Results 171 to 180 of about 4,646,407 (331)

Activation of USP30 Disrupts Endothelial Cell Function and Aggravates Acute Lung Injury Through Regulating the S‐Adenosylmethionine Cycle

open access: yesAdvanced Science, EarlyView.
USP30 deubiquitinates and stabilizes MAT2A, thereby regulating DNA methylation and miRNA expression, which ultimately leads to disruption of lung endothelial barrier and inflammation. Abstract Microvascular dysfunction is a key contributor to the development of acute inflammatory diseases, characterized by heightened vascular hyperpermeability and ...
Baoyinna Baoyinna   +11 more
wiley   +1 more source

Medical informatics

open access: bronze, 1986
J A Cooper
openalex   +1 more source

AP2M1 Amplification Orchestrates Notch‐Mediated Chemoresistance in Hematopoietic Stem Cells of Acute Myeloid Leukemia Patients

open access: yesAdvanced Science, EarlyView.
The precise regulation of AP2M1 is essential for normal hematopoiesis. Overexpression of AP2M1 negatively affects the clinical outcomes of AML patients. Upregulation of AP2M1 enhances stemness and chemoresistance in HSPC cells of AML. AP2M1 modulates NOTCH1 expression, enhancing the Notch1 signaling pathway.
Hansong Lee   +28 more
wiley   +1 more source

Nanoscopic Mapping of the Extracellular Space in Amyloid Plaque‐rich Cortex

open access: yesAdvanced Science, EarlyView.
The extracellular space and diffusion around amyloid plaques are examined using shadow imaging and single‐particle tracking. Increased diffusivity is found near plaques, extracellular matrix alterations, and plaque core penetrability that varies with amyloid phenotype.
Juan Estaún‐Panzano   +11 more
wiley   +1 more source

Are Medical Informatics and Nursing Informatics Distinct Disciplines?: The 1999 ACMI Debate [PDF]

open access: bronze, 2000
Daniel R. Masys   +4 more
openalex   +1 more source

Uncertainty‐Quantified Primary Particle Size Prediction in Li‐Rich NCM Materials via Machine Learning and Chemistry‐Aware Imputation

open access: yesAdvanced Science, EarlyView.
This study demonstrates a machine learning framework that predicts the primary particle size of Lithium‐rich Nickel‐Cobalt‐Manganese (Li‐rich NCM) materials from synthesis conditions, even with incomplete literature data. By combining chemistry‐aware imputation with uncertainty‐quantified modeling, it identifies sintering temperature and time as ...
Benediktus Madika   +6 more
wiley   +1 more source

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