AIMC Topic: Biomarkers

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Artificial intelligence for predicting interstitial fibrosis and tubular atrophy using diagnostic ultrasound imaging and biomarkers.

BMJ health & care informatics
BACKGROUND: Chronic kidney disease (CKD) is a global health concern characterised by irreversible renal damage that is often assessed using invasive renal biopsy. Accurate evaluation of interstitial fibrosis and tubular atrophy (IFTA) is crucial for ...

Integrating biomarker clustering for improved diagnosis of interstitial cystitis/bladder pain syndrome: a review.

International urology and nephrology
Interstitial cystitis/bladder pain syndrome (IC/BPS) is a debilitating chronic condition characterized by pelvic pain and urinary disturbances, making its diagnosis challenging due to overlapping symptoms with various lower urinary tract disorders. C...

Defining lipedema's molecular hallmarks by multi-omics approach for disease prediction in women.

Metabolism: clinical and experimental
Lipedema is a chronic disease in females characterized by pathologic subcutaneous adipose tissue expansion and hitherto remains without druggable targets. In this observational study, we investigated the molecular hallmarks of lipedema using an unbia...

Surrogate markers of insulin resistance and coronary artery disease in type 2 diabetes: U-shaped TyG association and insights from machine learning integration.

Lipids in health and disease
BACKGROUND: Surrogate insulin resistance (IR) indices are simpler and more practical alternatives to insulin-based IR indicators for clinical use. This study explored the association between surrogate IR indices, including triglyceride-glucose index ...

TEDML: a new machine learning (ML) approach for predicting thyroid eye disease and identifying key biomarkers.

The Journal of endocrinology
Thyroid eye disease (TED) features immune infiltration and metabolic dysregulation. Understanding these processes and identifying potential biomarkers are crucial for improving diagnosis and treatment. To this end, immune cell infiltration was analyz...

Circulating lncRNAs as biomarkers for severe dengue using a machine learning approach.

The Journal of infection
OBJECTIVES: Dengue virus (DENV) infection is a significant global health concern, causing severe morbidity and mortality. While many cases present as a mild febrile illness, some progress to life-threatening severe dengue (SD). Early intervention is ...

Biological age prediction using a DNN model based on pathways of steroidogenesis.

Science advances
Aging involves the progressive accumulation of cellular damage, leading to systemic decline and age-related diseases. Despite advances in medicine, accurately predicting biological age (BA) remains challenging due to the complexity of aging processes...

Multi-omics integration and machine learning identify and validate neutrophil extracellular trap-associated gene signatures in chronic rhinosinusitis with nasal polyps.

Clinical immunology (Orlando, Fla.)
This study aimed to explore the molecular characteristics of neutrophil extracellular traps (NETs) in chronic rhinosinusitis with nasal polyps (CRSwNP). Differentially expressed gene analysis, weighted gene co-expression network analysis, and machine...

Machine learning based model for the early detection of Gestational Diabetes Mellitus.

BMC medical informatics and decision making
BACKGROUND: Gestational Diabetes Mellitus (GDM) is one of the most common medical complications during pregnancy. In the Gulf region, the prevalence of GDM is higher than in other parts of the world. Thus, there is a need for the early detection of G...

Biological age prediction and NAFLD risk assessment: a machine learning model based on a multicenter population in Nanchang, Jiangxi, China.

BMC gastroenterology
BACKGROUND: The objective was to develop a biological age prediction model (NC-BA) for the Chinese population to enrich the relevant studies in this population. And to investigate the association between accelerated age and NAFLD.