Results 171 to 180 of about 28,920,622 (304)

Circular RNA expression landscapes in myelodysplastic neoplasms: Associations with mutational signatures and disease progression

Molecular Oncology, EarlyView.
In this explorative study, the abundance of circular RNA molecules in bone marrow stem cells was found to be elevated in patients with high‐risk myelodysplastic neoplasms, and to be associated with an increased risk of progression to acute myeloid leukemia.
Eileen Wedge, Morten Tulstrup, Gabriele Todisco, Pedro Moura, Christophe Côme, Jakob Werner Hansen, Jakob Schmidt Jespersen, Balthasar Schlotmann, Maria Creignou, Mette Dahl, Claudia Schöllkopf, Klas Raaschou‐Jensen, Krister Wennerberg, Bo Porse, Joachim Weischenfeldt, Eva Hellström‐Lindberg, Lasse Sommer Kristensen, Kirsten Grønbæk   +17 more
wiley   +1 more source

Research note: Identification of SNP-based haplotypes for the A, D, E, I, and L alloantigen systems in the chicken reference genome line UCD-001. [PDF]

Poult Sci
Taylor RL, McCarron AM, Fulton JE.
europepmc   +1 more source

Basroparib inhibits YAP‐driven cancers by stabilizing angiomotin

Molecular Oncology, EarlyView.
Basroparib, a selective tankyrase inhibitor, suppresses Wnt signaling and attenuates YAP‐driven oncogenic programs by stabilizing angiomotin. It promotes AMOT–YAP complex formation, enforces cytoplasmic YAP sequestration, inhibits YAP/TEAD transcription, and sensitizes YAP‐active cancers, including KRAS‐mutant colorectal cancer, to MEK inhibition.
Young‐Ju Kwon, Dong Young Kim, Yuna Kim, Uk‐Il Kim, Jae‐Sung Kim   +4 more
wiley   +1 more source

RaMBat: Accurate identification of medulloblastoma subtypes from diverse data sources with severe batch effects

Molecular Oncology, EarlyView.
To integrate multiple transcriptomics data with severe batch effects for identifying MB subtypes, we developed a novel and accurate computational method named RaMBat, which leveraged subtype‐specific gene expression ranking information instead of absolute gene expression levels to address batch effects of diverse data sources.
Mengtao Sun, Jieqiong Wang, Shibiao Wan
wiley   +1 more source

Prevalence and Drivers of Missed Healthcare Appointments in Poland: Insights From a 2025 Survey. [PDF]

Med Sci Monit
Olearczyk A, Sękowski K, Jankowski M, Moczeniat G, Kamińska A, Grudziąż-Sękowska J.   +5 more
europepmc   +1 more source

Identification of serum protein biomarkers for pre‐cancerous lesions associated with pancreatic ductal adenocarcinoma

Molecular Oncology, EarlyView.
This work identified serum proteins associated with pancreatic epithelial neoplasms (PanINs) and early‐stage PDAC. Proteomics screens assessed genetically engineered mice with abundant PanINs, KPC mice (Lox‐STOP‐Lox‐KrasG12D/+ Lox‐STOP‐Lox‐Trp53R172H/+ Pdx1‐Cre) before PDAC development and also early‐stage PDAC patients (n = 31), compared to benign ...
Hannah Mearns, Jaclyn S. Long, Sergio Lilla, Kelly Hodge, Marcus J. G. W. Ladds, Colin Nixon, Paula Fernández‐Palanca, Sara Zanivan, Pilar Acedo, Stephen P. Pereira, Kevin M. Ryan   +10 more
wiley   +1 more source

Enhancing antimicrobial resistance (AMR) surveillance data use in Uganda. [PDF]

Antimicrob Steward Healthc Epidemiol
Chukwuma U, Mayito J, Twemanye V, Namusoosa R, Kakyakumaiso D, Otita M, Tabajjwa D, Guma C, David Brett-Major D, Walmema R.   +9 more
europepmc   +1 more source

Subtype‐specific enhancer RNAs define transcriptional regulators and prognosis in breast cancers

Molecular Oncology, EarlyView.
This study employed machine learning methodologies to perform the subtype‐specific classification of RNA‐seq data sets, which are mapped on enhancers from TCGA‐derived breast cancer patients. Their integration with gene expression (referred to as ProxCReAM eRNAs) and chromatin accessibility profiles has the potential to identify lineage‐specific and ...
Aamena Y. Patel, Peyman Zarrineh, Nathnael T. Tuffa, Jigar H. Sheth, Sumitra Mohan, Mudassar Iqbal, Sankari Nagarajan   +6 more
wiley   +1 more source

Simulating enzyme catalysis with electrostatically embedded machine learning potentials. [PDF]

Chem Sci
Gradisteanu V, Chan EW, Hedges L, Malagarriga M, David R, de la Puente M, Laage D, Tuñón I, van der Kamp MW, Zinovjev K.   +9 more
europepmc   +1 more source

Virtual Reference System

netbib, 2005
openaire   +2 more sources