Results 181 to 190 of about 141,564 (263)

Targeting GALNT7 Disrupts the TAZ O‐GalNAcylation Feedback Loop to Suppress Gallbladder Cancer Progression

open access: yesAdvanced Science, EarlyView.
Aberrant GALNT7‐mediated O‐GalNAcylation stabilizes TAZ to drive gallbladder cancer progression through a feed‐forward transcriptional loop. Structure‐based screening identifies Olaparib as a potent GALNT7 antagonist that disrupts this oncogenic axis, providing an immediate therapeutic strategy for this aggressive malignancy.
Peng Qiu   +11 more
wiley   +1 more source

Pediatric Oncology

open access: yesIntegrative Cancer Therapies, 2006
openaire   +2 more sources

Histone H3K18 Lactylation Promotes the Malignant Progression of Wilms Tumor via a PSRC1/AKT/HIF‐1α Positive Feedback Loop

open access: yesAdvanced Science, EarlyView.
In nephroblastoma, aberrant glycolysis drives lactate accumulation, which elevates histone H3K18 lactylation via p300. Lactylation of the PSRC1 promoter activates its transcription. PSRC1 competitively binds AKT, relieving PTEN‐mediated inhibition and triggering AKT/mTOR/HIF‐1α signaling.
Yanping Wang   +6 more
wiley   +1 more source

Pasta, a Versatile Transcriptomic Clock, Maps the Chemical and Genetic Determinants of Aging and Rejuvenation

open access: yesAdvanced Science, EarlyView.
Pasta is a transcriptomic aging clock built on an age‐shift learning framework and trained on 17 000 samples across 21 datasets. It accurately predicts relative biological age across tissues, platforms, and species, captures stemness‐to‐senescence transitions, and identifies age‐modulatory perturbations.
Jérôme Salignon   +6 more
wiley   +1 more source

Can Machine Learning Reduce Unnecessary Surgeries? A Retrospective Analysis Using Threshold Optimization to Prevent Negative Appendectomies in Adults

open access: yesAnnals of Gastroenterological Surgery, EarlyView.
Threshold‐optimized machine learning models using routine clinical and laboratory data in 623 adults undergoing appendectomy. Logistic regression (AUC = 0.765) and random forest (AUC = 0.785) were the best‐performing models for appendicitis detection and complicated appendicitis prediction, respectively.
Ivan Males   +8 more
wiley   +1 more source

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