Results 71 to 80 of about 36,775 (295)
Pancreatic neuroendocrine tumors frequently silence MEN1 through epigenetic mechanisms. Here, SIRT7 recruits DNMT1 to the MEN1 promoter, drives hypermethylation, and enhances DNA repair. Inhibiting SIRT7 restores MEN1, reduces MRN complex abundance, impairs double‐strand break repair, and sensitizes PanNET models to radiation, supporting SIRT7 as a ...
Jianyun Jiang +11 more
wiley +1 more source
IntroductionThe increasing adoption of large language models (LLMs) in public health has raised significant concerns about hallucinations-factually inaccurate or misleading outputs that can compromise clinical communication and policy decisions.MethodsWe
Shan Xu +5 more
doaj +1 more source
G3BP1 Succinylation at K413 is Critical for Cardiac Function by Modulating PI3K‐AKT‐mTOR Signal Axis
Schematic illustrating the impact of G3BP1 succinylation at K413 on cardiac function. In the healthy human heart, G3BP1 succinylation maintains homeostatic mTOR signaling. In patients with dilated cardiomyopathy (DCM) and heart failure (HF), G3BP1 de‐succinylation induces RagA expression and disrupts the binding of the TSC1/2 complex, leading to the ...
Yuan Zhang +9 more
wiley +1 more source
Icariin promoted the growth of Akk by enhancing the activity of N‐acetylgalactosaminidase (Amuc_0920), which enhanced mucin utilization and provided a favorable nutrient environment for bacterial growth. This icariin‐mediated enrichment of Akk further reshaped the tumor microenvironment and promoted CD8+ T cell infiltration, ultimately synergizing with
Shuangying Qiao +12 more
wiley +1 more source
Using Retrieval vs. Cache Augmented Generation for a Pok´emon Chatbot
Cache Augmented Generation (CAG) can be an alternative to Retrieval Augmented Generation (RAG). There are differences between the two frameworks, but they work largely in the same way.
Cengiz Gunay, Jonathan Tran
doaj +1 more source
Question Decomposition for Retrieval-Augmented Generation
Grounding large language models (LLMs) in verifiable external sources is a well-established strategy for generating reliable answers. Retrieval-augmented generation (RAG) is one such approach, particularly effective for tasks like question answering: it retrieves passages that are semantically related to the question and then conditions the model on ...
Paul J. L. Ammann +2 more
openaire +2 more sources
Rethinking Relevance: How Noise and Distractors Impact Retrieval-Augmented Generation [PDF]
Retrieval-Augmented Generation (RAG) systems enhance the performance of Large Language Models (LLMs) by incorporating external information fetched from a retriever component.
Campagnano, Cesare +7 more
core
ABSTRACT Liver metastasis is a leading cause of mortality in colorectal cancer (CRC), where the inflammatory tumor microenvironment, specifically neutrophil infiltration, significantly promotes metastatic colonization. This study reveals a pro‐metastatic role for alpha‐1 antitrypsin (A1AT) in CRC liver metastasis via a dual mechanism involving ...
Qian Fei +11 more
wiley +1 more source
Retrieval-Augmented Generation with Graphs (GraphRAG)
Retrieval-augmented generation (RAG) is a powerful technique that enhances downstream task execution by retrieving additional information, such as knowledge, skills, and tools from external sources. Graph, by its intrinsic "nodes connected by edges" nature, encodes massive heterogeneous and relational information, making it a golden resource for RAG in
Haoyu Han 0001 +17 more
openaire +2 more sources
A single‐cell atlas of pancreatic ductal adenocarcinoma development reveals progressive ductal‐fibroblast‐immune crosstalk. Tumor‐derived LAMB3 drives the formation of immunosuppressive LRRC15+ fibroblasts through the ITGB1/FAK/MAPK/FOSL2 signaling. Glycolytic reprogramming upregulates LAMB3 and correlates with LRRC15+ fibroblast enrichment.
Xuqing Shi +23 more
wiley +1 more source

