Biomedical Engineering and Computational Biology最新文献

Background: Acute pancreatitis (AP) is a common disease of acute abdominal pain, the incidence of which is increasing annually, but its pathogenesis remains incompletely understood.

Methods: Gene expression profiles of AP were obtained from the Gene Expression Omnibus (GEO) database. R software was used to identify differentially expressed genes (DEGs) and perform functional analysis. The diagnostic value of HLA-DR-related genes was assessed by receiver operating characteristic (ROC) curves. Monocyte infiltration abundance in AP and normal groups was analyzed by Cibersort method, and the correlation between HLA-DR-related genes and monocyte abundance was analyzed. The modules highly correlated with HLA-DR-related genes were clarified by WGCNA modeling, and the core genes regulating HLA-DR were obtained by using LASSO regression. Finally, potential drugs targeting the above genes were analyzed by Enrichr database.

Result: A Total of 3 HLA-DR-related genes (HLA-DRA, HLA-DRB1, and HLA-DRB5) were identified, which were negatively correlated with the severity of AP and had excellent disease diagnostic value (AUC = 0.761, 0.761, and 0.718), were were positively correlated with monocyte abundance. We identified 110 genes that positively regulate HLA-DR and 130 genes that negatively regulate HLA-DR. LASSO regression identified UCP2, GK, and SAMHD1 as the core nodes of the regulated genes. Compared with the normal group, UCP2 and SAMHD1 were reduced in AP, and the opposite was true for GK, and SAMHD1 had better sensitivity and specificity in diagnosing AP. Drug sensitivity analysis predicted 12 drugs acting on HLA-DRA, HLA-DRB1, and HLA-DRB5 and 8 drugs acting on UCP2, GK, and SAMHD1.

Conclusion: We identified 3 HLA-DR-related genes (HLA-DRA, HLA-DRB1, and HLA-DRB5) and 3 coregulatory nodes (UCP2, GK, and SAMHD1), which were associated with AP severity and monocyte abundance. Based on these genes, we predicted 20 potential therapeutic agents for AP.

The spinal diseases commonly faced by people in the 19th century included intervertebral disc degeneration, tuberculosis and congenital defects that resulted in neurological impairment and global disability. To address these issues, cervical spine surgery was performed. Modern techniques currently used in spine surgery include interbody devices, pedicle screws, artificial discs and bone grafts. The postoperative complications clinically reported during follow-up include nonunion and implant subsidence, which remain significant drawbacks. The objective of this study is to develop a 3-dimensional finite element model of the C2-C7 cervical spine and validate it against existing experimental studies. The loading conditions considered for this study include a compressive preload of 50 N and a 1 Nm moment applied to the C2 vertebra, with the C7 vertebra fixed at the bottom. In this study, the biomechanical alterations of 4 different cage morphologies were analysed using finite element analysis. Valeo cages with 4 distinct designs were implanted at the C5-C6 level, and physiological motion at the surgical site was studied. Cage subsidence and migration, which can lead to adjacent segment disc degeneration, were also examined. Subsidence was primarily attributed to higher stress encountered in the cage, so stress distribution within the cages was evaluated. Additionally, stress distribution in the anterior plate and screws was analysed. The study concludes that introducing anterior plate and screw fixation helps prevent cage subsidence. Physiological motion at the surgical level was reduced compared to the intact model. Adjacent disc stress was also evaluated and found to be lower than in the intact model.

This work presents an enhanced identification procedure utilising bioinformatics data, employing optimisation techniques to tackle crucial difficulties in healthcare operations. A system model is designed to tackle essential difficulties by analysing major contributions, including risk factors, data integration and interpretation, error rates and data wastage and gain. Furthermore, all essential aspects are integrated with deep learning optimisation, encompassing data normalisation and hybrid learning methodologies to efficiently manage large-scale data, resulting in personalised healthcare solutions. The implementation of the suggested technology in real time addresses the significant disparity between data-driven and healthcare applications, hence facilitating the seamless integration of genetic insights. The contributions are illustrated in real time, and the results are presented through simulation experiments encompassing 4 scenarios and 2 case studies. Consequently, the comparison research reveals that the efficacy of bioinformatics for enhancing routes stands at 7%, while complexity diminish to 1%, thereby indicating that healthcare operations can be transformed by computational biology.

Introduction: The rate of acute hepatitis C increased by 7% between 2020 and 2021, after the number of cases doubled between 2014 and 2020. With the current adoption of pan-genotypic HCV therapy, there is a need for improved availability and accessibility of this therapy. However, double and triple DAA-resistant variants have been identified in genotypes 1 and 5 with resistance-associated amino acid substitutions (RAASs) in NS3/4A, NS5A, and NS5B. The role of this research was to screen for novel potential NS5B inhibitors from the cannabis compound database (CBD) using Deep Learning.

Methods: Virtual screening of the CBD compounds was performed using a trained Graph Neural Network (GNN) deep learning model. Re-docking and conventional docking were used to validate the results for these ligands since some had rotatable bonds >10. About 31 of the top 67 hits from virtual screening and docking were selected after ADMET screening. To verify their candidacy, 6 random hits were taken for FEP/MD and Molecular Simulation Dynamics to confirm their candidacy.

Results: The top 200 compounds from the deep learning virtual screening were selected, and the virtual screening results were validated by re-docking and conventional docking. The ADMET profiles were optimal for 31 hits. Simulated complexes indicate that these hits are likely inhibitors with suitable binding affinities and FEP energies. Phytil Diphosphate and glucaric acid were suggested as possible ligands against NS5B.

Schistosomiasis, which causes over 200 000 deaths annually, has since the 1970s been controlled by praziquintel. The reliance on a single drug to combat schistosomiasis, and reports of laboratory resistance to the drug, has created an urgent need in the scientific community to develop new chemotherapies to complement or supplement praziquantel. Medicinal plants are a potential reservoir of compounds with schistosomicidal activity. In the current study, we carried out computer-aided screening of Abrus precatorius compounds to discover compounds with potential to inhibit Schistosoma mansoni purine nucleoside phosphorylase (SmPNP). Thus, 99 compounds retrieved from Lotus Natural Compounds Database were docked into the active site of SmPNP. The top-ranked compounds were subjected to Lipinski's druglikeness and toxicity risk predictions. Three lead compounds, abrusogenin, cirsimaritin and hispidulin, were identified as having high binding affinities, favourable interactions with SmPNP active site residues and good toxicity risk prediction results. Molecular dynamics (MD) simulations were used to assess the stability of the interactions of these lead compounds with SmPNP. Collectively, analyses of the MD trajectories confirms that the lead compounds bound and interacted stably with active site residues of SmPNP. We conclude that abrusogenin, cirsimaritin and hispidulin could serve as hit compounds for the development of new antischistosomal drugs, based on plant-derived natural products. However, experimental studies are required to further evaluate the potentials of these compounds as possible therapeutics against schistosomiasis.

Trends in health care delivery systems have shifted as a result of the modern uses of biomaterials in medicine. Contrary to traditional medicine, modern healthcare are now useful in solving problems that were considered impossible some years back. One of the most significant factors to the most recent advancements in implant development has been the use of calcium based materials in the creation of necessary implants in the form of soft and hard tissues. With the advent of naturally sourced materials in the manufacturing of biomaterials, lots of attention are now focused on the different sources of agro-based resources that can be used for the product developments. These agro-based materials are now been considered for sustainable and ecological purposes in several areas of applications globally in the recent times. Hence, the review was carried out with focus on the sources, relevance, processing techniques and applications of tricalcium phosphate based biomaterials in modern day healthcare delivery. This review provides a historical and prospective picture of the crucial functions that materials based on tricalcium phosphate will play in fulfilling human requirements for medication.

A temporal neural code reliant on the pattern of spike times rather than spike rates offers a feasible mechanism for encoding information from weak periodic external stimuli, such as static or extremely low-frequency electromagnetic fields. Our model focuses on the influence of magnetic fields on neurotransmitter dynamics near the neuron membrane. Neurotransmitter binding to specific receptor sites on membrane proteins can regulate biochemical reactions. The duration a neurotransmitter spends in the bonded state serves as a metric for the magnetic field's capacity as a chemical regulator. By initiating a physical analysis of ligand-receptor binding, utilizing the alpha function for synaptic conductance, and employing a modified version of Bell's law, we quantified the impact of magnetic fields on the bond half-life time and, consequently, on postsynaptic spike timing.

Background: Colorectal cancer (CRC) remains a significant health burden globally, necessitating a deeper understanding of its molecular landscape and prognostic markers. This study characterized ferroptosis-related genes (FRGs) to construct models for predicting overall survival (OS) across various CRC datasets.

Methods: In TCGA-COAD dataset, differentially expressed genes (DEGs) were identified between tumor and normal tissues using DESeq2 package. Prognostic genes were identified associated with OS, disease-specific survival, and progression-free interval using survival package. Additionally, FRGs were downloaded from FerrDb website, categorized into unclassified, marker, and driver genes. Finally, multiple models (Coxboost, Elastic Net, Gradient Boosting Machine, LASSO Regression, Partial Least Squares Regression for Cox Regression, Ridge Regression, Random Survival Forest [RSF], stepwise Cox Regression, Supervised Principal Components analysis, and Support Vector Machines) were employed to predict OS across multiple datasets (TCGA-COAD, GSE103479, GSE106584, GSE17536, GSE17537, GSE29621, GSE39084, GSE39582, and GSE72970) using intersection genes across DEGs, OS, disease-specific survival, and progression-free interval, and FRG categories.

Results: Six intersection genes (ASNS, TIMP1, H19, CDKN2A, HOTAIR, and ASMTL-AS1) were identified, upregulated in tumor tissues, and associated with poor survival outcomes. In the TCGA-COAD dataset, the RSF model demonstrated the highest concordance index. Kaplan-Meier analysis revealed significantly lower OS probabilities in high-risk groups identified by the RSF model. The RSF model exhibited high accuracy with AUC values of 0.978, 0.985, and 0.965 for 1-, 3-, and 5-year survival predictions, respectively. Calibration curves demonstrated excellent agreement between predicted and observed survival probabilities. Decision curve analysis confirmed the clinical utility of the RSF model. Additionally, the model's performances were validated in GSE29621 dataset.

Conclusions: The study underscores the prognostic relevance of 6 intersection genes in CRC, providing insights into potential therapeutic targets and biomarkers for patient stratification. The RSF model demonstrates robust predictive performance, suggesting its utility in clinical risk assessment and personalized treatment strategies.

The growth and advancements done in solid mechanics and metallurgy have come up with various characterization techniques that help in prediction of elastic properties of different types of materials-isotropic, anisotropic, transverse isotropic, etc. Soft tissues which refer to fibrous tissues, fat, blood vessels, muscles and other tissues that support the body were found to have some control over its mechanical properties. This mechanical behavior of soft tissues has recently shifted the attention of many researchers to develop methods to characterize and describe the mechanical response of soft tissues. The paper discusses the biomechanical nature of soft tissues and the work done to characterize their elastic properties. The paper gives a review of the behavior and characteristics of soft tissues extracted from various experimental tests employed in their characterization. Soft tissues exhibit complex behavior and various complexities are involved in their experimental testing due to their small size and fragile nature. The paper focuses on the conventionally used tensile and compression tests and the difficulties encountered in soft tissue characterization. It also describes the utility of ultrasound technique which is a non-destructive method to characterize soft tissues. Tensile and compression test used to characterize materials are destructive in nature. Ultrasound technique can provide a better way to characterize material in a non-destructive manner.

Cao et al. introduce PANDA, an AI model designed for the early detection of pancreatic ductal adenocarcinoma (PDAC) using non-contrast CT scans. While the model shows great promise, it faces several challenges. Notably, its training predominantly on East Asian datasets raises concerns about generalizability across diverse populations. Additionally, PANDA's ability to detect rare lesions, such as pancreatic neuroendocrine tumors (PNETs), could be improved by integrating other imaging modalities. High specificity is a strength, but it also poses risks of false positives, which may lead to unnecessary procedures and increased healthcare costs. Implementing a tiered diagnostic approach and expanding training data to include a wider demographic are essential steps for enhancing PANDA's clinical utility and ensuring its successful global implementation, ultimately shifting the focus from late diagnosis to proactive early detection.