Results 131 to 140 of about 7,290,657 (291)
Extracellular vesicle (EV) lipidomic profiling of plasma and ascites from patients with high‐grade serous ovarian cancer and of ovarian cancer cell lines reveals enrichment of triglycerides (TGs), diacylglycerols (DGs), phosphatidylcholine (PCs), sphingomyelins (SMs), reflecting tumor metabolic reprogramming.
Shikha Rani +9 more
wiley +1 more source
Peristomal Dermatitis: Challenges in Interpreting Patch Test Positivity. [PDF]
Mouna K +4 more
europepmc +1 more source
Loss of eIF5A rewires thapsigargin‐mediated endoplasmic reticulum stress responses in multiple myeloma cells by selective translation of mRNA transcripts, consequently dampening thapsigargin‐induced unfolded protein response (UPR) activation and shifting the balance from induction of apoptosis toward cell survival and proliferation.
Demi Both +7 more
wiley +1 more source
AI-Assisted Automated Two-Stage Patch Test Interpretation System Using Vision Transformer. [PDF]
Lee JJ +6 more
europepmc +1 more source
In head and neck squamous cell carcinoma (HNSCC) p53 and p63 exert opposite roles on the transcription regulation of the lncRNA NEAT1. Under basal conditions, p53 levels are low and p63 represses NEAT1 expression. Upon genotoxic stress, p53 is rapidly induced, displacing p63 from the NEAT1 promoter leading to NEAT1 transcriptional activation and ...
Sara De Domenico +5 more
wiley +1 more source
Hydroxychloroquine Patch Test Positivity in a Patient With Systemic Lupus Erythematosus. [PDF]
Ries AG +3 more
europepmc +1 more source
Isocitrate dehydrogenase 1 (IDH1) mutations are highly recurrent in multiple human cancer types, including cholangiocarcinoma and glioma. IDH1 R132C is the most common IDH1 mutation in cholangiocarcinoma and likely arises from APOBEC3A‐ or APOBEC3B‐mediated deamination.
Kelly E. Butler +3 more
wiley +1 more source
A database of human predictive patch test data for skin sensitization. [PDF]
Strickland J +11 more
europepmc +1 more source
Patch level confusion matrix for test set using GoogLeNet classifier (Patch size (256 × 256), Accuracy = 78.79%, Malignant sensitivity = 76.04%.
Mahnaz Mohammadi (14209860) +9 more
core +1 more source

