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Identification of Ferroptosis-Related Hub Genes as Diagnosis Biomarkers and Therapeutic Monitoring for Major Depressive Disorder Diagnosis.

Major Depressive Disorder (MDD) is linked to increased neurodegenerative risk. Emerging evidence implicates ferroptosis in neuropsychiatric disorders, prompting investigation of its role in MDD through key gene identification. Three microarray datasets from the GEO database were analysed. Weighted gene co-expression network analysis (WGCNA) identified MDD-related module genes (MRGs) while ferroptosis-related genes (FRGs) were extracted from the FerrDb database. Overlapping genes between MRGs and FRGs were prioritised for mechanistic exploration. Functional enrichment (GO/KEGG) and protein-prot

Predicting emergency department visits for non-traumatic dental-related conditions among Medicaid beneficiaries.

Patients who use the emergency department (ED) for non-traumatic dental-related conditions are likely to return for multiple visits, accounting for a disproportionate share of healthcare cost. Reliance on the ED for receiving dental care continues to rise, particularly among Medicaid beneficiaries. We sought to predict future "ED users" for non-traumatic dental-related conditions using machine learning algorithms and identify associated risk factors to help Medicaid outreach programs better target populations in most need of improvement in access to dental care. Among 6.4 million Medicaid bene

Optimization of CO2 absorption into MDEA-PZ-sulfolane hybrid solution using machine learning algorithms and RSM.

This study presents the modeling and simulation of carbon dioxide (CO₂) absorption in hybrid amine solutions using machine learning algorithms and response surface methodology (RSM). The process was governed by adjustable input parameters, including pressure (0.50-7.76 bar), temperature (292.8-343.1 K), time (0-1680 s), and solvent concentrations (1-5 wt% for N-methyl diethanolamine (MDEA), 1-5 wt% for sulfolane, and 1-5 wt% for piperazine (PZ)). The primary objective was to leverage the synergistic effects of chemical and physical solvents for enhanced CO₂ absorption. Seven machine learning m

Integration of Metabolomics, Lipidomics, and Machine Learning for Developing a Biomarker Panel to Distinguish the Severity of Metabolic-Associated Fatty Liver Disease.

Metabolic-associated fatty liver disease (MAFLD), a global health challenge linked to metabolic syndrome, requires accurate severity stratification for clinical management. Current invasive diagnostic methods limit practical implementation. This study integrated multi-omics and machine learning to establish a non-invasive biomarker panel for the early stage of MAFLD assessment. Transcriptomic analysis of GEO datasets via weighted gene co-expression network (WGCNA) and differential expression revealed critical pathways associated with MAFLD progression. Subsequently, LC-MS/MS-based widely targe

Prediction of diabetic retinopathy using machine learning and its association with dementia risk in older adults with type 2 diabetes mellitus.

AIMS: Diabetic Retinopathy (DR), a common microvascular complication of diabetes, has been associated with an increased risk of dementia. This study aimed to develop Machine Learning (ML) models to predict DR occurrence and evaluate its potential as a prognostic biomarker for dementia. METHODS: We included 27,929 patients aged ≥ 50 years newly diagnosed with type 2 diabetes mellitus without prior dementia or eye disease. Prediction models for DR within one year were developed using three ML algorithms: extreme gradient boosting (XGBoost), random forest, and least absolute shrinkage and select

Artificial intelligence in muscle-invasive bladder cancer: opportunities, challenges, and clinical impact.

PURPOSE OF REVIEW: Muscle-invasive bladder cancer (MIBC) represents an aggressive malignancy with significant morbidity and mortality. Recent advances in artificial intelligence (AI) offer promising opportunities to enhance patient care across the entire MIBC management spectrum. This comprehensive review examines the current state and future potential of AI applications in MIBC, from diagnosis through treatment to response assessment. RECENT FINDINGS: In the diagnostic domain, AI systems demonstrate superior accuracy in cystoscopic cancer detection and staging, with deep learning models achi

Machine Learning Models for Differentiating Causes of Dyspnea in Emergency Department Patients: A Retrospective Study.

BACKGROUND: Dyspnea is a frequent symptom in emergency departments (ED) with multifactorial causes, including cardiac and pulmonary conditions. Accurate and timely diagnosis is crucial to guide appropriate management and improve patient outcomes. Machine learning (ML) may aid this process. METHODS: This retrospective study analyzed 787 adult patients presenting with dyspnea to the ED at Kantonsspital Baselland (Switzerland) in 2022. Clinical, laboratory, diagnostic data and final diagnoses were collected. ML models including decision trees, random forest and boosted decision trees were traine

VITALdb: to select the best viroinformatics tools for a desired virus or application.

The recent pandemics of viral diseases, COVID-19/mpox (humans) and lumpy skin disease (cattle), have kept us glued to viral research. These pandemics along with the recent human metapneumovirus outbreak have exposed the urgency for early diagnosis of viral infections, vaccine development, and discovery of novel antiviral drugs and therapeutics. To support this, there is an armamentarium of virus-specific computational tools that are currently available. VITALdb (VIroinformatics Tools and ALgorithms database) is a resource of ~360 viroinformatics tools encompassing all major viruses (SARS-CoV-2

Fully Automated Deep Learning Enabled Miniature Mass Spectrometry System for Psychoactive Therapeutic Drug Monitoring.

Advancing precision medicine requires efficient small molecule biomarker detection in biofluids, yet existing methods encounter challenges in complexity, portability, and throughput. This study presents an integrated miniature blood processing and mass spectrometry (MS) analysis system, which incorporates automated magnetic solid-phase extraction, self-aspiration sampling miniature mass spectrometer, and deep learning algorithms for automated quantitative analysis. It achieves full automation from sample preparation to detection, demonstrating the capability to analyze serum psychoactive drugs

Applications of artificial intelligence in diagnosis and treatment planning of orthodontics: a narrative review.

Artificial intelligence (AI) has recently gained significant interest in orthodontics due to its ability to enhance diagnostic accuracy, guide treatment planning, and improve therapeutic outcomes. This review aimed to explore the relevance and applications of AI across various aspects of orthodontics. A comprehensive literature search was performed from January 2010 to 1 March 2025 in databases including PubMed, EMBASE, Web of Science, Scopus, and Cochrane. Letters to the editor, case reports, systematic reviews, and animal studies were excluded. Artificial intelligence models, especially thos

Establishment of a Stacking Machine Learning Model Predicting Cardiac Phenotype in Ectopia Lentis Patients Based on Genotype and Ocular Phenotype.

Purpose: To establish a stacking machine learning model for cardiac phenotype prediction in ectopia lentis (EL) patients on the basis of their genotype and ocular phenotype. Methods: We enrolled 151 patients with congenital EL and divided them into three groups according to their echocardiograph (normal group, reflux group, and organic lesion group). All the subjects underwent genetic screening and an up-to-1-year ophthalmic and cardiac follow-up. Patients were randomly divided into training set and validation set in a 3:1 ratio. Six statistically significant parameters based on one-way ANOVA

Advancing Telerehabilitation with AI: Predicting Balance Scores from Early Sessions.

Telerehabilitation systems have transformed the management of balance and gait impairments, particularly in older adults with chronic low back pain. This study integrates multimodal balance exercises, cognitive retraining, and augmented reality within the HOLOBALANCE system. Using data from an eight-week rehabilitation program, predictive models based on machine learning (XGBoost and AdaBoost) were developed to estimate final balance outcomes using as few as 3 to 6 sessions. The results indicate that XGBoost performs optimally for shorter prediction horizons (i.e., 3-4 sessions) with RMSE valu

Regulation of reactive oxygen species and the role of mitochondrial apoptotic-related genes in rheumatoid arthritis.

Previous research suggests mitochondrial apoptosis alleviates rheumatoid arthritis (RA), but the role of mitochondrial apoptosis-related genes (MARGs) is unclear. Urgent exploration of RA-related mitochondrial apoptosis biomarkers is needed. Gene Expression Ontology (GEO)-derived RA datasets were used to identify differentially expressed genes (DEGs) compared to normal controls, intersected with MARGs to obtain differentially expressed mitochondrial apoptosis-related genes (DE-MARGs). Three ML algorithms screened diagnostic biomarkers. A nomogram was built and validated by receiver operating c

Deciphering the role of angiogenesis in glioblastoma: Integrative insights from transcriptomic profiling, single-cell sequencing and interpretable machine learning approaches.

Glioblastoma (GBM), a highly vascularized and aggressive primary brain tumor, presents unresolved questions regarding the functional significance of angiogenesis-related genes (ARGs) in disease progression. To systematically investigate this, we employed univariate Cox regression followed by LASSO regression analysis to identify prognosis-associated ARGs. These candidates were subsequently evaluated using six machine learning-derived survival prediction algorithms, with model interpretability achieved through SHapley Additive exPlanations (SHAP) analysis. Our studies revealed that six hub ARGs…

Comparative assessment of physiochemical metrics for the clustering of adaptive immune receptor repertoires.

A continued challenge in the analysis of adaptive immune receptor repertoires (AIRRs) is the prediction of antigen reactivity from primary sequence data. Many algorithms infer antigen-specific responses by measuring sequence similarity between receptors. Similarity is often scored using tools for protein alignment such as the BLOSUM matrices. However, these metrics were designed to identify homology in genomic proteins, not VDJ-recombined immune receptors. Comparison of these metrics against other approaches is underexplored. We used matrix factorization to make physiochemical-based alternativ

A new integrative learning framework for integrating multiple secondary outcomes into primary outcome analysis: a case study on liver health.

In the era of big data, secondary outcomes have become increasingly important alongside primary outcomes. These secondary outcomes, which can be derived from traditional endpoints in clinical trials, compound measures, or risk prediction scores, hold the potential to enhance the analysis of primary outcomes. Our method is motivated by the challenge of utilizing multiple secondary outcomes, such as blood biochemistry markers and urine assays, to improve the analysis of the primary outcome related to liver health. Current integration methods often fall short, as they impose strong model assumpti

Identification of T-cell epitopes of the intracellular parasite Babesia bovis by immunopeptidomic analysis of BoLA-II presented peptides.

Immunity to the bovine parasite Babesia bovis requires both innate and adaptive mechanisms that include macrophages, CD4+ T cells and neutralizing antibodies. Therefore, the development of new vaccines that replace the use of attenuated strains should include antigens and delivery systems targeting both arms of the immune response. Bioinformatic pipelines for the prediction of B-cell epitopes have been widely used by many research groups. However, despite intensive efforts, only a limited number of T-cell epitopes have been identified so far for B. bovis. Here, we report for the first time the

Глубинные пространственные омики MLADI-MS, управляемые квантовой каскадной лазерной микроскопией среднего инфракрасного изображения.

В пространственной "omics " высокоуверенные молекулярные идентификации необходимы для исследования сложной биологии и для пространственной биомаркировки. Однако нынешняя масс-спектрометрическая съемка (MSI) на основе пространственной "омики" должна скомпрометировать скорость получения данных и глубину биохимического профилирования. Здесь мы введём быструю, без этикеток квантовую каскадную лазерную микроскопию среднего инфракрасного изображения (QCL-MIR), чтобы направить MSI в высокоинтересные области тканей, такие маленькие, как гломерули почки, культурные многоклеточные сфероидные я

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In spatial'omics, highly confident molecular identifications are indispensable for the investigation of complex biology and for spatial biom … mice used as ground truth concept and provided chemical rationales for improvements to ion mobility prediction algorithms. Using this workflow, we characterized sclerotic spinal cord lesions in mice with experimental autoimmune encephalomyelitis (EAE), a model of multiple sclerosis, and identified upregulation of inflammation-related ceramide-1-phosphate and ceramide p …

Integrated Ultrasound-Enrichment and Machine Learning in Colorimetric Lateral Flow Assay for Accurate and Sensitive Clinical Alzheimer's Biomarker Diagnosis.

The colloidal gold nanoparticle (AuNP)-based colorimetric lateral flow assay (LFA) is one of the most promising analytical tools for point-of-care disease diagnosis. However, the low sensitivity and insufficient accuracy still limit its clinical application. In this work, a machine learning (ML)-optimized colorimetric LFA with ultrasound enrichment is developed to achieve the sensitive and accurate detection of tau proteins for early screening of Alzheimer's disease (AD). The LFA device is integrated with a portable ultrasonic actuator to rapidly enrich microparticles using ultrasound, which i

A novel approach for ASD recognition based on graph attention networks.

Early detection and diagnosis of Autism Spectrum Disorder (ASD) can significantly improve the quality of life for affected individuals. Identifying ASD based on brain functional connectivity (FC) poses a challenge due to the high heterogeneity of subjects' fMRI data in different sites. Meanwhile, deep learning algorithms show efficacy in ASD identification but lack interpretability. In this paper, a novel approach for ASD recognition is proposed based on graph attention networks. Specifically, we treat the region of interest (ROI) of the subjects as node, conduct wavelet decomposition of the B