Results 131 to 140 of about 2,585,016 (323)

Supervision of Supervision: How to be “Meta” to a Metaposition

open access: yesJournal of Strategic and Systemic Therapies, 1983
Wright, Lorraine M.   +1 more
openaire   +3 more sources

ITGAV and SMAD4 influence the progression and clinical outcome of pancreatic ductal adenocarcinoma

open access: yesMolecular Oncology, EarlyView.
In SMAD4‐positive pancreatic ductal adenocarcinoma (PDAC), integrin subunit alpha V (ITGAV) activates latent TGF‐β, which binds to the TGF‐β receptor and phosphorylates SMAD2/3. The activated SMAD2/3 forms a complex with SMAD4, and together they translocate to the nucleus, modulating gene expression to promote proliferation, migration, and invasion. In
Daniel K. C. Lee   +9 more
wiley   +1 more source

Stellungnahme der Redaktion zur Replik

open access: yesForum Supervision, 2021
Forum Supervision - Redaktion
doaj   +1 more source

Targeting carbonic anhydrase IX/XII prevents the anti‐ferroptotic effect of stromal lactic acid in prostate carcinoma

open access: yesMolecular Oncology, EarlyView.
In prostate carcinoma, lactic acid, secreted by highly glycolytic cancer‐associated fibroblasts, is imported into tumor cells through the MCT1 transporter and prevents RSL3 and erastin‐induced ferroptosis (A). Targeting of carbonic anhydrase IX/XII, the main extracellular pH regulators, in tumor and stromal cells reduces microenvironmental acidosis and
Elisa Pardella   +18 more
wiley   +1 more source

Machine learning for identifying liver and pancreas cancers through comprehensive serum glycopeptide spectra analysis: a case‐control study

open access: yesMolecular Oncology, EarlyView.
This study presents a novel AI‐based diagnostic approach—comprehensive serum glycopeptide spectra analysis (CSGSA)—that integrates tumor markers and enriched glycopeptides from serum. Using a neural network model, this method accurately distinguishes liver and pancreatic cancers from healthy individuals.
Motoyuki Kohjima   +6 more
wiley   +1 more source

Exploring Self-Supervised Regularization for Supervised and Semi-Supervised Learning

open access: yes, 2019
Recent advances in semi-supervised learning have shown tremendous potential in overcoming a major barrier to the success of modern machine learning algorithms: access to vast amounts of human-labeled training data. Previous algorithms based on consistency regularization can harness the abundance of unlabeled data to produce impressive results on a ...
openaire   +2 more sources

Home - About - Disclaimer - Privacy