Objective To investigate the learning curve of real-life vaginal delivery,including its difficult steps and influencing factors,to optimize the future training of vaginal delivery for residents in department of obstetrics and gynecology.Methods From 25 Sep 2020 to 12 Mar 2022,OBGYN residents without previous experiences in vaginal delivery were prospectively enrolled in Obstetrics and Gynecology Hospital,Fudan University.Residents performed normal vaginal delivery under the supervision of senior obstetricians and midwives.The performance score(PS)of vaginal delivery and its 9 steps were evaluated via a questionnaire fulfilled by the supervisor once each delivery was finished.Logistic regression models were performed for univariate and multivariate analyses to evaluate the factors that might be correlated with the PS.Results Eventually,233 deliveries performed by 60 residents were analyzed.Results showed that more than 10 deliveries were needed for 70%of residents to obtain minimal competence of vaginal delivery.Perineal protection,delivery of the fetal head,delivery of the fetal shoulders and repair of episiotomy or laceration were found to be the most difficult steps,which required more practices to achieve minimal competence level.Univariate analyses showed the delivery experience,the times of observation/simulation/training,and humanistic care skills might influence the total PS(P<0.05).However,only delivery experience(OR=1.43,95%CI:1.22-1.67)and the times of observation(OR=1.02,95%CI:1.00-1.04)were found to be independently correlated with the total PS in multivariate analyses.Conclusion More than 10 real-life practices were required to achieve the minimal competence of normal vaginal delivery.Enhancing the training on the four difficult steps of vaginal delivery might improve the learning efficiency when delivery opportunities are limited.

With the rapid development of generative artificial intelligence(GAI)technologies,their widespread application in academic research and writing is continuously expanding the boundaries of sci-entific inquiry.However,this trend has also raised a series of ethical and regulatory challenges,inclu-ding issues related to authorship,content authenticity,citation accuracy,and accountability.In light of the growing involvement of AI in generating academic content,establishing an open,controllable,and trustworthy ethical governance framework has become a key task for safeguarding research integrity and maintaining trust within the academic community.This expert consensus outlines ethical requirements across key stages of AI-assisted academic writing-including topic selection,data management,citation practices,and authorship attribution.It aims to clarify the boundaries and ethical obligations surrounding AI use in academic writing,ensuring that technological tools enhance efficiency without compromising in-tegrity.The goal is to provide guidance and institutional support for building a responsible and sustainable research ecosystem.

Objective To predict the risk of coronary atherosclerosis using cerebral small vessel disease and cerebral artery stenosis in order to assist early identification of coronary artery disease(CHD).Methods A retrospective analysis was conducted on 130 patients(aged≥65 years)who underwent cerebral MRI,magnetic resonance angiography(MRA),and coronary computed tomo-graphy angiography in the Chinese PLA General Hospital between January 2019 and December 2024.Based on coronary computed tomography angiography results,the patients were categorized into an asymptomatic CHD group(56 cases)and a non-CHD group(74 cases).Cerebral MRI and MRA were used to assess white matter hyperintensity,enlarged perivascular spaces,lacunar infarcts,cerebral microbleeds(CMB)scores,total score of cerebral small vessel disease and degree of cerebral artery stenosis.Multivariate logistic regression analysis was employed to identify the clinical and radiological indicators related to CHD.Then a predictive model was constructed,and then its performance in early predicting CHD was evaluated.Results Blood glucose,low-density lipoprotein cholesterol,CMB score≥ 1,and≥50％stenosis of the posterior cerebral artery were independent risk factors for CHD(P＜0.05).The combined predictive model integrating clinical indicators,cerebral small vessel disease,and cerebral artery stenosis demonstrated the best performance,with an AUC value of 0.867(95％CI:0.807-0.927),outperforming the clinical model and the clinical-cerebral small vessel disease model.Conclusion cerebral small vessel dis-ease and cerebral artery stenosis are closely related to coronary atherosclerosis.The predictive model integrating clinical features and cerebrovascular disease imaging markers provides impor-tant reference for early screening of CHD.

Objective To investigate the learning curve of real-life vaginal delivery,including its difficult steps and influencing factors,to optimize the future training of vaginal delivery for residents in department of obstetrics and gynecology.Methods From 25 Sep 2020 to 12 Mar 2022,OBGYN residents without previous experiences in vaginal delivery were prospectively enrolled in Obstetrics and Gynecology Hospital,Fudan University.Residents performed normal vaginal delivery under the supervision of senior obstetricians and midwives.The performance score(PS)of vaginal delivery and its 9 steps were evaluated via a questionnaire fulfilled by the supervisor once each delivery was finished.Logistic regression models were performed for univariate and multivariate analyses to evaluate the factors that might be correlated with the PS.Results Eventually,233 deliveries performed by 60 residents were analyzed.Results showed that more than 10 deliveries were needed for 70%of residents to obtain minimal competence of vaginal delivery.Perineal protection,delivery of the fetal head,delivery of the fetal shoulders and repair of episiotomy or laceration were found to be the most difficult steps,which required more practices to achieve minimal competence level.Univariate analyses showed the delivery experience,the times of observation/simulation/training,and humanistic care skills might influence the total PS(P<0.05).However,only delivery experience(OR=1.43,95%CI:1.22-1.67)and the times of observation(OR=1.02,95%CI:1.00-1.04)were found to be independently correlated with the total PS in multivariate analyses.Conclusion More than 10 real-life practices were required to achieve the minimal competence of normal vaginal delivery.Enhancing the training on the four difficult steps of vaginal delivery might improve the learning efficiency when delivery opportunities are limited.

With the rapid development of generative artificial intelligence(GAI)technologies,their widespread application in academic research and writing is continuously expanding the boundaries of sci-entific inquiry.However,this trend has also raised a series of ethical and regulatory challenges,inclu-ding issues related to authorship,content authenticity,citation accuracy,and accountability.In light of the growing involvement of AI in generating academic content,establishing an open,controllable,and trustworthy ethical governance framework has become a key task for safeguarding research integrity and maintaining trust within the academic community.This expert consensus outlines ethical requirements across key stages of AI-assisted academic writing-including topic selection,data management,citation practices,and authorship attribution.It aims to clarify the boundaries and ethical obligations surrounding AI use in academic writing,ensuring that technological tools enhance efficiency without compromising in-tegrity.The goal is to provide guidance and institutional support for building a responsible and sustainable research ecosystem.

Abdominal war trauma is a common and high-risk type of injury in the modern battlefield,with rapid changes in condition and a high mortality rate.There is an urgent need for emerging medical technologies to improve the efficiency and success rate of first aid for military casualties.With the development of artificial intelligence(AI),5G,and other emerging technologies,the concept of intelligent medical treatment is gradually forming and can assist in the diagnosis and treatment of abdominal trauma.This paper reviews the characteristics of abdominal war trauma in modern wars,discusses the application of intelligent medical treatment for abdominal war trauma and its drawbacks to be solved,aiming to provide reference for research related to abdominal war trauma.

Objective To establish and evaluate a nomogram prediction model for spontaneous peritonitis in HBV-related primary liver cancer.Methods A retrospective study was conducted on 1298 patients with HBV-related primary liver cancer hospitalized in the Kunming Third People's Hospital from January 2012 to December 2022.General data and serological indicators were collected,and patients were divided into infection group(n=262)and control group(n=1036)based on the occurrence of spontaneous peritonitis.Univariate and LASSO regression analyses were used to screen variables,followed by binary logistic regression to analyze the influencing factors of spontaneous peritonitis in HBV-related primary liver cancer patients,leading to the establishment of a nomogram prediction model.Finally,the Hosmer-lemeshow(H-L)goodness of fit test,receiver operating characteristic(ROC)curve,calibration curve,decision curve analysis(DCA)and clinical impact curve(CIC)were utilized to evaluate the fit degree,accuracy,calibration,and clinical practicability of the nomogram prediction model.Results Single factor analysis revealed significant differences between infection group and control group in portal vein cancer thrombus(PVTT),Child-Pugh grade,China Liver Cancer Staging(CNLC)stage,alcohol consumption history,smoking history,white blood cell count(WBC),neutrophil count(NE),hemoglobin(Hb),fibrinogen(FIB),abnormal prothrombin(PIVKA-Ⅱ),aspartate aminotransferase(AST),alanine aminotransferase(ALT),total protein(TP),prealbumin(PA),γ-glutamyltransferase(GGT),alkaline phosphatase(ALP),cholinesterase(CHE),total bile acid(TBA),total cholesterol(TC),low density lipoprotein(LDL),creatinine(Cr),HBV DNA,CD3+T cells count,CD4+T cells count,CD8+T cells count,CD4+T cells/CD8+T cells ratio,procalcitonin(PCT),serum amyloid A(SAA),interleukin-6(IL-6),high-sensitivity C-reactive protein(hs-CRP),alpha-fetoprotein(AFP),and IL-4(P＜0.05).LASSO regression analysis identified 5 variables:Child-Pugh grade,PVTT,WBC,CHE and hs-CRP.Binary logistic regression analysis indicated that Child-Pugh grade(Grade B:OR=5.780,95％CI 3.271-10.213,P＜0.001;Grade C:OR=14.818,95％CI 7.697-28.526,P＜0.001),PVTT(OR=2.893,95％CI 2.037-4.108,P＜0.001),WBC(OR=1.088,95％CI 1.031-1.148,P=0.002),and hs-CRP(OR=1.005,95％CI 1.001-1.010,P=0.026)were the independent risk factors of spontaneous peritonitis in HBV-related primary liver cancer patients.Using these 4 variables,a nomogram prediction model was constructed and evaluated.The P-value of the H-L goodness of fit test was 0.760.Moreover,the area under ROC curve(AUC)was 0.866,with a sensitivity of 0.870 and a specificity of 0.716.The average absolute error of the calibration curve is 0.022.DCA and CIC analyses demonstrated that the nomogram prediction model possessed some clinical utility.Conclusion The nomogram prediction model for spontaneous peritonitis in HBV-related primary liver cancer patients,constructed using Child-Pugh grade,PVTT,WBC and hs-CRP,exhibits a high fitting degree and accuracy,with the prediction probability highly consistent with the actual occurrence probability,and possesses certain clinical practicability.

Stent entrapment is a rare complication of percutaneous coronary intervention.In recent years,with the development of distal radial artery puncture technology,the rare complications related to distal radial artery have been gradually understood.This article describes a patient who underwent coronary intervention through a distal radial approach,and the stent was dislodged and trapped in the far radial artery.The patient came to our hospital for stent implantation because of acute extensive anterolateral myocardial infarction.During the intervention,the balloon could not be filled when the stent was released from the left anterior descending artery,and the retracting stent could not be used to remove the guide catheter.The stent was dislodged and embedded in the distal vessel.The sheath was inserted through the proximal radial reverse puncture,and the stent was captured with a snare and removed.

Stent entrapment is a rare complication of percutaneous coronary intervention.In recent years,with the development of distal radial artery puncture technology,the rare complications related to distal radial artery have been gradually understood.This article describes a patient who underwent coronary intervention through a distal radial approach,and the stent was dislodged and trapped in the far radial artery.The patient came to our hospital for stent implantation because of acute extensive anterolateral myocardial infarction.During the intervention,the balloon could not be filled when the stent was released from the left anterior descending artery,and the retracting stent could not be used to remove the guide catheter.The stent was dislodged and embedded in the distal vessel.The sheath was inserted through the proximal radial reverse puncture,and the stent was captured with a snare and removed.

Objective To predict the risk of coronary atherosclerosis using cerebral small vessel disease and cerebral artery stenosis in order to assist early identification of coronary artery disease(CHD).Methods A retrospective analysis was conducted on 130 patients(aged≥65 years)who underwent cerebral MRI,magnetic resonance angiography(MRA),and coronary computed tomo-graphy angiography in the Chinese PLA General Hospital between January 2019 and December 2024.Based on coronary computed tomography angiography results,the patients were categorized into an asymptomatic CHD group(56 cases)and a non-CHD group(74 cases).Cerebral MRI and MRA were used to assess white matter hyperintensity,enlarged perivascular spaces,lacunar infarcts,cerebral microbleeds(CMB)scores,total score of cerebral small vessel disease and degree of cerebral artery stenosis.Multivariate logistic regression analysis was employed to identify the clinical and radiological indicators related to CHD.Then a predictive model was constructed,and then its performance in early predicting CHD was evaluated.Results Blood glucose,low-density lipoprotein cholesterol,CMB score≥ 1,and≥50％stenosis of the posterior cerebral artery were independent risk factors for CHD(P＜0.05).The combined predictive model integrating clinical indicators,cerebral small vessel disease,and cerebral artery stenosis demonstrated the best performance,with an AUC value of 0.867(95％CI:0.807-0.927),outperforming the clinical model and the clinical-cerebral small vessel disease model.Conclusion cerebral small vessel dis-ease and cerebral artery stenosis are closely related to coronary atherosclerosis.The predictive model integrating clinical features and cerebrovascular disease imaging markers provides impor-tant reference for early screening of CHD.