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This viewpoint article first explores the ethical challenges associated with the future application of large language models (LLMs) in the context of medical education. These challenges include not only ethical concerns related to the development of LLMs, such as artificial intelligence (AI) hallucinations, information bias, privacy and data risks, and deficiencies in terms of transparency and interpretability but also issues concerning the application of LLMs, including deficiencies in emotional intelligence, educational inequities, problems with academic integrity, and questions of responsibility and copyright ownership. This paper then analyzes existing AI-related legal and ethical frameworks and highlights their limitations with regard to the application of LLMs in the context of medical education. To ensure that LLMs are integrated in a responsible and safe manner, the authors recommend the development of a unified ethical framework that is specifically tailored for LLMs in this field. This framework should be based on 8 fundamental principles: quality control and supervision mechanisms; privacy and data protection; transparency and interpretability; fairness and equal treatment; academic integrity and moral norms; accountability and traceability; protection and respect for intellectual property; and the promotion of educational research and innovation. The authors further discuss specific measures that can be taken to implement these principles, thereby laying a solid foundation for the development of a comprehensive and actionable ethical framework. Such a unified ethical framework based on these 8 fundamental principles can provide clear guidance and support for the application of LLMs in the context of medical education. This approach can help establish a balance between technological advancement and ethical safeguards, thereby ensuring that medical education can progress without compromising the principles of fairness, justice, or patient safety and establishing a more equitable, safer, and more efficient environment for medical education.

Objective To develop and validate an individualized prediction model for risk of restenosis in patients with arteriosclerosis obliterans (ASO) after percutaneous transluminal angioplasty (PTA). Methods A case-control trial was conducted on 137 patients who received PTA treatment for ASO in the Department of Vascular Surgery of the First Affiliated Hospital of Chongqing Medical University from February 2020 to May 2022. Their clinical data were collected and analyzed. The patients were assigned into training set (n=97) and validation set (n=40) at a ratio of 7 ∶3. The primary endpoint was the occurrence of restenosis within 12 months after interventional procedure. Based on the training set, single and multiple factor Cox regression models were used to screen the independent influencing factors of restenosis within 12 months. Then the obtained regression coefficients were then employed to establish a prediction model and draw a nomogram. The data from the training and validation sets were adopted to validate the model internally and externally, and the area under the receiver operating characteristic curve (AUC) and calibration chart were used to evaluate the model's recognition. The decision curve chart was established to evaluate the benefit of using the model to treat patients. Results Intravascular ultrasound guidance, lesion type, preoperative residual outflow tract, lesion length, and intraoperative treatment were independent predictors of restenosis after PTA in ASO patients (P < 0.05) and included in the model. The AUC of the nomograph for model's internal validation was 0.84 (95%CI: 0.75~0.92), with a specificity and sensitivity of 0.797 and 0.737, respectively. The AUC of the external validation using the validation set was 0.86 (95%CI: 0.74~0.98), with a specificity and sensitivity of 0.963 and 0.615, respectively. The calibration curve in the 2 cohort groups showed that the predictive value of the nomogram matched the actual observation. The decision curve indicated that this model could significantly increase the net benefit to patients. Conclusion Our nomogram prediction model can predict vessel patency of ASO patient within 12 months after PTA, and it has been developed and validated as an individualized risk prediction model for this specific group.

Objective To explore the characteristics of macroautophagy and molecular chaperone-mediated autophagy (CMA) in the oxidative stress injury of human umbilical vein endothelial cells (HUVECs). Methods After HUVECs were treated with oxidized low density lipoprotein (OX-LDL) at different concentrations(0、20、50、100、200 μg/mL), the production of reactive oxygen species (ROS) was measured to screen the optimal concentration to induce HUVECs oxidative stress. The cells were divided into 4 groups: control group (without any treatment), macroautophagy inhibition group (treated with 10 mmol/L 3-MA, specific inhibitor of macroautophagy), oxidative stress group (treated with 50 μg/mL OX-LDL), and macroautophagy inhibition-oxidative stress group (treated with 10 mmol/L 3-MA+50 μg/mL OX-LDL). The expression levels of proliferation marker protein PCNA, apoptosis marker Caspase-3, macroautophagy marker Beclin-1, and CMA markers Hsc70 and Lamp-2 were detected in each group with Western blotting. The proliferation, apoptosis, 2 kinds of autophagy under different treatments were evaluated respectively. After the cells of the oxidative stress group and macroautophagy inhibition-oxidative stress group were treated for different duration (1, 3, 6, 12 and 48 h), the proliferation and apoptosis of HUVECs were tested by CCK-8 assay and Annexin-V/PI double staining, respectively. Western blotting was adopted to determine the protein levels of Beclin-1, Hsc70 and Lamp-2. Finally, the time regulation characteristics of the 2 autophagy pathways before and after macroautophagy inhibition were analyzed. Results Under OX-LDL induced oxidative stress, the ROS level was elevated with the increase of OX-LDL concentration, and 50 μg/mL was finally selected as the appropriate concentration for subsequent experiments. As compared with the control group, the levels of Caspase-3, Beclin-1, Hsc70 and Lamp-2 were up-regulated, while that of PCNA was down-regulated in the oxidative stress group (P < 0.05). With the prolongation of treatment time, proliferation inhibition rate, overall apoptotic rate and Lamp-2 level were increased within 48 h, Hsc70 level was elevated after 6 h, and that of Beclin-1 was improved within 12 h in the oxidative stress group (P < 0.05). In the macroautophagy inhibition-oxidative stress group, the expression levels of Caspase-3, Hsc70 and Lamp-2 were also up-regulated, that of PCNA down-regulated (P < 0.05), those of Hsc70 and Lamp-2 were increased at the same time and that of Hsc70 was in an elevation after 3 h of treatment (P < 0.05). Conclusion Macroautophagy and CMA are activated successively during the process of oxidative stress, and CMA can be enhanced after macroautophagy inhibition. The early activation contributes to attenuate oxidative stress-induced damage to endothelial cells.

The heating methods currently used at oil delivery sites and transfer stations on North China Oilfield include electric bar heating, fuel oil heating, gas heating, etc. The traditional heating methods have adverse effect on energy conservation and environmental protection. They have low energy efficiency of heating and produces great pollution to the environment. The air source heat pump technology uses the heat in the air as the low-temperature heat source and transforms it to the high-temperature hot water, which will later serve as a heat tracing for crude oil gathering and transportation. No combustion effluent is produced in operation and the energy-saving effect is outstanding with high social-benefit.

Labor education is an important content of quality education, and the promotion of higher education cannot be separated from the implementation and practice of labor education. The labor education platform and labor education resource base in colleges and universities are established using the big data platform, which combines Hadoop technology and the HBase database. In order to help students obtain labor education teaching resources more efficiently on the labor education platform, reinforcement learning is combined with a deep neural network to optimize the scheduling of teaching resources, and α -the dispersion recommendation algorithm is used to realize personalized recommendations of teaching resources. With regard to the effectiveness of the labor education platform, load testing and application effects were carried out, and the influencing factors of labor behavior and craftsmanship cultivation of college labor education students were analyzed in depth. The labor education platform’s transaction response time increases by 22.5 seconds when the number of users reaches 5000, according to the results. The personal motivation for craftsmanship in the process of labor education will be affected by the campus culture, and its correlation coefficient is 0.565, and the student’s satisfaction with the platform of labor education reaches 0.825. The innovation and development of labor education in colleges and universities need to fully rely on the big data platform to promote the optimization and sharing of labor education resources and to promote the development of labor education resources. The cultivation of student labor creation and craftsmanship can be ensured through the optimization and sharing of educational resources.