COVID-19

Geometric deep learning-based coronary wall shear stress estimation from real-world patients.

BACKGROUND: Coronary wall shear stress (WSS) derived from computational fluid dynamics (CFD) provides mechanistic insight and prognostic information, but its clinical translation is hindered by modeling complexity and computation time. We evaluated a geometric deep learning framework based on gauge-equivariant mesh graph convolutional network (GEM-GCN) to estimate coronary WSS directly in geometri...

[Utilizing machine learning and SHAP analysis to develop a prognostic survival prediction model for gastric cancer patients following radical gastrectomy].

Objective: To construct a prediction model and website for the overall survival (OS) of gastric cancer patients after radical gastrectomy based on SHapley Additive exPlanations (SHAP) and machine learning models. Methods: This retrospective cohort study included 234 patients who underwent radical gastrectomy at Henan Cancer Hospital from October 2018 to December 2022, selected according to the fol...

Large-scale analysis of DNA quantification metrics and SNP sequencing performance in unidentified human remains.

Massively parallel sequencing (MPS)-based single nucleotide polymorphism (SNP) profiling is being increasingly used in forensic DNA testing, yet the i...

Integrative bioinformatics and machine learning combined with experimental validation in a doxorubicin-induced model identify BACH2, NXPH4, CD1E, and LIF as sodium overload-related molecular signatures in dilated cardiomyopathy.

INTRODUCTION: Dilated cardiomyopathy (DCM) is a leading cause of heart failure and remains a major clinical challenge due to its complex etiology and ...

Transcriptome Profiling of Peripheral Blood for Inflammatory Subtype Classification and Diagnostic Model Construction in Asthma.

OBJECTIVE: This study aimed to identify inflammatory subtypes of asthma using peripheral blood transcriptomics and to derive candidate biomarkers supp...

Predicting low oxygen in patients with acute COVID-19 infection isolating at home: a clinical prediction model.

RATIONALE: Identifying patients who could be safely managed at home versus those needing hospitalisation was a particular concern during early COVID-1...

Survey on mathematical modeling of infectious disease dynamics: insights and applications.

Mathematical modeling has become an indispensable tool for understanding, predicting, and controlling the spread of infectious diseases. Over the year...

Identification of the immune-related diagnostic biomarkers between Graves' disease and thyroid carcinoma based on comprehensive bioinformatics analysis and machine learning.

Increasing evidence suggests that Graves' disease (GD) may increase the risk of thyroid cancer (THCA), but diagnostic biomarkers associated with it re...

Improving the Understandability of Clinical Guidelines: Development and Evaluation of a GPT-4-Based Pipeline.

BACKGROUND: Difficulty in finding and understanding information in clinical guidelines contributes to medication errors. Large language models (LLMs) ...

Designing an explainable algorithm based on XGBoost and genetic algorithm for predicting hospitalization needs of COVID-19 patients.

Timely identification of COVID-19 outpatients who are at risk of hospitalization is critical for preventing clinical deterioration and optimizing heal...

Influence of sex differences among endoscopists in gastric cancer and precancerous condition detection rate during EGD in the artificial intelligence era: a retrospective study.

AIMS: This study investigated whether endoscopist sex is associated with the detection of gastric precancerous conditions and neoplasia during esophag...

Using machine learning to predict and analyze complex trait diseases: Lessons from a simple abstract model.

The ability to predict individual genetic susceptibility to a complex trait disease is a major challenge in modern medicine. One approach to addressin...

Mask - Region-based Convolutional Neural Networks (R-CNN) with Radiomics Integration and Gray Level Co-occurrence Matrix (GLCM) for brain tumor detection and segmentation.

Early diagnosis of brain tumors is important for successful treatment and better patient consequences in industrial information systems. This research...

Advances in chromatography for biomacromolecules and bioparticles (2019-2024): Focus on materials and industrial applications.

Chromatographic technology is the core of downstream separation and purification of biological macromolecules and bioparticles. In recent years, with ...

The emerging role of antibody-drug conjugates in EGFR-mutant non-small cell lung cancer with acquired resistance to third-generation EGFR tyrosine kinase inhibitors.

Resistance to third-generation epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors (TKIs) presents a significant clinical challenge for...

Memory-guided mask reconstruction with central contrastive learning for robust multivariate time series anomaly detection.

Mask reconstruction-based unsupervised multivariate time series anomaly detection (MTSAD) methods employ mask operations to model fine-grained local d...

Immunotherapy in Triple-Negative Breast Cancer: From Molecular Mechanisms to Precision Medicine-Overcoming Resistance and Optimizing Clinical Outcomes.

Triple-negative breast cancer (TNBC) is the most violent type of breast cancer, in which estrogen receptors (ER), progesterone receptors (PR), and hum...

Inadequate Preventive Dental Care Is Associated With Higher Cardiovascular Risk Identified by ECG-Based Artificial Intelligence Algorithms.

BACKGROUND: Inadequate preventive dental care may contribute to inflammatory conditions such as periodontitis, increasing cardiovascular disease risk....

A hybrid blockchain based deep learning model for multivector attack detection in internet of things enabled healthcare systems.

Novel advances in healthcare-related Internet of Things (IoT) systems have recently had significant impacts on clinical decision-support systems (CDSS...

Higher artificial intelligence-ECG atrial fibrillation prediction model output and estimated physiologic aging predict higher risk of adverse vascular events in patients with migraine.

OBJECTIVE: To investigate the ability of artificial intelligence-enabled electrocardiogram (AI-ECG) atrial fibrillation (AF) prediction model output a...