Results 171 to 180 of about 554,772 (269)

PRMT9 Aggravated Dopaminergic Neurodegeneration in Parkinson's Disease Model by Facilitating the Degradation of DUSP26 and Inducing Mitochondrial Dysfunction

open access: yesAdvanced Science, EarlyView.
In the pathological state of PD induced by MPP+, the upregulated PRMT9 in dopaminergic neurons translocates into mitochondrion and interacts with DUSP26 and catalyzes its arginine methylation, leading to the ubiquitin‐proteasomal degradation of DUSP26 mediated by Trim32.
Tengfei Liu   +13 more
wiley   +1 more source

Micro‐Galvanic Coupling Programs the Therapeutic Zinc Ion Window to Reconfigure Immune Cascades for Pro‐Regenerative Bone Healing

open access: yesAdvanced Science, EarlyView.
This study utilized alloy micro‐galvanic coupling design to regulate the release of essential elements, thereby programming immune responses and promoting regeneration. The sacrificial anodic process of Zn‐0.8Mg reduced Zn2+ release compared to the “large cathode‐small anode” coupling of Zn‐0.8Fe.
Chaoyang Sun   +14 more
wiley   +1 more source

Oral Leukemia Cutis and Facial Sweet's Syndrome Associated with Acute Myeloid Leukemia.

open access: yesIndian J Dermatol
Manzone SZ   +5 more
europepmc   +1 more source

A Robust Deep Temporal Causal Discovery Platform for Single‐Cell Gene Regulatory Network Reconstruction

open access: yesAdvanced Intelligent Discovery, EarlyView.
scTIGER2.0 is a deep‐learning framework that infers gene regulatory networks from single‐cell RNA sequencing data. By integrating correlation, pseudotime ordering, deep learning and bootstrap‐based significance testing, it reduces false positives and reveals directional gene interactions.
Nishi Gupta   +3 more
wiley   +1 more source

AI‐Driven Cancer Multi‐Omics: A Review From the Data Pipeline Perspective

open access: yesAdvanced Intelligent Discovery, EarlyView.
The exponential growth of cancer multi‐omics data brings opportunities and challenges for precision oncology. This review systematically examines AI's role in addressing these challenges, covering generative models, integration architectures, Explainable AI for clinical trust, clinical applications, and key directions for clinical translation.
Shilong Liu, Shunxiang Li, Kun Qian
wiley   +1 more source

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