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Biomarkers

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Metabolic reprogramming and macrophage expansion define ACPA-negative rheumatoid arthritis: insights from single-cell RNA sequencing.

Frontiers in immunology
BACKGROUND: Anti-citrullinated peptide antibodies (ACPA)-negative (ACPA-) rheumatoid arthritis (RA) presents significant diagnostic and therapeutic challenges due to the absence of specific biomarkers, underscoring the need to elucidate its distincti...

Selective diagnostics of Amyotrophic Lateral Sclerosis, Alzheimer's and Parkinson's Diseases with machine learning and miRNA.

Metabolic brain disease
The diagnosis of neurological diseases can be expensive, invasive, and inaccurate, as it is often difficult to distinguish between different types of diseases with similar motor symptoms. However, the dysregulation of miRNAs can be used to create a r...

Fibrosis and inflammatory activity diagnosis of chronic hepatitis C based on extreme learning machine.

Scientific reports
The traditional diagnosis of chronic hepatitis C usually relies on liver biopsy. Diagnosing chronic hepatitis C based on serum indices provides a non-invasive way to determine the stage of chronic hepatitis C without liver biopsy. In this paper, we p...

Utilizing integrated bioinformatics and machine learning approaches to elucidate biomarkers linking sepsis to purine metabolism-associated genes.

Scientific reports
Sepsis, characterized as a systemic inflammatory response triggered by pathogen invasion, represents a continuum that may progress from mild systemic infection to severe sepsis, potentially culminating in septic shock and multiple organ dysfunction s...

Artificial intelligence-based cardiovascular/stroke risk stratification in women affected by autoimmune disorders: a narrative survey.

Rheumatology international
Women are disproportionately affected by chronic autoimmune diseases (AD) like systemic lupus erythematosus (SLE), scleroderma, rheumatoid arthritis (RA), and Sjögren's syndrome. Traditional evaluations often underestimate the associated cardiovascul...

Identifying proteomic prognostic markers for Alzheimer's disease with survival machine learning: The Framingham Heart Study.

The journal of prevention of Alzheimer's disease
BACKGROUND: Protein abundance levels, sensitive to both physiological changes and external interventions, are useful for assessing the Alzheimer's disease (AD) risk and treatment efficacy. However, identifying proteomic prognostic markers for AD is c...

Non-Invasive Diagnosis of Moyamoya Disease Using Serum Metabolic Fingerprints and Machine Learning.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)
Moyamoya disease (MMD) is a progressive cerebrovascular disorder that increases the risk of intracranial ischemia and hemorrhage. Timely diagnosis and intervention can significantly reduce the risk of new-onset stroke in patients with MMD. However, t...

Identification of sepsis-associated encephalopathy biomarkers through machine learning and bioinformatics approaches.

Scientific reports
Sepsis-associated encephalopathy (SAE) is common in septic patients, characterized by acute and long-term cognitive impairment, and is associated with higher mortality. This study aimed to identify SAE-related biomarkers and evaluate their diagnostic...

Novel anoikis-related diagnostic biomarkers for aortic dissection based on machine learning.

Scientific reports
Aortic dissection (AD) is one of the most dangerous diseases of the cardiovascular system, which is characterized by acute onset and poor prognosis, while the pathogenesis of AD is still unclear and may affect or even delay the diagnosis of AD. Ancho...

Application of deep learning models on single-cell RNA sequencing analysis uncovers novel markers of double negative T cells.

Scientific reports
Double negative T (DNT) cells are a unique subset of CD3 + TCRαβ + T lymphocytes that lack CD4, CD8, or NK1.1 expression and constitute 3-5% of the total T cell population in C57BL/6 mice. They have increasingly gained recognition for their novel rol...