AIMC Topic: Biomarkers

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Predictive value of ATN biomarker profiles in estimating disease progression in Alzheimer's disease dementia.

Alzheimer's & dementia : the journal of the Alzheimer's Association
We aimed to evaluate the value of ATN biomarker classification system (amyloid beta [A], pathologic tau [T], and neurodegeneration [N]) for predicting conversion from mild cognitive impairment (MCI) to dementia. In a sample of people with MCI (n = 41...

Optical coherence tomography (OCT) angiolytics: a review of OCT angiography quantitative biomarkers.

Survey of ophthalmology
Optical coherence tomography angiography (OCTA) provides a non-invasive method to obtain angiography of the chorioretinal vasculature leading to its recent widespread adoption. With a growing number of studies exploring the use of OCTA, various bioma...

Artificial-intelligence-driven discovery of prognostic biomarker for sarcopenia.

Journal of cachexia, sarcopenia and muscle
BACKGROUND: Sarcopenia is defined as muscle wasting, characterized by a progressive loss of muscle mass and function due to ageing. Diagnosis of sarcopenia typically involves both muscle imaging and the physical performance of people exhibiting signs...

Random survival forests for dynamic predictions of a time-to-event outcome using a longitudinal biomarker.

BMC medical research methodology
BACKGROUND: Risk prediction models for time-to-event outcomes play a vital role in personalized decision-making. A patient's biomarker values, such as medical lab results, are often measured over time but traditional prediction models ignore their lo...

Deep learning evaluation of biomarkers from echocardiogram videos.

EBioMedicine
BACKGROUND: Laboratory testing is routinely used to assay blood biomarkers to provide information on physiologic state beyond what clinicians can evaluate from interpreting medical imaging. We hypothesized that deep learning interpretation of echocar...

Predicting cognitive impairment in outpatients with epilepsy using machine learning techniques.

Scientific reports
Many studies report predictions for cognitive function but there are few predictions in epileptic patients; therefore, we established a workflow to efficiently predict outcomes of both the Mini-Mental State Examination (MMSE) and Montreal Cognitive A...

Digital pathology and artificial intelligence in translational medicine and clinical practice.

Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc
Traditional pathology approaches have played an integral role in the delivery of diagnosis, semi-quantitative or qualitative assessment of protein expression, and classification of disease. Technological advances and the increased focus on precision ...

Integrating Statistical and Machine-Learning Approach for Meta-Analysis of Bisphenol A-Exposure Datasets Reveals Effects on Mouse Gene Expression within Pathways of Apoptosis and Cell Survival.

International journal of molecular sciences
Bisphenols are important environmental pollutants that are extensively studied due to different detrimental effects, while the molecular mechanisms behind these effects are less well understood. Like other environmental pollutants, bisphenols are bei...

Prediction of post-stroke cognitive impairment using brain FDG PET: deep learning-based approach.

European journal of nuclear medicine and molecular imaging
PURPOSE: Post-stroke cognitive impairment can affect up to one third of stroke survivors. Since cognitive function greatly contributes to patients' quality of life, an objective quantitative biomarker for early prediction of dementia after stroke is ...

Non-invasive diagnostic tool for Parkinson's disease by sebum RNA profile with machine learning.

Scientific reports
Parkinson's disease (PD) is a progressive neurodegenerative disease presenting with motor and non-motor symptoms, including skin disorders (seborrheic dermatitis, bullous pemphigoid, and rosacea), skin pathological changes (decreased nerve endings an...