Results 151 to 160 of about 41,326 (302)
Zmat1 deficiency mitigates pathological bone loss by impairing osteoclastogenesis and promoting osteoblastogenesis. Mechanistically, in osteoclasts, Zmat1 loss relieves transcriptional repression of the E3 ligase TRIM46, promoting YAP1 degradation and inhibiting osteoclastogenic genes.
Xinyu Chang +13 more
wiley +1 more source
This study reveals that the Fgl2‐FcγRIIB signaling axis is a key mechanism by which MDSCs mediate tumor immune evasion. Tumor‐derived exosomes systemically activate MDSCs via this pathway, positioning this axis as a promising broad‐spectrum target for cancer immunotherapy.
Fenglin Lin +12 more
wiley +1 more source
Vorinostat Potentiates Chemoimmunotherapy in Immune‐Enriched Pancreatic Cancer
Immune‐enriched pancreatic cancer does not confer a significant survival advantage. SAHA sensitizes these “hot” tumors to chemoimmunotherapy by disrupting a FASN/PARP9‐driven “metabolic trap” and enhancing CD8+ T cell function. A CD8high/FASNhigh/PARP9high signature identifies patients who are most likely to benefit from the “gemcitabine‐nivolumab‐SAHA”
Chen Chen +13 more
wiley +1 more source
Perceptions of chiropractic students on digital literacy skills at a South African university: A cross-sectional study. [PDF]
Pyper CN, Moore BA, Ismail F.
europepmc +1 more source
ABSTRACT Advancements in tissue engineering have revolutionized therapeutic paradigms for diabetic tissue defects; however, the lack of applicable scaffold containing various bioactive substance aggregates remained a critical bottleneck hindering satisfactory repair effect.
Tao Wang +8 more
wiley +1 more source
Conceptual pitfalls in the forest carbon debate : Comment solicited by Prof Yiping (Rocky) Wu, 30 Aug. 2025, revised Dec 2025. [PDF]
Körner C.
europepmc +1 more source
Leveraging Artificial Intelligence and Large Language Models for Cancer Immunotherapy
Cancer immunotherapy faces challenges in predicting treatment responses and understanding resistance mechanisms. Artificial intelligence (AI) and machine learning (ML) offer powerful solutions for cancer immunotherapy in patient stratification, biomarker discovery, treatment strategy optimization, and foundation model development.
Xinchao Wu +4 more
wiley +1 more source

