Biomedical Engineering and Computational Biology最新文献

{"title":"Computer-Aided Discovery of <i>Abrus precatorius</i> Compounds With Anti-Schistosomal Potential.","authors":"Ryman Shoko, Allen Mazadza","doi":"10.1177/11795972241294112","DOIUrl":"https://doi.org/10.1177/11795972241294112","url":null,"abstract":"<p><p>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 <i>Abrus precatorius</i> compounds to discover compounds with potential to inhibit <i>Schistosoma mansoni</i> purine nucleoside phosphorylase (<i>Sm</i>PNP). Thus, 99 compounds retrieved from Lotus Natural Compounds Database were docked into the active site of <i>Sm</i>PNP. 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 <i>Sm</i>PNP 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 <i>Sm</i>PNP. Collectively, analyses of the MD trajectories confirms that the lead compounds bound and interacted stably with active site residues of <i>Sm</i>PNP. 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.</p>","PeriodicalId":42484,"journal":{"name":"Biomedical Engineering and Computational Biology","volume":"15 ","pages":"11795972241294112"},"PeriodicalIF":2.3,"publicationDate":"2024-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11552047/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142629718","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Synthesis and Application of Sustainable Tricalcium Phosphate Based Biomaterials From Agro-Based Materials: A Review.","authors":"Isiaka Oluwole Oladele, Samson Ademola Adekola, Newton Itua Agbeboh, Baraka Abiodun Isola-Makinde, Benjamin Omotayo Adewuyi","doi":"10.1177/11795972241293525","DOIUrl":"https://doi.org/10.1177/11795972241293525","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":42484,"journal":{"name":"Biomedical Engineering and Computational Biology","volume":"15 ","pages":"11795972241293525"},"PeriodicalIF":2.3,"publicationDate":"2024-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11544672/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142629719","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Physical Framework to Study the Effect of Magnetic Fields on the Spike-Time Coding.","authors":"Manuel Rivas, Marina Martinez-Garcia","doi":"10.1177/11795972241272380","DOIUrl":"10.1177/11795972241272380","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":42484,"journal":{"name":"Biomedical Engineering and Computational Biology","volume":"15 ","pages":"11795972241272380"},"PeriodicalIF":2.3,"publicationDate":"2024-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11536361/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142584326","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Construction of Prognostic Prediction Models for Colorectal Cancer Based on Ferroptosis-Related Genes: A Multi-Dataset and Multi-Model Analysis.","authors":"Tao Gan, Xiaomeng Wei, Yuanhao Xing, Zhili Hu","doi":"10.1177/11795972241293516","DOIUrl":"10.1177/11795972241293516","url":null,"abstract":"<p><strong>Background: </strong>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.</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>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.</p><p><strong>Conclusions: </strong>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.</p>","PeriodicalId":42484,"journal":{"name":"Biomedical Engineering and Computational Biology","volume":"15 ","pages":"11795972241293516"},"PeriodicalIF":2.3,"publicationDate":"2024-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11531666/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142570088","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"On Mechanical Behavior and Characterization of Soft Tissues.","authors":"Radhika Chavan, Nitin Kamble, Chetan Kuthe, Sandeep Sarnobat","doi":"10.1177/11795972241294115","DOIUrl":"10.1177/11795972241294115","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":42484,"journal":{"name":"Biomedical Engineering and Computational Biology","volume":"15 ","pages":"11795972241294115"},"PeriodicalIF":2.3,"publicationDate":"2024-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11531667/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142570005","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Commentary on \"Large-Scale Pancreatic Cancer Detection via Non-Contrast CT and Deep Learning\".","authors":"Ibrahem Alshybani","doi":"10.1177/11795972241293521","DOIUrl":"10.1177/11795972241293521","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":42484,"journal":{"name":"Biomedical Engineering and Computational Biology","volume":"15 ","pages":"11795972241293521"},"PeriodicalIF":2.3,"publicationDate":"2024-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11528658/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142570055","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Correspondence to \"Conceptualizing Patient as an Organization with the Adoption of Digital Health\".","authors":"Hinpetch Daungsupawong, Viroj Wiwanitkit","doi":"10.1177/11795972241293514","DOIUrl":"10.1177/11795972241293514","url":null,"abstract":"","PeriodicalId":42484,"journal":{"name":"Biomedical Engineering and Computational Biology","volume":"15 ","pages":"11795972241293514"},"PeriodicalIF":2.3,"publicationDate":"2024-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11528574/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142570093","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Breast Cancer Diagnosis Using Virtualization and Extreme Learning Algorithm Based on Deep Feed Forward Networks.","authors":"G Siva Shankar, Edeh Michael Onyema, Balasubramanian Prabhu Kavin, Venkataramaiah Gude, Bvv Siva Prasad","doi":"10.1177/11795972241278907","DOIUrl":"10.1177/11795972241278907","url":null,"abstract":"<p><p>One of the leading causes of death for women worldwide is breast cancer. Early detection and prompt treatment can reduce the risk of breast cancer-related death. Cloud computing and machine learning are crucial for disease diagnosis today, but they are especially important for those who live in distant places with poor access to healthcare. While machine learning-based diagnosis tools act as primary readers and aid radiologists in correctly diagnosing diseases, cloud-based technology can also assist remote diagnostics and telemedicine services. The promise of techniques based on Artificial Neural Networks (ANN) for sickness diagnosis has attracted the attention of several re-searchers. The 4 methods for the proposed research include preprocessing, feature extraction, and classification. A Smart Window Vestige Deletion (SWVD) technique is initially suggested for preprocessing. It consists of Savitzky-Golay (S-G) smoothing, updated 2-stage filtering, and adaptive time window division. This technique separates each channel into multiple time periods by adaptively pre-analyzing its specificity. On each window, an altered 2-stage filtering process is then used to retrieve some tumor information. After applying S-G smoothing and integrating the broken time sequences, the process is complete. In order to deliver effective feature extraction, the Deep Residual based Multiclass for architecture (DRMFA) is used. In histological photos, identify characteristics at the cellular and tissue levels in both tiny and large size patches. Finally, a fresh customized strategy that combines a better crow forage-ELM. Deep learning and the Extreme Learning Machine (ELM) are concepts that have been developed (ACF-ELM). When it comes to diagnosing ailments, the cloud-based ELM performs similarly to certain cutting-edge technology. The cloud-based ELM approach beats alternative solutions, according to the DDSM and INbreast dataset results. Significant experimental results show that the accuracy for data inputs is 0.9845, the precision is 0.96, the recall is 0.94, and the F1 score is 0.95.</p>","PeriodicalId":42484,"journal":{"name":"Biomedical Engineering and Computational Biology","volume":"15 ","pages":"11795972241278907"},"PeriodicalIF":2.3,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11528671/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142570052","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Uncovering the Therapeutic Target and Molecular Mechanism of Upadacitinib on Sjogren's Syndrome.","authors":"Youguo Yang, Yuan Liu, Xiaofen Li, Yongping Zeng, Weiqian He, Juan Zhou","doi":"10.1177/11795972241293519","DOIUrl":"10.1177/11795972241293519","url":null,"abstract":"<p><strong>Objective: </strong>Upadacitinib, a selective Janus associated kinase 1 (JAK-1) inhibitor, can be prescribed particularly for the clinical treatment with Crohn's disease or rheumatoid arthritis. It is clinically observed that upadacitinib has been found with potential therapeutic effectiveness on Sjogren's syndrome (SS). However, the anti-SS targets and mechanisms involved in upadacitinib treatment remain uninvestigated.</p><p><strong>Materials and methods: </strong>Thus, this study was designed to identify therapeutic targets and mechanisms of upadacitinib for treating SS through conducting network pharmacology and molecular docking analyses.</p><p><strong>Results: </strong>In total, we identified 298 upadacitinib-related target genes, 1339 SS-related targets before collecting 56 overlapped target genes and 12 hub target genes. Upadacitinib largely exerted the critical biological processes including regulation of microenvironment homeostasis, inflammatory response, and cell apoptosis, and largely acted on pivotal molecular mechanisms including hypoxia-inducible factor 1 (HIF-1) signaling pathway, apoptosis pathway, phosphatidylinositol 3-kinase/protein kinase B (PI3K/Akt) signaling pathway, or Th17 cell differentiation pathway. Molecular docking data suggested that upadacitinib exhibited the high affinities with signal transducer and activator of transcription 3 (STAT3), HIF1A, poly(ADP-ribose) polymerase 1 (PARP1) target proteins, in which the structural interactions between upadacitinib and STAT3, HIF1A, PARP1 showed potential therapeutic activities against SS.</p><p><strong>Conclusion: </strong>In conclusion, upadacitinib possesses the bright anti-inflammatory and anti-apoptotic activities on SS, and this study can provide a theoretical basis for clinical therapy of SS using upadacitinib.</p>","PeriodicalId":42484,"journal":{"name":"Biomedical Engineering and Computational Biology","volume":"15 ","pages":"11795972241293519"},"PeriodicalIF":2.3,"publicationDate":"2024-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11528621/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142570025","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cranial Defect Repair With 3D Designed Models.","authors":"Sambardhan Dabadi, Raju Raj Dhungel","doi":"10.1177/11795972241291777","DOIUrl":"https://doi.org/10.1177/11795972241291777","url":null,"abstract":"<p><p>Cranioplasty is one of the most common neurosurgical procedure performed to repair cranial defect. Many materials and fabrication technique are used to prepare cranial implant in cases where autologous bone is not available. Polymethyl Methacrylate (PMMA) is one of the most common polymer used as bone substitute. PMMA fabricated using 3D printed models have shown better fit, symmetrical shape, and restore esthetic looks of patients. The use of 3D printed implants in medical procedures has several advantages over traditional manufacturing methods. 3D printing allows for greater precision, customization, and quicker implant time.</p>","PeriodicalId":42484,"journal":{"name":"Biomedical Engineering and Computational Biology","volume":"15 ","pages":"11795972241291777"},"PeriodicalIF":2.3,"publicationDate":"2024-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11474787/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142477123","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}