Computer Simulation

This article proposes a dispersal strategy for infected individuals in a spatial susceptible-infected-susceptible (SIS) epidemic model. The presence of spatial heterogeneity and the movement of individuals play crucial roles in determining the persistence and eradication of infectious diseases. To capture these dynamics, we introduce a moving strategy called risk-induced dispersal (RID) for infected individuals in a continuous-time patch model of the SIS epidemic. First, we establish a continuous-time -patch model and verify that the RID strategy is an effective approach for attaining a disease-free state. This is substantiated through simulations conducted on 7-patch models and analytical results derived from 2-patch models. Second, we extend our analysis by adapting the patch model into a diffusive epidemic model. This extension allows us to explore further the impact of the RID movement strategy on disease transmission and control. We validate our results through simulations, which provide the effects of the RID dispersal strategy.

Hyperspectral dark-field microscopy (HSDFM) and data cube analysis algorithms demonstrate successful detection and classification of various tissue types, including carcinoma regions in human post-lumpectomy breast tissues excised during breast-conserving surgeries.

The enormous LysR-type transcriptional regulators (LTTRs), which are diversely distributed amongst prokaryotes, play crucial roles in transcription regulation of genes involved in basic metabolic pathways, virulence and stress resistance. However, the precise transcription activation mechanism of these genes by LTTRs remains to be explored. Here, we determine the cryo-EM structure of a LTTR-dependent transcription activation complex comprising of Escherichia coli RNA polymerase (RNAP), an essential LTTR protein GcvA and its cognate promoter DNA. Structural analysis shows two N-terminal DNA binding domains of GcvA (GcvA_DBD) dimerize and engage the GcvA activation binding sites, presenting the -35 element for specific recognition with the conserved σR4. In particular, the versatile C-terminal domain of α subunit of RNAP directly interconnects with GcvA_DBD, σR4 and promoter DNA, providing more interfaces for stabilizing the complex. Moreover, molecular docking supports glycine as one potential inducer of GcvA, and single molecule photobleaching experiments kinetically visualize the occurrence of tetrameric GcvA-engaged transcription activation complex as suggested for the other LTTR homologs. Thus, a general model for tetrameric LTTR-dependent transcription activation is proposed. These findings will provide new structural and functional insights into transcription activation of the essential LTTRs.

Dysregulation of RNA splicing processes is intricately linked to tumorigenesis in various cancers, especially breast cancer. Cdc2-like kinase 2 (CLK2), an oncogenic RNA-splicing kinase pivotal in breast cancer, plays a significant role, particularly in the context of triple-negative breast cancer (TNBC), a subtype marked by substantial medical challenges due to its low survival rates. In this study, we employed a structure-based virtual screening (SBVS) method to identify potential CLK2 inhibitors with novel chemical structures for treating TNBC. Compound 670551 emerged as a novel CLK2 inhibitor with a 50% inhibitory concentration (IC) value of 619.7 nM. Importantly, Compound 670551 exhibited high selectivity for CLK2 over other protein kinases. Functionally, this compound significantly reduced the survival and proliferation of TNBC cells. Results from a cell-based assay demonstrated that this inhibitor led to a decrease in RNA splicing proteins, such as SRSF4 and SRSF6, resulting in cell apoptosis. In summary, we identified a novel CLK2 inhibitor as a promising potential treatment for TNBC therapy.

Peach fruit is prone to chilling injury (CI) during low-temperature storage, resulting in quality deterioration and economic losses. Our previous studies have found that exogenous trehalose treatment can alleviate the CI symptoms of peach by increasing sucrose accumulation. The purpose of this study was to explore the potential molecular mechanism of trehalose treatment in alleviating CI in postharvest peach fruit. Transcriptome analysis showed that trehalose induced gene expression in pathways of plant MAPK signaling, calcium signaling, and reactive oxygen species (ROS) signaling. Furthermore, molecular docking analysis indicated that PpCDPK24 may activate the ROS signaling pathway by phosphorylating PpRBOHE. Besides, PpWRKY40 mediates the activation of PpMAPKKK2-induced ROS signaling pathway by interacting with the PpRBOHE promoter. Accordingly, trehalose treatment significantly enhanced the activities of antioxidant-related enzymes such as superoxide dismutase (SOD), catalase (CAT), ascorbate peroxidase (APX), and gluathione reductase (GR), as well as the transcription levels AsA-GSH cycle related gene, which led to the reduction of HO and malondialdehyde (MDA) content in peach during cold storage. In summary, our results suggest that the potential molecular mechanism of trehalose treatment is to enhance antioxidant capacity by activating CDPK-mediated Ca -ROS signaling pathway and WRKY-mediated MAPK-WRKY-ROS signaling pathway, thereby reducing the CI in peach fruit.

Bioactive phytocompounds are crucial components in all plants. Since the time of traditional medicine, the utilization of plants has been grounded in the potential of these bioactive compounds to treat or manage specific illnesses. These natural bioactive compounds have sparked growing interest in employing medicinal plants for addressing various conditions, such as inflammatory diseases, diabetes, and cancer. This study focuses on assessing the qualitative phytochemical composition, antioxidant potential, and cytotoxic effects of blueberry (Vaccinium sect. Cyanococcus) extract using three different solvents, namely water, ethanol, and methanol. The extract exhibited notable antioxidant activities, as evidenced by DPPH and HO free radical scavenging assays. The cell viability assay also demonstrated cell growth inhibition in A549 cells. Furthermore, nine specific phytocompounds sourced from existing literature were selected for molecular docking studies against CDK6 and, AMPK key protein kinases which enhance the cancer progression. The molecular docking results also revealed favorable binding scores, with a high score of -9.5 kcal/mol in CDK6 protein and a maximum score of AMPK with targets of -8.8 kcal/mol. The selected phytocompounds' pharmacodynamic properties such as ADMET also supported the study. Furthermore, rutin stated that pre-dominantly present in blueberry plants shows a potent cytotoxicity effect in A549 cells. Functional annotations by bioinformatic analysis for rutin also revealed the strong enrichment in the involvement of PI3K/AKT1/STAT, and p53 signaling pathways. Based on this analysis, the identified rutin and other compounds hold a promising anticancer activity. Overall, the comprehensive evaluation of both in vitro and in silico data suggests that the Vaccinium sect. Cyanococcus extract could serve as a valuable source of pharmaceutical agents and may prove effective in future therapeutic applications.

The identification of an effective inhibitor is an important starting step in drug development. Unfortunately, many issues such as the characterization of protein binding sites, the screening library, materials for assays, etc., make drug screening a difficult proposition. As the size of screening libraries increases, more resources will be inefficiently consumed. Thus, new strategies are needed to preprocess and focus a screening library towards a targeted protein. Herein, we report an ensemble machine learning (ML) model to generate a CDK8-focused screening library. The ensemble model consists of six different algorithms optimized for CDK8 inhibitor classification. The models were trained using a CDK8-specific fragment library along with molecules containing CDK8 activity. The optimized ensemble model processed a commercial library containing 1.6 million molecules. This resulted in a CDK8-focused screening library containing 1,672 molecules, a reduction of more than 99.90%. The CDK8-focused library was then subjected to molecular docking, and 25 candidate compounds were selected. Enzymatic assays confirmed six CDK8 inhibitors, with one compound producing an IC value of ≤100 nM. Analysis of the ensemble ML model reveals the role of the CDK8 fragment library during training. Structural analysis of molecules reveals the hit compounds to be structurally novel CDK8 inhibitors. Together, the results highlight a pipeline for curating a focused library for a specific protein target, such as CDK8.

To explore the potential mechanism in Cuscuta sinensis on diarrhea-type irritable bowel syndrome using network pharmacology and molecular docking techniques. First, the active components and related targets of Cuscuta were found setting oral utilization >30% and drug-like properties greater than or equal to 0.18 as filter information from TCMSP database. The targets of diarrheal irritable bowel syndrome were compiled by searching DrugBank, GeneCards, OMIM, PharmGkb, and TTD databases. The intersections of drugs and targets related to the disease were taken for gene ontology enrichment and Kyoto encyclopedia of genes and genomes enrichment analyses, to elucidate the potential molecular mechanisms and pathway information of Cuscuta sinensis for the treatment of diarrheal irritable bowel syndrome. The protein-protein interaction network was constructed by using the STRING database and visualized with Cytoscape_v3.10.0 software to find the protein-protein interaction network core At last, molecular docking was performed to validate the combination of active compounds with the core target. The target information of Cuscuta and diarrhea-type irritable bowel syndrome was compiled, which can be resulted in 11 active compounds such as quercetin, kaempferol, isorhamnetin, β-sitosterol, and another 17 core targets such as TP53, IL6, AKT1, IL1B, TNF, EGFR, etc, whose Kyoto encyclopedia of genes and genomes was enriched in the pathways of lipids and atherosclerosis, chemical carcinogenesis-receptor activation, PI3K-Akt signaling pathway, and fluid shear stress and atherosclerosis, etc. Docking demonstrated that the core targets and the active compounds were able to be better combined. Cuscuta chinensis may exert preventive effects on diarrhea-type irritable bowel syndrome by reducing intestinal inflammation, protecting intestinal mucosa, and playing an important role in antioxidant response through multi-targets and multi-pathways.

Glioma is a typical malignant tumor of the nervous system. It is of great significance to identify new biomarkers for accurate diagnosis of glioma. In this context, THOC6 has been studied as a highly diagnostic prognostic biomarker, which contributes to improve the dilemma in diagnosing gliomas. We used online databases and a variety of statistical methods, such as Wilcoxon rank sum test, Dunn test and t test. We analyzed the mutation, location and expression profile of THOC6, revealing the network of THOC6 interaction with disease. Wilcoxon rank sum test showed that THOC6 is highly expressed in gliomas (P < 0.001). Dunn test, Wilcoxon rank sum test and t test showed that THOC6 expression was correlated with multiple clinical features. Logistic regression analysis further confirmed that THOC6 gene expression was a categorical dependent variable related to clinical features of poor prognosis. Kaplan-Meier survival analysis showed that the overall survival (OS) of glioma patients with high expression of THOC6 was poor (P < 0.001). Both univariate (P < 0.001) and multivariate (P = 0.04) Cox analysis confirmed that THOC6 gene expression was an independent risk factor for OS in patients with glioma. ROC curve analysis showed that THOC6 had a high diagnostic value in glioma (AUC = 0.915). Based on this, we constructed a nomogram to predict patient survival. Enrichment analysis showed that THOC6 expression was associated with multiple signal pathways. Immuno-infiltration analysis showed that the expression of THOC6 in glioma was closely related to the infiltration level of multiple immune cells. Molecular docking results showed that THOC6 might be the target of anti-glioma drugs. THOC6 is a novel diagnostic factor and prognostic biomarker of glioma.

Modern medicine has no cure for the xerostomia caused by the early onset of Sjögren's syndrome. Mume Fructus is a common Chinese herbal medicine used to relieve xerostomia. However, the molecular mechanisms of the effects of Mume Fructus are unknown. In this study, network pharmacology and molecular docking were used to investigate the mechanisms of action of Mume Fructus on Sjögren's syndrome.

Knee varus (KV) deformity leads to abnormal forces in the different compartments of the joint cavity and abnormal mechanical loading thus leading to knee osteoarthritis (KOA). This study used computer-aided design to create 3-dimensional simulation models of KOA with varying varus angles to analyze stress distribution within the knee joint cavity using finite element analysis for different varus KOA models and to compare intra-articular loads among these models. Additionally, we developed a cartilage loading model of static KV deformity to correlate with dynamic clinical cases of cartilage injury. Different KV angle models were accurately simulated with computer-aided design, and the KV angles were divided into (0°, 3°, 6°, 9°, 12°, 15°, and 18°) 7 knee models, and then processed with finite element software, and the Von-Mises stress distribution and peak values of the cartilage of the femoral condyles, medial tibial plateau, and lateral plateau were obtained by simulating the human body weight in axial loading while performing the static extension position. Finally, intraoperative endoscopy visualization of cartilage injuries in clinical cases corresponding to KV deformity subgroups was combined to find cartilage loading and injury correlations. With increasing varus angle, there was a significant increase in lower limb mechanical axial inward excursion and peak Von-Mises stress in the medial interstitial compartment. Analysis of patients' clinical data demonstrated a significant correlation between varus deformity angle and cartilage damage in the knee, medial plateau, and patellofemoral intercompartment. Larger varus deformity angles could be associated with higher medial cartilage stress loads and increased cartilage damage in the corresponding peak stress area. When the varus angle exceeds 6°, there is an increased risk of cartilage damage, emphasizing the importance of early surgical correction to prevent further deformity and restore knee function.

Danhong injection, a compound injection of Chinese herbal medicine, has been widely used in idiopathic pulmonary fibrosis (IPF) at present as an adjuvant treatment. However, the clinical efficacy and molecular mechanism of IPF are still unclear. This study will evaluate and explore the clinical efficacy and molecular mechanism of Danhong injection in the treatment of IPF.

Electron paramagnetic resonance (EPR) imaging is an advanced in vivo oxygen imaging modality. The main drawback of EPR imaging is the long scanning time. Sparse-view projections collection is an effective fast scanning pattern. However, the commonly-used filtered backprojection (FBP) algorithm is not competent to accurately reconstruct images from sparse-view projections because of the severe streak artifacts. The aim of this work is to develop an advanced algorithm for sparse reconstruction of 3D EPR imaging.

This work presents the design and multiphysics simulation of a cylindrical waveguide-based optical switch using germanium-antimony-tellurium (GST) as an active phase change material. The innovative cylindrical architecture is theoretically analyzed and evaluated at 1550 nm wavelength for telecommunication applications. The dispersion relation is derived analytically for the first time to model the optical switch, while finite element method (FEM) and finite difference time domain (FDTD) techniques are utilized to simulate the optical modes, light propagation, and phase change dynamics. The fundamental TE and HE modes are studied in detail, enabling switching between low-loss amorphous and high-loss crystalline GST phases. Increasing the GST thickness is found to increase absorption loss in the crystalline state but also slows down phase transition kinetics, reducing switching speeds. A 10 nm GST layer results in competitive performance metrics of 0.79 dB insertion loss, 13.47 dB extinction ratio, 30 nJ average power consumption, and 3.5 Mb/s bit rate. The combined optical, thermal, and electrical simulation provides comprehensive insights towards developing integrated non-volatile photonic switches and modulators utilizing phase change materials.

This study explores the impact of virtual simulation training on the transformation of teamwork attitudes among pharmacy students in a simulated severe COVID-19 pneumonia scenario in the emergency department.

Within hospital systems, diverse subsets of patients are subject to minimally invasive procedures that provide therapeutic relief and necessary health data that are often perceived as anxiogenic or painful. These feelings are particularly relevant to patients experiencing procedures where they are conscious and not sedated or placed under general anesthesia that renders them incapacitated. Pharmacologic pain management and topical anesthetic creams are used to manage these feelings; however, distraction-based methods can provide nonpharmacologic means to modify the painful experience and discomfort often associated with these procedures. Recent studies support distraction as a useful method for reducing anxiety and pain and as a result, improving patient experience. Virtual reality (VR) is an emerging technology that provides an immersive user experience and can operate through a distraction-based method to reduce the negative or painful experience often related to procedures where the patient is conscious. Given the possible short-term and long-term outcomes of poorly managed pain and enduring among patients, health care professionals are challenged to improve patient well-being during medically essential procedures.

Polypharmacology drugs-compounds that inhibit multiple proteins-have many applications but are difficult to design. To address this challenge we have developed POLYGON, an approach to polypharmacology based on generative reinforcement learning. POLYGON embeds chemical space and iteratively samples it to generate new molecular structures; these are rewarded by the predicted ability to inhibit each of two protein targets and by drug-likeness and ease-of-synthesis. In binding data for >100,000 compounds, POLYGON correctly recognizes polypharmacology interactions with 82.5% accuracy. We subsequently generate de-novo compounds targeting ten pairs of proteins with documented co-dependency. Docking analysis indicates that top structures bind their two targets with low free energies and similar 3D orientations to canonical single-protein inhibitors. We synthesize 32 compounds targeting MEK1 and mTOR, with most yielding >50% reduction in each protein activity and in cell viability when dosed at 1-10 μM. These results support the potential of generative modeling for polypharmacology.

The dissipative particle dynamics (DPD) simulation was used to study the morphologies and structures of the paclitaxel-loaded PLA-b-PEO-b-PLA polymeric micelle. We focused on the influences of PLA block length, PLA-b-PEO-b-PLA copolymer concentration, paclitaxel drug content on morphologies and structures of the micelle. Our simulations show that: (i) with the PLA block length increase, the self-assemble structure of PLA-b-PEO-b-PLA copolymers with paclitaxel vary between onion-like structure (core-middle layer-shell) to spherical core-shell structure. The PEO shell thins and the size of the PLA core increases. The onionlike structures are comprised of the PEO hydrophilic core, the PLA hydrophobic middle layer, and the PEO hydrophilic shell, the distribution of the paclitaxel drug predominantly occurs within the hydrophobic intermediate layer; (ii) The system forms a spherical core-shell structure when a small amount of the drug is added, and within a certain range, the size of the spherical structure increases as the drug amount increases. When the drug contents (volume fraction) c = 10%, it can be observed that the PLA-b-PEO-b-PLA spherical structures connect to form rod-shaped structures. With the length of PLA block N = 8, as the paclitaxel drug concentrations c = 4%, PEO has been insufficient to completely encapsulate the PLA and paclitaxel drug beads. To enhance drug loading capacity while maintaining stability of the system in aqueous solution, the optimal composition for loading paclitaxel is PLA-b-PEO-b-PLA; the drug content is not higher than 4%; (iii) The paclitaxel-loaded PLA-b-PEO-b-PLA micelle undergo the transition from onionlike (core-middle layer-shell) to spherical (core-shell) to rod-shaped and lamellar structure as the PLA-b-PEO-b-PLA copolymer concentration increases from c = 10% to 40%.

Due to the complex interactions between multiple infectious diseases, the spreading of diseases in human bodies can vary when people are exposed to multiple sources of infection at the same time. Typically, there is heterogeneity in individuals' responses to diseases, and the transmission routes of different diseases also vary. Therefore, this paper proposes an SIS disease spreading model with individual heterogeneity and transmission route heterogeneity under the simultaneous action of two competitive infectious diseases. We derive the theoretical epidemic spreading threshold using quenched mean-field theory and perform numerical analysis under the Markovian method. Numerical results confirm the reliability of the theoretical threshold and show the inhibitory effect of the proportion of fully competitive individuals on epidemic spreading. The results also show that the diversity of disease transmission routes promotes disease spreading, and this effect gradually weakens when the epidemic spreading rate is high enough. Finally, we find a negative correlation between the theoretical spreading threshold and the average degree of the network. We demonstrate the practical application of the model by comparing simulation outputs to temporal trends of two competitive infectious diseases, COVID-19 and seasonal influenza in China.

Monkeypox (Mpox) is an important human pathogen without etiological treatment. A viral-host interactome study may advance our understanding of molecular pathogenesis and lead to the discovery of suitable therapeutic targets.

In this work, the vapor-liquid-liquid equilibrium (VLLE) of the water/-pentane, water/-hexane, water/-octane, and water/-decane binary systems is calculated by computer simulation using the NVT-Gibbs ensemble (in the version of three simulation boxes) combined with the configurational bias Monte Carlo method. The combination of both methods, the molecular potential models used, and the simulation details allowed us to calculate the triphasic equilibrium properties of the systems studied: the densities of the three phases in equilibrium, their compositions, and potential energies. In previous works, these simulations were not carried out at a temperature range nor water/-alkanes systems simulated in this work, probably because they are highly nonideal systems; so, to the best of our knowledge, this is the first time that this phenomenon is studied in detail. The results from VLLE simulations of the water/-pentane system for temperatures from 343.2 to 435 K, the water/-hexane system for temperatures from 373.11 to 473.15 K, the water/-octane system for temperatures from 310.9 to 500 K, and for the water/-decane system for temperatures from 374.15 to 525 K are reported here. The temperature range was selected in concordance with the experimental data available for an adequate study of the VLLE simulation results. The subcritical densities (vapor and liquid rich in -alkane phases) at various temperatures fit well with the scaling law and the law of rectilinear diameters, allowing the estimation of upper critical end point temperature and density of the VLLE. The simulation results show a good prediction with experimental data reports in the literature.

Prior research has demonstrated high levels of color constancy in real-world scenarios featuring single light sources, extensive fields of view, and prolonged adaptation periods. However, exploring the specific cues humans rely on becomes challenging, if not unfeasible, with actual objects and lighting conditions. To circumvent these obstacles, we employed virtual reality technology to craft immersive, realistic settings that can be manipulated in real time. We designed forest and office scenes illuminated by five colors. Participants selected a test object most resembling a previously shown achromatic reference. To study color constancy mechanisms, we modified scenes to neutralize three contributors: local surround (placing a uniform-colored leaf under test objects), maximum flux (keeping the brightest object constant), and spatial mean (maintaining a neutral average light reflectance), employing two methods for the latter: changing object reflectances or introducing new elements. We found that color constancy was high in conditions with all cues present, aligning with past research. However, removing individual cues led to varied impacts on constancy. Local surrounds significantly reduced performance, especially under green illumination, showing strong interaction between greenish light and rose-colored contexts. In contrast, the maximum flux mechanism barely affected performance, challenging assumptions used in white balancing algorithms. The spatial mean experiment showed disparate effects: Adding objects slightly impacted performance, while changing reflectances nearly eliminated constancy, suggesting human color constancy relies more on scene interpretation than pixel-based calculations.

Understanding the intricate relationships between diseases is critical for both prevention and recovery. However, there is a lack of suitable methodologies for exploring the precedence relationships within multiple censored time-to-event data, resulting in decreased analytical accuracy. This study introduces the Censored Event Precedence Analysis (CEPA), which is a nonparametric Bayesian approach suitable for understanding the precedence relationships in censored multivariate events. CEPA aims to analyze the precedence relationships between events to predict subsequent occurrences effectively. We applied CEPA to neonatal data from the National Health Insurance Service, identifying the precedence relationships among the seven most commonly diagnosed diseases categorized by the International Classification of Diseases. This analysis revealed a typical diagnostic sequence, starting with respiratory diseases, followed by skin, infectious, digestive, ear, eye, and injury-related diseases. Furthermore, simulation studies were conducted to demonstrate CEPA suitability for censored multivariate datasets compared to traditional models. The performance accuracy reached 76% for uniform distribution and 65% for exponential distribution, showing superior performance in all four tested environments. Therefore, the statistical approach based on CEPA enhances our understanding of disease interrelationships beyond competitive methodologies. By identifying disease precedence with CEPA, we can preempt subsequent disease occurrences and propose a healthcare system based on these relationships.

Candida auris is an invasive fungal pathogen of high concern due to acquired drug tolerance against antifungals used in clinics. The prolonged persistence on biotic and abiotic surfaces can result in onset of hospital outbreaks causing serious health threat. An in depth understanding of pathology of C. auris is highly desirable for development of efficient therapeutics. Non-coding RNAs play crucial role in fungal pathology. However, the information about ncRNAs is scanty to be utilized. Herein our aim is to identify long noncoding RNAs with potent role in pathobiology of C. auris. Thereby, we analyzed the transcriptomics data of C. auris infection in blood for identification of potential lncRNAs with regulatory role in determining invasion, survival or drug tolerance under infection conditions. Interestingly, we found 275 lncRNAs, out of which 253 matched with lncRNAs reported in Candidamine, corroborating for our accurate data analysis pipeline. Nevertheless, we obtained 23 novel lncRNAs not reported earlier. Three lncRNAs were found to be under expressed throughout the course of infection, in the transcriptomics data. 16 of potent lncRNAs were found to be coexpressed with coding genes, emphasizing for their functional role. Noteworthy, these ncRNAs are expressed from intergenic regions of the genes associated with transporters, metabolism, cell wall biogenesis. This study recommends for possible association between lncRNA expression and C. auris pathogenesis.

A popular and widely suggested measure for assessing unilateral hand motor skills in stroke patients is the box and block test (BBT). Our study aimed to create an augmented reality enhanced version of the BBT (AR-BBT) and evaluate its correlation to the original BBT for stroke patients. Following G-power analysis, clinical examination, and inclusion-exclusion criteria, 31 stroke patients were included in this study. AR-BBT was developed using the Open Source Computer Vision Library (OpenCV). The MediaPipe's hand tracking library uses a palm and a hand landmark machine learning model to detect and track hands. A computer and a depth camera were employed in the clinical evaluation of AR-BBT following the principles of traditional BBT. A strong correlation was achieved between the number of blocks moved in the BBT and the AR-BBT on the hemiplegic side (Pearson correlation = 0.918) and a positive statistically significant correlation (p = 0.000008). The conventional BBT is currently the preferred assessment method. However, our approach offers an advantage, as it suggests that an AR-BBT solution could remotely monitor the assessment of a home-based rehabilitation program and provide additional hand kinematic information for hand dexterities in AR environment conditions. Furthermore, it employs minimal hardware equipment.

The present work elicits a novel approach to combating COVID-19 by synthesizing a series of azo-anchored 3,4-dihydroimidazo[4,5-b]indole derivatives. The envisaged methodology involves the L-proline-catalyzed condensation of para-amino-functionalized azo benzene, indoline-2,3-dione, and ammonium acetate precursors with pertinent aryl aldehyde derivatives under ultrasonic conditions. The structures of synthesized compounds were corroborated through FT-IR, H NMR, C NMR, and mass analysis data. Molecular docking studies assessed the inhibitory potential of these compounds against the main protease (M) of SARS-CoV-2. Remarkably, in silico investigations revealed significant inhibitory action surpassing standard drugs such as Remdesivir, Paxlovid, Molnupiravir, Chloroquine, Hydroxychloroquine (HCQ), and (N3), an irreversible Michael acceptor inhibitor. Furthermore, the highly active compound was also screened for cytotoxicity activity against HEK-293 cells and exhibited minimal toxicity across a range of concentrations, affirming its favorable safety profile and potential suitability. The pharmacokinetic properties (ADME) of the synthesized compounds have also been deliberated. This study paves the way for in vitro and in vivo testing of these scaffolds in the ongoing battle against SARS-CoV-2.

The rapid growth of the ageing population underscores the critical need for dementia care training among care providers. Innovative virtual reality (VR) technology has created opportunities to improve dementia care training. This scoping review will specifically focus on the barriers, facilitators and impacts of implementing fully immersive VR training for dementia care among staff in long-term care (LTC) settings.

Response functions are a fundamental aspect of physics; they represent the link between experimental observations and the underlying quantum many-body state. However, this link is often under-appreciated, as the Lehmann formalism for obtaining response functions in linear response has no direct link to experiment. Within the context of quantum computing, and via a linear response framework, we restore this link by making the experiment an inextricable part of the quantum simulation. This method can be frequency- and momentum-selective, avoids limitations on operators that can be directly measured, and can be more efficient than competing methods. As prototypical examples of response functions, we demonstrate that both bosonic and fermionic Green's functions can be obtained, and apply these ideas to the study of a charge-density-wave material on the ibm_auckland superconducting quantum computer. The linear response method provides a robust framework for using quantum computers to study systems in physics and chemistry.

We unify evolutionary dynamics on graphs in strategic uncertainty through a decaying Bayesian update. Our analysis focuses on the Price theorem of selection, which governs replicator(-mutator) dynamics, based on a stratified interaction mechanism and a composite strategy update rule. Our findings suggest that the replication of a certain mutation in a strategy, leading to a shift from competition to cooperation in a well-mixed population, is equivalent to the replication of a strategy in a Bayesian-structured population without any mutation. Likewise, the replication of a strategy in a Bayesian-structured population with a certain mutation, resulting in a move from competition to cooperation, is equivalent to the replication of a strategy in a well-mixed population without any mutation. This equivalence holds when the transition rate from competition to cooperation is equal to the relative strength of selection acting on either competition or cooperation in relation to the selection differential between cooperators and competitors. Our research allows us to identify situations where cooperation is more likely, irrespective of the specific payoff levels. This approach provides new perspectives into the intended purpose of Price's equation, which was initially not designed for this type of analysis.

The article introduces a revolutionary Nanorouter structure, which is a crucial component in the Nano communication regime. To complete the connection, many key properties of Nanorouters are investigated and merged. QCA circuits with better speed and reduced power dissipation aid in meeting internet standards. Cryptography based on QCA design methodologies is a novel concept in digital circuit design. Data security in nano-communication is crucial in data transmission and reception; hence, cryptographic approaches are necessary. The data entering the input line is encrypted by an encoder, and then sent to the designated output line, where it is decoded and transferred. The Nanorouter is offered as a data path selector, and the proposed study analyses the cell count of QCA and the circuit delay. In this manuscript, novel designs of (4:1)) Mux and (1:4) Demux designs are utilized to implement the proposed nanorouter design. The proposed (4:1) Mux design requires 3-5% fewer cell counts and 20-25% fewer area, and the propsoed (1:4) Demux designs require 75-80% fewer cell counts and 90-95% fewer area compared to their latest counterparts. The QCAPro utility is used to analyse the power consumption of several components that make up the router. QCADesigner 2.0.3 is used to validate the simulation results and output validity.

To develop and validate a 3D simulation model to calculate laser ablation (LA) zone size and estimate the volume of treated tissue for thyroid applications, a model was developed, taking into account dynamic optical and thermal properties of tissue change. For validation, ten Yorkshire swines were equally divided into two cohorts and underwent thyroid LA at 3 W/1,400 J and 3 W/1,800 J respectively with a 1064-nm multi-source laser (Echolaser X4 with Orblaze technology; ElEn SpA, Calenzano, Italy). The dataset was analyzed employing key statistical measures such as mean and standard deviation (SD). Model simulation data were compared with animal gross histology. Experimental data for longitudinal length, width (transverse length), ablation volume and sphericity were 11.0 mm, 10.0 mm, 0.6 mL and 0.91, respectively at 1,400 J and 14.6 mm, 12.4 mm, 1.12 mL and 0.83, respectively at 1,800 J. Gross histology data showed excellent reproducibility of the ablation zone among same laser settings; for both 1,400 J and 1,800 J, the SD of the in vivo parameters was ≤ 0.7 mm, except for width at 1,800 J, for which the SD was 1.1 mm. Simulated data for longitudinal length, width, ablation volume and sphericity were 11.6 mm, 10.0 mm, 0.62 mL and 0.88, respectively at 1,400 J and 14.2 mm, 12.0 mm, 1.06 mL and 0.84, respectively at 1,800 J. Experimental data for ablation volume, sphericity coefficient, and longitudinal and transverse lengths of thermal damaged area showed good agreement with the simulation data. Simulation datasets were successfully incorporated into proprietary planning software (Echolaser Smart Interface, Elesta SpA, Calenzano, Italy) to provide guidance for LA of papillary thyroid microcarcinomas. Our mathematical model showed good predictability of coagulative necrosis when compared with data from in vivo animal experiments.

In recent years, virtual reality (VR) has gained significant importance in medical education. Radiology education also has seen the induction of VR technology. However, there is no comprehensive review in this specific area. This review aims to fill this knowledge gap.

Ergot alkaloids (EAs) are a diverse group of indole alkaloids known for their complex structures, significant pharmacological effects, and toxicity to plants. The biosynthesis of these compounds begins with chanoclavine-I aldehyde (CC aldehyde, 2), an important intermediate produced by the enzyme EasD or its counterpart FgaDH from chanoclavine-I (CC, 1). However, how CC aldehyde 2 is converted to chanoclavine-I acid (CC acid, 3), first isolated from Ipomoea violacea several decades ago, is still unclear. In this study, we provide in vitro biochemical evidence showing that EasD not only converts CC 1 to CC aldehyde 2 but also directly transforms CC 1 into CC acid 3 through two sequential oxidations. Molecular docking and site-directed mutagenesis experiments confirmed the crucial role of two amino acids, Y166 and S153, within the active site, which suggests that Y166 acts as a general base for hydride transfer, while S153 facilitates proton transfer, thereby increasing the acidity of the reaction. KEY POINTS: • EAs possess complicated skeletons and are widely used in several clinical diseases • EasD belongs to the short-chain dehydrogenases/reductases (SDRs) and converted CC or CC aldehyde to CC acid • The catalytic mechanism of EasD for dehydrogenation was analyzed by molecular docking and site mutations.

In many organisms, most notably , homologous chromosomes associate in somatic cells, a phenomenon known as somatic pairing, which takes place without double strand breaks or strand invasion, thus requiring some other mechanism for homologs to recognize each other. Several studies have suggested a "specific button" model, in which a series of distinct regions in the genome, known as buttons, can associate with each other, mediated by different proteins that bind to these different regions. Here, we use computational modeling to evaluate an alternative "button barcode" model, in which there is only one type of recognition site or adhesion button, present in many copies in the genome, each of which can associate with any of the others with equal affinity. In this model, buttons are nonuniformly distributed, such that alignment of a chromosome with its correct homolog, compared with a nonhomolog, is energetically favored; since to achieve nonhomologous alignment, chromosomes would be required to mechanically deform in order to bring their buttons into mutual register. By simulating randomly generated nonuniform button distributions, many highly effective button barcodes can be easily found, some of which achieve virtually perfect pairing fidelity. This model is consistent with existing literature on the effect of translocations of different sizes on homolog pairing. We conclude that a button barcode model can attain highly specific homolog recognition, comparable to that seen in actual cells undergoing somatic homolog pairing, without the need for specific interactions. This model may have implications for how meiotic pairing is achieved.

Clinical trials often involve some form of interim monitoring to determine futility before planned trial completion. While many options for interim monitoring exist (e.g., alpha-spending, conditional power), nonparametric based interim monitoring methods are also needed to account for more complex trial designs and analyses. The upstrap is one recently proposed nonparametric method that may be applied for interim monitoring.

Ultracold atoms provide a platform for analog quantum computer capable of simulating the quantum turbulence that underlies puzzling phenomena like pulsar glitches in rapidly spinning neutron stars. Unlike other platforms like liquid helium, ultracold atoms have a viable theoretical framework for dynamics, but simulations push the edge of current classical computers. We present the largest simulations of fermionic quantum turbulence to date and explain the computing technology needed, especially improvements in the Eigenvalue soLvers for Petaflop Applications library that enable us to diagonalize matrices of record size (millions by millions). We quantify how dissipation and thermalization proceed in fermionic quantum turbulence by using the internal structure of vortices as a new probe of the local effective temperature. All simulation data and source codes are made available to facilitate rapid scientific progress in the field of ultracold Fermi gases.

To investigate the effect of particle size on liver by Monte Carlo simulation and phantom studies at both 1.5 T and 3.0 T.

Feline immunodeficiency virus (FIV) is a common infection found in domesticated and wild cats worldwide. Despite the wealth of therapeutic understanding of the disease in humans, considerably less information exists regarding the treatment of the disease in felines. Current treatment relies on drugs developed for the related human immunodeficiency virus (HIV) and includes compounds of the popular non-nucleotide reverse transcriptase (NNRTI) class. This is despite FIV-RT being only 67% similar to HIV-1 RT at the enzyme level, increasing to 88% for the allosteric pocket targeted by NNRTIs. The goal of this project was to try to quantify how well the more extensive pharmacological knowledge available for human disease translates to felines. To this end we screened known NNRTIs and 10 diverse pyrimidine analogs identified virtually. We use this chemo-centric probe approach to (a) assess the similarity between the two related RT targets based on the observed experimental inhibition values, (b) try to identify more potent inhibitors at FIV, and (c) gain a better appreciation of the structure-activity relationships (SAR). We found the correlation between ICs at the two targets to be strong (r = 0.87) and identified compound 1 as the most potent inhibitor of FIV with IC of 0.030 μM ± 0.009. This compared to FIV IC values of 0.22 ± 0.17 μM, 0.040 ± 0.010 μM and >160 μM for known anti HIV-1 RT drugs Efavirenz, Rilpivirine, and Nevirapine, respectively. This knowledge, along with an understanding of the structural origin that give rise to any differences could improve the way HIV drugs are repurposed for FIV.

Effective interventions that support blood donor retention are needed. Yet, integrating an intervention into the time-pressed and operationally sensitive context of a blood donation center requires justification for disruptions to an optimized process. This research provides evidence that virtual reality (VR) paradigms can serve as a research environment in which interventions can be tested prior to being delivered in blood donation centers. Study 1 (N = 48) demonstrated that 360°-video VR blood donation environments elicit a similar profile of emotional experience to a live donor center. Presence and immersion were high, and cybersickness symptoms low. Study 2 (N = 134) was an experiment deploying the 360°-video VR environments to test the impact of an intervention on emotional experience and intentions to donate. Participants in the intervention condition who engaged in a suite of tasks drawn from the process model of emotion regulation (including attentional deployment, positive reappraisal, and response modulation) reported more positive emotion than participants in a control condition, which in turn increased intentions to donate blood. By showing the promise for benefitting donor experience via a relatively low-cost and low-resource methodology, this research supports the use of VR paradigms to trial interventions prior to deployment in operationally-context field settings.

Shengxian decoction, a traditional Chinese medicinal prescription, has been shown to alleviate doxorubicin-induced chronic heart failure. This study established an ultra-performance liquid chromatography-quadrupole time-of-flight mass spectrometry method to separate and characterize the complex chemical compositions of Shengxian decoction, and the absorbed compounds in the bio-samples of the cardiotoxicity rats with chronic heart failure after its oral delivery. Note that 116 chemical compounds were identified from Shengxian decoction in vitro, 81 more than previously detected. Based on the three-dimensional data of these compounds, 28 absorbed compounds were confirmed in vivo. Network pharmacology and molecular docking experiments indicated that timosaponin B-II, timosaponin A-III, gitogenin, and 7,8-didehydrocimigenol were recognized as the key effective compounds to exert effects against doxorubicin cardiotoxicity by acting on targets such as caspase 3, cyclin-dependent kinase 1, cyclin-dependent kinase 4, receptor tyrosine-protein kinase erbB-2, and mitogen-activated protein kinase 1 in p53 and phosphatidylinositol 3-kinase-Akt signaling pathways. This study developed the understanding of the composition of Shengxian decoction for the treatment of doxorubicin cardiotoxicity, as well as a feasible strategy to elucidate the effective constituents in traditional Chinese medicines.

Based on the specific binding of drug molecules to cell membrane receptors, a screening and separation method for active compounds of natural products was established by combining phospholipase C (PLC) sensitized hollow fiber microscreening by a solvent seal with high-performance liquid chromatography technology. In the process, the factors affecting the screening were optimized. Under the optimal screening conditions, we screened honokiol (HK), magnolol (MG), negative control drug carbamazepine, and positive control drug amentoflavone, the repeatability of the method was tested. The PLC activity was determined before and after the screening. Experimental results showed that the sensitization factors of PLC of HK and MG were 61.0 and 48.5, respectively, and amentoflavone was 15.0, carbamazepine could not bind to PLC. Moreover, the molecular docking results were consistent with this measurement, indicating that HK and MG could be combined with PLC, and they were potential interacting components with PLC. This method used organic solvent to seal the PLC greatly ensuring the activity, so this method had the advantage of integrating separation, and purification with screening, it not only exhibited good reproducibility and high sensitivity but was also suitable for screening the active components in natural products by various targets in vitro.

Tuberculosis (TB) a disease caused by Mycobacterium tuberculosis (Mtb) poses a significant threat to human life, and current BCG vaccinations only provide sporadic protection, therefore there is a need for developing efficient vaccines. Numerous immunoinformatic methods have been utilized previously, here for the first time a deep learning framework based on Deconvolutional Neural Networks (DCNN) and Bidirectional Long Short-Term Memory (DCNN-BiLSTM) was used to predict Mtb Multiepitope vaccine (MtbMEV) subunits against six Mtb H37Rv proteins. The trained model was used to design MEV within a few minutes against TB better than other machine learning models with 99.5% accuracy. The MEV has good antigenicity, and physiochemical properties, and is thermostable, soluble, and hydrophilic. The vaccine's BLAST search ruled out the possibility of autoimmune reactions. The secondary structure analysis revealed 87% coil, 10% beta, and 2% alpha helix, while the tertiary structure was highly upgraded after refinement. Molecular docking with TLR3 and TLR4 receptors showed good binding, indicating high immune reactions. Immune response simulation confirmed the generation of innate and adaptive responses. In-silico cloning revealed the vaccine is highly expressed in E. coli. The results can be further experimentally verified using various analyses to establish a candidate vaccine for future clinical trials.

Degenerative lumbar spine disease (DLD) is a prevalent condition in middle-aged and elderly individuals. DLD frequently results in pain, muscle weakness, and motor impairment, which affect postural stability and functional performance in daily activities. Simulated skateboarding training could enable patients with DLD to engage in exercise with less pain and focus on single-leg weight-bearing. The purpose of this study was to investigate the effects of virtual reality (VR) skateboarding training on balance and functional performance in patients with DLD.

Understanding the functional connectivity between brain regions and its emergent dynamics is a central challenge. Here we present a theory-experiment hybrid approach involving iteration between a minimal computational model and in vivo electrophysiological measurements. Our model not only predicted spontaneous persistent activity (SPA) during Up-Down-State oscillations, but also inactivity (SPI), which has never been reported. These were confirmed in vivo in the membrane potential of neurons, especially from layer 3 of the medial and lateral entorhinal cortices. The data was then used to constrain two free parameters, yielding a unique, experimentally determined model for each neuron. Analytic and computational analysis of the model generated a dozen quantitative predictions about network dynamics, which were all confirmed in vivo to high accuracy. Our technique predicted functional connectivity; e. g. the recurrent excitation is stronger in the medial than lateral entorhinal cortex. This too was confirmed with connectomics data. This technique uncovers how differential cortico-entorhinal dialogue generates SPA and SPI, which could form an energetically efficient working-memory substrate and influence the consolidation of memories during sleep. More broadly, our procedure can reveal the functional connectivity of large networks and a theory of their emergent dynamics.

We compared computed tomography (CT) images and holograms (HG) to assess the number of arteries of the lung lobes undergoing lobectomy and assessed easiness in interpretation by radiologists and thoracic surgeons with both techniques.

The perception of bitter and sweet tastes is a crucial aspect of human sensory experience. Concerns over the long-term use of aspartame, a widely used sweetener suspected of carcinogenic risks, highlight the importance of developing new taste modifiers. This study utilizes Large Language Models (LLMs) such as GPT-3.5 and GPT-4 for predicting molecular taste characteristics, with a focus on the bitter-sweet dichotomy. Employing random and scaffold data splitting strategies, GPT-4 demonstrated superior performance, achieving an impressive 86% accuracy under scaffold partitioning. Additionally, ChatGPT was employed to extract specific molecular features associated with bitter and sweet tastes. Utilizing these insights, novel molecular compounds with distinct taste profiles were successfully generated. These compounds were validated for their bitter and sweet properties through molecular docking and molecular dynamics simulation, and their practicality was further confirmed by ADMET toxicity testing and DeepSA synthesis feasibility. This research highlights the potential of LLMs in predicting molecular properties and their implications in health and chemical science.

Worms create complex paths when moving through sediment to feed. This research applies computer simulation models to provide a unique approach to visualise and quantify the process by which complex worm paths can emerge from simple local movement decisions. A grid environment is proposed in which worms can move with choice of up to 8 directions at each step. This uses a square grid with diagonal paths which has not been investigated before and the resulting number of complex paths is increased compared to triangular grids. Results identify many novel worm paths. Some of the resulting paths are symmetrical, others produce repetitive looping paths, others return to the origin. Interesting worm paths are identified with chaotic movement. Some include oscillating between chaotic and ordered movement for which the outcome is still unknown after millions of steps. A conclusion that may be extrapolated to other creatures is that local movement decisions of a species substantially determine the overall global search strategy that emerges.

We integrate evolutionary predictions based on the neutral theory of molecular evolution with protein dynamics to generate mechanistic insight into the molecular adaptations of the SARS-COV-2 spike (S) protein. With this approach, we first identified candidate adaptive polymorphisms (CAPs) of the SARS-CoV-2 S protein and assessed the impact of these CAPs through dynamics analysis. Not only have we found that CAPs frequently overlap with well-known functional sites, but also, using several different dynamics-based metrics, we reveal the critical allosteric interplay between SARS-CoV-2 CAPs and the S protein binding sites with the human ACE2 (hACE2) protein. CAPs interact far differently with the hACE2 binding site residues in the open conformation of the S protein compared to the closed form. In particular, the CAP sites control the dynamics of binding residues in the open state, suggesting an allosteric control of hACE2 binding. We also explored the characteristic mutations of different SARS-CoV-2 strains to find dynamic hallmarks and potential effects of future mutations. Our analyses reveal that Delta strain-specific variants have non-additive (i.e., epistatic) interactions with CAP sites, whereas the less pathogenic Omicron strains have mostly additive mutations. Finally, our dynamics-based analysis suggests that the novel mutations observed in the Omicron strain epistatically interact with the CAP sites to help escape antibody binding.

Treatment planning through the diagnostic dimension of theranostics provides insights into predicting the absorbed dose of RPT, with the potential to individualize radiation doses for enhancing treatment efficacy. However, existing studies focusing on dose prediction from diagnostic data often rely on organ-level estimations, overlooking intra-organ variations. This study aims to characterize the intra-organ theranostic heterogeneity and utilize artificial intelligence techniques to localize them, i.e. to predict voxel-wise absorbed dose map based on pre-therapy PET.

Proline-rich antimicrobial peptides (PrAMPs) inhibit bacterial protein biosynthesis by binding to the polypeptide exit tunnel (PET) near the peptidyl transferase center. Api137, an optimized derivative of honeybee PrAMP apidaecin, inhibits protein expression by trapping release factors (RFs), which interact with stop codons on ribosomes to terminate translation. This study uses cryo-EM, functional assays and molecular dynamic (MD) simulations to show that Api137 additionally occupies a second binding site near the exit of the PET and can repress translation independently of RF-trapping. Api88, a C-terminally amidated (-CONH) analog of Api137 (-COOH), binds to the same sites, occupies a third binding pocket and interferes with the translation process presumably without RF-trapping. In conclusion, apidaecin-derived PrAMPs inhibit bacterial ribosomes by multimodal mechanisms caused by minor structural changes and thus represent a promising pool for drug development efforts.

Accurate development of satellite maneuvers necessitates a broad orbital dynamical system and efficient nonlinear control techniques. For achieving the intended formation, a framework of a discrete fractional difference satellite model is constructed by the use of commensurate and non-commensurate orders for the control and synchronization of fractional-order chaotic satellite system. The efficacy of the suggested framework is evaluated employing a numerical simulation of the concerning dynamic systems of motion while taking into account multiple considerations such as Lyapunov exponent research, phase images and bifurcation schematics. With the aid of discrete nabla operators, we monitor the qualitative behavioural patterns of satellite systems in order to provide justification for the structure's chaos. We acquire the fixed points of the proposed trajectory. At each fixed point, we calculate the eigenvalue of the satellite system's Jacobian matrix and check for zones of instability. The outcomes exhibit a wide range of multifaceted behaviours resulting from the interaction with various fractional-orders in the offered system. Additionally, the sample entropy evaluation is employed in the research to determine complexities and endorse the existence of chaos. To maintain stability and synchronize the system, nonlinear controllers are additionally provided. The study highlights the technique's vulnerability to fractional-order factors, resulting in exclusive, changing trends and equilibrium frameworks. Because of its diverse and convoluted behaviour, the satellite chaotic model is an intriguing and crucial subject for research.

Prior to September 2021, 55,000-90,000 hospital inpatients in England were identified as having a potentially nosocomial SARS-CoV-2 infection. This includes cases that were likely missed due to pauci- or asymptomatic infection. Further, high numbers of healthcare workers (HCWs) are thought to have been infected, and there is evidence that some of these cases may also have been nosocomially linked, with both HCW to HCW and patient to HCW transmission being reported. From the start of the SARS-CoV-2 pandemic interventions in hospitals such as testing patients on admission and universal mask wearing were introduced to stop spread within and between patient and HCW populations, the effectiveness of which are largely unknown.

The efficacy of flow diverters is influenced by the strut configuration changes resulting from size discrepancies between the stent and the parent artery. This study aimed to quantitatively analyze the impact of size discrepancies between flow diverters and parent arteries on the flow diversion effects, using computational fluid dynamics. Four silicone models with varying parent artery sizes were developed. Real flow diverters were deployed in these models to assess stent configurations at the aneurysm neck. Virtual stents were generated based on these configurations for computational fluid dynamics analysis. The changes in the reduction rate of the hemodynamic parameters were quantified to evaluate the flow diversion effect. Implanting 4.0 mm flow diverters in aneurysm models with parent artery diameters of 3.0-4.5 mm, in 0.5 mm increments, revealed that a shift from oversized to undersized flow diverters led to an increase in the reduction rates of hemodynamic parameter, accompanied by enhanced metal coverage rate and pore density. However, the flow diversion effect observed transitioning from oversizing to matching was less pronounced when moving from matching to undersizing. This emphasizes the importance of proper sizing of flow diverters, considering the benefits of undersizing and not to exceed the threshold of advantages.

Basic life support (BLS) is a mandatory skill for nurses. The confidence of the BLS provider should be enhanced by regular training. Traditionally, BLS training has used low-fidelity manikins, but more recent studies have suggested the use of high-fidelity manikins and alternative levels of simulation such as virtual reality.

In 2019, a BMJ Rapid Recommendation advised against colorectal cancer (CRC) screening for adults with a predicted 15-year CRC risk below 3%. Using Switzerland as a case study, we estimated the population-level impact of this recommendation.

Older age and cognitive inactivity have been associated with cognitive impairment, which in turn is linked to economic and societal burdens due to the high costs of care, especially for care homes and informal care. Emerging non-pharmacological interventions using new technologies, such as virtual reality (VR) delivered on a head-mounted display (HMD), might offer an alternative to maintain or improve cognition. The study aimed to evaluate the efficacy and safety of a VR-based Digital Therapeutics application for improving cognitive functions among healthy older adults.

In transcatheter aortic valve implantation (TAVI), final device position may be affected by device interaction with the whole aortic landing zone (LZ) extending to ascending aorta. We investigated the impact of aortic LZ curvature and angulation on TAVI implantation depth, comparing short-frame balloon-expanding (BE) and long-frame self-expanding (SE) devices. Patients (n = 202) treated with BE or SE devices were matched based on one-to-one propensity score. Primary endpoint was the mismatch between the intended (H) and the final (H) implantation depth. LZ curvature and angulation were calculated based on the aortic centerline trajectory available from pre-TAVI computed tomography. Total LZ curvature ( ) and LZ angulation distal to aortic annulus ( ) were greater in the SE compared to the BE group (P < 0.001 for both). In the BE group, H was significantly higher than H at both cusps (P < 0.001). In the SE group, H was significantly deeper than H only at the left coronary cusp (P = 0.013). At multivariate analysis, was the only independent predictor (OR = 1.11, P = 0.002) of deeper final implantation depth with a cut-off value of 17.8°. Aortic LZ curvature and angulation significantly affected final TAVI implantation depth, especially in high stent-frame SE devices reporting, upon complete release, deeper implantation depth with respect to the intended one.

Physiologically based pharmacokinetic (PBPK) modeling is a mechanistic concept, which helps to judge the effects of biopharmceutical properties of drug product such as in vitro dissolution on its pharmacokinetic and in vivo performance. With the application of virtual bioequivalence (VBE) study, the drug product development using model-based approach can help in evaluating the possibility of extending BCS-based biowaiver. Therefore, the current study was intended to develop PBPK model as well as in vitro in vivo extrapolation (IVIVE) for BCS class III drug i.e. cefadroxil. A PBPK model was created in GastroPlus™ 9.8.3 utilizing clinical data of immediate-release cefadroxil formulations. By the examination of simulated and observed plasma drug concentration profiles, the predictability of the proposed model was assessed for the prediction errors. Furthermore, mechanistic deconvolution was used to create IVIVE, and the plasma drug concentration profiles and pharmacokinetic parameters were predicted for different virtual formulations with variable cefadroxil in vitro release. Virtual bioequivalence study was also executed to assess the bioequivalence of the generic verses the reference drug product (Duricef). The developed PBPK model satisfactorily predicted C and AUC after cefadroxil single and multiple oral dose administrations, with all individual prediction errors within the limits except in a few cases. Second order polynomial correlation function obtained accurately predict in vivo drug release and plasma concentration profile of cefadroxil test and reference (Duricef) formulation. The VBE study also proved test formulation bioequivalent to reference formulation and the statistical analysis on pharmacokinetic parameters reported 90% confidence interval for C and AUC in the FDA acceptable limits. The analysis found that a validated and verified PBPK model with a mechanistic background is as a suitable approach to accelerate generic drug development.

Collaborative hunting, in which predators play different and complementary roles to capture prey, has been traditionally believed to be an advanced hunting strategy requiring large brains that involve high-level cognition. However, recent findings that collaborative hunting has also been documented in smaller-brained vertebrates have placed this previous belief under strain. Here, using computational multi-agent simulations based on deep reinforcement learning, we demonstrate that decisions underlying collaborative hunts do not necessarily rely on sophisticated cognitive processes. We found that apparently elaborate coordination can be achieved through a relatively simple decision process of mapping between states and actions related to distance-dependent internal representations formed by prior experience. Furthermore, we confirmed that this decision rule of predators is robust against unknown prey controlled by humans. Our computational ecological results emphasize that collaborative hunting can emerge in various intra- and inter-specific interactions in nature, and provide insights into the evolution of sociality.

Xylanases are key biocatalysts in the degradation of the β-1,4-glycosidic linkages in the xylan backbone of hemicellulose. These enzymes are potentially applied in a wide range of bioprocessing industries under harsh conditions. Metagenomics has emerged as powerful tools for the bioprospection and discovery of interesting bioactive molecules from extreme ecosystems with unique features, such as high temperatures. In this study, an innovative combination of function-driven screening of a compost metagenomic library and automatic extraction of halo areas with in-house MATLAB functions resulted in the identification of a promising clone with xylanase activity (LP4). The LP4 clone proved to be an effective xylanase producer under submerged fermentation conditions. Sequence and phylogenetic analyses revealed that the xylanase, Xyl4, corresponded to an endo-1,4-β-xylanase belonging to glycosyl hydrolase family 10 (GH10). When xyl4 was expressed in Escherichia coli BL21(DE3), the enzyme activity increased about 2-fold compared to the LP4 clone. To get insight on the interaction of the enzyme with the substrate and establish possible strategies to improve its activity, the structure of Xyl4 was predicted, refined, and docked with xylohexaose. Our data unveiled, for the first time, the relevance of the amino acids Glu133 and Glu238 for catalysis, and a close inspection of the catalytic site suggested that the replacement of Phe316 by a bulkier Trp may improve Xyl4 activity. Our current findings contribute to enhancing the catalytic performance of Xyl4 towards industrial applications. KEY POINTS: • A GH10 endo-1,4-β-xylanase (Xyl4) was isolated from a compost metagenomic library • MATLAB's in-house functions were developed to identify the xylanase-producing clones • Computational analysis showed that Glu133 and Glu238 are crucial residues for catalysis.

Severe fever with thrombocytopenia syndrome (SFTS) is an emerging tick-borne viral disease caused by the SFTS virus (Dabie bandavirus), which has become a substantial risk to public health. No specific treatment is available now, that calls for an effective vaccine. Given this, we aimed to develop a multi-epitope DNA vaccine through the help of bioinformatics. The final DNA vaccine was inserted into a special plasmid vector pVAX1, consisting of CD8 T cell epitopes, CD4 T cell epitopes and B cell epitopes (six epitopes each) screened from four genome-encoded proteins--nuclear protein (NP), glycoprotein (GP), RNA-dependent RNA polymerase (RdRp), as well as nonstructural protein (NSs). To ascertain if the predicted structure would be stable and successful in preventing infection, an immunological simulation was run on it. In conclusion, we designed a multi-epitope DNA vaccine that is expected to be effective against Dabie bandavirus, but in vivo trials are needed to verify this claim.

With the aid of a brain computer interface (BCI), users can communicate and receive signals wirelessly or over wired connections to operate smart devices. A BCI classifier's architecture is quite difficult since numerous elements should be combined. These elements are made up of brain signals, which also include high levels of weak sounds that could provide reliable participant recordings of daily activities. We must use computer vision techniques to create a model in order to control those information. The high dimension and volume of signals present the classification classifier with its primary obstacles.

The Wnt-planar cell polarity (Wnt-PCP) pathway is crucial in establishing cell polarity during development and tissue homoeostasis. This pathway is found to be dysregulated in many pathological conditions, including cancer and autoimmune disorders. The central event in Wnt-PCP pathway is the activation of Weak-similarity guanine nucleotide exchange factor (WGEF) by the adapter protein Dishevelled (Dvl). The PDZ domain of Dishevelled2 (Dvl2) binds and activates WGEF by releasing it from its autoinhibitory state. However, the actual Dvl2 binding site of WGEF and the consequent activation mechanism of the GEF have remained elusive. Using biochemical and molecular dynamics studies, we show that a unique "internal-PDZ binding motif" (IPM) of WGEF mediates the WGEF-Dvl2 interaction to activate the GEF. The residues at P, P, P and P positions of IPM play an important role in stabilizing the WGEF-Dvl2 interaction. Furthermore, MD simulations of modelled Dvl2-WGEF complexes suggest that WGEF-Dvl2 interaction may differ from the reported Dvl2-IPM interactions. Additionally, the apo structure of human Dvl2 shows conformational dynamics different from its IPM peptide bound state, suggesting an induced fit mechanism for the Dvl2-peptide interaction. The current study provides a model for Dvl2 induced activation of WGEF.

Aspartyl dipeptidase (dipeptidase E) can hydrolyze Asp-X dipeptides (where X is any amino acid), and the enzyme plays a key role in the degradation of peptides as nutrient sources. Dipeptidase E remains uncharacterized in Streptomyces. Orf2 from Streptomyces sp. 139 is located in the exopolysaccharide biosynthesis gene cluster, which may be a novel dipeptidase E with "S134-H170-D198" catalytic triad by sequence and structure comparison. Herein, recombinant Orf2 was expressed in E. coli and characterized dipeptidase E activity using the Asp-ρNA substrate. The optimal pH and temperature for Orf2 are 7.5 and 40 ℃; Vmax and Km of Orf2 are 0.0787 mM·min and 1.709 mM, respectively. Orf2 exhibits significant degradation activities to Asp-Gly-Gly, Asp-Leu, Asp-His, and isoAsp-Leu and minimal activities to Asp-Pro and Asp-Ala. Orf2 contains a Ser-His-Asp catalytic triad characterized by point mutation. In addition, the Asp147 residue of Orf2 is also proven to be critical for the enzyme's activity through molecular docking and point mutation. Transcriptome analysis reveals the upregulation of genes associated with ribosomes, amino acid biosynthesis, and aminoacyl-tRNA biosynthesis in the orf2 mutant strain. Compared with the orf2 mutant strain and WT, the yield of crude polysaccharide does not change significantly. However, crude polysaccharides from the orf2 mutant strain exhibit a wider range of molecular weight distribution. The results indicate that the Orf2 links nutrient stress to secondary metabolism as a novel dipeptidase E. KEY POINTS: • A novel dipeptidase E with a Ser-His-Asp catalytic triad was characterized from Streptomyces sp. 139. • Orf2 was involved in peptide metabolism both in vitro and in vivo. • Orf2 linked nutrient stress to mycelia formation and secondary metabolism in Streptomyces.

Arteriosclerosis is a major risk factor for cardiovascular disease and results in arterial vessel stiffening. Velocity estimation of the pulse wave sent by the heart and propagating into the arteries is a widely accepted biomarker. This symmetrical pulse wave propagates at a speed which is related to the Young's modulus through the Moens Korteweg (MK) equation. Recently, an antisymmetric flexural wave has been observed in vivo. Unlike the symmetrical wave, it is highly dispersive. This property offers promising applications for monitoring arterial stiffness and early detection of atheromatous plaque. However, as far as it is known, no equivalent of the MK equation exists for flexural pulse waves. To bridge this gap, a beam based theory was developed, and approximate analytical solutions were reached. An experiment in soft polymer artery phantoms was built to observe the dispersion of flexural waves. A good agreement was found between the analytical expression derived from beam theory and experiments. Moreover, numerical simulations validated wave speed dependence on the elastic and geometric parameters at low frequencies. Clinical applications, such as arterial age estimation and arterial pressure measurement, are foreseen.

Morphogenetic programs coordinate cell signaling and mechanical interactions to shape organs. In systems and synthetic biology, a key challenge is determining optimal cellular interactions for predicting organ shape, size, and function. Physics-based models defining the subcellular force distribution facilitate this, but it is challenging to calibrate parameters in these models from data. To solve this inverse problem, we created a Bayesian optimization framework to determine the optimal cellular force distribution such that the predicted organ shapes match the experimentally observed organ shapes. This integrative framework employs Gaussian Process Regression, a non-parametric kernel-based probabilistic machine learning modeling paradigm, to learn the mapping functions relating to the morphogenetic programs that maintain the final organ shape. We calibrated and tested the method on Drosophila wing imaginal discs to study mechanisms that regulate epithelial processes ranging from development to cancer. The parameter estimation framework successfully infers the underlying changes in core parameters needed to match simulation data with imaging data of wing discs perturbed with collagenase. The computational pipeline identifies distinct parameter sets mimicking wild-type shapes. It enables a global sensitivity analysis to support the regulation of actomyosin contractility and basal ECM stiffness to generate and maintain the curved shape of the wing imaginal disc. The optimization framework, combined with experimental imaging, identified that Piezo, a mechanosensitive ion channel, impacts fold formation by regulating the apical-basal balance of actomyosin contractility and elasticity of ECM. This workflow is extensible toward reverse-engineering morphogenesis across organ systems and for real-time control of complex multicellular systems.

Practical limitations of quality and quantity of data can limit the precision of parameter identification in mathematical models. Model-based experimental design approaches have been developed to minimise parameter uncertainty, but the majority of these approaches have relied on first-order approximations of model sensitivity at a local point in parameter space. Practical identifiability approaches such as profile-likelihood have shown potential for quantifying parameter uncertainty beyond linear approximations. This research presents a genetic algorithm approach to optimise sample timing across various parameterisations of a demonstrative PK-PD model with the goal of aiding experimental design. The optimisation relies on a chosen metric of parameter uncertainty that is based on the profile-likelihood method. Additionally, the approach considers cases where multiple parameter scenarios may require simultaneous optimisation. The genetic algorithm approach was able to locate near-optimal sampling protocols for a wide range of sample number (n = 3-20), and it reduced the parameter variance metric by 33-37% on average. The profile-likelihood metric also correlated well with an existing Monte Carlo-based metric (with a worst-case r > 0.89), while reducing computational cost by an order of magnitude. The combination of the new profile-likelihood metric and the genetic algorithm demonstrate the feasibility of considering the nonlinear nature of models in optimal experimental design at a reasonable computational cost. The outputs of such a process could allow for experimenters to either improve parameter certainty given a fixed number of samples, or reduce sample quantity while retaining the same level of parameter certainty.

To ensure that an external force can break the interaction between a protein and a ligand, the steered molecular dynamics simulation requires a harmonic restrained potential applied to the protein backbone. A usual practice is that all or a certain number of protein's heavy atoms or Cα atoms are fixed, being restrained by a small force. This present study reveals that while fixing both either all heavy atoms and or all Cα atoms is not a good approach, while fixing a too small number of few atoms sometimes cannot prevent the protein from rotating under the influence of the bulk water layer, and the pulled molecule may smack into the wall of the active site. We found that restraining the Cα atoms under certain conditions is more relevant. Thus, we would propose an alternative solution in which only the Cα atoms of the protein at a distance larger than 1.2 nm from the ligand are restrained. A more flexible, but not too flexible, protein will be expected to lead to a more natural release of the ligand.

Breast cancer (BC) is the most widespread cancer worldwide. Over 2 million new cases of BC were identified in 2020 alone. Despite previous studies, the lack of specific biomarkers and signaling pathways implicated in BC impedes the development of potential therapeutic strategies. We employed several RNAseq datasets to extract differentially expressed genes (DEGs) based on the intersection of all datasets, followed by protein-protein interaction network construction. Using the shared DEGs, we also identified significant gene ontology (GO) and KEGG pathways to understand the signaling pathways involved in BC development. A molecular docking simulation was performed to explore potential interactions between proteins and drugs. The intersection of the four datasets resulted in 146 DEGs common, including AURKB, PLK1, TTK, UBE2C, CDCA8, KIF15, and CDC45 that are significant hub-proteins associated with breastcancer development. These genes are crucial in complement activation, mitotic cytokinesis, aging, and cancer development. We identified key microRNAs (i.e., hsa-miR-16-5p, hsa-miR-1-3p, hsa-miR-147a, hsa-miR-195-5p, and hsa-miR-155-5p) that are associated with aggressive tumor behavior and poor clinical outcomes in BC. Notable transcription factors (TFs) were FOXC1, GATA2, FOXL1, ZNF24 and NR2F6. These biomarkers are involved in regulating cancer cell proliferation, invasion, and migration. Finally, molecular docking suggested Hesperidin, 2-amino-isoxazolopyridines, and NMS-P715 as potential lead compounds against BC progression. We believe that these findings will provide important insight into the BC progression as well as potential biomarkers and drug candidates for therapeutic development.

Cheminformatics-based machine learning (ML) has been employed to determine optimal reaction conditions, including catalyst structures, in the field of synthetic chemistry. However, such ML-focused strategies have remained largely unexplored in the context of catalytic molecular transformations using Lewis-acidic main-group elements, probably due to the absence of a candidate library and effective guidelines (parameters) for the prediction of the activity of main-group elements. Here, the construction of a triarylborane library and its application to an ML-assisted approach for the catalytic reductive alkylation of aniline-derived amino acids and C-terminal-protected peptides with aldehydes and H is reported. A combined theoretical and experimental approach identified the optimal borane, i.e., B(2,3,5,6-Cl-CH)(2,6-F-3,5-(CF)-CH), which exhibits remarkable functional-group compatibility toward aniline derivatives in the presence of 4-methyltetrahydropyran. The present catalytic system generates HO as the sole byproduct.

Fast collective motions are widely present in biomolecules, but their functional relevance remains unclear. Herein, we reveal that fast collective motions of backbone are critical to the water transfer of aquaporin Z (AqpZ) by using solid-state nuclear magnetic resonance (ssNMR) spectroscopy and molecular dynamics (MD) simulations. A total of 212 residue site-specific dipolar order parameters and 158 N spin relaxation rates of the backbone are measured by combining the C- and H-detected multidimensional ssNMR spectra. Analysis of these experimental data by theoretic models suggests that the small-amplitude (~10°) collective motions of the transmembrane α helices on the nanosecond-to-microsecond timescales are dominant for the dynamics of AqpZ. The MD simulations demonstrate that these collective motions are critical to the water transfer efficiency of AqpZ by facilitating the opening of the channel and accelerating the water-residue hydrogen bonds renewing in the selectivity filter region.

Cell deformability is an essential determinant for tissue-scale mechanical nature, such as fluidity and rigidity, and is thus crucial for tissue homeostasis and stable developmental processes. However, large-scale simulations of deformable cells have been restricted to those of polygonal-shaped cells, limiting our understanding of populations of arbitrarily deformable cells, such as mesenchymal, amoeboid cells, and nonconfluent epithelial cells. Here, we present an efficient approach for simulating large populations of nonpolygonally deformable cells with considerably higher computational efficiency than existing methods. Using the method, we demonstrate that the densely packed active cell population interacting via excluded volume interactions exhibits a fluid-to-fluid transition. An experimentally measurable index of topological defects, defined using the number of neighboring cells, is also proposed to characterize this transition. This study provides a flexible approach to tissue-scale cell population and a broader perspective on the biological fluid phases.

Inhibition of prolylhydroxylase-2 (PHD-2) in both normoxic and hypoxic cells is a critical component of solid tumours. The present study aimed to identify small molecules with PHD-2 activation potential. Virtually screening 4342 chemical compounds for structural similarity to R59949 and docking with PHD-2. To find the best drug candidate, hits were assessed for drug likeliness, antihypoxic and antineoplastic potential. The selected drug candidate's PHD-2 activation, cytotoxic and apoptotic potentials were assessed using 2-oxoglutarate, MTT, AO/EtBr and JC-1 staining. The drug candidate was also tested for its in-vivo chemopreventive efficacy against DMBA-induced mammary gland cancer alone and in combination with Tirapazamine (TPZ). Virtual screening and 2-oxoglutarate assay showed BBAP-6 as lead compound. BBAP-6 exhibited cytotoxic and apoptotic activity against ER+ MCF-7. In carmine staining and histology, BBAP-6 alone or in combination with TPZ restored normal surface morphology of the mammary gland after DMBA produced malignant alterations. Immunoblotting revealed that BBAP-6 reduced NF-κB expression, activated PHD-2 and induced intrinsic apoptotic pathway. Serum metabolomics conducted with 1H NMR confirmed that BBAP-6 prevented HIF-1α and NF-κB-induced metabolic changes in DMBA mammary gland cancer model. In a nutshell, it can be concluded that BBAP-6 activates PHD-2 and exhibits anticancer potential.

The standard errors (SE) of the maximum likelihood estimates (MLE) of the population parameter vector in nonlinear mixed effect models (NLMEM) are usually estimated using the inverse of the Fisher information matrix (FIM). However, at a finite distance, i.e. far from the asymptotic, the FIM can underestimate the SE of NLMEM parameters. Alternatively, the standard deviation of the posterior distribution, obtained in Stan via the Hamiltonian Monte Carlo algorithm, has been shown to be a proxy for the SE, since, under some regularity conditions on the prior, the limiting distributions of the MLE and of the maximum a posterior estimator in a Bayesian framework are equivalent. In this work, we develop a similar method using the Metropolis-Hastings (MH) algorithm in parallel to the stochastic approximation expectation maximisation (SAEM) algorithm, implemented in the saemix R package. We assess this method on different simulation scenarios and data from a real case study, comparing it to other SE computation methods. The simulation study shows that our method improves the results obtained with frequentist methods at finite distance. However, it performed poorly in a scenario with the high variability and correlations observed in the real case study, stressing the need for calibration.

Compared with conventional topological insulator that carries topological state at its boundaries, the higher-order topological insulator exhibits lower-dimensional gapless boundary states at its corners and hinges. Leveraging the form similarity between Schrödinger equation and diffusion equation, research on higher-order topological insulators has been extended from condensed matter physics to thermal diffusion. Unfortunately, all the corner states of thermal higher-order topological insulator reside within the band gap. Another kind of corner state, which is embedded in the bulk states, has not been realized in pure diffusion systems so far. Here, we construct higher-dimensional Su-Schrieffer-Heeger models based on sphere-rod structure to elucidate these corner states, which we term "in-bulk corner states." Because of the anti-Hermitian properties of diffusive Hamiltonian, we investigate the thermal behavior of these corner states through theoretical calculation, simulation, and experiment. Furthermore, we study the different thermal behaviors of in-bulk corner state and in-gap corner state. Our results would open a different gate for diffusive topological states and provide a distinct application for efficient heat dissipation.

The coronavirus disease 2019 (COVID-19) pandemic caused global health, economic, and population loss. Variants of the coronavirus contributed to the severity of the disease and persistent rise in infections. This study aimed to identify potential drug candidates from fifteen approved antiviral drugs against SARS-CoV-2 (6LU7), SARS-CoV (5B6O), and SARS-CoV-2 spike protein (6M0J) using virtual screening and pharmacokinetics to gain insights into COVID-19 therapeutics.

Tumor necrosis factor-α-induced protein 8-like 3 (TNFAIP8L3 or TIPE3) functions as a transfer protein for lipid second messengers. TIPE3 is highly upregulated in several human cancers and has been established to significantly promote tumor cell proliferation, migration, and invasion and inhibit the apoptosis of cancer cells. Thus, inhibiting the function of TIPE3 is expected to be an effective strategy against cancer. The advancement of artificial intelligence (AI)-driven drug development has recently invigorated research in anti-cancer drug development. In this work, we incorporated DFCNN, Autodock Vina docking, DeepBindBC, MD, and metadynamics to efficiently identify inhibitors of TIPE3 from a ZINC compound dataset. Six potential candidates were selected for further experimental study to validate their anti-tumor activity. Among these, three small-molecule compounds (K784-8160, E745-0011, and 7238-1516) showed significant anti-tumor activity in vitro, leading to reduced tumor cell viability, proliferation, and migration and enhanced apoptotic tumor cell death. Notably, E745-0011 and 7238-1516 exhibited selective cytotoxicity toward tumor cells with high TIPE3 expression while having little or no effect on normal human cells or tumor cells with low TIPE3 expression. A molecular docking analysis further supported their interactions with TIPE3, highlighting hydrophobic interactions and their shared interaction residues and offering insights for designing more effective inhibitors. Taken together, this work demonstrates the feasibility of incorporating deep learning and MD simulations in virtual drug screening and provides inhibitors with significant potential for anti-cancer drug development against TIPE3-.

Bisindole alkaloids are a source of inspiration for the design and discovery of new-generation anticancer agents. In this study, we investigated the cytotoxic and antiproliferative activities of three spirobisindole alkaloids from the traditional anticancer Philippine medicinal plant , along with their mechanisms of action. Thus, the alkaloids globospiramine (), deoxyvobtusine (), and vobtusine lactone () showed in vitro cytotoxicity and antiproliferative activities against the tested cell lines (L929, KB3.1, A431, MCF-7, A549, PC-3, and SKOV-3) using MTT and CellTiter-Blue assays. Globospiramine () was also screened against a panel of breast cancer cell lines using the sulforhodamine B (SRB) assay and showed moderate cytotoxicity. It also promoted the activation of apoptotic effector caspases 3 and 7 using Caspase-Glo 3/7 and CellEvent-3/7 apoptosis assays. Increased expressions of cleaved caspase 3 and PARP in A549 cells treated with were also observed. Apoptotic activity was also confirmed when globospiramine () failed to promote the rapid loss of membrane integrity according to the HeLa cell membrane permeability assay. Network pharmacology analysis, molecular docking, and molecular dynamics simulations identified MAPK14 (p38α), a pharmacological target leading to cancer cell apoptosis, as a putative target. Low toxicity risks and favorable drug-likeness were also predicted for . Overall, our study demonstrated the anticancer potentials and apoptotic mechanisms of globospiramine (), validating the traditional medicinal use of .

Breast cancer (BC) patients and survivors can experience immense emotional and psychosocial trauma. Treatment modalities for BC, including surgery, chemotherapy and radiotherapy are associated with certain displeasing and undesirable effects, including physical restrictions as well as mental stress. However, it has been ascertained that appropriate supportive and rehabilitative strategies can significantly help to alleviate the distress. Along with several conventional physical therapy options, the novel Virtual Reality (VR) tool has opened a new gateway in rehabilitative approaches in patients with BC. We reviewed the role of VR based management for BC-related incapacitations and found that its efficacy is comparable to that of contemporary therapy options. It has the additional benefits of modulating pain perceptions, improving mobility, and overall enhancing the quality of life of BC survivors.

This study aimed to efficiently reduce the release of radon from water bodies to protect the environment.

In recent times, dentistry has seen significant technological advancements that have transformed various specialized areas within the field. Developed into applications for mobile devices, augmented reality (AR) seamlessly merges digital components with the physical world, enhancing both realms while maintaining their individual separateness. On the other hand, virtual reality (VR) relies on advanced, tailored software to visualize a digital 3D environment stimulating the operator's senses through computer generated sensations and feedback. The current advances use the application of VR, haptic simulators, the use of an AI algorithm and many more that provides new opportunities for smart learning and enhance the teaching environment. As this technology continues to evolve, it is poised to become even more remarkable, enabling specialists to potentially visualize both soft and hard tissues within the patient's body for effective treatment planning. This literature aims to present the newest advancements and ongoing development of AR and VR in dentistry and medicine. It highlights their diverse applications while identifying areas needing further research for effective integration into clinical practice.

To assess the knowledge and awareness of Virtual Reality (VR) and Augmented Reality (AR) technology in dentistry.

Association testing has been widely used to study the relationship between genetic variants and phenotypes. Most association testing methods are genotype-based, i.e. first estimate genotype and then regress phenotype on estimated genotype and other variables. Directly testing methods based on next generation sequencing (NGS) data without genotype calling have been proposed and shown advantage over genotype-based methods in the scenarios when genotype calling is not accurate. NGS data-based single-variant testing have been proposed including our previously proposed single-variant testing method, i.e. UNC combo method [1]. NGS data-based group testing methods for continuous phenotype have also been proposed by us using a linear model framework which can handle continuous responses [2]. In this paper, we extend our linear model-based framework to a generalized linear model-based framework so that the methods can handle other types of responses especially binary responses which is commonly-faced in association studies. We have conducted extensive simulation studies to evaluate the performance of different estimators and compare our estimators with their corresponding genotype-based methods. We found that all methods have Type I errors controlled, and our NGS data-based testing methods have better performance than their corresponding genotype-based methods in the literature for other types of responses including binary responses (logistic regression) and count responses (Poisson regression especially when sequencing depth is low. In conclusion, we have extended our previous linear model (LM) framework to a generalized linear model (GLM) framework and derived NGS data-based testing methods for a group of genetic variants. Compared with our previously proposed LM-based methods [2], the new GLM-based methods can handle more complex responses (for example, binary responses and count responses) in addition to continuous responses. Our methods have filled the literature gap and shown advantage over their corresponding genotype-based methods in the literature.

According to the 2018 WHO R&D Blueprint, Nipah virus (NiV) is a priority disease, and the development of a vaccine against NiV is strongly encouraged. According to criteria used to categorize zoonotic diseases, NiV is a stage III disease that can spread to people and cause unpredictable outbreaks. Since 2001, the NiV virus has caused annual outbreaks in Bangladesh, while in India it has caused occasional outbreaks. According to estimates, the mortality rate for infected individuals ranges from 70 to 91%. Using immunoinformatic approaches to anticipate the epitopes of the MHC-I, MHC-II, and B-cells, they were predicted using the NiV glycoprotein and nucleocapsid protein. The selected epitopes were used to develop a multi-epitope vaccine construct connected with linkers and adjuvants in order to improve immune responses to the vaccine construct. The 3D structure of the engineered vaccine was anticipated, optimized, and confirmed using a variety of computer simulation techniques so that its stability could be assessed. According to the immunological simulation tests, it was found that the vaccination elicits a targeted immune response against the NiV. Docking with TLR-3, 7, and 8 revealed that vaccine candidates had high binding affinities and low binding energies. Finally, molecular dynamic analysis confirms the stability of the new vaccine. Codon optimization and in silico cloning showed that the proposed vaccine was expressed to a high degree in Escherichia coli. The study will help in identifying a potential epitope for a vaccine candidate against NiV. The developed multi-epitope vaccine construct has a lot of potential, but they still need to be verified by in vitro & in vivo studies.

The joint analysis of two datasets [Formula: see text] and [Formula: see text] that describe the same phenomena (e.g. the cellular state), but measure disjoint sets of variables (e.g. mRNA vs. protein levels) is currently challenging. Traditional methods typically analyze single interaction patterns such as variance or covariance. However, problem-tailored external knowledge may contain multiple different information about the interaction between the measured variables. We introduce MIASA, a holistic framework for the joint analysis of multiple different variables. It consists of assembling multiple different information such as similarity vs. association, expressed in terms of interaction-scores or distances, for subsequent clustering/classification. In addition, our framework includes a novel qualitative Euclidean embedding method (qEE-Transition) which enables using Euclidean-distance/vector-based clustering/classification methods on datasets that have a non-Euclidean-based interaction structure. As an alternative to conventional optimization-based multidimensional scaling methods which are prone to uncertainties, our qEE-Transition generates a new vector representation for each element of the dataset union [Formula: see text] in a common Euclidean space while strictly preserving the original ordering of the assembled interaction-distances. To demonstrate our work, we applied the framework to three types of simulated datasets: samples from families of distributions, samples from correlated random variables, and time-courses of statistical moments for three different types of stochastic two-gene interaction models. We then compared different clustering methods with vs. without the qEE-Transition. For all examples, we found that the qEE-Transition followed by Ward clustering had superior performance compared to non-agglomerative clustering methods but had a varied performance against ultrametric-based agglomerative methods. We also tested the qEE-Transition followed by supervised and unsupervised machine learning methods and found promising results, however, more work is needed for optimal parametrization of these methods. As a future perspective, our framework points to the importance of more developments and validation of distance-distribution models aiming to capture multiple-complex interactions between different variables.

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) caused the COVID-19 disease, which represents a new life-threatening disaster. Regarding viral infection, many therapeutics have been investigated to alleviate the epidemiology such as vaccines and receptor decoys. However, the continuous mutating coronavirus, especially the variants of Delta and Omicron, are tended to invalidate the therapeutic biological product. Thus, it is necessary to develop molecular entities as broad-spectrum antiviral drugs. Coronavirus replication is controlled by the viral 3-chymotrypsin-like cysteine protease (3CLpro) enzyme, which is required for the virus's life cycle. In the cases of severe acute respiratory syndrome coronavirus (SARS-CoV) and middle east respiratory syndrome coronavirus (MERS-CoV), 3CLpro has been shown to be a promising therapeutic development target. Here we proposed an attention-based deep learning framework for molecular graphs and sequences, training from the BindingDB 3CLpro dataset (114,555 compounds). After construction of such model, we conducted large-scale screening the in vivo/vitro dataset (276,003 compounds) from Zinc Database and visualize the candidate compounds with attention score. geometric-based affinity prediction was employed for validation. Finally, we established a 3CLpro-specific deep learning framework, namely GraphDPI-3CL (AUROC: 0.958) achieved superior performance beyond the existing state of the art model and discovered 10 molecules with a high binding affinity of 3CLpro and superior binding mode.

Microcystins (MCs) are the most widespread cyanobacterial toxins in eutrophic water body. As high toxic intermediate metabolites, linearized MCs are further catalyzed by linearized microcystinase (MlrB) of sp. USTB-05. Here MlrB structure was studied by comprizing with a model representative of the penicillin-recognizing enzyme family via homology modeling. The key active sites of MlrB were predicted by molecular docking, and further verified by site-directed mutagenesis. A comprehensive enzymatic mechanism for linearized MCs biodegradation by MlrB was proposed: S77 transferred a proton to H307 to promote a nucleophilic attack on the peptide bond (Ala-Leu in MC-LR or Ala-Arg in MC-RR) of linearized MCs to form the amide intermediate. Then water was involved to break the peptide bond and produced the tetrapeptide as product. Meanwhile, four amino acid residues (K80, Y171, N173 and D245) acted synergistically to stabilize the substrate and intermediate transition states. This study firstly revealed the enzymatic mechanism of MlrB for biodegrading linearized MCs with both computer simulation and experimental verification.

Bacterial pathogens have remained a major public health concern for several decades. This study investigated the antibacterial activities of Miang extracts (at non-neutral and neutral pH) against Bacillus cereus TISTR 747, Escherichia coli ATCC 22595, Salmonella enterica serovar Typhimurium TISTR 292 and Streptococcus mutans DMST 18777. The potential of Polyvinylpolypyrrolidone (PVPP)-precipitated tannin-free Miang extracts in growth-inhibition of the cariogenic Streptococcus mutans DMST 18777 and its biofilms was also evaluated. The tannin-rich fermented extracts had the best bacterial growth inhibition against S. mutans DMST 18777 with an MIC of 0.29 and 0.72 mg/mL for nonfilamentous fungi (NFP) Miang and filamentous-fungi-processed (FFP) Miang respectively. This observed anti-streptococcal activity still remained after PVPP-mediated precipitation of bioactive tannins especially, in NFP and FFP Miang. Characterization of the PVPP-treated extracts using High performance liquid chromatography quadrupole-time of flight-mass spectrometry (HPLC-QToF-MS) analysis, also offered an insight into probable compound classes responsible for the activities. In addition, Crystal violet-staining also showed better IC50 values for NFP Miang (4.30 ± 0.66 mg/mL) and FFP Miang (12.73 ± 0.11 mg/mL) against S. mutans DMST 18777 biofilms in vitro. Homology modeling and molecular docking analysis using HPLC-MS identified ligands in tannin-free Miang supernatants, was performed against modelled S. mutans DMST 18777 sortase A enzyme. The in silico analysis suggested that the inhibition by NFP and FFP Miang might be attributed to the presence of ellagic acid, flavonoid aglycones, and glycosides. Thus, these Miang extracts could be optimized and explored as natural active pharmaceutical ingredients (NAPIs) for applications in oral hygienic products.

This paper introduces a novel and improved double-resistor damped double-tuned passive power filter (DR-DDTF), designed using multi-objective optimization algorithms to mitigate harmonics and increase the hosting capacity of distribution systems with distributed energy resources. Although four different topologies of single-resistor damped double-tuned filters (DDTFs) have been studied before in the literature, the effectiveness of two different DR-DDTF configurations has not been examined. This work redresses this gap by demonstrating that via comprehensive simulations on two power systems, DR-DDTF provides better harmonic suppression and resonance mitigation than single-resistor alternatives. When it comes to optimizing the DR-DDTF for maximum hosting capacity and minimum system active power losses, the multi-objective artificial hummingbird outperformed six other algorithms in the benchmark. To allow for higher penetration of distributed generation without requiring grid upgrades, this newly developed harmonic mitigation filter provides a good alternative.

In the current study we investigated the impact of combination of rutin and vitamin A on glycated products, the glyoxalase system, oxidative markers, and inflammation in animals fed a high-fat high-fructose (HFFD) diet. Thirty rats were randomly divided into six groups (n = 5). The treatments, metformin (120 mg/kg), rutin (100 mg/kg), vitamin A (43 IU/kg), and a combination of rutin (100 mg/kg) and vitamin A (43 IU/kg) were given to relevant groups of rats along with high-fructose high-fat diet for 42 days. HbA1c, D-lactate, Glyoxylase-1, Hexokinase 2, malondialdehyde (MDA), glutathione peroxidase (GPx), catalase (CAT), nuclear transcription factor-B (NF-κB), interleukin-6 (IL-6), interleukin-8 (IL-8) and histological examinations were performed after 42 days. The docking simulations were conducted using Auto Dock package. The combined effects of rutin and vitamin A in treated rats significantly (p < 0.001) reduced HbA1c, hexokinase 2, and D-lactate levels while preventing cellular damage. The combination dramatically (p < 0.001) decreased MDA, CAT, and GPx in treated rats and decreased the expression of inflammatory cytokines such as IL-6 andIL-8, as well as the transcription factor NF-κB. The molecular docking investigations revealed that rutin had a strong affinity for several important biomolecules, including as NF-κB, Catalase, MDA, IL-6, hexokinase 2, and GPx. The results propose beneficial impact of rutin and vitamin A as a convincing treatment strategy to treat AGE-related disorders, such as diabetes, autism, alzheimer's, atherosclerosis.

Intuitively, combining multiple sources of evidence should lead to more accurate decisions than considering single sources of evidence individually. In practice, however, the proper computation may be difficult, or may require additional data that are inaccessible. Here, based on the concept of conditional independence, we consider expressions that can serve either as recipes for integrating evidence based on limited data, or as statistical benchmarks for characterizing evidence integration processes. Consider three events, A, B, and C. We find that, if A and B are conditionally independent with respect to C, then the probability that C occurs given that both A and B are known, P(C|A, B), can be easily calculated without the need to measure the full three-way dependency between A, B, and C. This simplified approach can be used in two general ways: to generate predictions by combining multiple (conditionally independent) sources of evidence, or to test whether separate sources of evidence are functionally independent of each other. These applications are demonstrated with four computer-simulated examples, which include detecting a disease based on repeated diagnostic testing, inferring biological age based on multiple biomarkers of aging, discriminating two spatial locations based on multiple cue stimuli (multisensory integration), and examining how behavioral performance in a visual search task depends on selection histories. Besides providing a sound prescription for predicting outcomes, this methodology may be useful for analyzing experimental data of many types.

Despite the huge importance that the centrality metrics have in understanding the topology of a network, too little is known about the effects that small alterations in the topology of the input graph induce in the norm of the vector that stores the node centralities. If so, then it could be possible to avoid re-calculating the vector of centrality metrics if some minimal changes occur in the network topology, which would allow for significant computational savings. Hence, after formalising the notion of centrality, three of the most basic metrics were herein considered (i.e., Degree, Eigenvector, and Katz centrality). To perform the simulations, two probabilistic failure models were used to describe alterations in network topology: Uniform (i.e., all nodes can be independently deleted from the network with a fixed probability) and Best Connected (i.e., the probability a node is removed depends on its degree). Our analysis suggests that, in the case of degree, small variations in the topology of the input graph determine small variations in Degree centrality, independently of the topological features of the input graph; conversely, both Eigenvector and Katz centralities can be extremely sensitive to changes in the topology of the input graph. In other words, if the input graph has some specific features, even small changes in the topology of the input graph can have catastrophic effects on the Eigenvector or Katz centrality.

Mendelian randomisation (MR) is the use of genetic variants as instrumental variables. Mode-based estimators (MBE) are one of the most popular types of estimators used in univariable-MR studies and is often used as a sensitivity analysis for pleiotropy. However, because there are no plurality valid regression estimators, modal estimators for multivariable-MR have been under-explored.

U.S. service members maintain constant situational awareness (SA) due to training and experience operating in dynamic and complex environments. Work examining how military experience impacts SA during visual search of a complex naturalistic environment, is limited. Here, we compare Active Duty service members and Civilians' physiological behavior during a navigational visual search task in an open-world virtual environment (VE) while cognitive load was manipulated. We measured eye-tracking and electroencephalogram (EEG) outcomes from Active Duty (N = 21) and Civilians (N = 15) while they navigated a desktop VE at a self-regulated pace. Participants searched and counted targets (N = 15) presented among distractors, while cognitive load was manipulated with an auditory Math Task. Results showed Active Duty participants reported significantly greater/closer to the correct number of targets compared to Civilians. Overall, Active Duty participants scanned the VE with faster peak saccade velocities and greater average saccade magnitudes compared to Civilians. Convolutional Neural Network (CNN) response (EEG P-300) was significantly weighted more to initial fixations for the Active Duty group, showing reduced attentional resources on object refixations compared to Civilians. There were no group differences in fixation outcomes or overall CNN response when comparing targets versus distractor objects. When cognitive load was manipulated, only Civilians significantly decreased their average dwell time on each object and the Active Duty group had significantly fewer numbers of correct answers on the Math Task. Overall, the Active Duty group explored the VE with increased scanning speed and distance and reduced cognitive re-processing on objects, employing a different, perhaps expert, visual search strategy indicative of increased SA. The Active Duty group maintained SA in the main visual search task and did not appear to shift focus to the secondary Math Task. Future work could compare how a stress inducing environment impacts these groups' physiological or cognitive markers and performance for these groups.

Ensuring the security and privacy of patient data is a critical concern in the healthcare industry. The growing utilization of electronic data transmission and storage in medical records has amplified apprehensions about data security. However, due to varying stakeholder interests, not all data can be freely shared, necessitating the development of secure protocols.

Soft crawling robots have been widely studied and applied because of their excellent environmental adaptability and flexible movement. However, most existing soft crawling robots typically exhibit a single-motion mode and lack diverse capabilities. Inspired by larvae, this paper proposes a compact soft crawling robot (weight, 13 g; length, 165 mm; diameter, 35 mm) with multimodal locomotion (forward, turning, rolling, and twisting). Each robot module uses 4 sets of high-power-density shape memory alloy actuators, endowing it with 4 degrees of motion freedom. We analyze the mechanical characteristics of the robot modules through experiments and simulation analysis. The plug-and-play modules can be quickly assembled to meet different motion and task requirements. The soft crawling robot can be remotely operated with an external controller, showcasing multimodal motion on various material surfaces. In a narrow maze, the robot demonstrates agile movement and effective maneuvering around obstacles. In addition, leveraging the inherent bistable characteristics of the robot modules, we used the robot modules as anchoring units and installed a microcamera on the robot's head for pipeline detection. The robot completed the inspection in horizontal, vertical, curved, and branched pipelines, adjusted the camera view, and twisted a valve in the pipeline for the first time. Our research highlights the robot's superior locomotion and application capabilities, providing an innovative strategy for the development of lightweight, compact, and multifunctional soft crawling robots.

α-zeins are amphiphilic maize seed storage proteins with material properties suitable for a multitude of applications e.g., in renewable plastics, foods, therapeutics and additive manufacturing (3D-printing). To exploit their full potential, molecular-level insights are essential. The difficulties in experimental atomic-resolution characterization of α-zeins have resulted in a diversity of published molecular models. However, deep-learning α-zein models are largely unexplored. Therefore, this work studies an AlphaFold2 (AF2) model of a highly expressed α-zein using molecular dynamics (MD) simulations. The sequence of the α-zein cZ19C2 gave a loosely packed AF2 model with 7 α-helical segments connected by turns/loops. Compact tertiary structure was limited to a C-terminal bundle of three α-helices, each showing notable agreement with a published consensus sequence. Aiming to chart possible α-zein conformations in practically relevant solvents, rather than the native solid-state, the AF2 model was subjected to MD simulations in water/ethanol mixtures with varying ethanol concentrations. Despite giving structurally diverse endpoints, the simulations showed several patterns: In water and low ethanol concentrations, the model rapidly formed compact globular structures, largely preserving the C-terminal bundle. At ≥ 50 mol% ethanol, extended conformations prevailed, consistent with previous SAXS studies. Tertiary structure was partially stabilized in water and low ethanol concentrations, but was disrupted in ≥ 50 mol% ethanol. Aggregated results indicated minor increases in helicity with ethanol concentration. β-sheet content was consistently low (∼1%) across all conditions. Beyond structural dynamics, the rapid formation of branched α-zein aggregates in aqueous environments was highlighted. Furthermore, aqueous simulations revealed favorable interactions between the protein and the crosslinking agent glycidyl methacrylate (GMA). The proximity of GMA epoxide carbons and side chain hydroxyl oxygens simultaneously suggested accessible reactive sites in compact α-zein conformations and pre-reaction geometries for methacrylation. The findings may assist in expanding the applications of these technologically significant proteins, e.g., by guiding chemical modifications.

The development of believable, natural, and interactive digital artificial agents is a field of growing interest. Theoretical uncertainties and technical barriers present considerable challenges to the field, particularly with regards to developing agents that effectively simulate human emotions. Large language models (LLMs) might address these issues by tapping common patterns in situational appraisal. In three empirical experiments, this study tests the capabilities of LLMs to solve emotional intelligence tasks and to simulate emotions. It presents and evaluates a new Chain-of-Emotion architecture for emotion simulation within video games, based on psychological appraisal research. Results show that it outperforms control LLM architectures on a range of user experience and content analysis metrics. This study therefore provides early evidence of how to construct and test affective agents based on cognitive processes represented in language models.

N-Methyl-D-aspartate (NMDA) receptors are glutamate-gated excitatory channels that play essential roles in brain functions. While high-resolution structures were solved for an allosterically inhibited form of functional NMDA receptor, other key functional states (particularly the active open-channel state) have not yet been resolved at atomic resolutions. To decrypt the molecular mechanism of the NMDA receptor activation, structural modeling and simulation are instrumental in providing detailed information about the dynamics and energetics of the receptor in various functional states. In this chapter, we describe coarse-grained modeling of the NMDA receptor using an elastic network model and related modeling/analysis tools (e.g., normal mode analysis, flexibility and hotspot analysis, cryo-EM flexible fitting, and transition pathway modeling) based on available structures. Additionally, we show how to build an atomistic model of the active-state receptor with targeted molecular dynamics (MD) simulation and explore its energetics and dynamics with conventional MD simulation. Taken together, these modeling and simulation can offer rich structural and dynamic information which will guide experimental studies of the activation of this key receptor.