Performance of ChatGPT on Chinese Master's Degree Entrance Examination in Clinical Medicine.

BACKGROUND: ChatGPT is a large language model designed to generate responses based on a contextual understanding of user queries and requests. This study utilised the entrance examination for the Master of Clinical Medicine in Traditional Chinese Medicine to assesses the reliability and practicality of ChatGPT within the domain of medical education. METHODS: We selected 330 single and multiple-choice questions from the 2021 and 2022 Chinese Master of Clinical Medicine comprehensive examinations, which did not include any images or tables. To ensure the test's accuracy and authenticity, we preserved the original format of the query and alternative test texts, without any modifications or explanations. RESULTS: Both ChatGPT3.5 and GPT-4 attained average scores surpassing the admission threshold. Noteworthy is that ChatGPT achieved the highest score in the Medical Humanities section, boasting a correct rate of 93.75%. However, it is worth noting that ChatGPT3.5 exhibited the lowest accuracy percentage of 37.5% in the Pathology division, while GPT-4 also displayed a relatively lower correctness percentage of 60.23% in the Biochemistry section. An analysis of sub-questions revealed that ChatGPT demonstrates superior performance in handling single-choice questions but performs poorly in multiple-choice questions. CONCLUSION: ChatGPT exhibits a degree of medical knowledge and the capacity to aid in diagnosing and treating diseases. Nevertheless, enhancements are warranted to address its accuracy and reliability limitations. Imperatively, rigorous evaluation and oversight must accompany its utilization, accompanied by proactive measures to surmount prevailing constraints.

Ze-Qi decoction inhibits non-small cell lung cancer growth and metastasis by modulating the PI3K/Akt/p53 signaling pathway.

Background: The Ze-Qi decoction (ZQD) is a traditional Chinese herbal formula commonly applied to treat lung cancer in China. This study aimed to assess the effective ingredients and molecular mechanisms of ZQD in treating non-small cell lung cancer (NSCLC) based on network pharmacology combined with experimental validation. Methods: Network pharmacology, bioinformatics, and molecular docking analyses were conducted to explore the mechanism of ZQD for treating NSCLC, which was further confirmed by animal experiments. Results: In total, 117 bioactive ingredients and 499 target proteins of ZQD were identified. Network pharmacology revealed 7 core active ingredients and 74 core target proteins. Kyoto Encyclopedia of Genes and Genomes enrichment analyses indicated that the PI3K/Akt and p53 signaling pathways may be crucial in NSCLC treatment. Molecular docking analysis revealed that the seven crucial bioactive ingredients complexed with PI3K, Akt, and p53. The animal experiment results validated that ZQD treatment promoted cell apoptosis and cell cycle arrest, thereby inhibiting NSCLC growth and metastasis. Furthermore, ZQD treatment caused a significant increase in p53 and Bax, while leading to a distinct reduction in p-PI3K (Tyr317), p-Akt (Ser473), VEGFA, CD31, MMP2, MMP9, Bcl2, and CDK2. Conclusions: ZQD inhibited the growth and metastasis of NSCLC subcutaneous tumors in C57BL/6J mice via the PI3K/Akt/p53 signaling pathway.

N-Methyl D-aspartate receptor subtype 2B/Ca2+/calmodulin-dependent protein kinase II signaling in the lateral habenula regulates orofacial allodynia and anxiety-like behaviors in a mouse model of trigeminal neuralgia.

Trigeminal neuralgia (TN) is a peripheral nerve disorder often accompanied by abnormalities in mood. The lateral habenula (LHb) plays important roles in the modulation of pain and emotion. In the present study, we investigated the involvement of the LHb in the mechanisms underlying allodynia and anxiety induced by partial transection of the infraorbital nerve (pT-ION) in mice. Our results indicated that pT-ION induced persistent orofacial allodynia and anxiety-like behaviors, which were correlated with increased phosphorylation of N-Methyl D-aspartate receptor (NMDAR) subtype 2B (p-NR2B) and Ca2+/calmodulin-dependent protein kinase II (p-CaMKII) in LHb neurons. Bilateral inhibition of NMDARs and CaMKII in the LHb attenuated the allodynia and anxiety-like behavior induced by pT-ION. Furthermore, bilateral activation of NMDARs in the LHb increased the expression of p-NR2B and p-CaMKII and induced orofacial allodynia and anxiety-like behaviors in naive mice. Adeno-associated virus (AAV)-mediated expression of hM3D(Gq) in CaMKII+ neurons of the bilateral LHb, followed by clozapine-N-oxide (CNO) administration, also triggered orofacial allodynia and anxiety-like behaviors in naïve mice with successful virus infection in LHb neurons (verified based on immunofluorescence). In conclusion, these findings suggest that activation of NMDA/CaMKII signaling in the LHb contributes to the occurrence and development of TN and related anxiety-like behaviors. Therefore, suppressing the activity of CaMKII+ neurons in the bilateral LHb by targeting NMDA/CaMKII may represent a novel strategy for treating pain and anxiety associated with TN.

Development of a Clinical Prognostic Model for Metabolism-Related Genes in Squamous Lung Cancer and Correlation Analysis of Immune Microenvironment.

Background: The incidence of squamous lung cancer (LUSC) has substantially increased. Systematic studies of metabolic genomic patterns are fundamental for the treatment and prediction of LUSC. Because cancer metabolism and immune cell metabolism have been studied in depth, metabolism and the state and function of immune cells have become key factors in tumor development. This also indicates that metabolic genes and the tumor immune microenvironment (TME) are crucial in tumor treatment. This study is aimed at dissecting the connection between TME and LUSC digestion-related qualities. Methods: The information used in this study was obtained from The Cancer Genome Atlas dataset. Metabolism-related genes in patients with LUSC were screened, and relevant clinical data were collated. Next, genes associated with prognosis were screened using univariate COX regression and LASSO regression analyses. Finally, a timer database study was conducted to analyze the molecular mechanisms of immune cell infiltration of LUSC prognosis-related metabolic genes at the immune cell level. Results: Nine metabolism-related genes were identified: ADCY7, ALDH3B1, CHIA, CYP2C18, ENTPD6, GGCT, HPRT1, PLA2G1B, and PTGIS. A clinical prediction model for LUSC based on metabolism-related genes was constructed. In addition, 22 subpopulations of tumor-infiltrating immune cells (TIIC) in the TME were analyzed using the CIBERSORT algorithm. Finally, we used the TIMER database to analyze the immune infiltration of LUSC and the relationship between metabolism-related genes and immune cells. Conclusion: Our study identified metabolic genes associated with the prognosis of LUSC, which are important markers for its diagnosis, clinically relevant assessments, and prognosis. The relationship between metabolic genes with prognostic impact and immune infiltration was also analyzed, and a metabolic gene-based clinical prediction model was identified, providing a new perspective for LUSC treatment.