Results 241 to 250 of about 1,557,979 (348)

Cuproptosis Signature Would Reveal the Acute‐Remitting Pattern in Patients with Neuromyelitis Optica Spectrum Disorder

open access: yesAdvanced Science, EarlyView.
Neuromyelitis optica spectrum disorder (NMOSD) has distinct acute‐remitting courses but lacks biomarkers for predicting relapse. Recent studies found cuproptosis‐related genes affect its course, and a model built accordingly can predict relapse to aid intervention.
Peidong Liu   +17 more
wiley   +1 more source

A Compartmentalized Joint‐on‐chip (JoC) Model to Unravel the Contribution of Cartilage and Synovium to Osteoarthritis Pathogenesis

open access: yesAdvanced Science, EarlyView.
A compartmentalized joint‐on‐chip (JoC) platform is here developed, modelling the interactions between cartilage and synovium in osteoarthritis (OA). Using independent culture and mechanical/biochemical stimulation, the JoC revealed how inflammation and mechanical damage drive mutual tissue changes.
Cecilia Palma   +6 more
wiley   +1 more source

Evaluation of 5-methylcytosine and 5-hydroxymethylcytosine levels in juvenile idiopathic arthritis and its types. [PDF]

open access: yesRev Assoc Med Bras (1992)
Dogantan S   +5 more
europepmc   +1 more source

Juvenile Idiopathic Arthritis

open access: yesPaediatric drugs, 2011
M. Beresford
semanticscholar   +1 more source

Cationic Peptoids for Systemic In Vivo Cartilage‐Targeting

open access: yesAdvanced Science, EarlyView.
Systemically‐dosed Cy5‐labeled cationic peptoid probe (NlysO)7 (yellow) binds to the cartilage's glycosaminoglycan content in vivo across the entire body of a neonatal mouse imaged by light sheet fluorescence microscopy, revealing fluorescence uptake in generic cartilage as well as the developing and ossifying bones.
Chaonan Zhang   +6 more
wiley   +1 more source

CellPhenoX: An Explainable Machine Learning Method for Identifying Cell Phenotypes To Predict Clinical Outcomes from Single‐Cell Multi‐Omics

open access: yesAdvanced Science, EarlyView.
CellPhenoX is an explainable machine learning framework that identifies cell‐specific phenotypes and interaction effects from single‐cell omics data. By leveraging interpretable models, it enables robust discovery of cell‐level phenotypes that contribute to clinical outcomes.
Jade Young   +4 more
wiley   +1 more source

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