Results 281 to 290 of about 23,904,458 (355)

YAP1::TFE3 mediates endothelial‐to‐mesenchymal plasticity in epithelioid hemangioendothelioma

open access: yesMolecular Oncology, EarlyView.
The YAP1::TFE3 fusion protein drives endothelial‐to‐mesenchymal transition (EndMT) plasticity, resulting in the loss of endothelial characteristics and gain of mesenchymal‐like properties, including resistance to anoikis, increased migratory capacity, and loss of contact growth inhibition in endothelial cells.
Ant Murphy   +9 more
wiley   +1 more source

Best Practices for Data Modernization Across the United States Public Health System: Scoping Review.

open access: yesJ Med Internet Res
Zeba Z   +10 more
europepmc   +1 more source

A Typology to Describe Community Context and Population Health Activities in Local Public Health Delivery Systems. [PDF]

open access: yesJ Public Health Manag Pract
Dankwa L   +5 more
europepmc   +1 more source

Emerging role of ARHGAP29 in melanoma cell phenotype switching

open access: yesMolecular Oncology, EarlyView.
This study gives first insights into the role of ARHGAP29 in malignant melanoma. ARHGAP29 was revealed to be connected to tumor cell plasticity, promoting a mesenchymal‐like, invasive phenotype and driving tumor progression. Further, it modulates cell spreading by influencing RhoA/ROCK signaling and affects SMAD2 activity. Rho GTPase‐activating protein
Beatrice Charlotte Tröster   +3 more
wiley   +1 more source

Machine Learning Applications in Population and Public Health: Guidelines for Development, Testing, and Implementation.

open access: yesJMIR Public Health Surveill
Pinto AD   +18 more
europepmc   +1 more source

Tumor‐agnostic detection of circulating tumor DNA in patients with advanced pancreatic cancer using targeted DNA methylation sequencing and cell‐free DNA fragmentomics

open access: yesMolecular Oncology, EarlyView.
We evaluated circulating tumor DNA (ctDNA) detection in advanced pancreatic cancer using DNA methylation, cell‐free DNA fragment lengths, and 5′ end motifs. Machine learning models were trained to estimate ctDNA levels from each feature and their combination.
Morten Lapin   +10 more
wiley   +1 more source

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