New York University

A New York University team released Neuro-JEPA, a multimodal foundation model that encodes T1w, T2w and FLAIR brain MRI contrasts in one architecture, pretrained on 1,551,862 scans from 428,647 studies. It outperformed BrainIAC, VoCo and NeuroVFM overall across 47 downstream tasks in three health systems and 12 public datasets while using less than one-third of NeuroVFM's pretraining data, and reached up to 15% higher few-shot AUROC on label-scarce tasks such as gliosis and multiple sclerosis, with roughly 15% on the NACC Alzheimer's disease task.

A New York University team released Neuro-JEPA, a multimodal foundation model that encodes T1w, T2w and FLAIR brain MRI contrasts in one architecture, pretrained on 1,551,862 scans from 428,647 studies. It outperformed BrainIAC, VoCo and NeuroVFM overall across 47 downstream tasks in three health systems and 12 public datasets while using less than one-third of NeuroVFM's pretraining data, and reached up to 15% higher few-shot AUROC on label-scarce tasks such as gliosis and multiple sclerosis, with roughly 15% on the NACC Alzheimer's disease task.

Using a three-round modified Delphi method, a 45-member panel of clinicians and researchers in diabetes, obesity and/or eating disorders plus people with lived experience reached an average 91.7% consensus on recommendations for GLP-1 receptor agonist use in eating disorder contexts, covering pre-treatment screening, risk-stratified monitoring, standardized educational materials and concurrent evidence-based psychotherapy, stating that these agents should generally be avoided in active eating disorders, particularly anorexia nervosa, atypical anorexia nervosa and bulimia nervosa with marked dietary restraint and/or significant weight suppression, and setting out research priorities in epidemiology, regulation and binge eating disorder trials.

Using a three-round modified Delphi method, a 45-member panel of clinicians and researchers in diabetes, obesity and/or eating disorders plus people with lived experience reached an average 91.7% consensus on recommendations for GLP-1 receptor agonist use in eating disorder contexts, covering pre-treatment screening, risk-stratified monitoring, standardized educational materials and concurrent evidence-based psychotherapy, stating that these agents should generally be avoided in active eating disorders, particularly anorexia nervosa, atypical anorexia nervosa and bulimia nervosa with marked dietary restraint and/or significant weight suppression, and setting out research priorities in epidemiology, regulation and binge eating disorder trials.

This review proposes reframing multidrug resistance (MDR) from conventional single-gene mechanism models toward systemic adaptive resistance as a complex and evolving biological phenomenon, and systematically reviews classical resistance mechanisms (drug uptake/efflux, compensatory pathway activation, apoptosis evasion), non-genetic resistance mechanisms (drug-tolerant persisters, DTPs; epithelial-mesenchymal transition, EMT; cancer stem cell, CSC, plasticity and related factors), how interactions among these mechanisms contribute to tumor persistence and MDR evolution, and translational barriers together with emerging pharmacological strategies including longitudinal molecular monitoring and artificial intelligence (AI)-assisted drug resistance prediction.

This review proposes reframing multidrug resistance (MDR) from conventional single-gene mechanism models toward systemic adaptive resistance as a complex and evolving biological phenomenon, and systematically reviews classical resistance mechanisms (drug uptake/efflux, compensatory pathway activation, apoptosis evasion), non-genetic resistance mechanisms (drug-tolerant persisters, DTPs; epithelial-mesenchymal transition, EMT; cancer stem cell, CSC, plasticity and related factors), how interactions among these mechanisms contribute to tumor persistence and MDR evolution, and translational barriers together with emerging pharmacological strategies including longitudinal molecular monitoring and artificial intelligence (AI)-assisted drug resistance prediction.