Objective To observe the impact of chat generative pre-trained transformer(ChatGPT)-based simulated emergency medical communication training on the communication skills of junior residents and compare patient satisfaction differences among physicians of different seniority levels in emergency scenarios.Methods A prospective controlled study was conducted,enrolling 32 newly hired junior residents from Shanghai Chest Hospital Affiliated Shanghai Jiao Tong University School of Medicine.Participants were divided into a trained junior resident group and an untrained junior resident group using a random number table method,16 physicians in each group.16 attending physicians(attending physician group)from the same hospital during the same period served as controls.The trained junior resident group underwent a 1-month ChatGPT-based simulation training covering emergency scenarios,including multilingual dialogue,emotional support,and cross-cultural communication.Patient satisfaction was assessed via anonymous questionnaires evaluating clarity of communication,empathy,emotional support,and information completeness.Compare the differences in patient satisfaction scores among different groups of physicians.Results Compared with the untrained junior resident group,the trained junior resident group and the attending physician group exhibited significantly higher patient satisfaction scores(4.50±0.28,4.45±0.25 vs.3.80±0.41,both P<0.05).Conclusion ChatGPT-based simulation training significantly enhances emergency medical communication skills in junior residents.These findings suggest that integrating ChatGPT into clinical teaching could optimize patient-physician interaction quality.

Objective To observe the impact of chat generative pre-trained transformer(ChatGPT)-based simulated emergency medical communication training on the communication skills of junior residents and compare patient satisfaction differences among physicians of different seniority levels in emergency scenarios.Methods A prospective controlled study was conducted,enrolling 32 newly hired junior residents from Shanghai Chest Hospital Affiliated Shanghai Jiao Tong University School of Medicine.Participants were divided into a trained junior resident group and an untrained junior resident group using a random number table method,16 physicians in each group.16 attending physicians(attending physician group)from the same hospital during the same period served as controls.The trained junior resident group underwent a 1-month ChatGPT-based simulation training covering emergency scenarios,including multilingual dialogue,emotional support,and cross-cultural communication.Patient satisfaction was assessed via anonymous questionnaires evaluating clarity of communication,empathy,emotional support,and information completeness.Compare the differences in patient satisfaction scores among different groups of physicians.Results Compared with the untrained junior resident group,the trained junior resident group and the attending physician group exhibited significantly higher patient satisfaction scores(4.50±0.28,4.45±0.25 vs.3.80±0.41,both P<0.05).Conclusion ChatGPT-based simulation training significantly enhances emergency medical communication skills in junior residents.These findings suggest that integrating ChatGPT into clinical teaching could optimize patient-physician interaction quality.

Objective:To study parameters of MRI image in different tissue differentiation,cell type of primary liver cancer.Methods:The pathological results and MRI data of regeneration nodules (27),hepatocellular carcinoma (HCC) (81 total;15,highly differentiated;40,moderately differentiated;26,low-grade differentiation),intrahepatic cholangiocarcinoma (ICC) (20) were retrospectively analyzed and compared To compare the difference of ADC values,strengthen degree among the regeneration nodules,HCC and ICC,and among HCC tissue differentiation.Results:Most cases of primary liver cancer can be accurately diagnosed by conventional MRI combined with LAVA.there are statistically significant differences of ADC values among regenerative nodules,HCC,ICC (P＜0.01),and among highly,moderately,poorly differentiated HCC groups (P＜0.01).But there is not actual clinical significance of the ADC values between moderately and poorly HCC.there is no statistically significant difference of the ADC values between highly differentiated HCC and ICC (P=0.27).Conclusion:Conventional MRI combining with DWI,LAVA can help distinguish the primary liver cancer differentiation degree and the cell type.

ObjectiveTo optimize the extraction technology of the active component,rosmarinic acid,an ester of caffeic acid and 3,4-dihydroxyphenyllactic acid,in perilla oil meal for the first time by a new homogenizing technology called smashing tissue extraction (STE).MethodsOrthogonal design was used to optimize the extraction condition.The content of rosmarinic acid was quantified from the methanol crude extract with the help of HPLC.ResultsThe optimization of STE process to get rosmarinic acid from the perilla oil meal was the ratio of liquid to solid material at 10∶1 and the power of extraction at 150 V,extracting twice (2 min for each time).ConclusionSTE could be applied to extracting the active ingredients from the oil meals due to its high extraction efficiency.This new homogenizing technology has advantages on saving extraction time,raising extraction efficiency,and maintaining the temperature sensitive constituents.