AIMC Topic: Biomarkers

Clear Filters Showing 451 to 460 of 2219 articles

Machine learning-based integration reveals reliable biomarkers and potential mechanisms of NASH progression to fibrosis.

Scientific reports
Non-alcoholic fatty liver disease (NAFLD) affects about 25% of adults worldwide. Its advanced form, non-alcoholic steatohepatitis (NASH), is a major cause of liver fibrosis, but there are no non-invasive tests for diagnosing or preventing it. In our ...

Leveraging machine learning in precision medicine to unveil organochlorine pesticides as predictive biomarkers for thyroid dysfunction.

Scientific reports
Exposure to organochlorine pesticides (OCPs) poses significant health risks, including cancer, endocrine dysregulation, neurological disorders, and reproductive disruption. This study investigates the association between OCP exposure and thyroid dist...

Oxidative Stress Markers and Prediction of Severity With a Machine Learning Approach in Hospitalized Patients With COVID-19 and Severe Lung Disease: Observational, Retrospective, Single-Center Feasibility Study.

JMIR formative research
BACKGROUND: Serious pulmonary pathologies of infectious, viral, or bacterial origin are accompanied by inflammation and an increase in oxidative stress (OS). In these situations, biological measurements of OS are technically difficult to obtain, and ...

The role of machine learning in predicting titanium dioxide nanoparticles induced pulmonary pathology using transcriptomic biomarkers.

Journal of hazardous materials
This study explores the application of machine learning (ML) in identifying transcriptomic changes associated with pulmonary pathologies induced by titanium dioxide nanoparticles (TiO-NPs). Such an approach significantly contributes to understanding ...

Predicting response to non-invasive brain stimulation in post-stroke upper extremity motor impairment: the importance of neurophysiological and clinical biomarkers.

Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology
BACKGROUND: Non-invasive brain stimulation (NIBS) is a promising approach to enhance upper extremity motor impairment (UEMI) recovery in post-stroke individuals. However, variability in treatment response poses a significant challenge. Identifying ne...

Identification of novel metabolism-related biomarkers of Kawasaki disease by integrating single-cell RNA sequencing analysis and machine learning algorithms.

Frontiers in immunology
BACKGROUND: The bile acid metabolism (BAM) and fatty acid metabolism (FAM) have been implicated in Kawasaki disease (KD), but their precise mechanisms remain unclear. Identifying signature cells and genes related to BAM and FAM could offer a deeper u...

Voice biomarkers as prognostic indicators for Parkinson's disease using machine learning techniques.

Scientific reports
Many people suffer from Parkinson's disease globally, a complicated neurological condition caused by the deficiency of dopamine, an organic chemical responsible for regulating movement in individuals. Patients with Parkinson face muscle stiffness or ...

Machine learning integration of multimodal data identifies key features of circulating NT-proBNP in people without cardiovascular diseases.

Scientific reports
N-Terminal Pro-Brain Natriuretic Peptide (NT-proBNP) is important for diagnosing and predicting heart failure or many other diseases. However, few studies have comprehensively assessed the factors correlated with NT-proBNP levels in people with cardi...

Single-Component Double-Emissive Ratiometric Probe: Toward Machine Learning Driven Detection and Discrimination of Neurological Biomarkers.

Analytical chemistry
This study presents an attractive single-component ratiometric fluorescent sensor that utilizes the oxidation of BSA-protected Au nanoclusters (BSA-Au NCs) by -Bromosuccinimide (NBS) to detect catecholamine neurotransmitters and their metabolites, wh...