Yantai Laiyang Central Hospital

Digital literacy and scientific creativity are becoming significant concerns in the cultivation of professional and innovative talents within medical higher education, particularly given the widespread application of digital technology in modern medicine. Although existing studies suggest a link between digital skills and students' performance, research specifically focusing on the relationship between digital literacy and scientific creativity remains limited. Furthermore, the internal mechanisms through which digital literacy influences scientific creativity have not been clearly elucidated. Knowledge conversion, as conceptualized in the SECI model, may mediate this relationship. This study aims to explore the relationship between digital literacy and scientific creativity among medical students and assesses the mediating role of knowledge conversion and its four dimensions. A cross-sectional survey of 770 medical students from Central South University (December 2024 to March 2025) was conducted using digital literacy scales, scientific creativity scales, and knowledge conversion scales. SPSS 24.0 and AMOS software were used to analyze the data, with mediating effects specifically examined via the Hayes PROCESS macro. Digital literacy exhibited a significant positive effect on medical students' scientific creativity. Mediation analysis revealed that knowledge conversion served as a significant mediator, explaining the vast majority of this effect, which underscores its role as a primary pathway. However, a significant direct effect of digital literacy remained, thus confirming a partial mediation model. Among the dimensions of knowledge conversion, externalization emerged as the most potent mediator. This study highlights the significant role of digital literacy in directly enhancing the scientific creativity of medical students, as well as the mediating effect of knowledge transformation, particularly in its externalization dimension. The findings provide valuable insights for educational interventions, indicating that strategies should not only focus on digital skills but also integrate knowledge management training. Furthermore, the proposed DLSC-SECI model provides a theoretical framework for systematic training aimed at improving the scientific creativity of medical students in the digital era.

Previous studies have typically focused on the effect of high preoperative serum creatinine (SCr) levels on the prognosis of hepatocellular carcinoma (HCC) patients but have often ignored the role of low SCr levels. This study aimed to assess the impact of low SCr levels on HCC patient prognosis. 3857 patients who underwent primary liver resection from seven centers were divided into low, normal and high SCr groups according to their baseline SCr levels. Factors independently associated with textbook outcome (TO) and overall survival (OS) were analyzed. Propensity score matching (PSM) was used to balance the baseline characteristics between the low- and normal-SCr groups, as well as between the high- and normal-SCr groups. TO was observed in 2403 (62.3%) patients. The TO rate in the normal SCr group was 63.4%, which was significantly higher than that in the low (48.5%, P < 0.001) and high (53.3%, P = 0.017) SCr groups in the whole cohort. Multivariate analysis confirmed that both low and high SCr levels independently contributed to non-TO achievement. The 5-year OS was 67.6% for the normal SCr group, which was significantly better than that of the low SCr group (51.1%, P < 0.001) but similar to that of the high SCr group (66.4%, P = 0.697) in the whole cohort. Similar results were observed after PSM. Multivariate analysis suggested that a low SCr level (HR = 1.328, 95% CI = 1.076–1.639, P = 0.008) was independently associated with OS, whereas a high SCr level (HR = 1.173, 95% CI = 0.870–1.583, P = 0.294) was not. Low SCr levels could affect both TO achievement and OS in HCC patients after liver resection. A low SCr level offers useful information for preoperative risk stratification, optimizes perioperative care and improves the prognosis of HCC patients. We should not ignore the effect of low preoperative SCr levels on HCC patient prognosis.

BackgroundChronic low-grade inflammation in nonalcoholic fatty liver disease (NAFLD) plays a critical role in the development of cardiovascular complications, particularly ischaemia with non-obstructive coronary arteries (INOCA). This study aimed to develop and evaluate models combining pericoronary adipose tissue (PCAT) radiomics, PCAT attenuation (PCATa), CCTA plaque parameters, and clinical risk factors to identify INOCA in NAFLD patients.MethodsThis retrospective study included 159 patients with NAFLD who underwent CCTA. The patients were randomly divided into the training (70%) and validation (30%) cohorts. Clinical features, CCTA imaging indicators, and right coronary artery PCAT radiomic features were analyzed. Five models were constructed using logistic regression: Model 1 (PCATa model), Model 2 (radiomics model), Model 3 (clinical factors model), Model 4 (combined imaging model), and Model 5 (combined imaging-clinical model). The models’ diagnostic performance was assessed using the area under the curve, reclassification metrics, and decision curve analysis (DCA).ResultsThe PCAT radiomics model exhibited higher diagnostic efficacy than the PCATa model in identifying INOCA (training cohort: AUC 0.734 vs. 0.674; validation cohort: AUC 0.706 vs. 0.637). The combined imaging model showed improved performance over the clinical factors model (training AUC 0.830, validation AUC 0.813). The model integrating imaging and clinical factors achieved the highest diagnostic accuracy (AUCs of 0.873 and 0.824 in the training and validation cohorts, respectively), demonstrating incremental value based on improved NRI, IDI, and DCA metrics. Calibration analysis indicated good agreement between predicted and observed outcomes.ConclusionsThe radiomics model provided better discrimination than the PCATa model for identifying INOCA among patients with NAFLD. Models incorporating radiomics and CCTA imaging parameters outperformed those based solely on clinical factors. The comprehensive imaging-clinical model achieved the best overall performance and may serve as a promising non-invasive approach for INOCA risk stratification in NAFLD, although external validation is still required.Supplementary InformationThe online version contains supplementary material available at 10.1186/s12872-025-05292-5.

Ellagic acid (EA) is a natural antioxidant, widely present in a lot of forms' soft fruits, nuts, and other plant tissues, and helpful for promoting human health; however, its protective effect on postharvest fruit and improving the quality index of postharvest fruit have rarely been studied. In this experiment, the strawberries were soaked in 0, 100, 200, 300, 400, and 500 mg L −1 EA, respectively, and the influential EA on fruit quality and the antioxidant system of strawberries were studied. Compared with the control, EA treatment can reduce the browning degree and rotting rate of strawberry fruit during storage and augment the soluble solid content (SSC). EA treatment can also increase the content of related stuff and enzyme activity in antioxidant systems; the gene expression level of polyphenol oxidase (PPO) in strawberries treated with EA was always down-regulated, correspondingly, the expression of other antioxidant enzyme genes was enhanced. Among the strawberry fruits treated with EA of different concentrations, 300 mg L −1 EA had the best effect in the process of strawberry preservation. The results suggested that the proper concentration of exogenous EA at 300 mg L −1 could maintain strawberries' quality and enhance the antioxidant system by improving the activities of antioxidative enzymes and the ascorbateglutathione (AsA-GSH) cycle during storage.

Aims: The purpose of this study was to investigate the relationship between nurses' perceived organizational support, work well-being, and medical narrative ability. Background: With the proposed bio-psycho-social medical model, nurses' medical narrative ability is closely related to patients' health problems and quality of life. Nurses' perceived organizational support and work well-being can improve nurses' empathy and reflection ability to a certain extent and promote patients' rehabilitation. However, the relationship between nurses' perceived organizational support, work well-being, and medical narrative ability is unclear. Methods: A total of 1831 nurses from 8 hospitals in China were surveyed using an online questionnaire that included nurses' sociodemographic, perceived organizational support, work well-being, and medical narrative ability. IBM SPSS 27.0 was used for Pearson's correlation analysis, one-way ANOVA, t-test, and mediation effect analysis using Model 4 in PROCESS (5000 resamples). Results: The total mean score of perceived organizational support (46.68 ± 11.00), work well-being (53.09 ± 10.81), and medical narrative ability (154.48 ± 22.93) among nurses was found to be moderate. The relationship between perceived organizational support, work well-being, and medical narrative ability was significant, with correlation coefficients ranging from 0.348 to 0.685 (p < 0.01). The relationship between perceived organizational support and medical narrative ability is partially mediated by work well-being. The intermediate effect accounted for 52.36% of the total effect. Conclusion: This study found that nurses' work well-being mediated the relationship between perceived organizational support and medical narrative ability. Implications for Nursing Management: This study evaluated nurses' medical narrative ability and explored the relationship between nurses' perceived organizational support, work well-being, and medical narrative ability. The results of this study can help nursing managers and educators to take appropriate measures to intervene nurses' perceived organizational support and work well-being, so as to improve nurses' medical narrative ability and optimize nursing quality.

In recent years, experts and scholars at home and abroad have continuously increased their research on AI technology, which has made the level of AI develop rapidly and has been applied in work, study and life. By studying big data, summarizing and discovering laws, we can conclude the differences with regularity to diagnose diseases. This paper studies the application of AI(artificial intelligence) technology in clinical auxiliary diagnosis and treatment equipment. An improved FA(Firefly algorithm) is proposed for diabetes feature selection, which relates the update of firefly position with the global optimal solution, so as to avoid falling into local optimal solution. PNN (Probability neural network) was used to establish a diabetes diagnosis model, and the diagnosis results were analyzed. The research results show that the improved FA algorithm is better than the results before feature selection in the dimension, classification accuracy and running time of the optimal feature subset, and can filter out redundant features well, so it is suitable for the reduction of diabetes feature set. Under the same conditions, the accuracy of the improved FA model network model is higher than that of the standard model. The reliability of the application of AI technology in clinical auxiliary diagnosis and treatment equipment is verified.