Computer Simulation

The purpose of this study is to investigate the molecular interactions and potential therapeutic uses of Eltrombopag (EPAG), a small molecule that activates the cMPL receptor. EPAG has been found to be effective in increasing platelet levels and alleviating thrombocytopenia. We utilized computational techniques to predict and confirm the complex formed by the ligand (EPAG) and the Thrombopoietin receptor (TPO-R) cMPL, elucidating the role of RAS, JAK-2, STAT-3, and other essential elements for downstream signaling. Molecular dynamics (MD) simulations were employed to evaluate the stability of the ligand across specific proteins, showing favorable characteristics. For the first time, we examined the presence of TPO-R in human umbilical cord mesenchymal stem cells (hUCMSC) and human gingival mesenchymal stem cells (hGMSC) proliferation. Furthermore, treatment with EPAG demonstrated angiogenesis and vasculature formation of endothelial lineage derived from both MSCs. It also indicated the activation of critical factors such as RUNX-1, GFI-1b, VEGF-A, MYB, GOF-1, and FLI-1. Additional experiments confirmed that EPAG could be an ideal molecule for protecting against UVB radiation damage, as gene expression (JAK-2, ERK-2, MCL-1, NFkB, and STAT-3) and protein CD90/cMPL analysis showed TPO-R activation in both hUCMSC and hGMSC. Overall, EPAG exhibits significant potential in treating radiation damage and mitigating the side effects of radiotherapy, warranting further clinical exploration.

Procyanidins, which are oligomerized flavan-3-ols with a polyphenolic structure, are bioactive substances that exhibit various biological effects. However, the relationship between the degree of polymerization (DP) of procyanidins and their bioactivities remains largely unknown. In this study, the preventive effects of procyanidins with different DP (EC, PB2 and PC1) on glucose improvement and liver lipid deposition were investigated using a high-fat diet/streptozotocin-induced diabetes mouse model. The results demonstrated that all the procyanidins with different DP effectively reduced fasting blood glucose and glucose/insulin tolerance, decreased the lipid profile (total cholesterol, triglyceride, and low-density lipoprotein cholesterol content) in serum and liver tissue as well as the liver oil red staining, indicating the improvement of glucose metabolism, insulin sensitivity and hepatic lipid deposition in diabetic mice. Furthermore, the procyanidins down-regulated expression of glucose regulated 78-kDa protein (GRP78) and C/EBP homologous protein (CHOP), indicating a regulation role of endoplasmic reticulum (ER) stress. The inhibition of ER stress by tauroursodeoxycholic acid (TUDCA) treatment abolished the effects of procyanidins with different DP in PA-induced HepG2 cells, confirming that procyanidins alleviate liver hyperlipidemia through the modulation of ER stress. Molecular docking results showed that EC and PB2 could better bind GRP78 and CHOP. Collectively, our study reveals that the structure of procyanidins, particularly DP, is not directly correlated with the improvement of blood glucose and lipid deposition, while highlighting the important role of ER stress in the bioactivities of procyanidins.

Fentanyl and its derivatives are a type of potent opioid analgesics, with the characteristics of diverse structure, high toxicity, extremely low content, and high fatality rate. Currently, they have become one of the most serious problems in international drug abuse control due to their extensive use in drug production and use. Therefore, the development of a rapid, sensitive, and accurate method for detecting trace fentanyl is of great significance. In this study, in view of its complex structure and trace concentration, a new molecular imprinting electrochemical sensor was developed through molecular simulations followed by experimental validation to detect trace fentanyl.

In this study, inactivation of mushroom polyphenol oxidase (PPO) by low intensity direct current (DC) electric field and its molecular mechanism were investigated. In the experiments under 3 V/cm, 5 V/cm, 7 V/cm and 9 V/cm electric fields, PPOs were all completely inactivated after different exposure times. Under 1 V/cm, a residual activity of 11.88 % remained. The inactivation kinetics confirms to Weibull model. Under 1-7 V/cm, n value closes to a constant about 1.3. The structural analysis of PPO under 3 V/cm and 5 V/cm by fluorescence emission spectroscopy and molecular dynamics (MD) simulation showed that the tertiary structure was slightly changed with increased radius of gyration, higher potential energy and rate of C-alpha fluctuation. After exposure to the electric field, most of the hydrophobic tryptophan (TRP) residues turned to the hydrophilic surface, resulting the fluorescence red-shifted and quenched. Molecular docking indicated that the receptor binding domain of catechol in PPO was changed. PPO under electric field was MD simulated the first time, revealing the changing mechanism of the electric field itself on PPO, a binuclear copper enzyme, which has a metallic center. All these suggest that the low intensity DC electric field would be a promising option for enzymatic browning inhibition or even enzyme activity inactivation.

BAX and BAK are proapoptotic members of the BCL2 family that directly mediate mitochondrial outer membrane permeabilition (MOMP), a central step in apoptosis execution. However, the molecular architecture of the mitochondrial apoptotic pore remains a key open question and especially little is known about the contribution of lipids to MOMP. By performing a comparative lipidomics analysis of the proximal membrane environment of BAK isolated in lipid nanodiscs, we find a significant enrichment of unsaturated species nearby BAK and BAX in apoptotic conditions. We then demonstrate that unsaturated lipids promote BAX pore activity in model membranes, isolated mitochondria and cellular systems, which is further supported by molecular dynamics simulations. Accordingly, the fatty acid desaturase FADS2 not only enhances apoptosis sensitivity, but also the activation of the cGAS/STING pathway downstream mtDNA release. The correlation of FADS2 levels with the sensitization to apoptosis of different lung and kidney cancer cell lines by co-treatment with unsaturated fatty acids supports the relevance of our findings. Altogether, our work provides an insight on how local lipid environment affects BAX and BAK function during apoptosis.

The advent of the fourth industrial revolution, characterized by artificial intelligence (AI) as its central component, has resulted in the mechanization of numerous previously labor-intensive activities. The use of in silico tools has become prevalent in the design of biopharmaceuticals. Upon conducting a comprehensive analysis of the genomes of many organisms, it has been discovered that their tissues can generate specific peptides that confer protection against certain diseases. This study aims to identify a selected group of neuropeptides (NPs) possessing favorable characteristics that render them ideal for production as neurological biopharmaceuticals. Until now, the construction of NP classifiers has been the primary focus, neglecting to optimize these characteristics. Therefore, in this study, the task of creating ideal NPs has been formulated as a multi-objective optimization problem. The proposed framework, NPpred, comprises two distinct components: NSGA-NeuroPred and BERT-NeuroPred. The former employs the NSGA-II algorithm to explore and change a population of NPs, while the latter is an interpretable deep learning-based model. The utilization of explainable AI and motifs has led to the proposal of two novel operators, namely p-crossover and p-mutation. An online application has been deployed at https://neuropred.anvil.app for designing an ideal collection of synthesizable NPs from protein sequences.

Materials with field-tunable polarization are of broad interest to condensed matter sciences and solid-state device technologies. Here, using hydrogen (H) donor doping, we modify the room temperature metallic phase of a perovskite nickelate NdNiO into an insulating phase with both metastable dipolar polarization and space-charge polarization. We then demonstrate transient negative differential capacitance in thin film capacitors. The space-charge polarization caused by long-range movement and trapping of protons dominates when the electric field exceeds the threshold value. First-principles calculations suggest the polarization originates from the polar structure created by H doping. We find that polarization decays within ~1 second which is an interesting temporal regime for neuromorphic computing hardware design, and we implement the transient characteristics in a neural network to demonstrate unsupervised learning. These discoveries open new avenues for designing ferroelectric materials and electrets using light-ion doping.

Sex, age, and education are associated with the level of cognitive performance. We investigated whether these factors modulate the change in cognitive performance in midlife by leveraging the longitudinal data from the Cardiovascular Risk in Young Finns Study (YFS).

We report herein, the design and synthesis of benzimidazole-oxadiazole derivatives as new inhibitors for vascular endothelial growth factor receptor-2 (VEGFR-2). The designed members were assessed for their in vitro anticancer activity against three cancer cell lines and two normal cell lines; A549, MCF-7, PANC-1, hTERT-HPNE and CCD-19Lu. Compounds 4c and 4d were found to be the most effective compounds against three cancer cell lines. Compounds 4c and 4d were then tested for their in vitro VEGFR-2 inhibitory activity, safety profiles, and selectivity indices using the normal hTERT-HPNE and CCD-19Lu cell lines. It was determined that compound 4c was the most effective and safe member of the produced chemical family. Vascular endothelial growth factor A (VEGFA) immunolocalizations of compounds 4c and 4d were evaluated relative to control by VEGFA immunofluorescence staining. Compounds 4c and 4d inhibited VEGFR-2 enzyme with half-maximal inhibitory concentration values of 0.475 ± 0.021 and 0.618 ± 0.028 µM, respectively. Molecular docking of the target compounds was carried out in the active site of VEGFR-2 (Protein Data Bank: 4ASD).

The COVID-19 has had a significant influence on people's lives across the world. The viral genome has undergone numerous unanticipated changes that have given rise to new varieties, raising alarm on a global scale. Bioactive phytochemicals derived from nature and synthetic sources possess lot of potential as pathogenic virus inhibitors. The goal of the recent study is to report new inhibitors of Schiff bases of 1,3-dipheny urea derivatives against SARS COV-2 spike protein through in-vitro and in-silico approach. Total 14 compounds were evaluated, surprisingly, all the compounds showed strong inhibition with inhibitory values between 79.60% and 96.00% inhibition. Here, compounds 3a (96.00%), 3d (89.60%), 3e (84.30%), 3f (86.20%), 3g (88.30%), 3h (86.80%), 3k (82.10%), 3l (90.10%), 3m (93.49%), 3n (85.64%), and 3o (81.79%) exhibited high inhibitory potential against SARS COV-2 spike protein. While 3c also showed significant inhibitory potential with 79.60% inhibition. The molecular docking of these compounds revealed excellent fitting of molecules in the spike protein receptor binding domain (RBD) with good interactions with the key residues of RBD and docking scores ranging from - 4.73 to - 5.60 kcal/mol. Furthermore, molecular dynamics simulation for 150 ns indicated a strong stability of a complex 3a:6MOJ. These findings obtained from the in-vitro and in-silico study reflect higher potency of the Schiff bases of 1,3-diphenyl urea derivatives. Furthermore, also highlight their medicinal importance for the treatment of SARS COV-2 infection. Therefore, these small molecules could be a possible drug candidate.

Dengue fever (DF) is an endemic disease that has become a public health concern around the globe. The NS3 protease-helicase enzyme is an important target for the development of antiviral drugs against DENV (dengue virus) due to its impact on viral replication. Inhibition of the activity of the NS3 protease-helicase enzyme complex significantly inhibits the infection associated with DENV. Unfortunately, there are no scientifically approved antiviral drugs for its prevention. However, this study has been developed to find natural bioactive molecules that can block the activity of the NS3 protease-helicase enzyme complex associated with DENV infection through molecular docking, MM-GBSA (molecular mechanics-generalized born surface area), and molecular dynamics (MD) simulations. Three hundred forty-two (342) compounds selected from twenty traditional medicinal plants were retrieved and screened against the NS3 protease-helicase protein by molecular docking and MM-GBSA studies, where the top six phytochemicals have been identified based on binding affinities. The six compounds were then subjected to pharmacokinetics and toxicity analysis, and we conducted molecular dynamics simulations on three protein-ligand complexes to validate their stability. Through computational analysis, this study revealed the potential of the two selected natural bioactive inhibitors (CID-440015 and CID-7424) as novel anti-dengue agents.

This study investigates quercetin complexes as potential synergistic agents against the important respiratory pathogen Streptococcus pneumoniae. Six quercetin complexes (QCX1-6) were synthesized by reacting quercetin with various metal salts and boronic acids and characterized using FTIR spectroscopy. Their antibacterial activity alone and in synergism with antibiotics was evaluated against S. pneumoniae ATCC 49619 using disc diffusion screening, broth microdilution MIC determination, and checkerboard assays. Complexes QCX-3 and QCX-4 demonstrated synergy when combined with levofloxacin via fractional inhibitory concentration indices ≤ 0.5 as confirmed by time-kill kinetics. Molecular docking elucidated interactions of these combinations with virulence enzymes sortase A and sialidase. A biofilm inhibition assay found the synergistic combinations more potently reduced biofilm formation versus monotherapy. Additionally, gene-gene interaction networks, biological activity predictions and in-silico toxicity profiling provided insights into potential mechanisms of action and safety.

Danshen, a prominent herb in traditional Chinese medicine (TCM), is known for its potential to enhance physiological functions such as blood circulation, immune response, and resolve blood stasis. Despite the effectiveness of COVID-19 vaccination efforts, some individuals still face severe complications post-infection, including pulmonary fibrosis, myocarditis arrhythmias and stroke. This study employs a network pharmacology and molecular docking approach to investigate the potential mechanisms underlying the therapeutic effects of candidate components and targets from Danshen in the treatment of complications in COVID-19. Candidate components and targets from Danshen were extracted from the TCMSP Database, while COVID-19-related targets were obtained from Genecards. Venn diagram analysis identified common targets. A Protein-Protein interaction (PPI) network and gene enrichment analysis elucidated potential therapeutic mechanisms. Molecular docking evaluated interactions between core targets and candidate components, followed by molecular dynamics simulations to assess stability. We identified 59 potential candidate components and 123 targets in Danshen for COVID-19 treatment. PPI analysis revealed 12 core targets, and gene enrichment analysis highlighted modulated pathways. Molecular docking showed favorable interactions, with molecular dynamics simulations indicating high stability of key complexes. Receiver operating characteristic (ROC) curves validated the docking protocol. Our study unveils candidate compounds, core targets, and molecular mechanisms of Danshen in COVID-19 treatment. These findings provide a scientific foundation for further research and potential development of therapeutic drugs.

Cyclic tetrapeptides c(Pro-Phe-Pro-Phe) obtained by the mechanosynthetic method using a ball mill were isolated in a pure stereochemical form as a homochiral system (all L-amino acids, sample A) and as a heterochiral system with D configuration at one of the stereogenic centers of Phe (sample B). The structure and stereochemistry of both samples were determined by X-ray diffraction studies of single crystals. In DMSO and acetonitrile, sample A exists as an equimolar mixture of two conformers, while only one is monitored for sample B. The conformational space and energetic preferences for possible conformers were calculated using DFT methods. The distinctly different conformational flexibility of the two samples was experimentally proven by Variable Temperature (VT) and 2D EXSY NMR measurements. Both samples were docked to histone deacetylase HDAC8. Cytotoxic studies proved that none of the tested cyclic peptide is toxic.

Rheumatoid arthritis (RA) is a chronic systemic autoimmune disease marked by inflammatory cell infiltration and joint damage. The Chinese government has approved the prescription medication sinomenine (SIN), an effective anti-inflammation drug, for treating RA. This study evaluated the possible anti-inflammatory actions of SIN in RA based on bioinformatics analysis and experiments. Six microarray datasets were acquired from the gene expression omnibus (GEO) database. We used R software to identify differentially expressed genes (DEGs) and perform function evaluations. The CIBERSORT was used to calculate the abundance of 22 infiltrating immune cells. The weighted gene co-expression network analysis (WGCNA) was used to discover genes associated with M1 macrophages. Four public datasets were used to predict the genes of SIN. Following that, function enrichment analysis for hub genes was performed. The cytoHubba and least absolute shrinkage and selection operator (LASSO) were employed to select hub genes, and their diagnostic effectiveness was predicted using the receiver operator characteristic (ROC) curve. Molecular docking was undertaken to confirm the affinity between the SIN and hub gene. Furthermore, the therapeutic efficacy of SIN was validated in LPS-induced RAW264.7 cells line using Western blot and Enzyme-linked immunosorbent assay (ELISA). The matrix metalloproteinase 9 (MMP9) was identified as the hub M1 macrophages-related biomarker in RA using bioinformatic analysis and molecular docking. Our study indicated that MMP9 took part in IL-17 and TNF signaling pathways. Furthermore, we found that SIN suppresses the MMP9 protein overexpression and pro-inflammatory cytokines, including tumor necrosis factor-α (TNF-α) and interleukin-6 (IL-6) in the LPS-induced RAW264.7 cell line. In conclusion, our work sheds new light on the pathophysiology of RA and identifies MMP9 as a possible RA key gene. In conclusion, the above findings demonstrate that SIN, from an emerging research perspective, might be a potential cost-effective anti-inflammatory medication for treating RA.

In this study, eleven novel chromene sulfonamide hybrids were synthesized by a convenient method in accordance with green chemistry. At first, chromene derivatives (1-9a) were prepared through the multi-component reaction between aryl aldehydes, malononitrile, and 3-aminophenol. Then, synthesized chromenes were reacted with appropriate sulfonyl chlorides by grinding method to give the corresponding chromene sulfonamide hybrids (1-11b). Synthesized hybrids were obtained in good to high yield and characterized by IR, HNMR, CNMR, CHN and melting point techniques. In addition, the broth microdilution assay was used to determine the minimal inhibitory concentration of newly synthesized chromene-sulfonamide hybrids. The MTT test was used to determine the cytotoxicity and apoptotic activity of the newly synthesized compounds against fibroblast L929 cells. The 3D‑QSAR analysis confirmed the experimental assays, demonstrating that our predictive model is useful for developing new antibacterial inhibitors. Consequently, molecular docking studies were performed to validate the findings of the 3D-QSAR analysis, confirming the potential binding interactions of the synthesized chromene-sulfonamide hybrids with the target enzymes. Molecular docking studies were employed to support the 3D-QSAR predictions, providing insights into the binding interactions between the newly synthesized chromene-sulfonamide hybrids and their target bacterial enzymes, thereby reinforcing the potential efficacy of these compounds as antibacterial agents. Also, some of the experimental outcomes supported or conflicted with the pharmacokinetic prediction (especially about compound carcinogenicity). The performance of ADMET predictor results was assessed. The work presented here proposes a computationally driven strategy for designing and discovering a new sulfonamide scaffold for bacterial inhibition.

Acute myeloid leukemia (AML) is fatal in the majority of adults. Identification of new therapeutic targets and their pharmacologic modulators are needed to improve outcomes. Previous studies had shown that immunization of rabbits with normal peripheral WBCs that had been incubated with fluorodinitrobenzene elicited high titer antibodies that bound to a spectrum of human leukemias. We report that proteomic analyses of immunoaffinity-purified lysates of primary AML cells showed enrichment of scaffolding protein IQGAP1. Immunohistochemistry and gene-expression analyses confirmed IQGAP1 mRNA overexpression in various cytogenetic subtypes of primary human AML compared to normal hematopoietic cells. shRNA knockdown of IQGAP1 blocked proliferation and clonogenicity of human leukemia cell-lines. To develop small molecules targeting IQGAP1 we performed in-silico screening of 212,966 compounds, selected 4 hits targeting the IQGAP1-GRD domain, and conducted SAR of the 'fittest hit' to identify UR778Br, a prototypical agent targeting IQGAP1. UR778Br inhibited proliferation, induced apoptosis, resulted in G2/M arrest, and inhibited colony formation by leukemia cell-lines and primary-AML while sparing normal marrow cells. UR778Br exhibited favorable ADME/T profiles and drug-likeness to treat AML. In summary, AML shows response to IQGAP1 inhibition, and UR778Br, identified through in-silico studies, selectively targeted AML cells while sparing normal marrow.

We use complex systems science to explore the emergent behavioral patterns that typify eusocial species, using collective ant foraging as a paradigmatic example. Our particular aim is to provide a methodology to quantify how the collective orchestration of foraging provides functional advantages to ant colonies. For this, we combine (i) a purpose-built experimental arena replicating ant foraging across realistic spatial and temporal scales, and (ii) a set of analytical tools, grounded in information theory and spin-glass approaches, to explore the resulting data. This combined approach yields computational replicas of the colonies; these are high-dimensional models that store the experimental foraging patterns through a training process, and are then able to generate statistically similar patterns, in an analogous way to machine learning tools. These in silico models are then used to explore the colony performance under different resource availability scenarios. Our findings highlight how replicas of the colonies trained under constant and predictable experimental food conditions exhibit heightened foraging efficiencies, manifested in reduced times for food discovery and gathering, and accelerated transmission of information under similar conditions. However, these same replicas demonstrate a lack of resilience when faced with new foraging conditions. Conversely, replicas of colonies trained under fluctuating and uncertain food conditions reveal lower efficiencies at specific environments but increased resilience to shifts in food location.

Cancer is the second leading cause of death globally and is responsible for an estimated 9.6 million deaths in 2018. Globally, about 1 in 6 deaths is due to cancer and the chemotherapeutic drugs available have high toxicity and have reported side effects hence, there is a need for the synthesis of novel drugs in the treatment of cancer.

Intracellular potassium (K) homeostasis is fundamental to cell viability. In addition to channels, K levels are maintained by various ion transporters. One major family is the proton-driven K efflux transporters, which in gram-negative bacteria is important for detoxification and in plants is critical for efficient photosynthesis and growth. Despite their importance, the structure and molecular basis for K-selectivity is poorly understood. Here, we report ~3.1 Å resolution cryo-EM structures of the Escherichia coli glutathione (GSH)-gated K efflux transporter KefC in complex with AMP, AMP/GSH and an ion-binding variant. KefC forms a homodimer similar to the inward-facing conformation of Na/H antiporter NapA. By structural assignment of a coordinated K ion, MD simulations, and SSM-based electrophysiology, we demonstrate how ion-binding in KefC is adapted for binding a dehydrated K ion. KefC harbors C-terminal regulator of K conductance (RCK) domains, as present in some bacterial K-ion channels. The domain-swapped helices in the RCK domains bind AMP and GSH and they inhibit transport by directly interacting with the ion-transporter module. Taken together, we propose that KefC is activated by detachment of the RCK domains and that ion selectivity exploits the biophysical properties likewise adapted by K-ion-channels.

Protein phosphatase and tensin homolog (PTEN) is a tumor suppressor protein with potential to be a new biotechnological drug for PTEN-deficient cancer treatment. This study aimed to develop PTEN-based chimeric proteins (CPP-PTEN-THP) for human epidermal growth factor receptor 2 (HER2)-positive breast cancer treatment, addressing current limitations like inadequate delivery, poor tumor penetration, and low selectivity, while assessing their potential HER2-specific anticancer effects.

A finite element model was developed for assessing the efficacy of rugby body padding in reducing the risk of sustaining cuts and abrasions. The model was developed to predict the onset of damage to a soft tissue simulant from concentrated impact loading (i.e., stud impact) and compared against a corresponding experiment. The damage modelling techniques involved defining an element deletion criterion, whereby those on the surface of the surrogate were deleted if their maximum principal stress reached a predefined value. Candidate maximum principal stress values for element deletion criteria were identified independently from puncture test simulations on the soft tissue simulant. Experimental impacts with a stud were carried out at three energies (2, 4 and 6 J), at three angular orientations (0°, 15° and 30°) and compared to corresponding simulations. Suitable maximum principal stress values for element deletion criteria settings were first identified for the 4 J impact, selecting the candidates that best matched the experimental results. The same element deletion settings were then applied in simulations at 2 and 6 J and the validity of the model was further assessed (difference < 15% for the force at tear and < 30% for time to tear). The damage modelling techniques presented here could be applied to other skin simulants to assess the onset of skin injuries and the ability of padding to prevent them.

Computational research methods, such as finite element analysis (FEA) and musculoskeletal multi-body simulation (MBS), are important in musculoskeletal biomechanics because they enable a better understanding of the mechanics of the musculoskeletal system, as well as the development and evaluation of orthopaedic implants. These methods are used to analyze clinically relevant issues in various anatomical regions, such as the hip, knee, shoulder joints and spine. Preoperative simulation can improve surgical planning in orthopaedics and predict individual results.

Aggression and negative activation in mental health inpatient units pose significant challenges for both patients and staff with severe physical and psychological ramifications. The Safewards model is an evidence-based conflict-containment framework including 10 strategies, such as 'Calm Down Methods'. As virtual reality (VR) scenarios have successfully enhanced anxiolytic and deactivating effects of therapeutic interventions, they are increasingly considered a means to enhance current models, like Safewards.

Toxoplasma infection in humans is considered due to direct contact with infected cats. Toxoplasma infection (an endemic disease) has the potential to affect various organs and systems (brain, eyes, heart, lungs, liver, and lymph nodes). Bilinear incidence rate and constant population (birth rate is equal to death rate) are used in the literature to explain the dynamics of Toxoplasmosis disease transmission in humans and cats. The goal of this study is to consider the mathematical model of Toxoplasma disease with harmonic mean type incident rate and also consider that the population of humans and cats is not equal (birth rate and the death rate are not equal). In examining Toxoplasma transmission dynamics in humans and cats, harmonic mean incidence rates are better than bilinear incidence rates. The disease dynamics are first schematically illustrated, and then the law of mass action is applied to obtain nonlinear ordinary differential equations (ODEs). Analysis of the boundedness, positivity, and equilibrium points of the system has been analyzed. The reproduction number is calculated using the next-generation matrix technique. The stability of disease-free and endemic equilibrium are analyzed. Sensitivity analysis is also done for reproduction number. Numerical simulation shows that the infection is spread in the population when the contact rate and increases while the infection is reduced when the recovery rate increases. This study investigates the impact of various optimal control strategies, such as vaccinations for the control of disease and the awareness of disease awareness, on the management of disease.

Radio(chemo)therapy is often required in pelvic malignancies (cancer of the anus, rectum, cervix). Direct irradiation adversely affects ovarian and endometrial function, compromising the fertility of women. While ovarian transposition is an established method to move the ovaries away from the radiation field, surgical procedures to displace the uterus are investigational. This study demonstrates the surgical options for uterine displacement in relation to the radiation dose received.  METHODS: The uterine displacement techniques were carried out sequentially in a human female cadaver to demonstrate each procedure step by step and assess the uterine positions with dosimetric CT scans in a hybrid operating room. Two treatment plans (anal and rectal cancer) were simulated on each of the four dosimetric scans (1. anatomical position, 2. uterine suspension of the round ligaments to the abdominal wall 3. ventrofixation of the uterine fundus at the umbilical level, 4. uterine transposition). Treatments were planned on Eclipse® System (Varian Medical Systems®,USA) using Volumetric Modulated Arc Therapy. Data about maximum (Dmax) and mean (Dmean) radiation dose received and the volume receiving 14 Gy (V14Gy) were collected.

Estimation of the SARS-CoV-2 incubation time distribution is hampered by incomplete data about infection. We discuss two biases that may result from incorrect handling of such data. Notified cases may recall recent exposures more precisely (differential recall). This creates bias if the analysis is restricted to observations with well-defined exposures, as longer incubation times are more likely to be excluded. Another bias occurred in the initial estimates based on data concerning travellers from Wuhan. Only individuals who developed symptoms after their departure were included, leading to under-representation of cases with shorter incubation times (left truncation). This issue was not addressed in the analyses performed in the literature.

It is a requirement that medical students are educated in emergencies and feel well prepared for practice as a doctor, yet national surveys show that many students feel underprepared. Virtual reality (VR), combined with 360-degree filming, provides an immersive, realistic, and interactive simulation experience. Unlike conventional in-person simulation, it is scalable with reduced workforce demands. We sought to compare students' engagement and enjoyment of VR simulation to desktop computer-based simulation.

Single-cell RNA sequencing (scRNA-seq) and spatially resolved transcriptomics (SRT) have led to groundbreaking advancements in life sciences. To develop bioinformatics tools for scRNA-seq and SRT data and perform unbiased benchmarks, data simulation has been widely adopted by providing explicit ground truth and generating customized datasets. However, the performance of simulation methods under multiple scenarios has not been comprehensively assessed, making it challenging to choose suitable methods without practical guidelines.

Virtual Reality (VR) laboratories are a new pedagogical approach to support psychomotor skills development in undergraduate programmes to achieve practical competency. VR laboratories are successfully used to carry out virtual experiments in science courses and for clinical skills training in professional courses. This paper describes the development and evaluation of a VR-based microbiology laboratory on Head-Mounted Display (HMD) for undergraduate students. Student and faculty perceptions and expectations were collected to incorporate into the laboratory design. An interactive 3-dimensional VR laboratory with a 360° view was developed simulating our physical laboratory setup. The laboratory environment was created using Unity with the (created) necessary assets and 3D models. The virtual laboratory was designed to replicate the physical laboratory environment as suggested by the students and faculty. In this VR laboratory, six microbiology experiments on Gram staining, bacterial streaking, bacterial motility, catalase test, oxidase test and biochemical tests were placed on the virtual platform. First-year biomedical science students were recruited to evaluate the VR laboratory. Students' perception of the virtual laboratory was positive and encouraging. About 70% of the students expressed they felt safe using the VR laboratory and that it was engaging. They felt that the VR laboratory provided an immersive learning experience. They appreciated that they could repeat each experiment multiple times without worrying about mistakes or mishaps. They could personalise their learning by concentrating on the specific experiments. Our in-house VR-based microbiology laboratory was later extended to other health professions programmes teaching microbiology.

The search for new bioactive natural compounds with anticancer activity is still of great importance. Even though their potential for diagnostics and treatment of cancer has already been proved, the availability is still limited. Hypericin, a naphthodianthrone isolated essentially from plant source Hypericum perforatum L. along with other related anthraquinones and bisanthraquinones belongs to this group of compounds. Although it has been proven that hypericin is synthesized by the polyketide pathway in plants, none of the candidate genes coding for key enzymes has been experimentally validated yet. Despite the rare occurrence of anthraquinones in plants, their presence in microorganisms, including endophytic fungi, is quite common. Unlike plants, several biosynthetic genes grouped into clusters (BGCs) in fungal endophytes have already been characterized.

Closed-loop deep brain stimulation (DBS) is a promising therapy for Parkinson's disease (PD) that works by adjusting DBS patterns in real time from the guidance of feedback neural activity. Current closed-loop DBS mainly uses threshold-crossing on-off controllers or linear time-invariant (LTI) controllers to regulate the basal ganglia (BG) Parkinsonian beta band oscillation power. However, the critical cortex-BG-thalamus network dynamics underlying PD are nonlinear, non-stationary, and noisy, hindering accurate and robust control of Parkinsonian neural oscillatory dynamics.

Mediation analysis is a powerful tool to identify factors mediating the causal pathway of exposure to health outcomes. Mediation analysis has been extended to study a large number of potential mediators in high-dimensional data settings. The presence of confounding in observational studies is inevitable. Hence, it's an essential part of high-dimensional mediation analysis (HDMA) to adjust for the potential confounders. Although the propensity score (PS) related method such as propensity score regression adjustment (PSR) and inverse probability weighting (IPW) has been proposed to tackle this problem, the characteristics with extreme propensity score distribution of the PS-based method would result in the biased estimation.

Stopping power is the rate at which a material absorbs the kinetic energy of a charged particle passing through it-one of many properties needed over a wide range of thermodynamic conditions in modeling inertial fusion implosions. First-principles stopping calculations are classically challenging because they involve the dynamics of large electronic systems far from equilibrium, with accuracies that are particularly difficult to constrain and assess in the warm-dense conditions preceding ignition. Here, we describe a protocol for using a fault-tolerant quantum computer to calculate stopping power from a first-quantized representation of the electrons and projectile. Our approach builds upon the electronic structure block encodings of Su et al. [ , 040332 (2021)], adapting and optimizing those algorithms to estimate observables of interest from the non-Born-Oppenheimer dynamics of multiple particle species at finite temperature. We also work out the constant factors associated with an implementation of a high-order Trotter approach to simulating a grid representation of these systems. Ultimately, we report logical qubit requirements and leading-order Toffoli costs for computing the stopping power of various projectile/target combinations relevant to interpreting and designing inertial fusion experiments. We estimate that scientifically interesting and classically intractable stopping power calculations can be quantum simulated with roughly the same number of logical qubits and about one hundred times more Toffoli gates than is required for state-of-the-art quantum simulations of industrially relevant molecules such as FeMoco or P450.

Chiari type 1 malformation is a neurological disorder characterized by an obstruction of the cerebrospinal fluid (CSF) circulation between the brain (intracranial) and spinal cord (spinal) compartments. Actions such as coughing might evoke spinal cord complications in patients with Chiari type 1 malformation, but the underlying mechanisms are not well understood. More insight into the impact of the obstruction on local and overall CSF dynamics can help reveal these mechanisms. Therefore, our previously developed computational fluid dynamics framework was used to establish a subject-specific model of the intracranial and upper spinal CSF space of a healthy control. In this model, we emulated a single cough and introduced porous zones to model a posterior (OBS-1), mild (OBS-2), and severe posterior-anterior (OBS-3) obstruction. OBS-1 and OBS-2 induced minor changes to the overall CSF pressures, while OBS-3 caused significantly larger changes with a decoupling between the intracranial and spinal compartment. Coughing led to a peak in overall CSF pressure. During this peak, pressure differences between the lateral ventricles and the spinal compartment were locally amplified for all degrees of obstruction. These results emphasize the effects of coughing and indicate that severe levels of obstruction lead to distinct changes in intracranial pressure.

Antifreeze proteins (AFPs) are remarkable biomolecules that suppress ice formation at trace concentrations. To inhibit ice growth, AFPs must not only bind to ice crystals, but also resist engulfment by ice. The highest supercooling, [Formula: see text], for which AFPs are able to resist engulfment is widely believed to scale as the inverse of the separation, [Formula: see text], between bound AFPs, whereas its dependence on the molecular characteristics of the AFP remains poorly understood. By using specialized molecular simulations and interfacial thermodynamics, here, we show that in contrast with conventional wisdom, [Formula: see text] scales as [Formula: see text] and not as [Formula: see text]. We further show that [Formula: see text] is proportional to AFP size and that diverse naturally occurring AFPs are optimal at resisting engulfment by ice. By facilitating the development of AFP structure-function relationships, we hope that our findings will pave the way for the rational design of AFPs.

This study aimed to comprehensively assess the biocompatibility and toxicity profiles of poly(methyl methacrylate) (PMMA) and its monomeric unit, methyl methacrylate (MMA), crucial components in dental materials for interim prosthetic restorations.

The estrogen receptor-α (ER) is thought to function only as a homodimer but responds to a variety of environmental, metazoan, and therapeutic estrogens at subsaturating doses, supporting binding mixtures of ligands as well as dimers that are only partially occupied. Here, we present a series of flexible ER ligands that bind to receptor dimers with individual ligand poses favoring distinct receptor conformations-receptor conformational heterodimers-mimicking the binding of two different ligands. Molecular dynamics simulations showed that the pairs of different ligand poses changed the correlated motion across the dimer interface to generate asymmetric communication between the dimer interface, the ligands, and the surface binding sites for epigenetic regulatory proteins. By examining the binding of the same ligand in crystal structures of ER in the agonist vs. antagonist conformers, we also showed that these allosteric signals are bidirectional. The receptor conformer can drive different ligand binding modes to support agonist vs. antagonist activity profiles, a revision of ligand binding theory that has focused on unidirectional signaling from the ligand to the coregulator binding site. We also observed differences in the allosteric signals between ligand and coregulator binding sites in the monomeric vs. dimeric receptor, and when bound by two different ligands, states that are physiologically relevant. Thus, ER conformational heterodimers integrate two different ligand-regulated activity profiles, representing different modes for ligand-dependent regulation of ER activity.

Core nursing procedures are essential for nursing students to master because of their high frequency in nursing practice. However, the experience of performing procedures in actual hospital settings decreased during the coronavirus disease 2019 pandemic, necessitating the development of various contents to supplement procedural training. This study investigated the effects of a straight catheterization program utilizing an immersive virtual reality simulation on nursing students' procedural performance, self-confidence, and immersion. The study employed a nonequivalent control group pretest-posttest design with 29 participants in the experimental group and 25 in the control group. The experimental group received training through a computer-based immersive virtual reality program installed in a virtual reality hospital, with three weekly sessions over 3 weeks. The control group underwent straight catheterization using manikin models. The research findings validated that virtual reality-based straight catheterization education significantly improved students' procedural skills, self-confidence, and flow state. Therefore, limited practical training can be effectively supplemented by immersive virtual reality programs.

This paper proposes a haptic guidance system to improve catheter navigation within a simulated environment.

Biomolecular condensates play a key role in cytoplasmic compartmentalization and cell functioning. Despite extensive research on the physico-chemical, thermodynamic, or crowding aspects of the formation and stabilization of the condensates, one less studied feature is the role of external perturbative fluid flow. In fact, in living cells, shear stress may arise from streaming or active transport processes. Here, we investigate how biomolecular condensates are deformed under different types of shear flows. We first model Couette flow perturbations via two-way coupling between the condensate dynamics and fluid flow by deploying Lattice Boltzmann Molecular Dynamics. We then show that a simplified approach where the shear flow acts as a static perturbation (one-way coupling) reproduces the main features of the condensate deformation and dynamics as a function of the shear rate. With this approach, which can be easily implemented in molecular dynamics simulations, we analyze the behavior of biomolecular condensates described through residue-based coarse-grained models, including intrinsically disordered proteins and protein/RNA mixtures. At lower shear rates, the fluid triggers the deformation of the condensate (spherical to oblated object), while at higher shear rates, it becomes extremely deformed (oblated or elongated object). At very high shear rates, the condensates are fragmented. We also compare how condensates of different sizes and composition respond to shear perturbation, and how their internal structure is altered by external flow. Finally, we consider the Poiseuille flow that realistically models the behavior in microfluidic devices in order to suggest potential experimental designs for investigating fluid perturbations in vitro.

This paper reports an important conclusion that self-diffusion is not a necessary condition for inducing Turing patterns, while taxis could establish complex pattern phenomena. We investigate pattern formation in a zooplankton-phytoplankton model incorporating phytoplankton-taxis, where phytoplankton-taxis describes the zooplankton that tends to move toward the high-densities region of the phytoplankton population. By using the phytoplankton-taxis sensitivity coefficient as the Turing instability threshold, one shows that the model exhibits Turing instability only when repulsive phytoplankton-taxis is added into the system, while the attractive-type phytoplankton-taxis cannot induce Turing instability of the system. In addition, the system does not exhibit Turing instability when the phytoplankton-taxis disappears. Numerically, we display the complex patterns in 1D, 2D domains and on spherical and zebra surfaces, respectively. In summary, our results indicate that the phytoplankton-taxis plays a pivotal role in giving rise to the Turing pattern formation of the model. Additionally, these theoretical and numerical results contribute to our understanding of the complex interaction dynamics between zooplankton and phytoplankton populations.

Phase separation plays a key role in determining the self-assembly of biological and soft-matter systems. In biological systems, liquid-liquid phase separation inside a cell leads to the formation of various macromolecular aggregates. The interaction among these aggregates is soft, i.e., they can significantly overlap at a small energy cost. From a computer simulation point of view, these complex macromolecular aggregates are generally modeled by soft particles. The effective interaction between two particles is defined via the generalized exponential model of index n, with n = 4. Here, using molecular dynamics simulations, we study the phase separation dynamics of a size-symmetric binary mixture of ultrasoft particles. We find that when the mixture is quenched to a temperature below the critical temperature, the two components spontaneously start to separate. Domains of the two components form, and the equal-time order parameter reveals that the domain sizes grow with time in a power-law manner with an exponent of 1/3, which is consistent with the Lifshitz-Slyozov law for conserved systems. Furthermore, the static structure factor shows a power-law decay with an exponent of 4, consistent with the Porod law.

Knee osteoarthritis (KOA) represents a widespread degenerative condition among adults that significantly affects quality of life. This study aims to elucidate the biomechanical implications of proximal fibular osteotomy (PFO), a proposed cost-effective and straightforward intervention for KOA, comparing its effects against traditional high tibial osteotomy (HTO) through in-silico analysis.

This study aimed to explore the active components and the effect of Hedyotis diffusa (HD) against Alzheimer's disease (AD) via network pharmacology, molecular docking, and experimental evaluations. We conducted a comprehensive screening process using the TCMSP, Swiss Target Prediction, and PharmMapper databases to identify the active components and their related targets in HD. In addition, we collected potential therapeutic targets of AD from the Gene Cards, Drugbank, and OMIM databases. Afterward, we utilized Cytoscape to establish both protein-protein interaction (PPI) networks and compound-target (C-T) networks. To gain further insights into the functional aspect, we performed GO and KEGG pathway analyses using the David database. Next, we employed Autodock vina to estimate the binding force between the components and the hub genes. To validate our network pharmacology findings, we conducted relevant experiments on Caenorhabditis elegans, further confirming the reliability of our results. Then a total of six active compounds and 149 therapeutic targets were detected. Through the analysis of the association between active compounds, therapeutic targets, and signaling pathways, it was observed that the therapeutic effect of HD primarily encompassed the inhibition of Aβ, suppression of AChE activity, and mitigating oxidative stress. Additionally, our investigation revealed that the key active compounds in HD primarily consisted of iridoids, which exhibited resistance against AD by acting on the Alzheimer's disease pathway and the AGE-RAGE signaling pathway in diabetic complications.

During macroautophagy, cytoplasmic constituents are engulfed by autophagosomes. Lysosomes fuse with closed autophagosomes but not with unclosed intermediate structures. This is achieved in part by the late recruitment of the autophagosomal SNARE syntaxin 17 (STX17) to mature autophagosomes. However, how STX17 recognizes autophagosome maturation is not known. Here, we show that this temporally regulated recruitment of STX17 depends on the positively charged C-terminal region of STX17. Consistent with this finding, mature autophagosomes are more negatively charged compared with unclosed intermediate structures. This electrostatic maturation of autophagosomes is likely driven by the accumulation of phosphatidylinositol 4-phosphate (PI4P) in the autophagosomal membrane. Accordingly, dephosphorylation of autophagosomal PI4P prevents the association of STX17 to autophagosomes. Furthermore, molecular dynamics simulations support PI4P-dependent membrane insertion of the transmembrane helices of STX17. Based on these findings, we propose a model in which STX17 recruitment to mature autophagosomes is temporally regulated by a PI4P-driven change in the surface charge of autophagosomes.

The health-care industry is a substantial contributor to global greenhouse gas emissions, yet the specific environmental impact of radiotherapy, a cornerstone of cancer treatment, remains under-explored. We aimed to quantify the emissions associated with the delivery of radiotherapy in the USA and propose a framework for reducing the environmental impact of oncology care.

A new series of quinoxaline-sulfonamide derivatives 3-12 were synthesized using fragment-based drug design by reaction of quinoxaline sulfonyl chloride (QSC) with different amines and hydrazines. The quinoxaline-sulfonamide derivatives were evaluated for antidiabetic and anti-Alzheimer's potential against α-glucosidase, α-amylase, and acetylcholinesterase enzymes. These derivatives showed good to moderate potency against α-amylase and α-glucosidase with inhibitory percentages between 24.34 ± 0.01%-63.09 ± 0.02% and 28.95 ± 0.04%-75.36 ± 0.01%, respectively. Surprisingly, bis-sulfonamide quinoxaline derivative 4 revealed the most potent activity with inhibitory percentages of 75.36 ± 0.01% and 63.09 ± 0.02% against α-glucosidase and α-amylase compared to acarbose (IP = 57.79 ± 0.01% and 67.33 ± 0.01%), respectively. Moreover, the quinoxaline derivative 3 exhibited potency as α-glucosidase and α-amylase inhibitory with a minute decline from compound 4 and acarbose with inhibitory percentages of 44.93 ± 0.01% and 38.95 ± 0.01%. Additionally, in vitro acetylcholinesterase inhibitory activity for designed derivatives exhibited weak to moderate activity. Still, sulfonamide-quinoxaline derivative 3 emerged as the most active member with inhibitory percentage of 41.92 ± 0.02% compared with donepezil (IP = 67.27 ± 0.60%). The DFT calculations, docking simulation, target prediction, and ADMET analysis were performed and discussed in detail.

Lensless imagers based on diffusers or encoding masks enable high-dimensional imaging from a single-shot measurement and have been applied in various applications. However, to further extract image information such as edge detection, conventional post-processing filtering operations are needed after the reconstruction of the original object images in the diffuser imaging systems. Here, we present the concept of a temporal compressive edge detection method based on a lensless diffuser camera, which can directly recover a time sequence of edge images of a moving object from a single-shot measurement, without further post-processing steps. Our approach provides higher image quality during edge detection, compared with the "conventional post-processing method." We demonstrate the effectiveness of this approach by both numerical simulation and experiments. The proof-of-concept approach can be further developed with other image post-processing operations or versatile computer vision assignments toward task-oriented intelligent lensless imaging systems.

Two propensity score (PS) based balancing covariate methods, the overlap weighting method (OW) and the fine stratification method (FS), produce superb covariate balance. OW has been compared with various weighting methods while FS has been compared with the traditional stratification method and various matching methods. However, no study has yet compared OW and FS. In addition, OW has not yet been evaluated in large claims data with low prevalence exposure and with low frequency outcomes, a context in which optimal use of balancing methods is critical. In the study, we aimed to compare OW and FS using real-world data and simulations with low prevalence exposure and with low frequency outcomes.

In this study, the synthesis of N-(5,6-methylenedioxybenzothiazole-2-yl)-2-[(substituted)thio/piperazine]acetamide/propanamide derivatives (3a-3k) and to investigate their acetylcholinesterase (AChE), butyrylcholinesterase (BChE) and β-secretase 1 (BACE-1) inhibition activity were aimed. Mass, H NMR, and C NMR spectra were utilized to determine the structure of the synthesized compounds. Compounds 3b, 3c, 3f, and 3j showed AChE inhibitory activity which compound 3c (IC = 0.030 ± 0.001 µM) showed AChE inhibitory activity as high as the reference drug donepezil (IC = 0.0201 ± 0.0010 µM). Conversely, none of the compounds showed BChE activity. Compounds 3c and 3j showed the highest BACE-1 inhibitory activity and IC value was found as 0.119 ± 0.004 µM for compound 3j whereas IC value was 0.110 ± 0.005 µM for donepezil, which is one of the reference substance. Molecular docking studies have been carried out using the data retrieved from the server of the Protein Data Bank (PDBID: 4EY7 and 2ZJM). Using in silico approach behavior active compounds (3c and 3j) and their binding modes clarified.

The paper presents a fault region identification method for the active distribution network (ADN) with limited PMU. First, PMU configuration and region division strategies are proposed based on the network topology. Next, the difference in three-phase current variations between the normal and fault regions of the ADN is analyzed. A multi-dimensional state monitoring matrix is built using the measured current data. The spectral norm ratio coefficient is constructed based on the 2-norm to lower the complexity of the multi-dimensional state monitoring matrix and quantify the difference in state changes before and after the fault occurs in each region. Then, the fault region is identified by combining each region's spectral norm ratio coefficient and the change of the current phase. Finally, an IEEE 33-node simulation model is created to simulate faults under different working conditions. According to the simulation results, the suggested approach is less impacted by fault type, neutral point grounding mode, and transition resistance. Furthermore, even if the communication does not match the rigorous synchronization requirements, the proposed method can still complete the fault identification of the distribution network correctly and has high robustness.

, like other helminthic parasites, lacks key components of cellular machinery required for endogenous lipid biosynthesis. This deficiency compels the parasite to obtain all of its lipid requirements from its host. The passage of lipids across the cell membrane is tightly regulated. To facilitate effective lipid transport, the cestode parasite utilizes certain lipid binding proteins called FABPs. These FABPs bind with the lipid ligands and allow the transport of lipids across the membranes and into the cytosol. Here, by integrating a computational with homology protein prediction tools, we had identified five FABPs in the proteome. We confirmed their presence by RNA expression analysis of respective genes from the parasite's cysticerci transcript. During the molecular modeling and MD simulation studies, two of them, TsM_000544100 and TsM_001185100, were most stable. Furthermore, they had a robust interaction with the IgG1 molecule, as evidenced by MD simulation. In addition, by employing screening, we had identified potential ligand interacting residues that are present on the probable druggable site. In combination with cysticidal assays, enalaprilat dihydrate showed efficacy against cysticerci, which suggests that FABPs play a significant role in the cysticercus life cycle. Together, we provided a detailed distribution of all FABPs expressed by cysticerci and the critical role of TsM_001185100 in cysticercus viability.

Utilizing molecular dynamics simulations, we examined how varying pore sizes affect the desalination capabilities of MoS membranes while keeping the total pore area constant. The total pore area within a MoS nanosheet was maintained at 200 Å, and the single-pore areas were varied, approximately 20, 30, 40, 50, and 60 Å. By comparing the water flux and ion rejection rates, we identified the optimal single-pore area for MoS membrane desalination. Our simulation results revealed that as the single-pore area expanded, the water flux increased, the velocity of water molecules passing the pores accelerated, the energy barrier decreased, and the number of water molecules within the pores rose, particularly between 30 and 40 Å. Balancing water flux and rejection rates, we found that a MoS2 membrane with a single-pore area of 40 Å offered the most effective water treatment performance. Furthermore, the ion rejection rate of MoS membranes was lower for ions with lower valences. This was attributed to the fact that higher-valence ions possess greater masses and radii, leading to slower transmembrane rates and higher transmembrane energy barriers. These insights may serve as theoretical guidance for future applications of MoS membranes in water treatment.

This paper contributes to the existing literature on variance estimators by utilizing supplementary information. The variance estimation problem of a finite population is a significant matter as sometimes, it is tough to control the variation. For this purpose, an optimum family of exponential variance estimators is suggested under simple random sampling. Moreover, different specific members of the proposed estimators are identified by incorporating various known characteristics of the supplementary variable in the suggested generalized class of estimators. The derivations for the expressions of bias as well as mean square error (MSE) of the proposed estimators are conducted. The suggested family of estimators is studied in different situations by using sets of real data and simulation studies for their performance. To evaluate the efficiency of the suggested estimators, R software is used for the analysis. The study compares the performance of the proposed estimators against the traditional estimators. The theoretical and numerical comparisons show that the estimators suggested in the study are superior in efficiency as compared to the existing estimators.

Given the COVID-19 pandemic, many Objective Structured Clinical Examinations (OSCEs) have been adapted to virtual formats without addressing whether physical examination maneuvers can or should be assessed virtually. In response, we developed a novel touchless physical examination station for a virtual OSCE and gathered validity evidence for its use.

There is a common concern that range of motion (ROM) is negatively affected by the Latarjet procedure. We hypothesize that the Latarjet procedure results in full recuperation of ROM postoperatively and significantly improved patient reported outcome measures.

Latent classes are a useful tool in developmental research, however there are challenges associated with embedding them within a counterfactual mediation model. We develop and test a new method "updated pseudo class draws (uPCD)" to examine the association between a latent class exposure and distal outcome that could easily be extended to allow the use of any counterfactual mediation method. UPCD extends an existing group of methods (based on pseudo class draws) that assume that the true values of the latent class variable are missing, and need to be multiply imputed using class membership probabilities. We simulate data based on the Avon Longitudinal Study of Parents and Children, examine performance for existing techniques to relate a latent class exposure to a distal outcome ("one-step," "bias-adjusted three-step," "modal class assignment," "non-inclusive pseudo class draws," and "inclusive pseudo class draws") and compare bias in parameter estimates and their precision to uPCD when estimating counterfactual mediation effects. We found that uPCD shows minimal bias when estimating counterfactual mediation effects across all levels of entropy. UPCD performs similarly to recommended methods (one-step and bias-adjusted three-step), but provides greater flexibility and scope for incorporating the latent grouping within any commonly-used counterfactual mediation approach.

This study introduces a novel Cardiac Electric Vector Simulation Model (CEVSM) to address the computational inefficiencies and low fidelity of traditional electrophysiological models in generating electrocardiograms (ECGs). Our approach leverages CEVSM to efficiently produce reliable ECG samples, facilitating data augmentation essential for the computer-aided diagnosis of myocardial infarction (MI). Significantly, experimental results show that our model dramatically reduces computation time compared to conventional models, with the self-adapting regression transformation matrix method (SRTM) providing clear advantages. SRTM not only achieves high fidelity in ECG simulations but also ensures exceptional consistency with the gold standard method, greatly enhancing MI localization accuracy by data augmentation. These advancements highlight the potential of our model to generate dependable ECG training samples, making it highly suitable for data augmentation and significantly advancing the development and validation of intelligent MI diagnostic systems. Furthermore, this study demonstrates the feasibility of applying life system simulations in the training of medical big models.

This study estimated the accumulation potential of tritium, a major radionuclide released from the Fukushima Daiichi Nuclear Power Station (FDNPS), into the olive flounder as organically bound tritium (OBT) using a computer simulation model. In this estimation, two transfer pathways into the OBT were assumed: formation from tritiated water (HTO) in the tricarboxylic acid (TCA) cycle, and ingestion of OBT through the food chain (from phytoplankton, small fish, to the flounder). The food chain structure was reconstructed based on fish growth model. The OBT concentration in the flounder was estimated on three scenarios: Tritium was supplied to the flounder as only HTO in seawater (Scenario 1), as HTO in seawater and OBT formed from HTO in the small fish (Scenario 2), and as HTO in seawater and OBT accumulated in the small fish through the formation and ingestion of OBT in phytoplankton (Scenario 3). The estimated OBT concentrations in the flounder were in the following order: Scenario 3 > 2 > 1. The ratio of the estimated concentration in Scenario 1 to that in Scenario 3 reached a certain value (66 % after a year from the start of HTO exposure), indicating that the tritium transfer from the seawater into the flounder more significantly contributed to this concentration than ingestions of the small fish and the phytoplankton. Additionally, the difference between the estimations of Scenarios 1 and 2 is significantly larger than that between Scenarios 2 and 3. This suggests that phytoplankton contributed weakly to the OBT concentration in the flounder compared to the small fish.

This paper proposes a new geometric fault detection and isolation (FDI) strategy for uncertain neutral time-delay systems (UNTDS). Firstly, the concept of unobservability subspace is extended to the considered system. Subsequently, utilizing the geometric properties of factor space and canonical projection, the fault is divided into different unobservability subspaces. Therefore, an algorithm for constructing the subspace is developed for fault isolation. Finally, a set of observers is designed for the subsystems, and generates a set of structured residuals which is sensitive only to a specific fault. Additionally, the H technique is utilized to suppress the disturbances and error signals due to time-varying delays on the residual. The simulation examples verify the effectiveness of the proposed approach.

Multi-drug combinations to treat bacterial populations are at the forefront of approaches for infection control and prevention of antibiotic resistance. Although the evolution of antibiotic resistance has been theoretically studied with mathematical population dynamics models, extensions to spatial dynamics remain rare in the literature, including in particular spatial evolution of multi-drug resistance. In this study, we propose a reaction-diffusion system that describes the multi-drug evolution of bacteria based on a drug-concentration rescaling approach. We show how the resistance to drugs in space, and the consequent adaptation of growth rate, is governed by a Price equation with diffusion, integrating features of drug interactions and collateral resistances or sensitivities to the drugs. We study spatial versions of the model where the distribution of drugs is homogeneous across space, and where the drugs vary environmentally in a piecewise-constant, linear and nonlinear manner. Although in many evolution models, per capita growth rate is a natural surrogate for fitness, in spatially-extended, potentially heterogeneous habitats, fitness is an emergent property that potentially reflects additional complexities, from boundary conditions to the specific spatial variation of growth rates. Applying concepts from perturbation theory and reaction-diffusion equations, we propose an analytical metric for characterization of average mutant fitness in the spatial system based on the principal eigenvalue of our linear problem, λ1. This enables an accurate translation from drug spatial gradients and mutant antibiotic susceptibility traits to the relative advantage of each mutant across the environment. Our approach allows one to predict the precise outcomes of selection among mutants over space, ultimately from comparing their λ1 values, which encode a critical interplay between growth functions, movement traits, habitat size and boundary conditions. Such mathematical understanding opens new avenues for multi-drug therapeutic optimization.

How the spatial arrangement of a population shapes its evolutionary dynamics has been of long-standing interest in population genetics. Most previous studies assume a small number of demes or symmetrical structures that, most often, act as well-mixed populations. Other studies use network theory to study more heterogeneous spatial structures, however they usually assume small, regular networks, or strong constraints on the strength of selection considered. Here we build network generation algorithms, conduct evolutionary simulations and derive general analytic approximations for probabilities of fixation in populations with complex spatial structure. We build a unifying evolutionary theory across network families and derive the relevant selective parameter, which is a combination of network statistics, predictive of evolutionary dynamics. We also illustrate how to link this theory with novel datasets of spatial organization and use recent imaging data to build the cellular spatial networks of the stem cell niches of the bone marrow. Across a wide variety of parameters, we find these networks to be strong suppressors of selection, delaying mutation accumulation in this tissue. We also find that decreases in stem cell population size also decrease the suppression strength of the tissue spatial structure.

PR65 is the HEAT repeat scaffold subunit of the heterotrimeric protein phosphatase 2A (PP2A) and an archetypal tandem repeat protein. Its conformational mechanics plays a crucial role in PP2A function by opening/closing substrate binding/catalysis interface. Using in silico saturation mutagenesis, we identified PR65 "hinge" residues whose substitutions could alter its conformational adaptability and thereby PP2A function, and selected six mutations that were verified to be expressed and soluble. Molecular simulations and nanoaperture optical tweezers revealed consistent results on the specific effects of the mutations on the structure and dynamics of PR65. Two mutants observed in simulations to stabilize extended/open conformations exhibited higher corner frequencies and lower translational scattering in experiments, indicating a shift toward extended conformations, whereas another displayed the opposite features, confirmed by both simulations and experiments. The study highlights the power of single-molecule nanoaperture-based tweezers integrated with in silico approaches for exploring the effect of mutations on protein structure and dynamics.

Although long-read sequencing enables the generation of complete genomes for unculturable microbes, its high cost limits the widespread adoption of long-read sequencing in large-scale metagenomic studies. An alternative method is to assemble short-reads with long-range connectivity, which can be a cost-effective way to generate high-quality microbial genomes. Here, we develop Pangaea, a bioinformatic approach designed to enhance metagenome assembly using short-reads with long-range connectivity. Pangaea leverages connectivity derived from physical barcodes of linked-reads or virtual barcodes by aligning short-reads to long-reads. Pangaea utilizes a deep learning-based read binning algorithm to assemble co-barcoded reads exhibiting similar sequence contexts and abundances, thereby improving the assembly of high- and medium-abundance microbial genomes. Pangaea also leverages a multi-thresholding algorithm strategy to refine assembly for low-abundance microbes. We benchmark Pangaea on linked-reads and a combination of short- and long-reads from simulation data, mock communities and human gut metagenomes. Pangaea achieves significantly higher contig continuity as well as more near-complete metagenome-assembled genomes (NCMAGs) than the existing assemblers. Pangaea also generates three complete and circular NCMAGs on the human gut microbiomes.

The two-pore domain potassium (K) channels TREK-1 and TREK-2 link neuronal excitability to a variety of stimuli including mechanical force, lipids, temperature and phosphorylation. This regulation involves the C-terminus as a polymodal stimulus sensor and the selectivity filter (SF) as channel gate. Using crystallographic up- and down-state structures of TREK-2 as a template for full atomistic molecular dynamics (MD) simulations, we reveal that the SF in down-state undergoes inactivation via conformational changes, while the up-state structure maintains a stable and conductive SF. This suggests an atomistic mechanism for the low channel activity previously assigned to the down state, but not evident from the crystal structure. Furthermore, experimentally by using (de-)phosphorylation mimics and chemically attaching lipid tethers to the proximal C-terminus (pCt), we confirm the hypothesis that moving the pCt towards the membrane induces the up-state. Based on MD simulations, we propose two gating pathways by which movement of the pCt controls the stability (i.e., conductivity) of the filter gate. Together, these findings provide atomistic insights into the SF gating mechanism and the physiological regulation of TREK channels by phosphorylation.

The method development process in gas chromatography can be accelerated by suitable computer simulation tools using knowledge about the solute-column interactions described by thermodynamic retention parameters. Since retention parameters usually are determined under isothermal conditions, the presented work offers a step to estimate one of the most important retention parameters, the characteristic temperature T by less laborious temperature programmed measurements. In the first part an empirical multivariate model was introduced describing the correlation between the elution temperature T of a solute and its characteristic temperature T. Now in the second part a simulation model of GC and available retention data from a retention database was used to investigate the correlation between T and T for an expanded range of heating rates and initial temperatures. In addition to part I, the simulation is used to investigate the influences of different properties of the separation column such as different phase ratios and column geometries like length and diameter or various stationary phases including SLB-5 ms, SPB-50, Stabilwax, Rtx-Dioxin2, Rxi-17Sil MS, Rxi-5Sil MS, ZB-PAH-CT, DB-5 ms, Rxi-5 ms, Rtx5 and FS5ms. The fit model is valid for all investigated stationary phases. The influence of the phase ratio to the correlation could be determined. Therefore, the model was expanded to this parameter. The expanded range of heating rates and the normalization for the system independent dimensionless heating rate required a further modification of the previously presented correlation model. The model now fits also under isothermal conditions. The results were used for estimation of the T of an analyte from the elution temperature in the temperature program. The prediction performance was investigated and evaluated for 20 different temperature program conditions and at two phase ratios (β=125 and β=250). Under best conditions the estimated and the measured T values show relative differences <0.5 %. With this novel model estimations for T are possible at 20 °C above the initial temperature, which expands the prediction range even for low and medium retained analytes compared to earlier approaches.

The SAVVY project aims to improve the analyses of adverse events (AEs) in clinical trials through the use of survival techniques appropriately dealing with varying follow-up times and competing events (CEs). This paper summarizes key features and conclusions from the various SAVVY papers.

Many drug formulations containing small active molecules are used for the treatment of coronary artery disease, which affects a significant part of the world's population. However, the inadequate profile of these molecules in terms of therapeutic efficacy has led to the therapeutic use of protein and peptide-based biomolecules with superior properties, such as target-specific affinity and low immunogenicity, in critical diseases. Protein‒protein interactions, as a consequence of advances in molecular techniques with strategies involving the combined use of in silico methods, have enabled the design of therapeutic peptides to reach an advanced dimension. In particular, with the advantages provided by protein/peptide structural modeling, molecular docking for the study of their interactions, molecular dynamics simulations for their interactions under physiological conditions and machine learning techniques that can work in combination with all these, significant progress has been made in approaches to developing therapeutic peptides that can modulate the development and progression of coronary artery diseases. In this scope, this review discusses in silico methods for the development of peptide therapeutics for the treatment of coronary artery disease and strategies for identifying the molecular mechanisms that can be modulated by these designs and provides a comprehensive perspective for future studies.

The excitable gap (EG), defined as the excitable tissue between two subsequent wavefronts of depolarization, is critical for maintaining reentry that underlies deadly ventricular arrhythmias. EG in the His-Purkinje Network (HPN) plays an important role in the maintenance of electrical wave reentry that underlies these arrhythmias.

Lactate dehydrogenase (LDH), a crucial enzyme in anaerobic glycolysis, plays a pivotal role in the energy metabolism of tumor cells, positioning it as a promising target for tumor treatment. Rutin, a plant-based flavonoid, offers benefits like antioxidant, antiapoptotic, and antineoplastic effects. This study employed diverse experiments to investigate the inhibitory mechanism of rutin on LDH through a binding perspective. The outcomes revealed that rutin underwent spontaneous binding within the coenzyme binding site of LDH, leading to the formation of a stable binary complex driven by hydrophobic forces, with hydrogen bonds also contributing significantly to sustaining the stability of the LDH-rutin complex. The binding constant (K) for the LDH-rutin system was 2.692 ± 0.015 × 10 M at 298 K. Furthermore, rutin induced the alterations in the secondary structure conformation of LDH, characterized by a decrease in α-helix and an increase in antiparallel and parallel β-sheet, and β-turn. Rutin augmented the stability of coenzyme binding to LDH, which could potentially hinder the conversion process among coenzymes. Specifically, Arg98 in the active site loop of LDH provided essential binding energy contribution in the binding process. These outcomes might explain the dose-dependent inhibition of the catalytic activity of LDH by rutin. Interestingly, both the food additives ascorbic acid and tetrahydrocurcumin could reduce the binding stability of LDH and rutin. Meanwhile, these food additives did not produce positive synergism or antagonism on the rutin binding to LDH. Overall, this research could offer a unique insight into the therapeutic potential and medicinal worth of rutin.

The population is constantly aging, and most older adults will experience many potential physiological changes as they age, leading to functional decline. Urinary and bowel dysfunction is the most common obstacle in older people. At present, the analysis of pelvic floor histological changes related to aging has not been fully elucidated, and the mechanism of improving intestinal control ability in older people is still unclear.

Binding affinity is an important factor in drug design to improve drug-target selectivity and specificity. In this study, in silico techniques based on molecular docking followed by molecular dynamics (MD) simulations were utilized to identify the key residue(s) for CSF1R binding affinity among 14 pan-tyrosine kinase inhibitors and 15 CSF1R-specific inhibitors. We found tryptophan at position 550 (W550) on the CSF1R binding site interacted with the inhibitors' aromatic ring in a π-π way that made the ligands better at binding. Upon W550-Alanine substitution (W550A), the binding affinity of trans-(-)-kusunokinin and imatinib to CSF1R was significantly decreased. However, in terms of structural features, W550 did not significantly affect overall CSF1R structure, but provided destabilizing effect upon mutation. The W550A also did not either cause ligand to change its binding site or conformational changes due to ligand binding. As a result of our findings, the π-π interaction with W550's aromatic ring could be still the choice for increasing binding affinity to CSF1R. Nevertheless, our study showed that the increasing binding to W550 of the design ligand may not ensure CSF1R specificity and inhibition since W550-ligand bound state did not induce significantly conformational change into inactive state.

To infer the treatment effect for a single treated unit using panel data, synthetic control (SC) methods construct a linear combination of control units' outcomes that mimics the treated unit's pre-treatment outcome trajectory. This linear combination is subsequently used to impute the counterfactual outcomes of the treated unit had it not been treated in the post-treatment period, and used to estimate the treatment effect. Existing SC methods rely on correctly modeling certain aspects of the counterfactual outcome generating mechanism and may require near-perfect matching of the pre-treatment trajectory. Inspired by proximal causal inference, we obtain two novel nonparametric identifying formulas for the average treatment effect for the treated unit: one is based on weighting, and the other combines models for the counterfactual outcome and the weighting function. We introduce the concept of covariate shift to SCs to obtain these identification results conditional on the treatment assignment. We also develop two treatment effect estimators based on these two formulas and generalized method of moments. One new estimator is doubly robust: it is consistent and asymptotically normal if at least one of the outcome and weighting models is correctly specified. We demonstrate the performance of the methods via simulations and apply them to evaluate the effectiveness of a pneumococcal conjugate vaccine on the risk of all-cause pneumonia in Brazil.

Multi-gene panel testing allows many cancer susceptibility genes to be tested quickly at a lower cost making such testing accessible to a broader population. Thus, more patients carrying pathogenic germline mutations in various cancer-susceptibility genes are being identified. This creates a great opportunity, as well as an urgent need, to counsel these patients about appropriate risk-reducing management strategies. Counseling hinges on accurate estimates of age-specific risks of developing various cancers associated with mutations in a specific gene, ie, penetrance estimation. We propose a meta-analysis approach based on a Bayesian hierarchical random-effects model to obtain penetrance estimates by integrating studies reporting different types of risk measures (eg, penetrance, relative risk, odds ratio) while accounting for the associated uncertainties. After estimating posterior distributions of the parameters via a Markov chain Monte Carlo algorithm, we estimate penetrance and credible intervals. We investigate the proposed method and compare with an existing approach via simulations based on studies reporting risks for two moderate-risk breast cancer susceptibility genes, ATM and PALB2. Our proposed method is far superior in terms of coverage probability of credible intervals and mean square error of estimates. Finally, we apply our method to estimate the penetrance of breast cancer among carriers of pathogenic mutations in the ATM gene.

Doubly adaptive biased coin design (DBCD), a response-adaptive randomization scheme, aims to skew subject assignment probabilities based on accrued responses for ethical considerations. Recent years have seen substantial advances in understanding DBCD's theoretical properties, assuming correct model specification for the responses. However, concerns have been raised about the impact of model misspecification on its design and analysis. In this paper, we assess the robustness to both design model misspecification and analysis model misspecification under DBCD. On one hand, we confirm that the consistency and asymptotic normality of the allocation proportions can be preserved, even when the responses follow a distribution other than the one imposed by the design model during the implementation of DBCD. On the other hand, we extensively investigate three commonly used linear regression models for estimating and inferring the treatment effect, namely difference-in-means, analysis of covariance (ANCOVA) I, and ANCOVA II. By allowing these regression models to be arbitrarily misspecified, thereby not reflecting the true data generating process, we derive the consistency and asymptotic normality of the treatment effect estimators evaluated from the three models. The asymptotic properties show that the ANCOVA II model, which takes covariate-by-treatment interaction terms into account, yields the most efficient estimator. These results can provide theoretical support for using DBCD in scenarios involving model misspecification, thereby promoting the widespread application of this randomization procedure.

Bacteriocins are a kind of antimicrobial peptides that kill or inhibit the growth of bacterial strains. The purpose of this study was to investigate the antibacterial effect of on several pathogenic bacterial strains. Bacteriocin produced by was purified by chromatography with Sephadex G-75 column, and its antibacterial effect on gram-negative bacteria, including ATCC 700928, PTCC 1707, PTCC 1621, PTCC 1693, and PTCC 1755, were evaluated by the disk diffusion method. The structure of bacteriocin was determined by nuclear magnetic resonance spectroscopy. The interaction of bacteriocin with the antigen 43 (Ag43) of was evaluated by the molecular docking method. Bacteriocin extracted from bacterial isolates had antibacterial activity on strains but not on other studied strains. Bioinformatics analysis also showed bacteriocin docking with Ag43 with an energy of -159.968 kJ/mol. Natural compounds, such as bacteriocin, can be an alternative to common chemical compounds and antibiotics. To reach a definite conclusion in this regard, there is a need for further research and understanding of their mechanism of action.

Cobalamin (B), an essential nutrient and growth cofactor for many living organisms on Earth, can be fully synthesized only by selected prokaryotes in nature. Therefore, microbial communities related to B biosynthesis could serve as an example subsystem to disentangle the underlying ecological mechanisms balancing the function and taxonomic make-up of complex functional assemblages. By anchoring microbial traits potentially involved in B biosynthesis, we depict the biogeographic patterns of B biosynthesis genes and the taxa harboring them in the global ocean, despite the limitations of detecting de novo B synthesizers via metagenomes alone. Both the taxonomic and functional composition of B biosynthesis genes were strongly shaped by depth, differentiating the epipelagic zones from the mesopelagic layers. Functional genes related to B biosynthesis were relatively stably distributed across different oceans, but the taxa harboring them varied considerably, showing clear functional redundancy among microbial systems. Microbial taxa carrying B biosynthesis genes in the surface water were influenced by environmental factors such as temperature, oxygen, and nitrate. However, the composition of functional genes was only weakly associated with these environmental factors. Null model analyses demonstrated that determinism governed the variations in B biosynthesis genes, whereas a higher degree of stochasticity was associated with taxonomic variations. Significant associations were observed between the chlorophyll concentration and B biosynthesis, confirming its importance in primary production in the global ocean. The results of this study reveal an essential ecological mechanism governing the assembly of microbes in nature: the environment selects for function rather than taxonomy; functional redundancy underlies stochastic community assembly.

Disentangling the assembly mechanisms controlling community composition, structure, distribution, functions, and dynamics is a central issue in ecology. Although various approaches have been proposed to examine community assembly mechanisms, quantitative characterization is challenging, particularly in microbial ecology. Here, we present a novel approach for quantitatively delineating community assembly mechanisms by combining the consumer-resource model with a neutral model in stochastic differential equations. Using time-series data from anaerobic bioreactors that target microbial 16S rRNA genes, we tested the applicability of three ecological models: the consumer-resource model, the neutral model, and the combined model. Our results revealed that model performances varied substantially as a function of population abundance and/or process conditions. The combined model performed best for abundant taxa in the treatment bioreactors where process conditions were manipulated. In contrast, the neutral model showed the best performance for rare taxa. Our analysis further indicated that immigration rates decreased with taxa abundance and competitions between taxa were strongly correlated with phylogeny, but within a certain phylogenetic distance only. The determinism underlying taxa and community dynamics were quantitatively assessed, showing greater determinism in the treatment bioreactors that aligned with the subsequent abnormal system functioning. Given its mechanistic basis, the framework developed here is expected to be potentially applicable beyond microbial ecology.

The creation of developing new generalized classes of distributions has attracted applied and theoretical statisticians owing to their properties of flexibility. The development of generalized distribution aims to find distribution flexibility and suitability for available data. In this decade, most authors have developed classes of distributions that are new, to become valuable for applied researchers.

American foulbrood (AFB), caused by the highly virulent, spore-forming bacterium , poses a significant threat to honey bee brood. The widespread use of antibiotics not only fails to effectively combat the disease but also raises concerns regarding honey safety. The current computational study was attempted to identify a novel therapeutic drug target against , a causative agent of American foulbrood disease in honey bee.

The Organic Cation Transporter Novel 1 (OCTN1), also known as SLC22A4, is widely expressed in various human tissues, and involved in numerous physiological and pathological processes remains. It facilitates the transport of organic cations, zwitterions, with selectivity for positively charged solutes. Ergothioneine, an antioxidant compound, and acetylcholine (Ach) are among its substrates. Given the lack of experimentally solved structures of this protein, this study aimed at generating a reliable 3D model of OCTN1 to shed light on its substrate-binding preferences and the role of sodium in substrate recognition and transport. A chimeric model was built by grafting the large extracellular loop 1 (EL1) from an AlphaFold-generated model onto a homology model. Molecular dynamics simulations revealed domain-specific mobility, with EL1 exhibiting the highest impact on overall stability. Molecular docking simulations identified cytarabine and verapamil as highest affinity ligands, consistent with their known inhibitory effects on OCTN1. Furthermore, MM/GBSA analysis allowed the categorization of substrates into weak, good, and strong binders, with molecular weight strongly correlating with binding affinity to the recognition site. Key recognition residues, including Tyr211, Glu381, and Arg469, were identified through interaction analysis. Ach demonstrated a low interaction energy, supporting the hypothesis of its one-directional transport towards to outside of the membrane. Regarding the role of sodium, our model suggested the involvement of Glu381 in sodium binding. Molecular dynamics simulations of systems at increasing levels of Na+ concentrations revealed increased sodium occupancy around Glu381, supporting experimental data associating Na+ concentration to molecule transport. In conclusion, this study provides valuable insights into the 3D structure of OCTN1, its substrate-binding preferences, and the role of sodium in the recognition. These findings contribute to the understanding of OCTN1 involvement in various physiological and pathological processes and may have implications for drug development and disease management.

Pyrazole is a well-known nucleus in the pharmacy field with a wide range of other activities in addition to anti-inflammatory and analgesic, i.e., anticonvulsant, antiviral, and anticancer activities. There are well-known marketed drugs having pyrazole moiety as celecoxib, and lonazolac as COX-II inhibitors.

The subject matter of surfaces in computer aided geometric design (CAGD) is the depiction and design of surfaces in the computer graphics arena. Due to their geometric features, modeling of Bézier curves and surfaces with their shape parameters is the most well-liked topic of research in CAGD/computer-aided manufacturing (CAM). The primary challenges in industries such as automotive, shipbuilding, and aerospace are the design of complex surfaces. In order to address this issue, the continuity constraints between surfaces are utilized to generate complex surfaces. The parametric and geometric continuities are the two metrics commonly used for establishing connections among surfaces. This paper proposes continuity constraints between two generalized Bézier-like surfaces (gBS) with different shape parameters to address the issue of modeling and designing surfaces. Initially, the generalized form of C3 and G3 of generalized Bézier-like curves (gBC) are developed. To check the validity of these constraints, some numerical examples are also analyzed with graphical representations. Furthermore, for a continuous connection among these gBS, the necessary and sufficient G1 and G2 continuity constraints are also developed. It is shown through the use of several geometric designs of gBS that the recommended basis can resolve the shape and position adjustment problems associated with Bézier surfaces more effectively than any other basis. As a result, the proposed scheme not only incorporates all of the geometric features of curve design schemes but also improves upon their faults, which are typically encountered in engineering. Mainly, by changing the values of shape parameters, we can alter the shape of the curve by our choice which is not present in the standard Bézier model. This is the main drawback of traditional Bézier model.

Inhibition of acetylcholinesterase (AChE) is a crucial target in the treatment of Alzheimer's disease (AD). Common anti-acetylcholinesterase drugs such as Galantamine, Rivastigmine, Donepezil, and Tacrine have significant inhibition potential. Due to side effects and safety concerns, we aimed to investigate a wide range of phytochemicals and structural analogues of these compounds. Compounds similar to the established drugs, and phytochemicals were investigated as potential inhibitors for AChE in treating AD. A total of 2,270 compound libraries were generated for further analysis. Initial virtual screening was performed using Pyrx software, resulting in 638 molecules showing higher binding affinities compared to positive controls Tacrine (-9.0 kcal/mol), Donepezil (-7.3 kcal/mol), Galantamine (-8.3 kcal/mol), and Rivastigmine (-6.4 kcal/mol). Subsequently, ADME properties were assessed, including blood-brain barrier permeability and Lipinski's rule of five violations, leading to 88 compounds passing the ADME analysis. Among the rivastigmine analogous, [3-(1-methylpiperidin-2-yl)phenyl] N,N-diethylcarbamate showed interaction with Tyr123, Tyr336, Tyr340, Phe337, Trp285 residues of AChE. Tacrine similar compounds, such as 4-amino-2-styrylquinoline, exhibited bindings with Tyr123, Phe337, Tyr336, Trp285, Trp85, Gly119, and Gly120 residues. A phytocompound (bisdemethoxycurcumin) showed interaction with Trp285, Tyr340, Trp85, Tyr71, and His446 residues of AChE with favourable binding. These findings underscore the potential of these compounds as novel inhibitors of AChE, offering insights into alternative therapeutic avenues for AD. A 100ns simulation analysis confirmed the stability of protein-ligand complex based on the RMSD, RMSF, ligand properties, PCA, DCCM and MMGBS parameters. The investigation suggested 3 ligands as a potent inhibitor of AChE which are [3-(1-methylpiperidin-2-yl)phenyl] N,N-diethylcarbamate, 4-Amino-2-styrylquinoline and bisdemethoxycurcumin. Furthermore, investigation, including in-vitro and in-vivo studies, is needed to validate the efficacy, safety profiles, and therapeutic potential of these compounds for AD treatment.

In this paper, the fused graphical lasso (FGL) method is used to estimate multiple precision matrices from multiple populations simultaneously. The lasso penalty in the FGL model is a restraint on sparsity of precision matrices, and a moderate penalty on the two precision matrices from distinct groups restrains the similar structure across multiple groups. In high-dimensional settings, an oracle inequality is provided for FGL estimators, which is necessary to establish the central limit law. We not only focus on point estimation of a precision matrix, but also work on hypothesis testing for a linear combination of the entries of multiple precision matrices. We apply a de-biasing technology, which is used to obtain a new consistent estimator with known distribution for implementing the statistical inference, and extend the statistical inference problem to multiple populations. The corresponding de-biasing FGL estimator and its asymptotic theory are provided. A simulation study and an application of the diffuse large B-cell lymphoma data show that the proposed test works well in high-dimensional situation.

Particle-in-cell (PIC) simulation serves as a widely employed method for investigating plasma, a prevalent state of matter in the universe. This simulation approach is instrumental in exploring characteristics such as particle acceleration by turbulence and fluid, as well as delving into the properties of plasma at both the kinetic scale and macroscopic processes. However, the simulation itself imposes a significant computational burden. This research proposes a novel implementation approach to address the computationally intensive phase of the electrostatic PIC simulation, specifically the Particle-to-Interpolation phase. This is achieved by utilizing a high-speed Field Programmable Gate Array (FPGA) computation platform. The suggested approach incorporates various optimization techniques and diminishes memory access latency by leveraging the flexibility and performance attributes of the Intel FPGA device. The results obtained from our study highlight the effectiveness of the proposed design, showcasing the capability to execute hundreds of functional operations in each clock cycle. This stands in contrast to the limited operations performed in a general-purpose single-core computation platform (CPU). The suggested hardware approach is also scalable and can be deployed on more advanced FPGAs with higher capabilities, resulting in a significant improvement in performance.

The article is devoted to investigation of energy-efficient moisture removal from capillary-porous materials. Moisture is removed by dispersion at collapse of cylindrical cavitation bubbles, formed by ultrasonic vibrations in the capillaries of the material. Mathematical model, which allowed to investigate the mechanism of moisture dispersion, has been developed. Necessity of realization of cavitation bubble full life cycle in capillary (slow growth, rapid expansion with deformation, collapse) was found. An optimal range of sound pressure levels from 150 dB ("critical level" at which dispersion of water from capillary starts) up to 170 dB (dispersion productivity growth stops due to cavitation bubbles reaching maximum size equal to diameter of capillary) was determined. It is shown that the size of the dewatered sample for maximum drying efficiency should correspond to the ultrasonic wavelength in air. Ultrasonic dispersion of liquid during drying was confirmed experimentally. It is found that for significant reduction of drying time (up to 50% and more) it is necessary to affect in the range of 165-170 dB. And the materials to be dried must be placed as particles or layers having dimensions or thicknesses corresponding to the length of the ultrasonic wave in air. The implementation of ultrasonic drying, on the example of food products (beets) provided a reduction in drying time of 1.9 times, while reducing energy costs by 1.7 times in comparison with convective drying.

Deep learning and multimodal remote and proximal sensing are widely used for analyzing plant and crop traits, but many of these deep learning models are supervised and necessitate reference datasets with image annotations. Acquiring these datasets often demands experiments that are both labor-intensive and time-consuming. Furthermore, extracting traits from remote sensing data beyond simple geometric features remains a challenge. To address these challenges, we proposed a radiative transfer modeling framework based on the Helios 3-dimensional (3D) plant modeling software designed for plant remote and proximal sensing image simulation. The framework has the capability to simulate RGB, multi-/hyperspectral, thermal, and depth cameras, and produce associated plant images with fully resolved reference labels such as plant physical traits, leaf chemical concentrations, and leaf physiological traits. Helios offers a simulated environment that enables generation of 3D geometric models of plants and soil with random variation, and specification or simulation of their properties and function. This approach differs from traditional computer graphics rendering by explicitly modeling radiation transfer physics, which provides a critical link to underlying plant biophysical processes. Results indicate that the framework is capable of generating high-quality, labeled synthetic plant images under given lighting scenarios, which can lessen or remove the need for manually collected and annotated data. Two example applications are presented that demonstrate the feasibility of using the model to enable unsupervised learning by training deep learning models exclusively with simulated images and performing prediction tasks using real images.

Angiogenesis, the process of forming new blood vessels from pre-existing ones, plays a crucial role in the development and advancement of cancer. Although blocking angiogenesis has shown success in treating different types of solid tumors, its relevance in prostate adenocarcinoma (PRAD) has not been thoroughly investigated.

Ankylosing spondylitis (AS) is a complex condition with a significant genetic component. This study explored circulating proteins as potential genetic drug targets or biomarkers to prevent AS, addressing the need for innovative and safe treatments.

The extent to which active noise cancelation (ANC), when combined with hearing assistance, can improve speech intelligibility in noise is not well understood. One possible source of benefit is ANC's ability to reduce the sound level of the direct (i.e., vent-transmitted) path. This reduction lowers the "floor" imposed by the direct path, thereby allowing any increases to the signal-to-noise ratio (SNR) created in the amplified path to be "realized" at the eardrum. Here we used a modeling approach to estimate this benefit. We compared pairs of simulated hearing aids that differ only in terms of their ability to provide ANC and computed intelligibility metrics on their outputs. The difference in metric scores between simulated devices is termed the "ANC Benefit." These simulations show that ANC Benefit increases as (1) the environmental sound level increases, (2) the ability of the hearing aid to improve SNR increases, (3) the strength of the ANC increases, and (4) the hearing loss severity decreases. The predicted size of the ANC Benefit can be substantial. For a moderate hearing loss, the model predicts improvement in intelligibility metrics of >30% when environments are moderately loud (>70 dB SPL) and devices are moderately capable of increasing SNR (by >4 dB). It appears that ANC can be a critical ingredient in hearing devices that attempt to improve SNR in loud environments. ANC will become more and more important as advanced SNR-improving algorithms (e.g., artificial intelligence speech enhancement) are included in hearing devices.

Since the COVID-19 outbreak in early 2020, researchers and studies are continuing to find drugs and/or vaccines against the disease. As shown before, medicinal plants can be very good sources against viruses because of their secondary compounds which may cure diseases and help in survival of patients. There is a growing trend in the filed patents in this field.

Breast cancer is the leading cause of cancer-related deaths in women worldwide, with Hormone Receptor (HR)+ being the predominant subtype. Tamoxifen (TAM) serves as the primary treatment for HR+ breast cancer. However, drug resistance often leads to recurrence, underscoring the need to develop new therapies to enhance patient quality of life and reduce recurrence rates. Artemisinin (ART) has demonstrated efficacy in inhibiting the growth of drug-resistant cells, positioning art as a viable option for counteracting endocrine resistance. This study explored the interaction between artemisinin and tamoxifen through a combined approach of bioinformatics analysis and experimental validation. Five characterized genes () and seven drug-disease crossover genes () were identified using WGCNA crossover analysis. Subsequent functional enrichment analyses were conducted. Our findings confirm a significant correlation between key cluster gene expression and immune cell infiltration in tamoxifen-resistant and -sensitized patients. scRNA-seq analysis revealed high expression of key cluster genes in epithelial cells, suggesting artemisinin's specific impact on tumor cells in estrogen receptor (ER)-positive BC tissues. Molecular target docking and experiments with artemisinin on LCC9 cells demonstrated a reversal effect in reducing migratory and drug resistance of drug-resistant cells by modulating relevant drug resistance genes. These results indicate that artemisinin could potentially reverse tamoxifen resistance in ER-positive breast cancer.

Cancer is a leading cause of death and a significant public health issue worldwide. Standard treatment methods such as chemotherapy, radiotherapy, and surgery are only sometimes effective. Therefore, new therapeutic approaches are needed for cancer treatment. Sea anemone actinoporins are pore-forming toxins (PFTs) with membranolytic activities. RTX-A is a type of PFT that interacts with membrane phospholipids, resulting in pore formation. The synthesis of recombinant proteins in a secretory form has several advantages, including protein solubility and easy purification. In this study, we aimed to discover suitable signal peptides for producing RTX-A in Bacillus subtilis in a secretory form.

Mitigating the spread of infectious diseases is of paramount concern for societal safety, necessitating the development of effective intervention measures. Epidemic simulation is widely used to evaluate the efficacy of such measures, but realistic simulation environments are crucial for meaningful insights. Despite the common use of contact-tracing data to construct realistic networks, they have inherent limitations. This study explores reconstructing simulation networks using link prediction methods as an alternative approach.

This study is a retrospective evaluation of the performance of deep learning models that were developed for the detection of COVID-19 from chest x-rays, undertaken with the goal of assessing the suitability of such systems as clinical decision support tools.

To develop a novel method for calculating small airway resistance using computational fluid dynamics (CFD) based on CT data and evaluate its value to identify COPD.