Objective] To discuss the effect of timely infection evaluation and intervention on reducing infection to low-body mass incubator infants of primary hospitals. [Method] Select 98 cases of low-body mass incubator infants of 1210~2000g as control group(routine prevention used), and another 95 cases of 1200~2010g as observation group(taking timely infection evaluation, giving pointing intervention to infants with various infective risk factors). Compare their infection and in-hospital period. [Result]In control group 98 cases, 45 cases(45.92%) had infection; in observation group 95 cases, 11 (11.58%) had infection; the infective rate of observation group was lower than control group, the difference had statistical meaning( P<0.05). [Conclusion] To take timely infection evaluation and preventive intervention can definitely reduce infection.

Objective    To explore the efficiency of Ki-67 expression and CT imaging features in predicting the degree of invasion of lung adenocarcinoma. Methods    The clinical data of 217 patients with pulmonary nodules, who were diagnosed as suspicious lung cancer by multi-disciplinary treatment clinic of pulmonary nodules in our hospital from September 2017 to August 2021, were retrospectively analyzed. There were 84 males and 133 females, aged 52 (25-84) years. The patients were divided into two groups according to the infiltration degree, including an adenocarcinoma in situ and microinvasive adenocarcinoma group (n=145) and an invasive adenocarcinoma group (n=72). Results    There was no statistical difference in the age and gender between the two groups (P>0.05). The univariate analysis showed that CK-7, P63, P40 and CK56 expressions were not different between the two groups (P=0.172, 0.468, 0.827, 0.313), while Napsin A, TTF-1 and Ki-67 expressions were statistically different (P=0.002, 0.020, <0.001). The multivariate analysis showed that Ki-67 expression was statistically different between the two groups (P<0.001). Ki-67 was positively correlated with malignant features of CT images and the degree of lung adenocarcinoma invasion (P<0.05). Ki-67 and CT imaging features alone could predict the degree of lung adenocarcinoma invasion, but their sensitivity and specificity were not high. Ki-67 combined with CT imaging features could achieve a higher prediction efficiency. Conclusion    Compared with Ki-67 or CT imaging features alone, the combined prediction of Ki-67 and imaging features is more effective, which is of great significance for clinicians to select the appropriate operation occasion.

Following the rapid advancement of artificial intelligence technologies, especially the development of large language models like ChatGPT, the field of medical clinical practice is undergoing an unprecedented technological revolution. These advanced technologies, through efficient processing and analysis of large datasets, not only provide medical professionals with auxiliary diagnoses and treatment suggestions but also significantly enhance the quality and efficiency of medical education. This study conducts a comprehensive analysis and review of the applications of large language models in various aspects, including clinical inquiry, history collection, medical literature writing, clinical decision support, optimization of medical portal websites, patient health management, medical education, academic research, and scientific writing. However, the application of these technologies is not without flaws and presents several limitations and ethical challenges. This paper focuses on challenges related to technological errors, academic dishonesty, abuse risks, over-reliance, possibilities of misdiagnosis and treatment errors, and issues of accountability. In conclusion, large language models demonstrate tremendous potential in the integration and advancement of medical practices. Nevertheless, while fully harnessing the benefits brought by ChatGPT, it is essential to acknowledge and address these ethical challenges to ensure that the application of ChatGPT in the medical field is responsible and effective.