We developed THER, a web tool integrating 63 hypoxia-related tumor transcriptomic datasets, enabling differential expression, expression profiling, correlation, enrichment, and drug sensitivity analyses. It helps identify valuable biomarkers, further reveal the molecular mechanisms of tumor hypoxia, and identify effective drugs, thus providing a scientific basis for tumor diagnosis and treatment. Experimental verification showed hypoxia reduces tumor cell sensitivity to chemotherapy drugs. Accessible at https://smuonco.shinyapps.io/THER/.

In a ground-breaking analysis, researchers have examined global safety databases to reveal increased risks of secondary primary malignancies following CAR-T cell therapy. These findings not only support recent FDA warnings but identify age-specific patterns showing younger patients face earlier onset of secondary cancers.

Researchers tested a large language model (LLM) on peer review tasks for cancer research papers. They found the AI could be abused to generate highly persuasive rejection letters and other fraudulent reviews, such as requests to cite unrelated papers. Crucially, current AI detection tools were largely unable to identify the AI-generated text, posing a significant, hidden threat to academic integrity.

The objective of this study is to assess the diagnostic performance of image analysis-capable generative AI (Gen-AI) (GPT-4-turbo, Google DeepMind's Gemini-pro-vision, and Anthropic’s Claude-3-opus) in interpreting CT images of lung cancer. This is the first study to integrate the diagnostic capabilities of these three models across distinct imaging settings. Additionally, a Likert scale is used to evaluate each model's internal tendencies. By examining the potential and limitations of multimodal large language models (MM-LLMs) for lung cancer diagnosis, this research aims to provide an evidence-based foundation for the future clinical applications of Gen-AI.