Computer Simulation

To alleviate skull defects and enhance the biological activity of taxifolin, this study utilized the thin-film dispersion method to prepare paclitaxel liposomes (TL). Thiolated chitosan (CSSH)-modified TL (CTL) was synthesized through charge interactions. Injectable hydrogels (BLG) were then prepared as hydrogel scaffolds loaded with TAX (TG), TL (TLG), and CTL (CTLG) using a Schiff base reaction involving oxidized dextran and carboxymethyl chitosan. The study investigated the bone reparative properties of CTLG through molecular docking, western blot techniques, and transcriptome analysis. The particle sizes of CTL were measured at 248.90 ± 14.03 nm, respectively, with zeta potentials of +36.68 ± 5.43 mV, respectively. CTLG showed excellent antioxidant capacity in vitro. It also has a good inhibitory effect on Escherichia coli and Staphylococcus aureus, with inhibition rates of 93.88 ± 1.59 % and 88.56 ± 2.83 % respectively. The results of 5-ethynyl-2 '-deoxyuridine staining, alkaline phosphatase staining and alizarin red staining showed that CTLG also had the potential to promote the proliferation and differentiation of mouse embryonic osteoblasts (MC3T3-E1). The study revealed that CTLG enhances the expression of osteogenic proteins by regulating the Wnt signaling pathway, shedding light on the potential application of TAX and bone regeneration mechanisms.

App-linked real-time feedback-devices for cardiopulmonary resuscitation (CPR) aim to improve laypersons' resuscitation quality. Resuscitation guidelines recommend these technologies in training settings. This is the first study comparing resuscitation quality of all App-linked feedback-devices currently on market.

Microgravity in the space environment can potentially have various negative effects on the human body, one of which is bone loss. Given the increasing frequency of human space activities, there is an urgent need to identify effective anti-osteoporosis drugs for the microgravity environment. Traditional microgravity experiments conducted in space suffer from limitations such as time-consuming procedures, high costs, and small sample sizes. In recent years, the in-silico drug discovery method has emerged as a promising strategy due to the advancements in bioinformatics and computer technology. In this study, we first collected a total of 184,915 literature articles related to microgravity and bone loss. We employed a combination of dependency path extraction and clustering techniques to extract data from the text. Afterwards, we conducted data cleaning and standardization to integrate data from several sources, including The Global Network of Biomedical Relationships (GNBR), Curated Drug-Drug Interactions Database (DDInter), Search Tool for Interacting Chemicals (STITCH), DrugBank, and Traditional Chinese Medicines Integrated Database (TCMID). Through this integration process, we constructed the Microgravity Biology Knowledge Graph (MBKG) consisting of 134,796 biological entities and 3,395,273 triplets. Subsequently, the TransE model was utilized to perform knowledge graph embedding. By calculating the distances between entities in the model space, the model successfully predicted potential drugs for treating osteoporosis and microgravity-induced bone loss. The results indicate that out of the top 10 ranked western medicines, 7 have been approved for the treatment of osteoporosis. Additionally, among the top 10 ranked traditional Chinese medicines, 5 have scientific literature supporting their effectiveness in treating bone loss. Among the top 20 predicted medicines for microgravity-induced bone loss, 15 have been studied in microgravity or simulated microgravity environments, while the remaining 5 are also applicable for treating osteoporosis. This research highlights the potential application of MBKG in the field of space drug discovery.

The risk posed by prolonged exposure to space radiation represents a significant obstacle to long-duration human space exploration. Of the ion species present in the galactic cosmic ray spectrum, relativistic protons are the most abundant and as such are a relevant point of interest with regard to the radiation protection of space crews involved in future long-term missions to the Moon, Mars, and beyond. This work compared the shielding effectiveness of a number of standard and composite materials relevant to the design and development of future spacecraft or planetary surface habitats. Absorbed dose was measured using AlO:C optically stimulated luminescence dosimeters behind shielding targets of varying composition and depth using the 1 GeV nominal energy proton beam available at the NASA Space Radiation Laboratory at the Brookhaven National Laboratory in New York. Absorbed dose scored from computer simulations performed using the multi-purpose Monte Carlo radiation transport code FLUKA agrees well with measurements obtained via the shielding experiments. All shielding materials tested and modeled in this study were unable to reduce absorbed dose below that measured by the (unshielded) front detector, even after depths as large as 30 g/cm. These results could be noteworthy given the broad range of proton energies present in the galactic cosmic ray spectrum, and the potential health and safety hazard such space radiation could represent to future human space exploration.

A sedentary lifestyle associated with unregulated diets rich in high-calorie foods have contributed to the great prevalence of metabolic dysfunction-associated steatotic liver disease (MASLD) latterly, with up to 60% in the high-risk population and 25% in the general population. The absence of specific pharmacological strategies for this syndrome represents one of the major problems in the management of MASLD patients. Lifestyle interventions and adherence to a healthy diet are the main cornerstones of current therapies. The identification of nutraceuticals useful in the treatment of MASLD appears to be one of the most promising strategies for the development of new effective and safe treatments for this disease. The onion, one of the most widely studied foods in the field of nutraceuticals, serves as an inexhaustible reservoir of potent compounds with various beneficial effects. The following preliminary study analyzes, mediating in silico studies, the iteration of a library of typical onion compounds with 3-hydroxy-3-methylglutaryl-coenzyme A reductase, liver receptors X α and β, as well as peroxisome proliferator-activated receptors α and γ. In this study, for the first time promising smart molecules from the onion that could have a beneficial action in MASLD patients were identified.

Complex microbial communities have been reported to be involved in endodontic infections. The microorganisms invade the dental pulp leading to pulpitis and initiating pulp inflammation. is a dominant bacterium implicated in both primary and secondary endodontic infections. Drugs targeting the molecular machinery of will minimize pulp infection. LpxA and LpxD are early acyltransferases involved in the formation of lipid A, a major component of bacterial membranes. The identification of leads which exhibit preference towards successive enzymes in a single pathway can also prevent the development of bacterial resistance. A stringent screening strategy utilizing physicochemical and pharmacokinetic parameters along with a virtual screening approach identified two compounds, Lomefloxacin and Enoxacin, with good binding affinity towards the early acyltransferases LpxA and LpxD. Lomefloxacin and Enoxacin, members of the fluoroquinolone antibiotic class, exhibit wide-ranging activity against diverse bacterial strains. Nevertheless, their effectiveness in the context of endodontic treatment requires further investigation. This study explored the potential of Lomefloxacin and Enoxacin to manage endodontic infections via computational analysis. Moreover, the compounds identified herein serve as a foundation for devising novel combinatorial libraries with enhanced efficacy for endodontic therapeutic strategies.

Schiff bases (SBs) are important ligands in coordination chemistry due to their unique structural properties. Their ability to form complexes with metal ions has been exploited for the environmental detection of emerging water contaminants. In this work, we evaluated the complexation ability of three newly proposed SBs, -, by complete conformational analysis, using a combination of Molecular Dynamics and Density Functional Theory studies, to understand their ability to coordinate toxic heavy metal (HMs) ions. From this study, it emerges that all the ligands present geometries that make them suitable to complex HMs through the -imino moieties or, in the case of , with the support of the oxygen atoms of the ethylene diether chain. In particular, this ligand shows the most promising coordination behavior, particularly with Pb.

, the striped flea beetle, is one of the most destructive pests in Brassicaceae plants worldwide. Given the drawbacks associated with long-term use of chemical insecticides, green strategies based on chemical ecology are an effective alternative for beetle control. However, the lack of information on beetle ecology has hindered the development of effective biocontrol strategies. In this report, we identified two odorants, (S)-cis-verbenol and (-)-verbenone, which displayed significant attraction for ( < 0.05), indicating their great potential for management. Using the "empty neuron" system, an antenna-biased odorant receptor, PstrOR17, was identified as responsible for the detection of (-)-verbenone and (S)-cis-verbenol. Furthermore, the interactions between PstrOR17 and (-)-verbenone or (S)-cis-verbenol were predicted via modeling and molecular docking. Finally, we used RNAi to confirm that PstrOR17 is essential for the detection of (-)-verbenone and (S)-cis-verbenol to elicit an attraction effect. Our results not only lay a foundation for the development of new and effective nonchemical insecticide strategies based on (S)-cis-verbenol and (-)-verbenone, but also provide new insight into the molecular basis of odorant recognition in .

In this study, we performed a computational study of binding mechanisms for the SARS-CoV-2 spike Omicron XBB lineages with the host cell receptor ACE2 and a panel of diverse class one antibodies. The central objective of this investigation was to examine the molecular factors underlying epistatic couplings among convergent evolution hotspots that enable optimal balancing of ACE2 binding and antibody evasion for Omicron variants BA.1, BA2, BA.3, BA.4/BA.5, BQ.1.1, XBB.1, XBB.1.5, and XBB.1.5 + L455F/F456L. By combining evolutionary analysis, molecular dynamics simulations, and ensemble-based mutational scanning of spike protein residues in complexes with ACE2, we identified structural stability and binding affinity hotspots that are consistent with the results of biochemical studies. In agreement with the results of deep mutational scanning experiments, our quantitative analysis correctly reproduced strong and variant-specific epistatic effects in the XBB.1.5 and BA.2 variants. It was shown that Y453W and F456L mutations can enhance ACE2 binding when coupled with Q493 in XBB.1.5, while these mutations become destabilized when coupled with the R493 position in the BA.2 variant. The results provided a molecular rationale of the epistatic mechanism in Omicron variants, showing a central role of the Q493/R493 hotspot in modulating epistatic couplings between convergent mutational sites L455F and F456L in XBB lineages. The results of mutational scanning and binding analysis of the Omicron XBB spike variants with ACE2 receptors and a panel of class one antibodies provide a quantitative rationale for the experimental evidence that epistatic interactions of the physically proximal binding hotspots Y501, R498, Q493, L455F, and F456L can determine strong ACE2 binding, while convergent mutational sites F456L and F486P are instrumental in mediating broad antibody resistance. The study supports a mechanism in which the impact on ACE2 binding affinity is mediated through a small group of universal binding hotspots, while the effect of immune evasion could be more variant-dependent and modulated by convergent mutational sites in the conformationally adaptable spike regions.

Clofazimine and Arbidol have both been reported to be effective in vitro SARS-CoV-2 fusion inhibitors. Both are promising drugs that have been repurposed for the treatment of COVID-19 and have been used in several previous and ongoing clinical trials. Small-molecule bindings to expressed constructs of the trimeric S2 segment of Spike and the full-length SARS-CoV-2 Spike protein were measured using a Surface Plasmon Resonance (SPR) binding assay. We demonstrate that Clofazimine, Toremifene, Arbidol and its derivatives bind to the S2 segment of the Spike protein. Clofazimine provided the most reliable and highest-quality SPR data for binding with S2 over the conditions explored. A molecular docking approach was used to identify the most favorable binding sites on the S2 segment in the prefusion conformation, highlighting two possible small-molecule binding sites for fusion inhibitors. Results related to molecular docking and modeling of the structure-activity relationship (SAR) of a newly reported series of Clofazimine derivatives support the proposed Clofazimine binding site on the S2 segment. When the proposed Clofazimine binding site is superimposed with other experimentally determined coronavirus structures in structure-sequence alignments, the changes in sequence and structure may rationalize the broad-spectrum antiviral activity of Clofazimine in closely related coronaviruses such as SARS-CoV, MERS, hCoV-229E, and hCoV-OC43.

Diabetes mellitus (DM) represents a problem for the healthcare system worldwide. DM has very serious complications such as blindness, kidney failure, and cardiovascular disease. In addition to the very bad socioeconomic impacts, it influences patients and their families and communities. The global costs of DM and its complications are huge and expected to rise by the year 2030. DM is caused by genetic and environmental risk factors. Genetic testing will aid in early diagnosis and identification of susceptible individuals or populations using ATP-sensitive potassium (K) channels present in different tissues such as the pancreas, myocardium, myocytes, and nervous tissues. The channels respond to different concentrations of blood sugar, stimulation by hormones, or ischemic conditions. In pancreatic cells, they regulate the secretion of insulin and glucagon. Mutations in the gene that encodes the Kir6.2 protein (a major constituent of K channels) were reported to be associated with Type 2 DM, neonatal diabetes mellitus (NDM), and maturity-onset diabetes of the young (MODY). Kir6.2 harbors binding sites for ATP and phosphatidylinositol 4,5-diphosphate (PIP2). The ATP inhibits the K channel, while the (PIP2) activates it. A Kir6.2 mutation at tyrosine330 (Y330) was demonstrated to reduce ATP inhibition and predisposes to NDM. In this study, we examined the effect of mutations on the Kir6.2 structure using bioinformatics tools and molecular dynamic simulations (SIFT, PolyPhen, SNAP2, PANTHER, PhD&SNP, SNP&Go, I-Mutant, MuPro, MutPred, ConSurf, HOPE, and GROMACS). Our results indicated that M199R, R201H, R206H, and Y330H mutations influence Kir6.2 structure and function and therefore may cause DM. We conclude that MD simulations are useful techniques to predict the effects of mutations on protein structure. In addition, the M199R, R201H, R206H, and Y330H variant in the Kir6.2 protein may be associated with DM. These results require further verification in protein-protein interactions, Kir6.2 function, and case-control studies.

The DC-DC dual active bridge (DAB) converter has become one of the essential units for bidirectional energy distribution and connecting various renewable energy sources. When it comes to regulating the converter's output voltage, integrating an extended state observer (ESO) offers the advantage of eliminating the need for a current sensor, thereby reducing system costs. The ESO with a high observer bandwidth tends to acquire a faster system convergence and greater tracking accuracy. However, its disturbance suppression performance will become poor compared to the ESO with a low observer bandwidth. Based on this, the adaptive ESO (AESO) is proposed in this study to make a compromise between tracking performance and disturbance suppression. When the system is subjected to a high voltage error, the observer bandwidth will increase to improve the tracking performance and decrease to enhance the disturbance suppression. In order to demonstrate that the proposed method is effective, it is compared to the ESO with a fixed observer bandwidth and the improved model-based phase-shift control (MPSC). These comparisons are made through simulation and experimental results in various operation scenarios.

With the emergence of wireless rechargeable sensor networks (WRSNs), the possibility of wirelessly recharging nodes using mobile charging vehicles (MCVs) has become a reality. However, existing approaches overlook the effective integration of node energy replenishment and mobile data collection processes. In this paper, we propose a joint energy replenishment and data collection scheme (D-JERDG) for WRSNs based on deep reinforcement learning. By capitalizing on the high mobility of unmanned aerial vehicles (UAVs), D-JERDG enables continuous visits to the cluster head nodes in each cluster, facilitating data collection and range-based charging. First, D-JERDG utilizes the K-means algorithm to partition the network into multiple clusters, and a cluster head selection algorithm is proposed based on an improved dynamic routing protocol, which elects cluster head nodes based on the remaining energy and geographical location of the cluster member nodes. Afterward, the simulated annealing (SA) algorithm determines the shortest flight path. Subsequently, the DRL model multiobjective deep deterministic policy gradient (MODDPG) is employed to control and optimize the UAV instantaneous heading and speed, effectively planning UAV hover points. By redesigning the reward function, joint optimization of multiple objectives such as node death rate, UAV throughput, and average flight energy consumption is achieved. Extensive simulation results show that the proposed D-JERDG achieves joint optimization of multiple objectives and exhibits significant advantages over the baseline in terms of throughput, time utilization, and charging cost, among other indicators.

In recent decades, technological advancements have transformed the industry, highlighting the efficiency of automation and safety. The integration of augmented reality (AR) and gesture recognition has emerged as an innovative approach to create interactive environments for industrial equipment. Gesture recognition enhances AR applications by allowing intuitive interactions. This study presents a web-based architecture for the integration of AR and gesture recognition, designed to interact with industrial equipment. Emphasizing hardware-agnostic compatibility, the proposed structure offers an intuitive interaction with equipment control systems through natural gestures. Experimental validation, conducted using Google Glass, demonstrated the practical viability and potential of this approach in industrial operations. The development focused on optimizing the system's software and implementing techniques such as normalization, clamping, conversion, and filtering to achieve accurate and reliable gesture recognition under different usage conditions. The proposed approach promotes safer and more efficient industrial operations, contributing to research in AR and gesture recognition. Future work will include improving the gesture recognition accuracy, exploring alternative gestures, and expanding the platform integration to improve the user experience.

Through the use of Underwater Smart Sensor Networks (USSNs), Marine Observatories (MOs) provide continuous ocean monitoring. Deployed sensors may not perform as intended due to the heterogeneity of USSN devices' hardware and software when combined with the Internet. Hence, USSNs are regarded as complex distributed systems. As such, USSN designers will encounter challenges throughout the design phase related to time, complexity, sharing diverse domain experiences (viewpoints), and ensuring optimal performance for the deployed USSNs. Accordingly, during the USSN development and deployment phases, a few Underwater Environmental Constraints (UECs) should be taken into account. These constraints may include the salinity level and the operational depth of every physical component (sensor, server, etc.) that will be utilized throughout the duration of the USSN information systems' development and implementation. To this end, in this article we present how we integrated an Artificial Intelligence (AI) Database, an extended ArchiMO meta-model, and a design tool into our previously proposed Enterprise Architecture Framework. This addition proposes adding new Underwater Environmental Constraints (UECs) to the AI Database, which is accessed by USSN designers when they define models, with the goal of simplifying the USSN design activity. This serves as the basis for generating a new version of our ArchiMO design tool that includes the UECs. To illustrate our proposal, we use the newly generated ArchiMO to create a model in the MO domain. Furthermore, we use our self-developed domain-specific model compiler to produce the relevant simulation code. Throughout the design phase, our approach contributes to the handling and controling of the uncertainties and variances of the provided quality of service that may occur during the performance of the USSNs, as well as reducing the design activity's complexity and time. It provides a way to share the different viewpoints of the designers in the domain of USSNs.

In this paper, we consider an integrated sensing, communication, and computation (ISCC) system to alleviate the spectrum congestion and computation burden problem. Specifically, while serving communication users, a base station (BS) actively engages in sensing targets and collaborates seamlessly with the edge server to concurrently process the acquired sensing data for efficient target recognition. A significant challenge in edge computing systems arises from the inherent uncertainty in computations, mainly stemming from the unpredictable complexity of tasks. With this consideration, we address the computation uncertainty by formulating a robust communication and computing resource allocation problem in ISCC systems. The primary goal of the system is to minimize total energy consumption while adhering to perception and delay constraints. This is achieved through the optimization of transmit beamforming, offloading ratio, and computing resource allocation, effectively managing the trade-offs between local execution and edge computing. To overcome this challenge, we employ a Markov decision process (MDP) in conjunction with the proximal policy optimization (PPO) algorithm, establishing an adaptive learning strategy. The proposed algorithm stands out for its rapid training speed, ensuring compliance with latency requirements for perception and computation in applications. Simulation results highlight its robustness and effectiveness within ISCC systems compared to baseline approaches.

Low-power wide-area (LPWA) is a communication technology for the IoT that allows low power consumption and long-range communication. Additionally, packet-level index modulation (PLIM) can transmit additional information using multiple frequency channels and time slots. However, in a competitive radio access environment, where multiple sensors autonomously determine packet transmission, packet collisions occur when transmitting the same information. The packet collisions cause a reduction in the throughput. A method has been proposed to design a mapping table that shows the correspondence between indexes and information using a packet collision minimization criterion. However, the effectiveness of this method depends on how the probability of the occurrence of the information to be transmitted is modeled. We propose an environment-aware adaptive data-gathering method that identifies the location of factors affecting sensor information and constructs a model for the probability of the occurrence of sensor information. The packet collision rate of the environment-aware adaptive data-gathering method was clarified through computer simulations and actual experiments on a 429 MHz LPWA. We confirm that the proposed scheme improves the packet collision rate by 15% in the computer simulation and 30% in the experimental evaluation, respectively.

Xylanase is an essential component used to hydrolyze the xylan in wheat flour to enhance the quality of bread. Presently, cold-activated xylanase is popularly utilized to aid in the development of dough. In this study, ancestral sequence reconstruction and molecular docking of xylanase and wheat xylan were used to enhance the activity and stability of a thermophilic xylanase. The results indicated that the ancestral enzyme TmxN3 exhibited significantly improved activity and thermal stability. The Vmax increased by 2.7 times, and the catalytic efficiency (K/K) increased by 1.7 times in comparison to TmxB. After being incubated at 100 °C for 120 min, it still retained 87.3% of its activity, and the half-life in 100 °C was 330 min, while the wild type xylanase was only 55 min. This resulted in an improved shelf life of bread, while adding TmxN3 considerably enhanced its quality with excellent volume and reduced hardness, chewiness, and gumminess. The results showed that the hardness was reduced by 55.2%, the chewiness was reduced by 40.11%, and the gumminess was reduced by 53.52%. To facilitate its industrial application, we further optimized the production conditions in a 5L bioreactor, and the xylanase activity reached 1.52 × 10 U/mL culture.

nitroreductase A (NfsA) is a candidate for gene-directed prodrug cancer therapy using bioreductively activated nitroaromatic compounds (ArNO). In this work, we determined the standard redox potential of FMN of NfsA to be -215 ± 5 mV at pH 7.0. FMN semiquinone was not formed during 5-deazaflavin-sensitized NfsA photoreduction. This determines the two-electron character of the reduction of ArNO and quinones (Q). In parallel, we characterized the oxidant specificity of NfsA with an emphasis on its structure. Except for negative outliers nitracrine and SN-36506, the reactivity of ArNO increases with their electron affinity (single-electron reduction potential, ) and is unaffected by their lipophilicity and Van der Waals volume up to 386 Å. The reactivity of quinoidal oxidants is not clearly dependent on , but 2-hydroxy-1,4-naphthoquinones were identified as positive outliers and a number of compounds with diverse structures as negative outliers. 2-Hydroxy-1,4-naphthoquinones are characterized by the most positive reaction activation entropy and the negative outlier tetramethyl-1,4-benzoquinone by the most negative. Computer modelling data showed that the formation of H bonds with Arg15, Arg133, and Ser40, plays a major role in the binding of oxidants to reduced NfsA, while the role of the π-π interaction of their aromatic structures is less significant. Typically, the calculated hydride-transfer distances during ArNO reduction are smallwer than for Q. This explains the lower reactivity of quinones. Another factor that slows down the reduction is the presence of positively charged aliphatic substituents.

The present study aimed to evaluate the leishmanicidal potential of the essential oil (EO) of () and to investigate its molecular mechanism of action by qPCR. Furthermore, in silicointeraction study of the major EO compounds with the enzyme cytochrome P450 sterol 14α-demethylase (CYP51) was also performed. EO was analyzed by gas chromatography-mass spectrometry (GC-MS). Results showed that α-pinene (26.44%), -cadinol (26.27%), caryophyllene Oxide (7.73 ± 1.04%), and α-Cadinene (3.79 ± 0.12%) are the major compounds of EO. However, limited antioxidant activity was observed, as this EO was ineffective in neutralizing DPPH free radicals and in inhibiting β-carotene bleaching. Interestingly, it displayed effective leishmanicidal potential against promastigote (IC of 6.79 and 5.25 μg/mL) and amastigote (IC of 8.04 and 7.32 μg/mL) forms of and , respectively. Molecular mechanism investigation showed that EO displayed potent inhibition on the thiol regulatory pathway. Furthermore, a docking study of the main components of the EO with cytochrome P450 sterol 14α-demethylase (CYP51) enzyme revealed that -cadinol exhibited the best binding energy values (-7.5 kcal/mol), followed by α-cadinene (-7.3 kcal/mol) and caryophyllene oxide (-7 kcal/mol). These values were notably higher than that of the conventional drug fluconazole showing weaker binding energy (-6.9 kcal/mol). These results suggest that EO could serve as a potent and promising candidate for the development of alternative antileishmanial agent in the treatment of leishmaniasis.

Soluble epoxide hydrolase (sEH) is an enzyme targeted for the treatment of inflammation and cardiovascular diseases. Activated inflammatory cells produce nitric oxide (NO), which induces oxidative stress and exacerbates inflammation. We identify an inhibitor able to suppress sEH and thus NO production. Five flavonoids - isolated from flowers were evaluated for their abilities to inhibit sEH with IC values of 12.1 ± 0.1 to 62.8 ± 1.8 µM and for their effects on enzyme kinetics. A simulation study using computational chemistry was conducted as well. Furthermore, five inhibitors (-) were confirmed to suppress NO levels at 10 µM. The results showed that flavonoids - exhibited inhibitory activity in all tests, with compound exhibiting the most significant efficacy. Thus, in the development of anti-inflammatory inhibitors, compound is a promising natural candidate.

COVID-19 caused by SARS-CoV-2 has spread around the world. The receptor-binding domain (RBD) of the spike protein of SARS-CoV-2 is a critical component that directly interacts with host ACE2. Here, we simulate the ACE2 recognition processes of RBD of the WT, Delta, and OmicronBA.2 variants using our recently developed supervised Gaussian accelerated molecular dynamics (Su-GaMD) approach. We show that RBD recognizes ACE2 through three contact regions (regions I, II, and III), which aligns well with the anchor-locker mechanism. The higher binding free energy in State d of the RBD.-ACE2 system correlates well with the increased infectivity of OmicronBA.2 in comparison with other variants. For RBD, the T478K mutation affects the first step of recognition, while the L452R mutation, through its nearby Y449, affects the RBD-ACE2 binding in the last step of recognition. For RBD., the E484A mutation affects the first step of recognition, the Q493R, N501Y, and Y505H mutations affect the binding free energy in the last step of recognition, mutations in the contact regions affect the recognition directly, and other mutations indirectly affect recognition through dynamic correlations with the contact regions. These results provide theoretical insights for RBD-ACE2 recognition and may facilitate drug design against SARS-CoV-2.

Changes during the production cycle of dairy cattle can leave these animals susceptible to oxidative stress and reduced antioxidant health. In particular, the periparturient period, when dairy cows must rapidly adapt to the sudden metabolic demands of lactation, is a period when the production of damaging free radicals can overwhelm the natural antioxidant systems, potentially leading to tissue damage and reduced milk production. Central to the protection against free radical damage and antioxidant defense is the transcription factor NRF2, which activates an array of genes associated with antioxidant functions and cell survival. The objective of this study was to evaluate the effect that two natural NRF2 modulators, the NRF2 agonist sulforaphane (SFN) and the antagonist brusatol (BRU), have on the transcriptome of immortalized bovine mammary alveolar cells (MACT) using both the RT-qPCR of putative NRF2 target genes, as well as RNA sequencing approaches. The treatment of cells with SFN resulted in the activation of many putative NRF2 target genes and the upregulation of genes associated with pathways involved in cell survival, metabolism, and antioxidant function while suppressing the expression of genes related to cellular senescence and DNA repair. In contrast, the treatment of cells with BRU resulted in the upregulation of genes associated with inflammation, cellular stress, and apoptosis while suppressing the transcription of genes involved in various metabolic processes. The analysis also revealed several novel putative NRF2 target genes in bovine. In conclusion, these data indicate that the treatment of cells with SFN and BRU may be effective at modulating the NRF2 transcriptional network, but additional effects associated with cellular stress and metabolism may complicate the effectiveness of these compounds to improve antioxidant health in dairy cattle via nutrigenomic approaches.

Bromodomain 4 and 9 (BRD4 and BRD9) have been regarded as important targets of drug designs in regard to the treatment of multiple diseases. In our current study, molecular dynamics (MD) simulations, deep learning (DL) and binding free energy calculations are integrated to probe the binding modes of three inhibitors (H1B, JQ1 and TVU) to BRD4 and BRD9. The MD trajectory-based DL successfully identify significant functional function domains, such as BC-loop and ZA-loop. The information from the post-processing analysis of MD simulations indicates that inhibitor binding highly influences the structural flexibility and dynamic behavior of BRD4 and BRD9. The results of the MM-GBSA calculations not only suggest that the binding ability of H1B, JQ1 and TVU to BRD9 are stronger than to BRD4, but they also verify that van der Walls interactions are the primary forces responsible for inhibitor binding. The hot spots of BRD4 and BRD9 revealed by residue-based free energy estimation provide target sites of drug design in regard to BRD4 and BRD9. This work is anticipated to provide useful theoretical aids for the development of selective inhibitors over BRD family members.

Individuals with obstructive sleep apnea (OSA) face increased accident risks due to excessive daytime sleepiness. PERCLOS, a recognized drowsiness detection method, encounters challenges from image quality, eyewear interference, and lighting variations, impacting its performance, and requiring validation through physiological signals. We propose visual-based scoring using adaptive thresholding for eye aspect ratio with OpenCV for face detection and Dlib for eye detection from video recordings. This technique identified 453 drowsiness (PERCLOS ≥ 0.3 || CLOSDUR ≥ 2 s) and 474 wakefulness episodes (PERCLOS < 0.3 and CLOSDUR < 2 s) among fifty OSA drivers in a 50 min driving simulation while wearing six-channel EEG electrodes. Applying discrete wavelet transform, we derived ten EEG features, correlated them with visual-based episodes using various criteria, and assessed the sensitivity of brain regions and individual EEG channels. Among these features, theta-alpha-ratio exhibited robust mapping (94.7%) with visual-based scoring, followed by delta-alpha-ratio (87.2%) and delta-theta-ratio (86.7%). Frontal area (86.4%) and channel F4 (75.4%) aligned most episodes with theta-alpha-ratio, while frontal, and occipital regions, particularly channels F4 and O2, displayed superior alignment across multiple features. Adding frontal or occipital channels could correlate all episodes with EEG patterns, reducing hardware needs. Our work could potentially enhance real-time drowsiness detection reliability and assess fitness to drive in OSA drivers.

Leishmaniasis, an infectious disease caused by pathogenic parasites, affects millions of people in developing countries, and its re-emergence in developed countries, particularly in Europe, poses a growing public health concern. The limitations of current treatments and the absence of effective vaccines necessitate the development of novel therapeutics. In this study, we focused on identifying small molecule inhibitors which prevents the interaction between peroxin 5 (PEX5) and peroxisomal targeting signal 1 (PTS1), pivotal for kinetoplastid parasite survival. The PEX5, containing a C-terminal tetratricopeptide repeat (TPR) domain, was expressed and purified, followed by the quantification of kinetic parameters of PEX5-PTS1 interactions. A fluorescence polarization-based high-throughput screening assay was developed and small molecules inhibiting the PEX5-PTS1 interaction were discovered through the screening of a library of 51,406 compounds. Based on the confirmatory assay, nine compounds showed half maximal inhibitory concentration (IC) values ranging from 3.89 to 24.50 µM. In silico docking using a homology model of PEX5 elucidated that the molecular interactions between PEX5 and the inhibitors share amino acids critical for PTS1 binding. Notably, compound P20 showed potent activity against the growth of promastigotes, promastigotes, and blood stream form, with IC values of 12.16, 19.21, and 3.06 μM, respectively. The findings underscore the potential of targeting PEX5-PTS1 interactions with small molecule inhibitors as a promising strategy for the discovery of new anti-parasitic compounds.

Using mathematical models of physiological systems in medicine has allowed for the development of diagnostic, treatment, and medical educational tools. However, their complexity restricts, in most cases, their application for predictive, preventive, and personalized purposes. Although there are strategies that reduce the complexity of applying models based on fitting techniques, most of them are focused on a single instant of time, neglecting the effect of the system's temporal evolution. The objective of this research was to introduce a dynamic fitting strategy for physiological models with an extensive array of parameters and a constrained amount of experimental data. The proposed strategy focused on obtaining better predictions based on the temporal trends in the system's parameters and being capable of predicting future states. The study utilized a cardiorespiratory model as a case study. Experimental data from a longitudinal study of healthy adult subjects undergoing aerobic exercise were used for fitting and validation. The model predictions obtained in a steady state using the proposed strategy and the traditional single-fit approach were compared. The most successful outcomes were primarily linked to the proposed strategy, exhibiting better overall results regarding accuracy and behavior than the traditional population fitting approach at a single instant in time. The results evidenced the usefulness of the dynamic fitting strategy, highlighting its use for predictive, preventive, and personalized applications.

Antibiotic resistance in Gram-negative bacteria remains one of the most pressing challenges to global public health. Blocking the transportation of lipopolysaccharides (LPS), a crucial component of the outer membrane of Gram-negative bacteria, is considered a promising strategy for drug discovery. In the transportation process of LPS, two components of the LPS transport (Lpt) complex, LptA and LptC, are responsible for shuttling LPS across the periplasm to the outer membrane, highlighting their potential as targets for antibacterial drug development. In the current study, a protein-protein interaction (PPI) model of LptA and LptC was constructed, and a molecular screening strategy was employed to search a protein-protein interaction compound library. The screening results indicated that compound 18593 exhibits favorable binding free energy with LptA and LptC. In comparison with the molecular dynamics (MD) simulations on currently known inhibitors, compound 18593 shows more stable target binding ability at the same level. The current study suggests that compound 18593 may exhibit an inhibitory effect on the LPS transport process, making it a promising hit compound for further research.

This study aimed to investigate the impact of a common non-synonymous gene variant (C>G, rs738409) in patatin-like phospholipase domain-containing 3 (), leading to the substitution of isoleucine with methionine at position 148 (-I148M), on susceptibility to nonalcoholic fatty liver disease (NAFLD) and explore potential therapeutic nutritional strategies targeting . It contributed to understanding sustainable dietary practices for managing NAFLD, recently referred to as metabolic-dysfunction-associated fatty liver. NAFLD had been diagnosed by ultrasound in a metropolitan hospital-based cohort comprising 58,701 middle-aged and older Korean individuals, identifying 2089 NAFLD patients. The interaction between and lifestyle factors was investigated. In silico analyses, including virtual screening, molecular docking, and molecular dynamics simulations, were conducted to identify bioactive compounds from foods targeting (I148M). Subsequent cellular experiments involved treating oleic acid (OA)-exposed HepG2 cells with selected bioactive compounds, both in the absence and presence of compound C (AMPK inhibitor), targeting expression. Carriers of the risk allele _rs738409G showed an increased association with NAFLD risk, particularly with adherence to a plant-based diet, avoidance of a Western-style diet, and smoking. Delphinidin 3-caffeoyl-glucoside, pyranocyanin A, delta-viniferin, kaempferol-7-glucoside, and petunidin 3-rutinoside emerged as potential binders to the active site residues of , exhibiting a reduction in binding energy. These compounds demonstrated a dose-dependent reduction in intracellular triglyceride and lipid peroxide levels in HepG2 cells, while pretreatment with compound C showed the opposite trend. Kaempferol-7-glucoside and petunidin-3-rutinoside showed potential as inhibitors of expression by enhancing AMPK activity, ultimately reducing intrahepatic lipogenesis. In conclusion, there is potential for plant-based diets and specific bioactive compounds to promote sustainable dietary practices to mitigate NAFLD risk, especially in individuals with genetic predispositions.

Slc4a genes encode various types of transporters, including Na-HCO cotransporters, Cl/HCO exchangers, or Na-driven Cl/HCO exchangers. Previous research has revealed that Slc4a9 (Ae4) functions as a Cl/HCO exchanger, which can be driven by either Na or K, prompting investigation into whether other Slc4a members facilitate cation-dependent anion transport. In the present study, we show that either Na or K drive Cl/HCO exchanger activity in cells overexpressing Slc4a8 or Slc4a10. Further characterization of cation-driven Cl/HCO exchange demonstrated that Slc4a8 and Slc4a10 also mediate Cl and HCO-dependent K transport. Full-atom molecular dynamics simulation on the recently solved structure of Slc4a8 supports the coordination of K at the Na binding site in S1. Sequence analysis shows that the critical residues coordinating monovalent cations are conserved among mouse Slc4a8 and Slc4a10 proteins. Together, our results suggest that Slc4a8 and Slc4a10 might transport K in the same direction as HCO ions in a similar fashion to that described for Na transport in the rat Slc4a8 structure.

Polygonati Rhizoma (PR) has certain neuroprotective effects as a homology of medicine and food. In this study, systematic pharmacology, molecular docking, and in vitro experiments were integrated to verify the antidepressant active ingredients in PR and their mechanisms. A total of seven compounds in PR were found to be associated with 45 targets of depression. Preliminarily, DFV docking with cyclooxygenase 2 (COX2) showed good affinity. In vitro, DFV inhibited lipopolysaccharide (LPS)-induced inflammation of BV-2 cells, reversed amoeba-like morphological changes, and increased mitochondrial membrane potential. DFV reversed the malondialdehyde (MDA) overexpression and superoxide dismutase (SOD) expression inhibition in LPS-induced BV-2 cells and decreased interleukin-1β (IL-1β), tumor necrosis factor-α (TNF-α), and IL-6 mRNA expression levels in a dose-dependent manner. DFV inhibited both mRNA and protein expression levels of COX2 induced by LPS, and the activation of NACHT, LRR, and PYD domains-containing protein 3 (NLRP3) and caspase1 was suppressed, thus exerting an antidepressant effect. This study proves that DFV may be an important component basis for PR to play an antidepressant role.

The diterpene 7α-acetoxy-6β-hydroxyroyleanone isolated from demonstrates promising antibacterial, anti-inflammatory and anticancer properties. However, its bioactivity may be enhanced via strategic structural modifications of such natural products through semisynthesis. The anticancer potential of 7α-acetoxy-6β-hydroxyroyleanone and five derivatives was analyzed in silico via the prediction of chemicals absorption, distribution, metabolism, excretion, and toxicity (ADMET), quantum mechanical calculations, molecular docking and molecular dynamic simulation. The protein targets included regulators of apoptosis and cell proliferation. Additionally, network pharmacology was used to identify potential targets and signaling pathways. Derivatives 7α-acetoxy-6β-hydroxy-12--(2-fluoryl)royleanone and 7α-acetoxy-6β-(4-fluoro)benzoxy-12--(4-fluoro)benzoylroyleanone achieved high predicted binding affinities towards their respective protein panels, with stable molecular dynamics trajectories. Both compounds demonstrated favorable ADMET parameters and toxicity profiles. Their stability and reactivity were confirmed via geometry optimization. Network analysis revealed their involvement in cancer-related pathways. Our findings justify the inclusion of 7α-acetoxy-6β-hydroxy-12--(2-fluoryl)royleanone and 7α-acetoxy-6β-(4-fluoro)benzoxy-12--(4-fluoro)benzoylroyleanone in in vitro analyses as prospective anticancer agents. Our binding mode analysis and stability simulations indicate their potential as selective inhibitors. The data will guide studies into their structure optimization, enhancing efficacy and drug-likeness.

The abietane diterpenoid 7α-acetoxy-6β-hydroxyroyleanone (Roy) isolated from demonstrates cytotoxicity across numerous cancer cell lines. To potentiate anticancer attributes, a series of semi-synthetic Roy derivatives were generated and examined computationally. ADMET predictions were used to evaluate drug-likeness and toxicity risks. The antineoplastic potential was quantified by PASS. The DFT models were used to assess their reactivity and stability. Molecular docking determined cancer-related protein binding. MS simulations examined ligand-protein stability. Additionally, network pharmacology was used to identify potential targets and signaling pathways. Favorable ADME attributes and acceptable toxicity profiles were determined for all compounds. Strong anticancer potential was shown across derivatives (Pa 0.819-0.879). Strategic modifications altered HOMO-LUMO gaps (3.39-3.79 eV) and global reactivity indices. Favorable binding was revealed against cyclin-dependent kinases, BCL-2, caspases, receptor tyrosine kinases, and p53. The ligand exhibited a stable binding pose in MD simulations. Network analysis revealed involvement in cancer-related pathways. In silico evaluations predicted Roy and derivatives as effective molecules with anticancer properties. Experimental progress is warranted to realize their chemotherapeutic potential.

Among the common applications of plenoptic cameras are depth reconstruction and post-shot refocusing. These require a calibration relating the camera-side light field to that of the scene. Numerous methods with this goal have been developed based on thin lens models for the plenoptic camera's main lens and microlenses. Our work addresses the often-overlooked role of the main lens exit pupil in these models, specifically in the decoding process of standard plenoptic camera (SPC) images. We formally deduce the connection between the refocusing distance and the resampling parameter for the decoded light field and provide an analysis of the errors that arise when the exit pupil is not considered. In addition, previous work is revisited with respect to the exit pupil's role, and all theoretical results are validated through a ray tracing-based simulation. With the public release of the evaluated SPC designs alongside our simulation and experimental data, we aim to contribute to a more accurate and nuanced understanding of plenoptic camera optics.

We report the results of large-scale molecular dynamics simulations of adsorption nanoparticles on solid surfaces. The particles were modeled as stiff aggregates of spherical segments. Three types of particles were studied: rods, rectangles, and triangles built of the same number of segments. We show how the particle shape affects the adsorption, the structure of the surface layer, and the degree of the removal of particles from the solvent. The systems with different segment-segment and segment-surface interactions and different concentrations of particles were investigated. The ordered structures formed in adsorption monolayers were also analyzed. The results are consistent with experimental observations.

Breast cancer is a major global health issue, causing high incidence and mortality rates as well as psychological stress for patients. Chemotherapy resistance is a common challenge, and the Aldo-keto reductase family one-member C3 enzyme is associated with resistance to anthracyclines like doxorubicin. Recent studies have identified celecoxib as a potential treatment for breast cancer. Virtual screening was conducted using a quantitative structure-activity relationship model to develop similar drugs; this involved backpropagation of artificial neural networks and structure-based virtual screening. The screening revealed that the C-6 molecule had a higher affinity for the enzyme (-11.4 kcal/mol), a lower half-maximal inhibitory concentration value (1.7 µM), and a safer toxicological profile than celecoxib. The compound C-6 was synthesized with an 82% yield, and its biological activity was evaluated. The results showed that C-6 had a more substantial cytotoxic effect on MCF-7 cells (62%) compared to DOX (63%) and celecoxib (79.5%). Additionally, C-6 had a less harmful impact on healthy L929 cells than DOX and celecoxib. These findings suggest that C-6 has promising potential as a breast cancer treatment.

Betulonic acid (B(O)A) is a pentacyclic lupane-type triterpenoid that widely exists in plants. There are scientific reports indicating anticancer activity of B(O)A, as well as the amides and esters of this triterpenoid. In the first step of the study, the synthesis of novel amide derivatives of B(O)A containing an acetylenic moiety was developed. Subsequently, the medium-soluble compounds (EB171 and EB173) and the parent compound, i.e., B(O)A, were investigated for potential cytotoxic activity against breast cancer (MCF-7 and MDA-MB-231) and melanoma (C32, COLO 829 and A375) cell lines, as well as normal human fibroblasts. Screening analysis using the WST-1 test was applied. Moreover, the lipophilicity and ADME parameters of the obtained derivatives were determined using experimental and in silico methods. The toxicity assay using zebrafish embryos and larvae was also performed. The study showed that the compound EB171 exhibited a significant cytotoxic effect on cancer cell lines: MCF-7, A-375 and COLO 829, while it did not affect the survival of normal cells. Moreover, studies on embryos and larvae showed no toxicity of EB171 in an animal model. Compared to EB171, the compound EB173 had a weaker effect on all tested cancer cell lines and produced less desirable effects against normal cells. The results of the WST-1 assay obtained for B(O)A revealed its strong cytotoxic activity on the examined cancer cell lines, but also on normal cells. In conclusion, this article describes new derivatives of betulonic acid-from synthesis to biological properties. The results allowed to indicate a promising direction for the functionalization of B(O)A to obtain derivatives with selective anticancer activity and low toxicity.

Alzheimer's disease (AD) is a complex neurodegenerative disease that can lead to the loss of cognitive function. The progression of AD is regulated by multiple signaling pathways and their associated targets. Therefore, multitarget strategies theoretically have greater potential for treating AD. In this work, a series of new hybrids were designed and synthesized by the hybridization of tacrine (, AChE: IC = 0.223 μM) with pyrimidone compound (GSK-3: IC = 3 μM) using the cysteamine or cystamine group as the connector. The biological evaluation results demonstrated that most of the compounds exhibited moderate to good inhibitory activities against acetylcholinesterase (AChE) and glycogen synthase kinase 3 (GSK-3). The optimal compound possessed potent dual AChE/GSK-3 inhibition (AChE: IC = 0.047 ± 0.002 μM, GSK-3: IC = 0.930 ± 0.080 μM). Further molecular docking and enzymatic kinetic studies revealed that this compound could occupy both the catalytic anionic site and the peripheral anionic site of AChE. The results also showed a lack of toxicity to SH-SY5Y neuroblastoma cells at concentrations of up to 25 μM. Collectively, this work explored the structure-activity relationships of novel tetrahydroacridin hybrids with sulfur-inserted linkers, providing a reference for the further research and development of new multitarget anti-AD drugs.

The pathogenesis of carcinoma is believed to come from the combined effect of polygenic variation, and the initiation and progression of malignant tumors are closely related to the dysregulation of biological pathways. Quantifying the alteration in pathway activation and identifying coordinated patterns of pathway dysfunction are the imperative part of understanding the malignancy process and distinguishing different tumor stages or clinical outcomes of individual patients. In this study, we have conducted in silico pathway activation analysis using Riemannian manifold (RiePath) toward pan-cancer personalized characterization, which is the first attempt to apply the Riemannian manifold theory to measure the extent of pathway dysregulation in individual patient on the tangent space of the Riemannian manifold. RiePath effectively integrates pathway and gene expression information, not only generating a relatively low-dimensional and biologically relevant representation, but also identifying a robust panel of biologically meaningful pathway signatures as biomarkers. The pan-cancer analysis across 16 cancer types reveals the capability of RiePath to evaluate pathway activation accurately and identify clinical outcome-related pathways. We believe that RiePath has the potential to provide new prospects in understanding the molecular mechanisms of complex diseases and may find broader applications in predicting biomarkers for other intricate diseases.

Cancer is a serious threat to human life and social development and the use of scientific methods for cancer prevention and control is necessary. In this study, HQSAR, CoMFA, CoMSIA and TopomerCoMFA methods are used to establish models of 65 imidazo[4,5-]pyridine derivatives to explore the quantitative structure-activity relationship between their anticancer activities and molecular conformations. The results show that the cross-validation coefficients of HQSAR, CoMFA, CoMSIA and TopomerCoMFA are 0.892, 0.866, 0.877 and 0.905, respectively. The non-cross-validation coefficients are 0.948, 0.983, 0.995 and 0.971, respectively. The externally validated complex correlation coefficients of external validation are 0.814, 0.829, 0.758 and 0.855, respectively. The PLS analysis verifies that the QSAR models have the highest prediction ability and stability. Based on these statistics, virtual screening based on group is performed using the ZINC database by the Topomer search technology. Finally, 10 new compounds with higher activity are designed with the screened new fragments. In order to explore the binding modes and targets between ligands and protein receptors, these newly designed compounds are conjugated with macromolecular protein (PDB ID: 1MQ4) by molecular docking technology. Furthermore, to study the nature of the newly designed compound in dynamic states and the stability of the protein-ligand complex, molecular dynamics simulation is carried out for N3, N4, N5 and N7 docked with 1MQ4 protease structure for 50 ns. A free energy landscape is computed to search for the most stable conformation. These results prove the efficient and stability of the newly designed compounds. Finally, ADMET is used to predict the pharmacology and toxicity of the 10 designed drug molecules.

It is important to search for cytostatic compounds in order to fight cancer. One of them could be 2'-methylthiamine, which is a thiamine antimetabolite with an additional methyl group at the C-2 carbon of thiazole. So far, the cytostatic potential of 2'-methylthiamine has not been studied. We have come forward with a simplified method of synthesis using commercially available substrates and presented a comparison of its effects, as boosted by oxythiamine, on normal skin fibroblasts and HeLa cancer cells, having adopted in vitro culture techniques. Oxythiamine has been found to inhibit the growth and metabolism of cancer cells significantly better than 2'-methylthiamine (GI 36 and 107 µM, respectively), while 2'-methylthiamine is more selective for cancer cells than oxythiamine (SI = 180 and 153, respectively). Docking analyses have revealed that 2'-methylthiamine (Δ -8.2 kcal/mol) demonstrates a better affinity with thiamine pyrophosphokinase than thiamine (Δ -7.5 kcal/mol ) and oxythiamine (Δ -7.0 kcal/mol), which includes 2'-methylthiamine as a potential cytostatic. Our results suggest that the limited effect of 2'-methylthiamine on HeLa arises from the related arduous transport as compared to oxythiamine. Given that 2'-methylthiamine may possibly inhibit thiamine pyrophosphokinase, it could once again be considered a potential cytostatic. Thus, research should be carried out in order to find the best way to improve the transport of 2'-methylthiamine into cells, which may trigger its cytostatic properties.

Two α-pyrone analogs were isolated from the endophytic fungus sp. CB10100, which is derived from the medicinal plant . These analogs included a new compound, diaporpyrone F (), and a known compound, diaporpyrone D (). The structure of was identified by a comprehensive examination of HRESIMS, 1D and 2D NMR spectroscopic data. Bioinformatics analysis revealed that biosynthetic gene clusters for α-pyrone analogs are common in fungi of species. The in vitro α-glucosidase inhibitory activity and antibacterial assay of revealed that it has a 46.40% inhibitory effect on α-glucosidase at 800 μM, while no antibacterial activity against methicillin-resistant (MRSA), () or at 64 μg/mL. Molecular docking and molecular dynamics simulations of with α-glucosidase further suggested that the compounds are potential α-glucosidase inhibitors. Therefore, α-pyrone analogs can be used as lead compounds for α-glucosidase inhibitors in more in-depth studies.

Chagas disease is one of the world's neglected tropical diseases, caused by the human pathogenic protozoan parasite . There is currently a lack of effective and tolerable clinically available therapeutics to treat this life-threatening illness and the discovery of modern alternative options is an urgent matter. glucokinase (GlcK) is a potential drug target because its product, d-glucose-6-phosphate, serves as a key metabolite in the pentose phosphate pathway, glycolysis, and gluconeogenesis. In 2019, we identified a novel cluster of GlcK inhibitors that also exhibited anti- efficacy called the 3-nitro-2-phenyl-2-chromene analogues. This was achieved by performing a target-based high-throughput screening (HTS) campaign of 13,040 compounds. The selection criteria were based on first determining which compounds strongly inhibited GlcK in a primary screen, followed by establishing on-target confirmed hits from a confirmatory assay. Compounds that exhibited notable in vitro trypanocidal activity over the infective form (trypomastigotes and intracellular amastigotes) co-cultured in NIH-3T3 mammalian host cells, as well as having revealed low NIH-3T3 cytotoxicity, were further considered. Compounds and were determined to inhibit GlcK quite well with IC values of 6.1 µM and 4.8 µM, respectively. Illuminated by these findings, we herein screened a small compound library consisting of thirteen commercially available 3-nitro-2-phenyl-2-chromene analogues, two of which were and (compounds and , respectively). Twelve of these compounds had a one-point change from the chemical structure of . The analogues were run through a similar primary screening and confirmatory assay protocol to our previous HTS campaign. Subsequently, three in vitro biological assays were performed where compounds were screened against (a) (Tulahuen strain) infective form co-cultured within NIH-3T3 cells, (b) (427 strain) bloodstream form, and (c) NIH-3T3 host cells alone. We report on the GlcK inhibitor constant determinations, mode of enzyme inhibition, in vitro antitrypanosomal IC determinations, and an assessment of structure-activity relationships. Our results reveal that the 3-nitro-2-phenyl--chromene scaffold holds promise and can be further optimized for both Chagas disease and human African trypanosomiasis early-stage drug discovery research.

Allergic rhinitis (AR) is a prevalent inflammatory condition affecting millions globally, with current treatments often associated with significant side effects. To seek safer and more effective alternatives, natural sources like (UD) are being explored. However, UD's mechanism of action remains unknown. Therefore, to elucidate it, we conducted an in silico evaluation of UD phytochemicals' effects on known therapeutic targets of allergic rhinitis: histamine receptor 1 (HR1), neurokinin 1 receptor (NK1R), cysteinyl leukotriene receptor 1 (CLR1), chemoattractant receptor-homologous molecule expressed on type 2 helper T cells (CRTH2), and bradykinin receptor type 2 (BK2R). The docking analysis identified amentoflavone, alpha-tocotrienol, neoxanthin, and isorhamnetin 3-O-rutinoside as possessing a high affinity for all the receptors. Subsequently, molecular dynamics (MD) simulations were used to analyze the key interactions; the free energy of binding was calculated through Generalized Born and Surface Area Solvation (MMGBSA), and the conformational changes were evaluated. Alpha-tocotrienol exhibited a high affinity while also inducing positive conformational changes across all targets. Amentoflavone primarily affected CRTH2, neoxanthin targeted NK1R, CRTH2, and BK2R, and isorhamnetin-3-O-rutinoside acted on NK1R. These findings suggest UD's potential to treat AR symptoms by inhibiting these targets. Notably, alpha-tocotrienol emerges as a promising multi-target inhibitor. Further in vivo and in vitro studies are needed for validation.

Directed structural modifications of natural products offer excellent opportunities to develop selectively acting drug candidates. Natural product hybrids represent a particular compound group. The components of hybrids constructed from different molecular entities may result in synergic action with diminished side effects. Steroidal homo- or heterodimers deserve special attention owing to their potentially high anticancer effect. Inspired by our recently described antiproliferative core-modified estrone derivatives, here, we combined them into heterodimers via Cu(I)-catalyzed azide-alkyne cycloaddition reactions. The two -16-azido-3-(-benzyl)-17-hydroxy-13α-estrone derivatives were reacted with 3--propargyl-D-secoestrone alcohol or oxime. The antiproliferative activities of the four newly synthesized dimers were evaluated against a panel of human adherent gynecological cancer cell lines (cervical: Hela, SiHa, C33A; breast: MCF-7, T47D, MDA-MB-231, MDA-MB-361; ovarian: A2780). One heterodimer () exerted substantial antiproliferative activity against all investigated cell lines in the submicromolar or low micromolar range. A pronounced proapoptotic effect was observed by fluorescent double staining and flow cytometry on three cervical cell lines. Additionally, cell cycle blockade in the G2/M phase was detected, which might be a consequence of the effect of the dimer on tubulin polymerization. Computational calculations on the taxoid binding site of tubulin revealed potential binding of both steroidal building blocks, mainly with hydrophobic interactions and water bridges.

The search for novel effective TAAR1 ligands continues to draw great attention due to the wide range of pharmacological applications related to TAAR1 targeting. Herein, molecular docking studies of known TAAR1 ligands, characterized by an oxazoline core, have been performed in order to identify novel promising chemo-types for the discovery of more active TAAR1 agonists. In particular, the oxazoline-based compound has been taken as a reference compound for the computational study, leading to the development of quite flat and conformationally locked ligands. The choice of a "Y-shape" conformation was suggested for the design of TAAR1 ligands, interacting with the protein cavity delimited by ASP103 and aromatic residues such as PHE186, PHE195, PHE268, and PHE267. The obtained results allowed us to preliminary in silico screen an in-house series of pyrimidinone-benzimidazoles (-) as a novel scaffold to target TAAR1. Combined ligand-based (LBCM) and structure based (SBCM) computational methods suggested the biological evaluation of compounds -, leading to the identification of derivatives - (hTAAR1 EC = 526.3-657.4 nM) as promising novel TAAR1 agonists.

Dendritic structures play a pivotal role in the computational processes occurring within neurons. Signal propagation along dendrites relies on both passive conduction and active processes related to voltage-dependent ion channels. Among these channels, extrasynaptic N-methyl-D-aspartate channels (exNMDA) emerge as a significant contributor. Prior studies have mainly concentrated on interactions between synapses and nearby exNMDA (100 nm-10 µm from synapse), activated by presynaptic membrane glutamate. This study concentrates on the correlation between synaptic inputs and distal exNMDA (>100 µm), organized in clusters that function as signal amplifiers. Employing a computational model of a dendrite, we elucidate the mechanism underlying signal amplification in exNMDA clusters. Our findings underscore the pivotal role of the optimal spatial positioning of the NMDA cluster in determining signal amplification efficiency. Additionally, we demonstrate that exNMDA subunits characterized by a large conduction decay constant. Specifically, NR2B subunits exhibit enhanced effectiveness in signal amplification compared to subunits with steeper conduction decay. This investigation extends our understanding of dendritic computational processes by emphasizing the significance of distant exNMDA clusters as potent signal amplifiers. The implications of our computational model shed light on the spatial considerations and subunit characteristics that govern the efficiency of signal amplification in dendritic structures, offering valuable insights for future studies in neurobiology and computational neuroscience.

Pyxinol, an active metabolite of ginsenosides in human hepatocytes, exhibits various pharmacological activities. Here, a series of C-3 modified pyxinol derivatives was designed and virtually screened by molecular docking with the key inflammation-related proteins of the nuclear factor kappa B (NF-κB) pathway. Some of the novel derivatives were synthesized to assess their effects in inhibiting the production of nitric oxide (NO) and mitochondrial reactive oxygen species (MtROS) in lipopolysaccharide-triggered RAW264.7 cells. Derivative exhibited the highest NO and MtROS inhibitory activities with low cytotoxicity. Furthermore, decreased the protein levels of interleukin 1β, tumor necrosis factor α, inducible nitric oxide synthase, and cyclooxygenase 2 and suppressed the activation of NF-κB signaling. Cellular thermal shift assays indicated that could directly bind with p65 and p50 in situ. Molecular docking revealed that 's binding to the p65-p50 heterodimer and p50 homodimer was close to their DNA binding sites. In summary, pyxinol derivatives possess potential for development as NF-κB inhibitors.

Throughout its lifecycle, encounters a variety of stressful conditions. This parasite possesses Heat Shock Response Elements (HSEs) which are crucial for regulating the expression of various genes, aiding in its adaptation and survival. These HSEs are regulated by Heat Shock Transcription Factors (EhHSTFs). Our research has identified seven such factors in the parasite, designated as EhHSTF1 through to EhHSTF7. Significantly, under heat shock conditions and in the presence of the antiamoebic compound emetine, EhHSTF5, EhHSTF6, and EhHSTF7 show overexpression, highlighting their essential role in gene response to these stressors. Currently, only EhHSTF7 has been confirmed to recognize the HSE as a promoter of the gene (HSE_), leaving the binding potential of the other EhHSTFs to HSEs yet to be explored. Consequently, our study aimed to examine, both in vitro and in silico, the oligomerization, and binding capabilities of the recombinant EhHSTF5 protein (rEhHSTF5) to HSE_. The in vitro results indicate that the oligomerization of rEhHSTF5 is concentration-dependent, with its dimeric conformation showing a higher affinity for HSE_ than its monomeric state. In silico analysis suggests that the alpha 3 α-helix (α3-helix) of the DNA-binding domain (DBD5) of EhHSTF5 is crucial in binding to the major groove of HSE, primarily through hydrogen bonding and salt-bridge interactions. In summary, our results highlight the importance of oligomerization in enhancing the affinity of rEhHSTF5 for HSE_ and demonstrate its ability to specifically recognize structural motifs within HSE_. These insights significantly contribute to our understanding of one of the potential molecular mechanisms employed by this parasite to efficiently respond to various stressors, thereby enabling successful adaptation and survival within its host environment.

High-performance vector hydrophones have been gaining attention for underwater target-monitoring applications. Nevertheless, there exists the mutual constraint between sensitivity and bandwidth of a single hydrophone. To solve this problem, a four-unit array piezoelectric bionic MEMS vector hydrophone (FPVH) was developed in this paper, which has a cross-beam and a bionic fish-lateral-line-nerve-cell-cilia unit array structure. Simulation analysis and optimization in the design of the bionic microstructure have been performed by COMSOL 6.1 software to determine the structure dimensions and the lead zirconate titanate (PZT) thin film distribution. The FPVH was manufactured using MEMS technology and tested in a standing wave bucket. The results indicate that the FPVH has a sensitivity of up to -167.93 dB@1000 Hz (0 dB = 1 V/μPa), which is 12 dB higher than that of the one-unit piezoelectric MEMS vector hydrophone (OPVH). Additionally, the working bandwidth of the FPVH reaches 20 Hz~1200 Hz, exhibiting a good cosine curve with an 8-shape. This work paves a new way for the development of multi-unit piezoelectric vector hydrophones for underwater acoustic detectors.

Base excision repair (BER), which involves the sequential activity of DNA glycosylases, apurinic/apyrimidinic endonucleases, DNA polymerases, and DNA ligases, is one of the enzymatic systems that preserve the integrity of the genome. Normal BER is effective, but due to single-nucleotide polymorphisms (SNPs), the enzymes themselves-whose main function is to identify and eliminate damaged bases-can undergo amino acid changes. One of the enzymes in BER is DNA polymerase β (Polβ), whose function is to fill gaps in DNA. SNPs can significantly affect the catalytic activity of an enzyme by causing an amino acid substitution. In this work, pre-steady-state kinetic analyses and molecular dynamics simulations were used to examine the activity of naturally occurring variants of Polβ that have the substitutions L19P and G66R in the dRP-lyase domain. Despite the substantial distance between the dRP-lyase domain and the nucleotidyltransferase active site, it was found that the capacity to form a complex with DNA and with an incoming dNTP is significantly altered by these substitutions. Therefore, the lower activity of the tested polymorphic variants may be associated with a greater number of unrepaired DNA lesions.

This study demonstrates a particular composited barrier structure of high-electron-mobility transistors (HEMTs) with an enhancement mode composed of p-GaN/GaN/AlN/AlGaN/GaN. The purpose of the composite barrier structure device is to increase the maximum drain current, reduce gate leakage, and achieve lower on-resistance (R) performance. A comparison was made between the conventional device without the composited barrier and the device with the composited barrier structure. The maximum drain current is significantly increased by 37%, and R is significantly reduced by 23%, highlighting the synergistic impact of the composite barrier structure on device performance improvement. This reason can be attributed to the undoped GaN (u-GaN) barrier layer beneath p-GaN, which was introduced to mitigate Mg diffusion in the capping layer, thus addressing its negative effects. Furthermore, the AlN barrier layer exhibits enhanced electrical properties, which can be attributed to the critical role of high-energy-gap properties that increase the 2DEG carrier density and block leakage pathways. These traps impact the device behavior mechanism, and the simulation for a more in-depth analysis of how the composited barrier structure brings improvement is introduced using Synopsys Sentaurus TCAD.

The human Vitamin K Epoxide Reductase Complex (hVKORC1), a key enzyme that converts vitamin K into the form necessary for blood clotting, requires for its activation the reducing equivalents supplied by its redox partner through thiol-disulphide exchange reactions. The functionally related molecular complexes assembled during this process have never been described, except for a proposed de novo model of a 'precursor' complex of hVKORC1 associated with protein disulphide isomerase (PDI). Using numerical approaches ( modelling and molecular dynamics simulation), we generated alternative 3D models for each molecular complex bonded either covalently or non-covalently. These models differ in the orientation of the PDI relative to hVKORC1 and in the cysteine residue involved in forming protein-protein disulphide bonds. Based on a comparative analysis of these models' shape, folding, and conformational dynamics, the most probable putative complexes, mimicking the 'precursor', 'intermediate', and 'successor' states, were suggested. In addition, we propose using these complexes to develop the '' necessary for treating blood diseases.

Mesenchymal stem cells (MSC) attract an increasing amount of attention due to their unique therapeutic properties. Yet, MSC can undergo undesirable genetic and epigenetic changes during their propagation in vitro. In this study, we investigated whether polyploidy can compromise MSC oncological safety and therapeutic properties. For this purpose, we compared the impact of polyploidy on the transcriptome of cancer cells and MSC of various origins (bone marrow, placenta, and heart). First, we identified genes that are consistently ploidy-induced or ploidy-repressed through all comparisons. Then, we selected the master regulators using the protein interaction enrichment analysis (PIEA). The obtained ploidy-related gene signatures were verified using the data gained from polyploid and diploid populations of early cardiomyocytes (CARD) originating from iPSC. The multistep bioinformatic analysis applied to the cancer cells, MSC, and CARD indicated that polyploidy plays a pivotal role in driving the cell into hypertranscription. It was evident from the upregulation of gene modules implicated in housekeeping functions, stemness, unicellularity, DNA repair, and chromatin opening by means of histone acetylation operating via DNA damage associated with the NUA4/TIP60 complex. These features were complemented by the activation of the pathways implicated in centrosome maintenance and ciliogenesis and by the impairment of the pathways related to apoptosis, the circadian clock, and immunity. Overall, our findings suggest that, although polyploidy does not induce oncologic transformation of MSC, it might compromise their therapeutic properties because of global epigenetic changes and alterations in fundamental biological processes. The obtained results can contribute to the development and implementation of approaches enhancing the therapeutic properties of MSC by removing polyploid cells from the cell population.

One of the challenges in augmentative biological control programs is the definition of releasing strategy for natural enemies, especially when macro-organisms are involved. Important information about the density of insects to be released and frequency of releases usually requires a great number of experiments, which implies time and space that are not always readily available. In order to provide science-based responses for these questions, computational models offer an in silico option to simulate different biocontrol agent releasing scenarios. This allows decision-makers to focus their efforts to more feasible options. The major insect pest in sugarcane crops is the sugarcane borer Diatraea saccharalis, which can be managed using the egg parasitoid Trichogramma galloi. The current strategy consists in releasing 50,000 insects per hectare for each release, in three weekly releases. Here, we present a simulation model to check whether this releasing strategy is optimal against the sugarcane borer. A sensitive analysis revealed that the population of the pest is more affected by the number of releases rather than by the density of parasitoids released. Only the number of releases demonstrated an ability to drive the population curve of the pest towards a negative growth. For example, releasing a total of 600,000 insects per hectare in three releases led to a lower pest control efficacy that releasing only 250,000 insects per hectare in five releases. A higher number of releases covers a wider range of time, increasing the likelihood of releasing parasitoids at the correct time given that the egg stage is short. Based on these results, it is suggested that, if modifications to the releasing strategy are desired, increasing the number of releases from 3 to 5 at weekly intervals is most likely preferable.

A considerable effort has been spent in the past decades to develop targeted therapies for the treatment of demyelinating diseases, such as multiple sclerosis (MS). Among drugs with free radical scavenging activity and oligodendrocyte protecting effects, Edaravone (Radicava) has recently received increasing attention because of being able to enhance remyelination in experimental in vitro and in vivo disease models. While its beneficial effects are greatly supported by experimental evidence, there is a current paucity of information regarding its mechanism of action and main molecular targets. By using high-throughput RNA-seq and biochemical experiments in murine oligodendrocyte progenitors and SH-SY5Y neuroblastoma cells combined with molecular docking and molecular dynamics simulation, we here provide evidence that Edaravone triggers the activation of aryl hydrocarbon receptor (AHR) signaling by eliciting AHR nuclear translocation and the transcriptional-mediated induction of key cytoprotective gene expression. We also show that an Edaravone-dependent AHR signaling transduction occurs in the zebrafish experimental model, associated with a downstream upregulation of the NRF2 signaling pathway. We finally demonstrate that its rapid cytoprotective and antioxidant actions boost increased expression of the promyelinating Olig2 protein as well as of an Olig2:GFP transgene in vivo. We therefore shed light on a still undescribed potential mechanism of action for this drug, providing further support to its therapeutic potential in the context of debilitating demyelinating conditions.

Investigations on binding strength differences of non-covalent protein complex components were performed by mass spectrometry. T4 fibritin foldon (T4Ff) is a well-studied miniprotein, which together with its biotinylated version served as model system to represent a compactly folded protein to which an Intrinsically Disordered Region (IDR) was attached. The apparent enthalpies of the gas phase dissociation reactions of the homo-trimeric foldon F-F-F and of the homo-trimeric triply biotinylated foldon bF-bF-bF have been determined to be rather similar (3.32 kJ/mol and 3.85 kJ/mol) but quite distinct from those of the singly and doubly biotinylated hetero-trimers F-F-bF and F-bF-bF (1.86 kJ/mol and 1.08 kJ/mol). Molecular dynamics simulations suggest that the ground states of the (biotinylated) T4Ff trimers are highly symmetric and well comparable to each other, indicating that the energy levels of all four (biotinylated) T4Ff trimer ground states are nearly indistinguishable. The experimentally determined differences and/or similarities in enthalpies of the complex dissociation reactions are explained by entropic spring effects, which are noticeable in the T4Ff hetero-trimers but not in the T4Ff homo-trimers. A lowering of the transition state energy levels of the T4Ff hetero-trimers seems likely because the biotin moieties, mimicking intrinsically disordered regions (IDRs), induced asymmetries in the transition states of the biotinylated T4Ff hetero-trimers. This transition state energy level lowering effect is absent in the T4Ff homo-trimer, as well as in the triply biotinylated T4Ff homo-trimer. In the latter, the IDR-associated entropic spring effects on complex stability cancel each other out. ITEM-FIVE enabled semi-quantitative determination of energy differences of complex dissociation reactions, whose differences were modulated by IDRs attached to compactly folded proteins.

The stability criterion approach is very important for estimating precise behavior before or after fabricating brain computer interface system applications.

In this article, we present new design techniques to improve the gain and impedance bandwidth of short backfire antennas. For the gain enhancement procedure, our approach was to flare the rim of the antenna, which simultaneously led to an increase in the impedance bandwidth of the antenna. Parametric studies were carried out to obtain the optimal flaring angle. The peak realized gain was obtained as 17.2 dBi with an impedance bandwidth of 55% (2.4 dB and 28.6% increase in gain and bandwidth, respectively, compared to the unflared antenna). To further enhance the impedance bandwidth, an inductive iris was added to improve impedance matching at the waveguide aperture. We varied the width of the iris to obtain the optimal width that provided the best gain and impedance bandwidth result of 17.1 dBi and 66% (~40% increase compared to the unflared antenna without iris). To experimentally verify the work, prototypes were fabricated and tested. We found good agreement between simulation and measurement. The results of this study indicate that gain and bandwidth can be enhanced through optimized geometrical modification of the SBF structure. Furthermore, our 3D-printed technique demonstrates a mass reduction compared with conventional metallic structures.

Degenerative diseases of the spine have a negative impact on the quality of life of patients. This study presents the results of numerical modelling of the mechanical behaviour of the lumbar spine with patient-specific conditions at physiological loads. This paper aims to numerically study the influence of degenerative changes in the spine and the presence of an endoprosthesis on the creation of conditions for tissue regeneration.

As a rapidly growing field, biophotonics demonstrates an increasingly higher demand for interdisciplinary professionals and requires the implementation of a structured approach to educational and outreach activities focused on appropriate curriculum, and teaching and learning for audiences with diverse technical backgrounds and learning styles. Our study shows the main findings upon applying this approach to biophotonics workshops delivered 2 consecutive years while updating and improving learning outcomes, teaching strategies, workshop content based on student and teacher feedback. We provided resources for a variety of lecture-based, experimental, computer simulation activities. Quality of subject matter, teaching, and overall learning was rated as "Very good" or "Good" by 88%, 76%, and 82% of students in average, respectively. Application of our teaching strategies and materials during short- and long-term workshops/courses could potentially increase the interest in pursuing careers in the biophotonics field and related areas, leading to standardized approaches in designing education and outreach events across centers.

The emergence of multidrug-resistant (MDR) microorganisms combined with the ever-draining antibiotic pipeline poses a disturbing and immensely growing public health challenge that requires a multidisciplinary approach and the application of novel therapies aimed at unconventional targets and/or applying innovative drug formulations. Hence, bacterial iron acquisition systems and bacterial Fe-containing enzymes have been identified as a plausible target of great potential. The intriguing "Trojan horse" approach deprives microorganisms from the essential iron. Recently, gallium's potential in medicine as an iron mimicry species has attracted vast attention. Different Ga formulations exhibit diverse effects upon entering the cell and thus supposedly have multiple targets. The aim of the current study is to specifically distinguish characteristics of great significance in regard to the initial gallium-based complex, allowing the alien cation to effectively compete with the native ferric ion for binding the siderophores pyochelin and pyoverdine secreted by the bacterium . Therefore, three gallium-based formulations were taken into consideration: the first-generation gallium nitrate, Ga(NO), metabolized to Ga-hydrated forms, the second-generation gallium maltolate (tris(3-hydroxy-2-methyl-4-pyronato)gallium), and the experimentally proven Ga carrier in the bloodstream-the protein transferrin. We employed a reliable in silico approach based on DFT computations in order to understand the underlying biochemical processes that govern the Ga/Fe rivalry for binding the two bacterial siderophores.

Whether cortical neurons operate in a strongly or weakly correlated dynamical regime determines fundamental information processing capabilities and has fueled decades of debate. We offer a resolution of this debate; we show that two important dynamical regimes, typically considered incompatible, can coexist in the same local cortical circuit by separating them into two different subspaces. In awake mouse motor cortex, we find a low-dimensional subspace with large fluctuations consistent with criticality-a dynamical regime with moderate correlations and multi-scale information capacity and transmission. Orthogonal to this critical subspace, we find a high-dimensional subspace containing a desynchronized dynamical regime, which may optimize input discrimination. The critical subspace is apparent only at long timescales, which explains discrepancies among some previous studies. Using a computational model, we show that the emergence of a low-dimensional critical subspace at large timescales agrees with established theory of critical dynamics. Our results suggest that the cortex leverages its high dimensionality to multiplex dynamical regimes across different subspaces.

Fernandoa adenophylla, due to the presence of phytochemicals, has various beneficial properties and is used in folk medicine to treat many conditions. This study aimed to isolate indanone derivative from F. adenophylla root heartwood and assess in-vitro anti-inflammatory and anti-diabetic characteristics at varying concentrations. Heat-induced hemolysis and glucose uptake by yeast cells assays were conducted to evaluate these properties. Besides, docking analyses were performed on four molecular targets. These studies were combined with molecular dynamics simulations to elucidate the time-evolving inhibitory effect of selected inhibitors within the active pockets of the target proteins (COX-1 and COX-2). Indanone derivative (10-100 µM) inhibited the lysis of human red blood cells from 9.12 ± 0.75 to 72.82 ± 4.36% and, at 5-100 µM concentrations, it significantly increased the yeast cells' glucose uptake (5.16 ± 1.28% to 76.59 ± 1.62%). Concluding, the isolated indanone might act as an anti-diabetic agent by interacting with critical amino acid residues of 5' adenosine monophosphate-activated protein kinase (AMPK), and it showed a binding affinity with anti-inflammatory targets COX-1, COX-2, and TNF-α. Besides, the obtained results may help to consider the indanone derivative isolated from F. adenophylla as a promising candidate for drug delivery, subject to outcomes of further in vivo and clinical studies.

The precision of workpiece machining is critically influenced by the geometric errors in the kinematics of grind robots, which directly affect their absolute positioning accuracy. To tackle this challenge, this paper introduces a logistic-tent chaotic mapping Levenberg Marquardt algorithm designed to accurately identify and compensate for this geometric error. the approach begins with the construction of a forward kinematic model and an error model specific to the robot. Then the algorithm is adopted to identify and compensate for the geometric error. The method establishes a mapping interval around the initial candidate solutions derived from iterative applications of the Levenberg Marquardt algorithm. Within this interval, the logistic-tent chaotic mapping method generates a diverse set of candidate solutions. These candidates are evaluated based on their fitness values, with the optimal solution selected for subsequent iterations. Empirical compensation experiments have validated the proposed method's precision and effectiveness, demonstrating a 6% increase in compensation accuracy and a 47.68% improvement in efficiency compared to existing state-of-the-art approaches. This process not only minimizes the truncation error inherent in the Levenberg Marquardt algorithm but also significantly enhances solution efficiency. Moreover, simulation experiments on grind processes further validate the method's ability to significantly improve the quality of workpiece machining.

In consideration of the chromones' therapeutic potential and anticancer activity, a new series of chromanone derivatives have been synthesized through a straightforward reaction between 6-formyl-7-hydroxy-5-methoxy-2-methylchromone (2) and various organic active compounds. The cytotoxic activity of the newly synthesized congeners was investigated against MCF-7 (human breast cancer), HCT-116 (colon cancer), HepG2 (liver cancer), and normal skin fibroblast cells (BJ1). The obtained data indicated that compounds 14b, 17, and 19 induce cytotoxic activity in the breast MCF7, while compounds 6a, 6b, 11 and 14c showed highly potent activity in the colon cancer cell lines. Overall, the results demonstrate that the potential cytotoxic effects of the studied compounds may be based on their ability to induce DNA fragmentation in cancer cell lines, down-regulate the expression level of CDK4 as well as the anti-apoptotic gene Bcl-2 and up-regulate the expression of the pro-apoptotic genes P53 and Bax. Furthermore, compounds 14b and 14c showed a dual mechanism of action by inducing apoptosis and cell cycle arrest. The docking studies showed that the binding affinity of the most active cytotoxic compounds within the active pocket of the CDK4 enzyme is stronger due to hydrophobic and H-bonding interactions. These results were found to be consistent with the experimental results.

Polyhedral models of metabolic networks are computationally tractable and can predict some cellular functions. A longstanding challenge is incorporating metabolites without losing tractability. In this paper, we do so using a new second-order cone representation of the Michaelis-Menten kinetics. The resulting model consists of linear stoichiometric constraints alongside second-order cone constraints that couple the reaction fluxes to metabolite concentrations. We formulate several new problems around this model: conic flux balance analysis, which augments flux balance analysis with metabolite concentrations; dynamic conic flux balance analysis; and finding minimal cut sets of networks with both reactions and metabolites. Solving these problems yields information about both fluxes and metabolite concentrations. They are second-order cone or mixed-integer second-order cone programs, which, while not as tractable as their linear counterparts, can nonetheless be solved at practical scales using existing software.

Fabaceae plays a crucial role in African traditional medicine as a source of large number of important folk medication, agriculture and food plants. In a search of potential antioxidant and anti-inflammatory candidates derived from locally cultivated plants, the flowers of Tipuana tipu (Benth.) Lillo growing in Egypt were subjected to extensive biological and phytochemical studies. The impact of the extraction technique on the estimated biological activities was investigated.

3D visualization technology applies computers and other devices to create a realistic virtual world for individuals with various sensory experiences such as 3D vision, touch, and smell to gain a more effective understanding of the relationships between real spatial structures and organizations. The purpose of this study was to comprehensively evaluate the effectiveness of 3D visualization technology in human anatomy teaching/training and explore the potential factors that affect the training effects to better guide the teaching of classroom/laboratory anatomy.

Due to population growth, climate change, and the urban heat island effect, heat exposure is becoming an important issue faced by urban built environments. Heat exposure assessment is a prerequisite for mitigation measures to reduce the impact of heat exposure. However, there is limited research on urban heat exposure assessment approaches that provides fine-scale spatiotemporal heat exposure information, integrated with meteorological status and human collective exposure as they move about in cities, to enable proactive heat exposure mitigation measures. Smart city digital twins (SCDTs) provide a new potential avenue for addressing this gap, enabling fine spatiotemporal scales, human-infrastructure interaction modeling, and predictive and decision support capabilities. This study aims to develop and test an SCDT for collective urban heat exposure assessment and forecasting. Meteorological sensors and computer vision techniques were implemented in Columbus, Georgia, to acquire temperature, humidity, and passersby count data. These data were then integrated into a collective temperature humidity index. A time-series prediction model and a crowd simulation were employed to predict future short-term heat exposures based on the data accumulated by this SCDT and to support heat exposure mitigation efforts. The results demonstrate the potential of SCDT to enhance public safety by providing city officials with a tool for discovering, predicting, and, ultimately, mitigating community exposure to extreme heat.

Antioxidant peptides are a class of biologically active peptides with low molecular weights and stable antioxidant properties that are isolated from proteins. In this review, the progress in research on the activity evaluation, action mechanism, and structure-activity relationships of natural antioxidant peptides are summarized. The methods used to evaluate antioxidant activity are mainly classified into three categories: in vitro chemical, in vitro cellular, and in vivo animal methods. Also, the biological effects produced by these three methods are listed: the scavenging of free radicals, chelation of metal ions, inhibition of lipid peroxidation, inhibition of oxidative enzyme activities, and activation of antioxidant enzymes and non-enzymatic systems. The antioxidant effects of natural peptides primarily consist of the regulation of redox signaling pathways, which includes activation of the Nrf2 pathway and the inhibition of the NF-κB pathway. The structure-activity relationships of the antioxidant peptides are investigated, including the effects of peptide molecular weight, amino acid composition and sequence, and secondary structure on antioxidant activity. In addition, four computer-assisted methods (molecular docking, molecular dynamics simulation, quantum chemical calculations, and the determination of quantitative structure-activity relationships) for analyzing the structure-activity effects of natural peptides are summarized. Thus, this review lays a theoretical foundation for the development of new antioxidants, nutraceuticals, and cosmetics.

Cybersickness is a global issue affecting users of immersive virtual reality. However, there is no agreement on the exact cause of cybersickness. Taking into consideration how it can differ greatly from one person to another, it makes it even more difficult to determine the exact cause or find a solution. Because cybersickness excludes so many prospective users, including healthcare professionals, from using immersive virtual reality as a learning tool, this research sought to find solutions in existing literature and construct a framework that can be used to revent or minimise bersickness during mmersive irtual linical imulation (CyPVICS). The Bestfit Framework by Carrol and authors were used to construct the CyPVICS framework. The process started by conducting two separate literature searchers using the BeHEMoTh (for models, theories, and frameworks) and SPIDER (for primary research articles) search techniques. Once the literature searches were completed the models, theories and framework were used to construct a priori framework. The models' theories and frameworks were analysed to determine aspects relevant to causes, reducing, eliminating, and detecting cybersickness. The priori framework was expanded by, first coding the findings of the primary research study into the existing aspects of the priori framework. Once coded the aspects that could not be coded were added in the relevant category, for example causes. After reviewing 1567 abstracts and titles as part of the BeHEMoTh search string,19 full text articles, a total of 15 papers containing models, theories, and frameworks, were used to construct the initial CyPVICS framework. Once the initial CyPVICS was created, a total 904 primary research studies (SPIDER) were evaluated, based on their titles and abstracts, of which 100 were reviewed in full text. In total, 67 articles were accepted and coded to expand the initial CyPVICS framework. This paper presents the CyPVICS framework for use, not only in health professions' education, but also in other disciplines, since the incorporated models, theories, frameworks, and primary research studies were not specific to virtual clinical simulation.

In this paper, we develop a method of analyzing long transient dynamics in a class of predator-prey models with two species of predators competing explicitly for their common prey, where the prey evolves on a faster timescale than the predators. In a parameter regime near a singular zero-Hopf bifurcation of the coexistence equilibrium state, we assume that the system under study exhibits bistability between a periodic attractor that bifurcates from the singular Hopf point and another attractor, which could be a periodic attractor or a point attractor, such that the invariant manifolds of the coexistence equilibrium point play central roles in organizing the dynamics. To find whether a solution that starts in a vicinity of the coexistence equilibrium approaches the periodic attractor or the other attractor, we reduce the equations to a suitable normal form, and examine the basin boundary near the singular Hopf point. A key component of our study includes an analysis of the long transient dynamics, characterized by their rapid oscillations with a slow variation in amplitude, by applying a moving average technique. We obtain a set of necessary and sufficient conditions on the initial values of a solution near the coexistence equilibrium to determine whether it lies in the basin of attraction of the periodic attractor. As a result of our analysis, we devise a method of identifying early warning signals, significantly in advance, of a future crisis that could lead to extinction of one of the predators. The analysis is applied to the predator-prey model considered in Sadhu (Discrete Contin Dyn Syst B 26:5251-5279, 2021) and we find that our theory is in good agreement with the numerical simulations carried out for this model.

Cinnamon and motherwort are traditional Chinese medicines and are often combined to treat benign prostatic hyperplasia; however, the specific therapeutic mechanisms involved remain unclear. Therefore, in this study, we applied a network pharmacology approach to investigate the potential mechanisms of action of the drug pair cinnamon and motherwort (PCM) for the treatment of benign prostatic hyperplasia. Relevant targets for the use of PCM to treat benign prostatic hyperplasia were obtained through databases. Protein-protein interactions were then identified by the STRING database and core targets were screened. Enrichment analysis was conducted through the Metascape platform. Finally, molecular docking experiments were carried out to evaluate the affinity between the target proteins and ligands of PCM. We identified 22 active ingredients in PCM, 315 corresponding targets and 130 effective targets of PCM for the treatment of benign prostatic hyperplasia. These targets were related to the PI3K-Akt, MAPK, FoxO, TNF, and IL-17 signaling pathways. Network pharmacology was used to identify the effective components and action targets of PCM. We also identified potential mechanisms of action for PCM in the treatment of benign prostatic hyperplasia. Our results provide a foundation for expanding the clinical application of PCM and provide new ideas and directions for further research on the mechanisms of action of PCM and its components for the treatment of benign prostatic hyperplasia.

In this study, Antarctic sponge-derived discorhabdin G was considered a hit for developing potential lead compounds acting as cholinesterase inhibitors. The hypothesis on the pharmacophore moiety suggested through molecular docking allowed us to simplify the structure of the metabolite. ADME prediction and drug-likeness consideration provided valuable support in selecting 5-methyl-2H-benzo[h]imidazo[1,5,4-de]quinoxalin-7(3H)-one as a candidate molecule. It was synthesized in a four-step sequence starting from 2,3-dichloronaphthalene-1,4-dione and evaluated as an inhibitor of electric eel acetylcholinesterase (eeAChE), human recombinant AChE (hAChE), and horse serum butyrylcholinesterase (BChE), together with other analogs obtained by the same synthesis. The candidate molecule showed a slightly lower inhibitory potential against eeAChE but better inhibitory activity against hAChE than discorhabdin G, with a higher selectivity for AChEs than for BChE. It acted as a reversible competitive inhibitor, as previously observed for the natural alkaloid. The findings from the in vitro assay were relatively consistent with the data available from the AutoDock Vina and Protein-Ligand ANTSystem (PLANTS) calculations.

In this work, the univariant two-phase coexistence line of the tetrahydrofuran (THF) hydrate is determined from 100 to 1000 bar by molecular dynamics simulations. This study is carried out by putting in contact a THF hydrate phase with a stoichiometric aqueous solution phase. Following the direct coexistence technique, the pressure is fixed, and the coexistence line is determined by analyzing if the hydrate phase grows or melts at different values of temperature. Water is described using the well-known TIP4P/Ice model. We have used two different models of THF based on the transferable parameters for phase equilibria-united atom approach (TraPPE-UA), the original (flexible) TraPPe-UA model and a rigid and planar version of it. Overall, at high pressures, small differences are observed in the results obtained by both models. However, large differences are observed in the computational efforts required by the simulations performed using both models, being the rigid and planar version much faster than the original one. The effect of the unlike dispersive interactions between the water and THF molecules is also analyzed at 250 bar using the rigid and planar THF model. In particular, we modify the Berthelot combining rule via a parameter ξO-THF that controls the unlike water-THF dispersive interactions. We analyze the effect on the dissociation temperature of the hydrate when ξO-THF is modified from 1.0 (original Berthelot combining rule) to 1.4 (modified Berthelot combining rule). We use the optimized value ξO-THF = 1.4 and the rigid THF model in a transferable way to predict the dissociation temperatures at other pressures. We find excellent agreement between computer simulation predictions and experimental data taken from the literature.

Patulin contamination has become a bottleneck problem in the safe production of fruit products, although biodegradation technology shows potential application value in patulin control. In the present study, the patulin biodegradation mechanism in a probiotic yeast, S15-8, was investigated. Firstly, the short-chain dehydrogenase PgSDR encoded by gene A5D9S1 was identified as a patulin degradation enzyme, through RNA sequencing and verification by qRT-PCR. Subsequently, the exogenous expression system of the degradation protein PgSDR-A5D9S1 in was successfully constructed and demonstrated a more significant patulin tolerance and degradation ability. Furthermore, the structure of PgSDR-A5D9S1 and its active binding sites with patulin were predicted via molecular docking analysis. In addition, the heat-excited protein HSF1 was predicted as the transcription factor regulating the patulin degradation protein PgSDR-A5D9S1, which may provide clues for the further analysis of the molecular regulation mechanism of patulin degradation. This study provides a theoretical basis and technical support for the industrial application of biodegradable functional strains.

An ethyl acetate extract of a marine actinomycete strain, SCSIO 53858, isolated from a deep-sea sediment sample in the South China Sea, exhibited anti-quorum-sensing (QS) activity against CV026. Guided by the anti-QS activity, a novel active compound was isolated and purified from the extract and was identified as 2,3-dimethoxycinnamic acid (2,3-DCA) through spectral data analysis. At a concentration of 150 μg/mL, 2,3-DCA exhibited robust inhibitory effects on three QS-regulated traits of . CV026: violacein production, swarming motility, and biofilm formation, with inhibition rates of 73.9%, 65.9%, and 37.8%, respectively. The quantitative reverse transcription polymerase chain reaction results indicated that 2,3-DCA can disrupt the QS system in . CV026 by effectively suppressing the expression of QS-related genes, including , , , and . Molecular docking analysis revealed that 2,3-DCA hinders the QS system by competitively binding to the same binding pocket on the CviR receptor as the natural signal molecule N-hexanoyl-L-homoserine lactone. Collectively, these findings suggest that 2,3-DCA exhibits promising potential as an inhibitor of QS systems, providing a potential solution to the emerging problem of bacterial resistance.

With the increasing aging population, rational design of drugs for Alzheimer's disease (AD) treatment has become an important research area. Based on the multifunctional design strategy, four diosmetin derivatives (1-4) were designed, synthesized, and characterized by H NMR, C NMR, and MS. Docking study was firstly applied to substantiate the design strategies and then the biological activities including cholinesterase inhibition, metal chelation, antioxidation and β-amyloid (Aβ) aggregation inhibition in vitro were evaluated. The results showed that 1-4 had good acetylcholinesterase (AChE) and butyrylcholinesterase (BuChE) inhibition, metal chelation (selective chelation of Cu ions), antioxidation, self-induced, Cu-induced, and AChE-induced Aβ aggregation inhibition activities, and suitable blood-brain barrier (BBB) permeability. Especially, compound 3 had the strongest inhibitory effect on AChE (10 M magnitude) and BuChE (10 M magnitude) and showed the best inhibition on AChE-induced Aβ aggregation with 66.14% inhibition ratio. Furthermore, compound 3 could also reduce intracellular reactive oxygen species (ROS) levels in Caenorhabditis elegans and had lower cytotoxicity. In summary, 3 might be considered as a potential multifunctional anti-AD ligand.

In view of the extensive potential applications of chitinase (ChiA) in various fields such as agriculture, environmental protection, medicine, and biotechnology, the development of a high-yielding strain capable of producing chitinase with enhanced activity holds significant importance. The objective of this study was to utilize the extracellular chitinase from Bacillus thuringiensis as the target, and Bacillus licheniformis as the expression host to achieve heterologous expression of ChiA with enhanced activity. Initially, through structural analysis and molecular dynamics simulation, we identified key amino acids to improve the enzymatic performance of chitinase, and the specific activity of chitinase mutant D116N/E118N was 48% higher than that of the natural enzyme, with concomitant enhancements in thermostability and pH stability. Subsequently, the expression elements of ChiA(D116N/E118N) were screened and modified in Bacillus licheniformis, resulting in extracellular ChiA activity reached 89.31 U/mL. Further efforts involved the successful knockout of extracellular protease genes aprE, bprA and epr, along with the gene clusters involved in the synthesis of by-products such as bacitracin and lichenin from Bacillus licheniformis. This led to the development of a recombinant strain, DW2△abelA, which exhibited a remarkable improvement in chitinase activity, reaching 145.56 U/mL. To further improve chitinase activity, a chitinase expression frame was integrated into the genome of DW2△abelA, resulting in a significant increas to 180.26 U/mL. Optimization of fermentation conditions and medium components further boosted shake flask enzyme activity shake flask enzyme activity, achieving 200.28 U/mL, while scale-up fermentation experiments yielded an impressive enzyme activity of 338.79 U/mL. Through host genetic modification, expression optimization and fermentation optimization, a high-yielding ChiA strain was successfully constructed, which will provide a solid foundation for the extracellular production of ChiA.

Organoid technology is an emerging and rapidly growing field that shows promise in studying organ development and screening therapeutic regimens. Although organoids have been proposed for a decade, concerns exist, including batch-to-batch variations, lack of the native microenvironment and clinical applicability.

Resuscitation is a team effort, and it is increasingly acknowledged that team cooperation requires training. Staff shortages in many healthcare systems worldwide, as well as recent pandemic restrictions, limit opportunities for collaborative team training. To address this challenge, a learner-centred approach known as flipped learning has been successfully implemented. This model comprises self-directed, asynchronous pre-course learning, followed by knowledge application and skill training during in-class sessions. The existing evidence supports the effectiveness of this approach for the acquisition of cognitive skills, but it is uncertain whether the flipped classroom model is suitable for the acquisition of team skills. The objective of this study was to determine if a flipped classroom approach, with an online workshop prior to an instructor-led course could improve team performance and key resuscitation variables during classroom training.

With the generation of spatially resolved transcriptomics of microbial biofilms, computational tools can be used to integrate this data to elucidate the multi-scale mechanisms controlling heterogeneous biofilm metabolism. This work presents a Multi-scale model of Metabolism In Cellular Systems (MiMICS) which is a computational framework that couples a genome-scale metabolic network reconstruction (GENRE) with Hybrid Automata Library (HAL), an existing agent-based model and reaction-diffusion model platform. A key feature of MiMICS is the ability to incorporate multiple -omics-guided metabolic models, which can represent unique metabolic states that yield different metabolic parameter values passed to the extracellular models. We used MiMICS to simulate Pseudomonas aeruginosa regulation of denitrification and oxidative stress metabolism in hypoxic and nitric oxide (NO) biofilm microenvironments. Integration of P. aeruginosa PA14 biofilm spatial transcriptomic data into a P. aeruginosa PA14 GENRE generated four PA14 metabolic model states that were input into MiMICS. Characteristic of aerobic, denitrification, and oxidative stress metabolism, the four metabolic model states predicted different oxygen, nitrate, and NO exchange fluxes that were passed as inputs to update the agent's local metabolite concentrations in the extracellular reaction-diffusion model. Individual bacterial agents chose a PA14 metabolic model state based on a combination of stochastic rules, and agents sensing local oxygen and NO. Transcriptome-guided MiMICS predictions suggested microscale denitrification and oxidative stress metabolic heterogeneity emerged due to local variability in the NO biofilm microenvironment. MiMICS accurately predicted the biofilm's spatial relationships between denitrification, oxidative stress, and central carbon metabolism. As simulated cells responded to extracellular NO, MiMICS revealed dynamics of cell populations heterogeneously upregulating reactions in the denitrification pathway, which may function to maintain NO levels within non-toxic ranges. We demonstrated that MiMICS is a valuable computational tool to incorporate multiple -omics-guided metabolic models to mechanistically map heterogeneous microbial metabolic states to the biofilm microenvironment.

Excessive oxidative stress in the brain is an important pathological factor in neurological diseases. Acetoxypachydiol (APHD) is a lipophilic germacrane-type diterpene extracted as a major component from different species of brown algae within the genus Dictyota. There have been no previous reports on the pharmacological activity of APHD. The present research aims to explore the potential neuroprotective properties of APHD and its underlying mechanisms.

Eugenol exhibits broad-spectrum antibacterial and anti-inflammatory properties. However, cytotoxicity at high concentrations limits the full utilization of eugenol-based drug complexes. Formulations of multidrug-loaded eugenol-based nanoemulsions have reduced cytotoxicity; however, it remains crucial to understand how these eugenol complexes interact with primary human carrier proteins to design and develop therapeutic alternatives. Consequently, this study primarily aims to investigate the impact on Human Serum Albumin (HSA) when it interacts with eugenol-based complexes loaded with first-line anti-tuberculosis drugs.

B-box (BBX) proteins are a type of zinc finger proteins containing one or two B-box domains. They play important roles in development and diverse stress responses of plants, yet their roles in wheat remain unclear.

In epidemics, waning immunity is common after infection or vaccination of individuals. Immunity levels are highly heterogeneous and dynamic. This work presents an immuno-epidemiological model that captures the fundamental dynamic features of immunity acquisition and wane after infection or vaccination and analyzes mathematically its dynamical properties. The model consists of a system of first order partial differential equations, involving nonlinear integral terms and different transfer velocities. Structurally, the equation may be interpreted as a Fokker-Planck equation for a piecewise deterministic process. However, unlike the usual models, our equation involves nonlocal effects, representing the infectivity of the whole environment. This, together with the presence of different transfer velocities, makes the proved existence of a solution novel and nontrivial. In addition, the asymptotic behavior of the model is analyzed based on the obtained qualitative properties of the solution. An optimal control problem with objective function including the total number of deaths and costs of vaccination is explored. Numerical results describe the dynamic relationship between contact rates and optimal solutions. The approach can contribute to the understanding of the dynamics of immune responses at population level and may guide public health policies.

MEMS accelerometers are significantly impacted by temperature and noise, leading to a considerable compromise in their accuracy. In response to this challenge, we propose a parallel denoising and temperature compensation fusion algorithm for MEMS accelerometers based on RLMD-SE-TFPF and GRU-attention. Firstly, we utilize robust local mean decomposition (RLMD) to decompose the output signal of the accelerometer into a series of product function (PF) signals and a residual signal. Secondly, we employ sample entropy (SE) to classify the decomposed signals, categorizing them into noise segments, mixed segments, and temperature drift segments. Next, we utilize the time-frequency peak filtering (TFPF) algorithm with varying window lengths to separately denoise the noise and mixed signal segments, enabling subsequent signal reconstruction and training. Considering the strong inertia of the temperature signal, we innovatively introduce the accelerometer's output time series as the model input when training the temperature compensation model. We incorporate gated recurrent unit (GRU) and attention modules, proposing a novel GRU-MLP-attention model (GMAN) architecture. Simulation experiments demonstrate the effectiveness of our proposed fusion algorithm. After processing the accelerometer output signal through the RLMD-SE-TFPF denoising algorithm and the GMAN temperature drift compensation model, the acceleration random walk is reduced by 96.11%, with values of 0.23032 g/h/Hz for the original accelerometer output signal and 0.00895695 g/h/Hz for the processed signal.

The aim of this study was to investigate targets through which Gualou Xiebai Banxia decoction aids in treating myocardial infarction (MI) using network pharmacology in combination with molecular docking. The principal active ingredients of Gualou Xiebai Banxia decoction were identified from the TCMSP database using the criteria of drug-likeness ≥30% and oral bioavailability ≥0.18. Interactions and pathway enrichment were investigated using protein-protein interaction (PPI) networks and Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis, respectively. Active component structures were docked with those of potential protein targets using AutoDock molecular docking relative softwares. HIF1A was of particular interest as it was identified by the PPI network, GO and KEGG pathway enrichment analyses. In conclusion, the use of network pharmacology prediction and molecular docking assessments provides further information on the active components and mechanisms of action Gualou Xiebai Banxia decoction.

Abnormal blood coagulation is a major health problem and natural anticoagulants from blood-feeding organisms have been investigated as novel therapeutics. NAPc2, a potent nematode-derived inhibitor of coagulation, has an unusual mode of action that requires coagulation factor Xa but does not inhibit it. Molecular dynamics simulations of NAPc2 and factor Xa were generated to better understand NAPc2. The simulations suggest that parts of NAPc2 become more rigid upon binding factor Xa and reveal that two highly conserved residues form an internal salt bridge that stabilises the bound conformation. Clotting time assays with mutants confirmed the utility of the salt bridge and suggested that it is a conserved mechanism for stabilising the bound conformation of secondary structure-poor protease inhibitors.

: This study aimed to examine the responsiveness and concurrent validity of a serious game and its correlation between the use of serious games and upper limbs (UL) performance in Parkinson's Disease (PD) patients. : Twenty-four consecutive upper limbs (14 males, 8 females, age: 55-83 years) of PD patients were assessed. The clinical assessment included: the Box and Block test (BBT), Nine-Hole Peg test (9HPT), and sub-scores of the Unified Parkinson's Disease Rating-Scale Motor section (UPDRS-M) to assess UL disability. Performance scores obtained in two different tests (Ex. A and Ex. B, respectively, the Trolley test and Mushrooms test) based on leap motion (LM) sensors were used to study the correlations with clinical scores. : The subjective fatigue experienced during LM tests was measured by the Borg Rating of Perceived Exertion (RPE, 0-10); the BBT and 9HPT showed the highest correlation coefficients with UPDRS-M scores (ICCs: -0.652 and 0.712, < 0.05). Exercise A (Trolley test) correlated with UPDRS-M (ICC: 0.31, < 0.05), but not with the 9HPT and BBT tests (ICCs: -0.447 and 0.390, < 0.05), while Exercise B (Mushroom test) correlated with UPDRS-M (ICC: -0.40, < 0.05), as did these last two tests (ICCs: -0.225 and 0.272, < 0.05). The mean RPE during LM tests was 3.4 ± 3.2. The evaluation of upper limb performance is feasible and does not induce relevant fatigue. : The analysis of the ICC supports the use of Test B to evaluate UL disability and performance in PD patients, while Test A is mostly correlated with disability. Specifically designed serious games on LM can serve as a method of impairment in the PD population.

Cell models play a crucial role in analyzing the mechanical response of cells and quantifying cellular damage incurred during micromanipulation. While traditional models can capture the overall mechanical behavior of cells, they often lack the ability to discern among distinct cellular components. Consequently, by employing dissipative particle dynamics, this study constructed a triangular network-like representation of the cell membrane along with cross-linked cytoskeletal chains. The mechanical properties of both the membrane and cytoskeleton were then analyzed through a series of simulated mechanical tests, validated against real-world experiments. The investigation utilized particle-tracking rheology to monitor changes in the mean square displacements of membrane particles over time, facilitating the analysis of the membrane's storage and loss moduli. Additionally, the cytoskeletal network's storage and loss moduli were examined via a double-plate oscillatory shear experiment. The simulation results revealed that both the membrane and cytoskeleton exhibit viscoelastic behavior, as evidenced by the power-law dependency of their storage and loss moduli on frequency. Furthermore, indentation and microinjection simulations were conducted to examine the overall mechanical properties of cells. In the indentation experiments, an increase in the shear modulus of the membrane's WLCs correlated with a higher Young's modulus for the entire cell. Regarding the microinjection experiment, augmenting the microinjection speed resulted in reduced deformation of the cell at the point of membrane rupture and a lower percentage of high strain.

The ATP-binding cassette (ABC) transporters are a superfamily of membrane proteins. These active transporters are involved in the export of different substances such as xenobiotics. ABC transporters from subfamily C (ABCC) have also been described as functional receptors for different insecticidal proteins from (Bt) in several lepidopteran species. Numerous studies have characterized the relationship between the ABCC2 transporter and Bt Cry1 proteins. Although other ABCC transporters sharing structural and functional similarities have been described, little is known of their role in the mode of action of Bt proteins. For , only the ABCC2 transporter and its interaction with Cry1A proteins have been studied to date. Here, we have searched for paralogs to the gene in , and identified two new ABC transporter genes: and . Furthermore, we have characterized their gene expression in the midgut and their protein topology, and compared them with that of ABCC2. Finally, we discuss their possible interaction with Bt proteins by performing protein docking analysis.

The Philips Visual Patient Avatar, a user-centered visualization technology, offers an alternative approach to patient monitoring. Computer-based simulation studies indicate that it increases diagnostic accuracy and confidence, while reducing perceived workload. About three months after the technology's integration into clinical practice, we conducted an assessment among anesthesia providers to determine their views on its strengths, limitations, and overall perceptions. This single-center qualitative study at the University Hospital of Zurich examined anesthesia providers' perceptions of the Philips Visual Patient Avatar after its implementation. The study included an online survey to identify medical personnel's opinions on the technology's strengths and areas for improvement, which were analyzed using thematic analysis. A total of 63 of the 377 invited anesthesia providers (16.7%) responded to the survey. Overall, 163 comments were collected. The most prevalent positive themes were good presentation of specific parameters (16/163; 9.8%) and quick overview/rapid identification of problems (15/163; 9.2%). The most common perceived area for improvement was the ability to adjust the visualization thresholds of Visual Patient Avatar, which represent the physiological upper and lower vital-sign limits (33/163; 20.3%). The study showed that users consider Philips Visual Patient Avatar a valuable asset in anesthesia, allowing for easier identification of underlying problems. However, the study also revealed a user desire for the ability to freely adjust the thresholds of the Visual Patient Avatar by the handling caregivers, which were fixed to the departmental standard during the study.

Fracture pattern acquisition and representation in human bones play a crucial role in medical simulation, diagnostics, and treatment planning. This article presents a comprehensive review of methodologies employed in acquiring and representing bone fracture patterns. Several techniques, including segmentation algorithms, curvature analysis, and deep learning-based approaches, are reviewed to determine their effectiveness in accurately identifying fracture zones. Additionally, diverse methods for representing fracture patterns are evaluated. The challenges inherent in detecting accurate fracture zones from medical images, the complexities arising from multifragmentary fractures, and the need to automate fracture reduction processes are elucidated. A detailed analysis of the suitability of each representation method for specific medical applications, such as simulation systems, surgical interventions, and educational purposes, is provided. The study explores insights from a broad spectrum of research articles, encompassing diverse methodologies and perspectives. This review elucidates potential directions for future research and contributes to advancements in comprehending the acquisition and representation of fracture patterns in human bone.

This study assessed the suitability of the complementarity-determining region 2 (CDR2) of the nanobody (Nb) as a template for the derivation of nanobody-derived peptides (NDPs) targeting active-state β-adrenergic receptor (βAR) conformation. Sequences of conformationally selective Nbs favoring the agonist-occupied βAR were initially analyzed by the informational spectrum method (ISM). The derived NDPs in complex with βAR were subjected to protein-peptide docking, molecular dynamics (MD) simulations, and metadynamics-based free-energy binding calculations. Computational analyses identified a 25-amino-acid-long CDR2-NDP of Nb71, designated P4, which exhibited the following binding free-energy for the formation of the βAR:P4 complex (ΔG = -6.8 ± 0.8 kcal/mol or a Ki = 16.5 μM at 310 K) and mapped the βAR:P4 amino acid interaction network. In vitro characterization showed that P4 (i) can cross the plasma membrane, (ii) reduces the maximum isoproterenol-induced cAMP level by approximately 40% and the isoproterenol potency by up to 20-fold at micromolar concentration, (iii) has a very low affinity to interact with unstimulated βAR in the cAMP assay, and (iv) cannot reduce the efficacy and potency of the isoproterenol-mediated βAR/β-arrestin-2 interaction in the BRET-based recruitment assay. In summary, the CDR2-NDP, P4, binds preferentially to agonist-activated βAR and disrupts Gαs-mediated signaling.

The rapid development of mobile communication devices has brought challenges to wireless networks, where data packets are able to organize and maintain local area networks more freely without the constraints of wired devices. Scholars have developed diverse network protocols on how to ensure data transmission while maintaining its self-organizational nature. However, it is difficult for traditional network protocols to meet the needs of increasingly complex networks. In order to solve the problem that the better node set may not be selected when selecting the node set responsible for forwarding in the traditional OLSR protocol, a multi-objective optimized OLSR algorithm is proposed in this paper, which incorporating a new MPR mechanism and an improved NSGA-II algorithm. In the process of route discovery, the intermediate nodes responsible for forwarding packets are determined by the new MPR mechanism, and then the main parameters in the OLSR protocol are provided by the multi-objective optimization algorithm. Matlab was used to build a self-organizing network in this study. In addition, the conventional OLSR protocol, NSGA-II algorithm and multi-objective simulated annealing algorithm are selected to compare with the proposed algorithm. Simulation results show that the proposed algorithm can effectively reduce packet loss and end-to-end delay while obtaining better results in HV and Spacing, two multi-objective optimization result evaluation metrics.

The metaverse, as a new digital interactive platform, is garnering significant attention and exploration across industries due to technological advancements and societal digital transformation. In occupational health, there is immense potential for leveraging the metaverse to enhance work environments and occupational health management. It offers companies more efficient and intelligent solutions for occupational health management while providing employees with safer and more comfortable work environments.

Whereas undetected species contribute to estimation of species diversity, undetected alleles have not been used to estimated genetic diversity. Although random sampling guarantees unbiased estimation of allele frequency and genetic diversity measures, using undetected alleles may provide biased but more precise estimators useful for conservation. We newly devised kernel density estimation (KDE) for allele frequency including undetected alleles and tested it in estimation of allele frequency and nucleotide diversity using population generated by coalescent simulation as well as well as real population data. Contrary to expectations, nucleotide diversity estimated by KDE had worse bias and accuracy. Allele frequency estimated by KDE was also worse except when the sample size was small. These might be due to finity of population and/or the curse of dimensionality. In conclusion, KDE of allele frequency does not contribute to genetic diversity estimation.

Advances in simulations, combined with technological developments in high-performance computing, have made it possible to produce a physically accurate dynamic representation of complex biological systems involving millions to billions of atoms over increasingly long simulation times. The analysis of these computed simulations is crucial, involving the interpretation of structural and dynamic data to gain insights into the underlying biological processes. However, this analysis becomes increasingly challenging due to the complexity of the generated systems with a large number of individual runs, ranging from hundreds to thousands of trajectories. This massive increase in raw simulation data creates additional processing and visualization challenges. Effective visualization techniques play a vital role in facilitating the analysis and interpretation of molecular dynamics simulations. In this paper, we focus mainly on the techniques and tools that can be used for visualization of molecular dynamics simulations, among which we highlight the few approaches used specifically for this purpose, discussing their advantages and limitations, and addressing the future challenges of molecular dynamics visualization.