AIMC Topic: Disease Progression

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Explainable machine learning model predicting neurological deterioration in Wilson's disease via MRI radiomics and clinical features.

Parkinsonism & related disorders
BACKGROUND: This study aims to build a machine learning (ML) model to predict the deterioration of neurological symptoms in Wilson's disease (WD) patients during short-term anti-copper therapy. The model combines brain T1WI MRI radiomics with clinica...

Autophagy crosstalk with the immune microenvironment in chronic myeloid leukemia and serves as a biomarker for diagnosis and progression.

Frontiers in immunology
BACKGROUND: Previous studies have shown that autophagy is closely related to the occurrence, development, and treatment resistance of chronic myeloid leukemia (CML) and has dual roles in promoting cell survival and inducing cell death.

Extrachromosomal circular DNA drives dynamic genome plasticity: emerging roles in disease progression and clinical potential.

Theranostics
Extrachromosomal circular DNA (eccDNA) has emerged as a dynamic and versatile genomic element with key roles in physiological regulation and disease pathology. This review synthesizes current knowledge on eccDNA, covering its classification, biogenes...

Metabolic signature of COVID-19 progression: potential prognostic markers for severity and outcome.

Metabolomics : Official journal of the Metabolomic Society
INTRODUCTION: There are significant challenges remain in accurately categorizing the risk of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) patients.

Early detection of Alzheimer's disease progression stages using hybrid of CNN and transformer encoder models.

Scientific reports
Alzheimer's disease (AD) is a neurodegenerative disorder that affects memory and cognitive functions. Manual diagnosis is prone to human error, often leading to misdiagnosis or delayed detection. MRI techniques help visualize the fine tissues of the ...

PFHxA and PFHxS promote breast cancer progression in 3D culture: MEX3C-associated immune infiltration revealed by bioinformatics and machine learning.

Journal of hazardous materials
Per- and polyfluoroalkyl substances (PFAS) are persistent environmental contaminants with widespread use and bioaccumulative potential. Short-chain PFAS such as perfluorohexanoic acid (PFHxA) and perfluorohexane sulfonate (PFHxS) have been introduced...

Machine Learning-Driven Identification of Hematological and Immunological Biomarkers for Predicting Proliferative Diabetic Retinopathy Progression.

Current eye research
PURPOSE: Proliferative Diabetic Retinopathy (PDR) is a severe complication of diabetes characterized by neovascularization and retinal detachment, leading to significant vision loss. This study investigates the predictive power of hematological and i...

Risk for ocular hypertension progression to early glaucoma: A predictive model and key predictors.

Photodiagnosis and photodynamic therapy
BACKGROUND: Ocular hypertension (OHT) is the most significant risk factor for glaucoma. We aimed to develop a model for predicting OHT progression to early glaucoma and to identify key predictors.

Identifying disease progression biomarkers in metabolic associated steatotic liver disease (MASLD) through weighted gene co-expression network analysis and machine learning.

Journal of translational medicine
BACKGROUND: Metabolic Associated Steatotic Liver Disease (MASLD), encompassing conditions simple liver steatosis (MAFL) and metabolic associated steatohepatitis (MASH), is the most prevalent chronic liver disease. Currently, the management of MASLD i...