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Oropharyngeal Microbiota Associated with Sore Throat in Acute Upper Respiratory Tract Infections.

Acute upper respiratory tract infections (AURI) can lead to an imbalance in the oropharyngeal microbiome. Specific bacteria may be responsib … igher expression of non-homologous end-joining repair mechanisms and xenobiotic biodegradation. The Support Vector Machine (SVM) model exhibited the best predictive performance. Machine learning models at the genus level identified Peptostreptococcus as a potential microbial feature associated with sore throat. Notably, further species-level analysis revealed complexity …

Multi-cohort transcriptomic analysis with machine learning identifies interferon-related candidate genes in systemic lupus erythematosus.

Systemic lupus erythematosus (SLE) is a heterogeneous autoimmune disease with complex molecular mechanisms. Although transcriptomic studies have revealed prominent interferon signatures in SLE, robust prioritization of candidate genes across independent cohorts remains challenging. In this study, we performed a multi-cohort transcriptomic analysis using publicly available GEO datasets. Differential expression analysis was conducted independently within each cohort to minimize cross-study confounding. Machine learning models, including LASSO, support vector machine, and random forest, were appl…

Deep learning for predicting lumbar segmental instability using neutral lateral lumbar radiographs: a retrospective study.

OBJECTIVE: To develop a deep learning model for predicting lumbar segmental instability (LSI) using neutral lateral lumbar radiographs and to identify key radiographic features associated with LSI. METHODS: A DenseNet121-based stacking ensemble model was integrated with Support Vector Machine, Random Forest, and Softmax classifiers. Model validation employed 10-fold cross-validation, with performance assessed using AUC, accuracy, sensitivity, specificity, and F1-score. Sensitivity analyses evaluated robustness across spinal/non-spinal regions, age/gender subgroups, and feature interactions. G…

Computational prediction of potential aggravating mechanisms of polyethylene terephthalate microplastics in diabetic foot ulcers: An integrated in silico approach combining network toxicology, bioinformatics, machine learning, and molecular dynamics simulations.

The increasing incidence of diabetic foot ulcer (DFU) and growing recognition of environmental pollutants have highlighted polyethylene tere … lysis. Machine learning models (least absolute shrinkage and selection operator, random forest, and support vector machine-recursive feature elimination) and SHapley Additive exPlanations analysis identified key genes, validated via nomogram, molecular dynamics simulation, and molecular docking. From 6723 DFU-related differentially expressed genes, 53 overlapping genes …

Morphological risk factors of parent arteries in intracranial aneurysm formation: an interpretable machine learning analysis.

PURPOSE: Intracranial aneurysms (IAs) pose a severe threat to human health. While the morphological features of the parent artery exert a si … al risk prediction model, an ensemble of ridge regression, linear discriminant analysis, and linear support vector machine, shows strong performance (accuracy: 0.856, AUC: 0.967). Additionally, feature contribution ranking highlights greater contributions from bifurcation angle, bifurcation diameter ratio, smaller daughter vessel taper, and smaller daughter vessel steno …

Machine learning-based prediction of post-thrombotic syndrome in unprovoked lower extremity deep vein thrombosis: a two-center study incorporating uric acid and clinical risk factors.

BACKGROUND: Post-thrombotic syndrome (PTS) is a common and debilitating complication after lower extremity deep vein thrombosis (LEDVT). Early risk assessment of PTS patients is still a clinical challenge. METHODS: The retrospective, multicenter cohort study included 265 patients with unprovoked LEDVT. Baseline clinical and biochemical data including serum uric acid, body mass index (BMI) and treatment delay were noted. Ten machine learning (ML) models (e.g., support vector machine [SVM], XGBoost and LightGBM) were constructed for predicting PTS occurrence in a 70:30 training-validation split…

Revealing Fibrosis Genes as Biomarkers of Ulcerative Colitis: A Bioinformatics Study Based on ScRNA and Bulk RNA Datasets.

OBJECTIVE: This study aimed to uncover biomarkers associated with fibroblasts to diagnose ulcerative colitis (UC) and predict sensitivity to TNFα inhibitors. METHODS: We identified fibrosis-related genes by analyzing eight bulk RNA and one single-cell RNA sequencing dataset from UC patients. Three machine learning algorithms were employed to identify common significant genes. We utilized five machine learning models, namely Random Forest (RF), Support Vector Machine (SVM), Xgboost, Multilayer Perceptron (MLP), and Logistic Regression, to develop diagnostic models for UC. Following hyperparame…

Machine Learning Models Based on CT Enterography for Differentiating Between Ulcerative Colitis and Colonic Crohn's Disease Using Intestinal Wall, Mesenteric Fat, and Visceral Fat Features.

PURPOSE: This study aimed to develop radiomic-based machine learning models using computed tomography enterography (CTE) features derived fr … tion analysis, SelectKBest, and least absolute shrinkage and selection operator (LASSO) regression. Support vector machine models were constructed using features from individual and combined regions, with model performance evaluated using the area under the ROC curve (AUC). RESULTS: The combined radiomic model, integrating features from all three regions, exhibited sup …

Predicting amyotrophic lateral sclerosis in the pre-symptomatic phase: Insights from SOD1G93A mouse gene expression profiles.

Amyotrophic lateral sclerosis (ALS) is a fast-paced fatal disease that requires immediate intervention to slow down the course of pathology … . Based on this ALS gene-associated signature, we tested three machine learning binary classifiers (Support Vector Machine, Neural Network and Linear Discriminant Analysis), which demonstrated highly significant predictive power in discriminating mutant SOD1G93A from controls mice, even at pre-symptomatic stages. This was evident in both the discovery cohort and in two …

Risk factors for early arteriovenous fistula failure in patients undergoing hemodialysis and comparison of diagnostic prediction models.

OBJECTIVE: The aim of this study was to investigate the incidence of early arteriovenous fistula (AVF) failure and identify associated facto … e models, including logistic regression and machine learning techniques such as random forest (RF), support vector machine (SVM), and eXtreme gradient boosting (XGBoost). RESULTS: Early failure was observed in 46 patients (6.5%), whereas 92 patients (12.8%) experienced AVF failure within 1 year, with early failure accounting for 50% of cases (46/92). The area under the …

A machine-learning-derived online prediction model for depression risk in COPD patients: A retrospective cohort study from CHARLS.

BACKGROUND: Depression associated with Chronic Obstructive Pulmonary Disease (COPD) is a detrimental complication that significantly impairs patients' quality of life. This study aims to develop an online predictive model to estimate the risk of depression in COPD patients. METHODS: This study included 2921 COPD patients from the 2018 China Health and Retirement Longitudinal Study (CHARLS), analyzing 36 behavioral, health, psychological, and socio-demographic indicators. LASSO regression filtered predictive factors, and six machine learning models-Logistic Regression, Support Vector Machine, …

Machine learning powered profiling: Rapid identification of Klebsiella Pneumoniae drug resistance from MALDI-TOF MS.

The early identification of drug-resistant phenotypes in Klebsiella pneumoniae is essential for effective clinical intervention, infection m … t Boosting (XGBoost), Adaptive Boosting (AdaBoost), Logistic Regression, Multilayer Perceptron, and Support Vector Machine. Performance was assessed using test score, ten-fold cross-validation, accuracy, specificity, F1 score, AUROC, and 95 %CI. The models performed best for Amikacin and Co-trimoxazole, whilst exhibiting the poorest predictive efficacy for levofloxacin. …

Prediction of lymph node metastasis in cervical cancer patients using AdaBoost machine learning model: analysis of risk factors.

This study focuses on the development and evaluation of machine learning models, particularly the Adaptive Boosting (AdaBoost) algorithm, for predicting lymph node metastasis (LNM) in cervical cancer (CC) patients. The findings show that AdaBoost outperformed traditional statistical methods and other machine learning models, including Random Forest, Support Vector Machine (SVM), and Least Absolute Shrinkage and Selection Operator (LASSO) regression, in predicting LNM. The areas under the curve (AUCs) for the training and validation sets were 0.882 and 0.857, respectively, indicating high predi…

Machine learning identification of tinnitus-related features in auditory peripheral spontaneous activity in a guinea pig noise-induced tinnitus model.

OBJECTIVES: Tinnitus affects millions globally, yet its clinical assessment relies on subjective reports, limiting diagnostic accuracy and t … ic ranges. These changes were reversed by EES, coinciding with behavioral improvement in GPIAS. The support vector machine achieved the highest classification performance (AUC = 0.962). SHAP analysis identified central frequency (1400-1600 Hz) and skewness (8000-9000 Hz; 16,000-17,000 Hz) as the most informative features. CONCLUSIONS: These findings suggest that specif …

Seasonal and Meteorological Drivers of Hand, Foot, and Mouth Disease Outbreaks Using Data-Driven Machine Learning Models.

Hand, Foot, and Mouth Disease (HFMD) predominantly affects children under the age of five and remains a significant public health concern in the Asia-Pacific region. HFMD outbreaks are closely linked to seasonal changes and meteorological factors, particularly in tropical and subtropical areas. In Thailand, a total of 657,570 HFMD cases were reported between 2011 and 2022 (12 years). This study aimed to identify the high- and low-risk HFMD outbreak areas using machine learning models: Logistic Regression (LR), Support Vector Machine (SVM), Decision Tree (DT), Random Forests (RF), Gradient Boos…

Конвергенты и дивергенты Митохондриалы в качестве драйверов и терапевтических целей в неврологических расстройствах.

Митохондриальная дисфункция затрагивает весь спектр неврологических заболеваний, однако ее причинно-следственная роль и механистическая специфичность остаются неясными. Для устранения этих пробелов в этом исследовании использовался многомерный комплексный анализ митохондриального генного экспрессии в болезни Альцгеймера (AD), амиотропическом боковом склерозе (ALS), множественном склерозе (MS) и болезни Паркинсона (PD). Мы комбинировали машинное обучение для прогнозирования моделирования с методами генетической причинно-следственной диагностики (Mendelian Randomization, Coлокализация, FheWAS),

Совпадение в аннотации оригинала
Mitochondrial dysfunction is implicated across a spectrum of neurological diseases, yet its causal role and mechanistic specificity remain u … llowed by drug enrichment analysis and molecular docking. Our machine learning models, particularly Support Vector Machine and Multi-layer Perceptron, effectively classified these conditions, with MS exhibiting the highest predictability (mean Accuracy: 0.758). Causal inference analyses identified specific gene-disease links; for instance, genetically predicted increase …

Predicting Success in Descemet Membrane Endothelial Keratoplasty Using Machine Learning.

PURPOSE: This study aimed to predict early graft failure (GF) in patients who underwent Descemet membrane endothelial keratoplasty based on … isual acuity. Five classification methods were experimented with the study data set: random forest, support vector machine, k-nearest neighbor, RUSBoosted tree, and neural networks. In holdout validation, 75% of the data were used in training and the remaining 25% used in testing. The predictive accuracy, sensitivity, specificity, f-score, and area under the receiver op …

New perspectives in the differential diagnosis of jaw lesions: Machine learning and inflammatory biomarkers.

This study aimed to assess the diagnostic performance of a machine learning approach that utilized radiomic features extracted from Cone Bea … d, along with machine learning techniques such as decision tree (DT), k-nearest neighbor (KNN), and support vector machine (SVM), to predict the outcomes. When individual inflammatory biomarkers were considered alone, their ability to differentiate between OKCs, UAs, and DCs was below 50 % accuracy. However, a linear regression model combining four inflammatory biomarke …

1-Year Mortality Prediction through Artificial Intelligence Using Hemodynamic Trace Analysis among Patients with ST Elevation Myocardial Infarction.

Background and Objectives: Patients presenting with ST Elevation Myocardial Infarction (STEMI) due to occlusive coronary arteries remain at … features. Then, three machine learning algorithms were employed: LDA, K-nearest neighbor (KNN), and support vector machine (SVM). Results: The performance of these algorithms, measured by the area under the curve (AUC), ranged from 0.73 to 0.77, with accuracy, specificity, and sensitivity ranging between 68% and 73%. Moreover, we extended the analysis by incorporating d …

Predictive model and risk analysis for coronary heart disease in people living with HIV using machine learning.

OBJECTIVE: This study aimed to construct a coronary heart disease (CHD) risk-prediction model in people living with human immunodeficiency virus (PLHIV) with the help of machine learning (ML) per electronic medical records (EMRs). METHODS: Sixty-one medical characteristics (including demography information, laboratory measurements, and complicating disease) readily available from EMRs were retained for clinical analysis. These characteristics further aided the development of prediction models by using seven ML algorithms [light gradient-boosting machine (LightGBM), support vector machine (SVM…