Artificial Intelligence and Complex Network Approaches Reveal Potential Gene Biomarkers for Hepatocellular Carcinoma

PPM1G promotes cell proliferation via modulating mutant GOF p53 protein expression in hepatocellular carcinoma

The serine/threonine protein phosphatase family involves series of cellular processes, such as pre-mRNA splicing. The function of one of its members, protein phosphatase, Mg2+/Mn2+ dependent 1G (PPM1G), remains unclear in hepatocellular carcinoma (HCC). Our results demonstrated that PPM1G was significantly overexpressed in HCC cells and tumor tissues compared with the normal liver tissues at both protein and RNA levels. High PPM1G expression is associated with shorter overall survival (p < 0.0001) and disease-free survival (p = 0.004) in HCC patients. Enhanced expression of PPM1G increases the cell proliferation rate, and knockdown of PPM1G led to a significant reduction in tumor volume in vivo. Further experiments illustrated that upregulated-PPM1G expression increased the protein expression of gain-of-function (GOF) mutant p53. Besides, the immunoprecipitation analysis revealed a direct interaction between PPM1G and GOF mutant p53. Collectively, PPM1G can be a powerful prognostic predictor and potential drug-target molecule.

ChatGPT's performance in German OB/GYN exams - paving the way for AI-enhanced medical education and clinical practice

Background

Chat Generative Pre-Trained Transformer (ChatGPT) is an artificial learning and large language model tool developed by OpenAI in 2022. It utilizes deep learning algorithms to process natural language and generate responses, which renders it suitable for conversational interfaces. ChatGPT's potential to transform medical education and clinical practice is currently being explored, but its capabilities and limitations in this domain remain incompletely investigated. The present study aimed to assess ChatGPT's performance in medical knowledge competency for problem assessment in obstetrics and gynecology (OB/GYN).

Methods

Two datasets were established for analysis: questions (1) from OB/GYN course exams at a German university hospital and (2) from the German medical state licensing exams. In order to assess ChatGPT's performance, questions were entered into the chat interface, and responses were documented. A quantitative analysis compared ChatGPT's accuracy with that of medical students for different levels of difficulty and types of questions. Additionally, a qualitative analysis assessed the quality of ChatGPT's responses regarding ease of understanding, conciseness, accuracy, completeness, and relevance. Non-obvious insights generated by ChatGPT were evaluated, and a density index of insights was established in order to quantify the tool's ability to provide students with relevant and concise medical knowledge.

Results

ChatGPT demonstrated consistent and comparable performance across both datasets. It provided correct responses at a rate comparable with that of medical students, thereby indicating its ability to handle a diverse spectrum of questions ranging from general knowledge to complex clinical case presentations. The tool's accuracy was partly affected by question difficulty in the medical state exam dataset. Our qualitative assessment revealed that ChatGPT provided mostly accurate, complete, and relevant answers. ChatGPT additionally provided many non-obvious insights, especially in correctly answered questions, which indicates its potential for enhancing autonomous medical learning.

Conclusion

ChatGPT has promise as a supplementary tool in medical education and clinical practice. Its ability to provide accurate and insightful responses showcases its adaptability to complex clinical scenarios. As AI technologies continue to evolve, ChatGPT and similar tools may contribute to more efficient and personalized learning experiences and assistance for health care providers.