Biomedical Engineering and Computational Biology最新文献

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.

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.

Objective: 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.

Materials and methods: Thus, this study was designed to identify therapeutic targets and mechanisms of upadacitinib for treating SS through conducting network pharmacology and molecular docking analyses.

Results: 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.

Conclusion: 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.

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.

Objective: The aim is to detect impacted teeth in panoramic radiology by refining the pretrained MedSAM model.

Study design: Impacted teeth are dental issues that can cause complications and are diagnosed via radiographs. We modified SAM model for individual tooth segmentation using 1016 X-ray images. The dataset was split into training, validation, and testing sets, with a ratio of 16:3:1. We enhanced the SAM model to automatically detect impacted teeth by focusing on the tooth's center for more accurate results.

Results: With 200 epochs, batch size equals to 1, and a learning rate of 0.001, random images trained the model. Results on the test set showcased performance up to an accuracy of 86.73%, F1-score of 0.5350, and IoU of 0.3652 on SAM-related models.

Conclusion: This study fine-tunes MedSAM for impacted tooth segmentation in X-ray images, aiding dental diagnoses. Further improvements on model accuracy and selection are essential for enhancing dental practitioners' diagnostic capabilities.

Tissue engineering is a multidisciplinary field that uses biomaterials to restore tissue function and assist with drug development. Over the last decade, the fabrication of three-dimensional (3D) multifunctional scaffolds has become commonplace in tissue engineering and regenerative medicine. Thanks to the development of 3D bioprinting technologies, these scaffolds more accurately recapitulate in vivo conditions and provide the support structure necessary for microenvironments conducive to cell growth and function. The purpose of this review is to provide a background on the leading 3D bioprinting methods and bioink selections for tissue engineering applications, with a specific focus on the growing field of developing multifunctional bioinks and possible future applications.