AIMC Topic: Biomarkers

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Unveiling the Immune Landscape of Delirium through Single-Cell RNA Sequencing and Machine Learning: Towards Precision Diagnosis and Therapy.

Psychogeriatrics : the official journal of the Japanese Psychogeriatric Society
BACKGROUND: Postoperative delirium (POD) poses significant clinical challenges regarding its diagnosis and treatment. Identifying biomarkers that can predict and diagnose POD is crucial for improving patient outcomes.

Interoception, cardiac health, and heart failure: The potential for artificial intelligence (AI)-driven diagnosis and treatment.

Physiological reports
"I see, I forget, I read aloud, I remember, and when I do read purposefully by writing it, I do not forget it." This phenomenon is known as "interoception" and refers to the sensing and interpretation of internal body signals, allowing the brain to c...

Use of Machine Learning Models to Predict Microaspiration Measured by Tracheal Pepsin A.

American journal of critical care : an official publication, American Association of Critical-Care Nurses
BACKGROUND: Enteral feeding intolerance, a common type of gastrointestinal dysfunction leading to underfeeding, is associated with increased mortality. Tracheal pepsin A, an indicator of microaspiration, was found in 39% of patients within 24 hours o...

Unsupervised Dimensionality Reduction Techniques for the Assessment of ASD Biomarkers.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
Autism Spectrum Disorder (ASD) encompasses a range of developmental disabilities marked by differences in social functioning, cognition, and behavior. Both genetic and environmental factors are known to contribute to ASD, yet the exact etiological fa...

A Comprehensive Review on Deep Learning Techniques in Alzheimer's Disease Diagnosis.

Current topics in medicinal chemistry
Alzheimer's Disease (AD) is a serious neurological illness that causes memory loss gradually by destroying brain cells. This deadly brain illness primarily strikes the elderly, impairing their cognitive and bodily abilities until brain shrinkage occu...

Machine Learning and Augmented Intelligence Enables Prognosis of Type 2 Diabetes Prior to Clinical Manifestation.

Current diabetes reviews
BACKGROUND: The global incidence of type 2 diabetes (T2D) persists at epidemic proportions. Early diagnosis and/or preventive efforts are critical to attenuate the multi-systemic clinical manifestation and consequent healthcare burden. Despite enormo...

Multi-Omics Integration With Machine Learning Identified Early Diabetic Retinopathy, Diabetic Macula Edema and Anti-VEGF Treatment Response.

Translational vision science & technology
PURPOSE: Identify optimal metabolic features and pathways across diabetic retinopathy (DR) stages, develop risk models to differentiate diabetic macular edema (DME), and predict anti-vascular endothelial growth factor (anti-VEGF) therapy response.

Identification of KCNQ1 as a diagnostic biomarker related to endoplasmic reticulum stress for intervertebral disc degeneration based on machine learning and experimental evidence.

Medicine
Intervertebral disc degeneration (IDD) is a primary cause of low back pain and disability. Cellular senescence and apoptosis due to endoplasmic reticulum stress (ERS) are key in IDD pathology. Identifying biomarkers linked to ERS in IDD is crucial fo...

A deep learning feature importance test framework for integrating informative high-dimensional biomarkers to improve disease outcome prediction.

Briefings in bioinformatics
Many human diseases result from a complex interplay of behavioral, clinical, and molecular factors. Integrating low-dimensional behavioral and clinical features with high-dimensional molecular profiles can significantly improve disease outcome predic...

Toward molecular diagnosis of major depressive disorder by plasma peptides using a deep learning approach.

Briefings in bioinformatics
Major depressive disorder (MDD) is a severe psychiatric disorder that currently lacks any objective diagnostic markers. Here, we develop a deep learning approach to discover the mass spectrometric features that can discriminate MDD patients from heal...