AIMC Topic: Biomarkers

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Revolutionizing sepsis diagnosis using machine learning and deep learning models: a systematic literature review.

BMC infectious diseases
Sepsis is a life-threatening condition resulting from a dysregulated immune response to infection, often leading to organ failure and death. Early detection is vital, as delays significantly worsen outcomes. In recent years, the integration of artifi...

Multimodal integration of plasma biomarkers, MRI, and genetic risk to predict cerebral amyloid burden in Alzheimer's disease.

NeuroImage
Alzheimer's disease (AD), the most prevalent neurodegenerative disorder, is marked by the accumulation of amyloid-β (Aβ) plaques. Although cerebral Aβ positron emission tomography (Aβ-PET) remains the gold standard for assessing cerebral Aβ burden, i...

Plasma multi-omics and machine learning reveal predictive biomarkers for type 2 diabetes and retinopathy in Qatar biobank cohort.

Journal of translational medicine
BACKGROUND: Type 2 diabetes (T2D) and its vascular complications, including diabetic retinopathy (DR), are escalating in prevalence globally, with disproportionately high prevalence in Middle Eastern populations, where genetic predispositions and lif...

Decoding dynamic lipase trajectory patterns and in-hospital mortality in acute pancreatitis: insights from machine learning in intensive care units.

European journal of medical research
BACKGROUND: Serum lipase levels are crucial biomarkers in acute pancreatitis (AP), yet their dynamic patterns and prognostic implications remain incompletely understood. This study aimed to identify distinct lipase trajectory phenotypes and evaluate ...

Mass spectrometry combined with machine learning identifies novel protein signatures as demonstrated with multisystem inflammatory syndrome in children.

Scientific reports
Rapid and accurate diagnosis of emerging inflammatory illnesses is challenging due to overlapping clinical features with existing conditions. We demonstrate an approach that integrates proteomic analysis with machine learning to identify diagnostic p...

Defining endotypes of bronchopulmonary dysplasia in preterm infants to improve precision-based therapies.

JCI insight
Bronchopulmonary dysplasia (BPD) remains a debilitating disease in premature infants. The chronic pathogenesis of BPD with complex prenatal and postnatal programming challenges attempts at precisely defining or treating disease. While existing BPD de...

Evaluation of inflammatory markers in survival analysis of patients undergoing radical cystectomy using machine learning.

World journal of urology
BACKGROUND: We aimed to create a Machine learning (ML) model using patient demographic, clinical and pathological data for prediction of overall survival in patients treated with radical cystectomy (RC). Secondly, we evaluated whether inflammatory ma...

Unveiling manganese metabolism-related biomarkers in Alzheimer's disease: Insights into diagnosis and therapeutic targets.

Clinica chimica acta; international journal of clinical chemistry
BACKGROUND: Alzheimer's disease (AD), a neurodegenerative disorder with multifactorial etiologies, has been closely associated with disturbances in manganese metabolism. However, its specific biomarkers remain insufficiently characterized. This study...

Lactate/albumin ratio predicts mortality in critically ill COVID-19 patients: a retrospective machine learning study.

Scientific reports
Severe COVID-19 often progresses to critical illness, requiring accurate prognostic biomarkers. Lactate-to-albumin ratio (LAR) has been proposed as a novel indicator to estimate the likelihood of death. Using data from the MIMIC database, this retros...