Objective To analyze the effect of releasing the lower pulmonary ligament on right residual lung expansion after right upper lobe resection under different body mass index (BMI) levels. Methods The clinical data of patients who underwent thoracoscopic right upper lobe resection in the First Affiliated Hospital with Nanjing Medical University from 2021 to 2022 were retrospectively analyzed. Patients were divided into a group A (17 kg/m2＜BMI≤23 kg/m2), a group B (23 kg/m2＜BMI≤29 kg/m2) and a group C (BMI＞29 kg/m2) according to BMI. The presence of residual cavity was judged by chest X-ray at 7-10 days after operation, the degree of compensation change of the right main bronchus angle was measured, and the changes in lung volume were determined by CT three-dimensional reconstruction. Results A total of 157 patients who underwent thoracoscopic right upper lobe resection were included, including 71 males and 86 females, with an average age of (59.7±11.2) years. There were 50 patients in the group A, 75 patients in the group B, and 32 patients in the group C. In the group A, compared with those without releasing the lower pulmonary ligament, patients with releasing had a lower incidence of postoperative residual cavity (P=0.016), greater changes in bronchus angle (P<0.001), and smaller changes in lung volume (P<0.001). In the group B and C, there was no significant effect of releasing the lower pulmonary ligament on postoperative residual cavity, bronchus angle, and lung volume changes (P>0.05). Conclusion For patients with thin and long body shape and low BMI, releasing the lower pulmonary ligament is helpful to promote the expansion of the residual lung after right upper lobe resection and reduce the occurrence of postoperative residual cavity in patients.

Body weight abnormalities, including overweight, obesity, and underweight, have become a dual public health challenge in Chinese adults: overweight and obesity lead to a variety of chronic complications, while underweight increases the risks of malnutrition, sarcopenia, and organ dysfunction. To systematically address these issues, multidisciplinary experts in endocrinology, sports science, nutrition, and psychiatry from various regions have held multiple weight management seminars. Based on the latest epidemiological data and clinical evidence, they expanded the guideline to include assessment and intervention strategies for underweight, in addition to the core content of obesity management. This guideline outlines the etiological mechanisms, evaluation methods, and multidimensional management strategies for overweight and obesity, covering key areas such as diagnosis and assessment, medical nutrition therapy, exercise prescription, pharmacological intervention, and psychological support. It is intended to provide a scientific and standardized approach to weight management across the adult population, aiming to curb the rising prevalence of obesity, mitigate complications associated with abnormal body weight, and improve nutritional status and overall quality of life.

Objective To explore the regulatory effect of amylin on the feeding behavior of mice through the ERK pathway of paraventricular nucleus of the hypothalamus(PVN).Methods A total of 54 C57BL/6J mice were randomly divided into multiple groups for experiments.First,immunofluorescence was used to detect the distribution of ERK and calcitonin receptor(CTR)neurons in the brains of 4 mice.A chemical lesion model of PVN neurons was established,and 16 mice were divided into four groups(4 mice per group):control(Ctrl)group,ibotenic acid(IBO)-lesioned group,amylin group,and amylin+IBO group.The number of ERK-positive neurons in the PVN region and food intake were then measured.Subsequently,16 mice were divided into four groups(4 mice per group),Ctrl group,amylin group,ERK antagonist U0126 group,and amylin+U0126 group.The expression level of the immediate early gene c-fos in the PVN was detected by Real-time PCR.Immunofluorescence was used to detect the distribution of p-ERK positive neurons in the brains of 4 saline-treated mice and 4 amylin-treated mice.Finally,18 mice were randomly divided into three groups(6 mice per group)through intracerebroventricular injection,Ctrl group,amylin group,and amylin+receptor antagonist AC 187 group.Then,triple immunofluorescence was used to detect the co-expression of p-ERK,neuronal nitric oxide synthase(nNOS),and CTR-immunopositive neurons in the PVN region,and food intake within 3 hours after treatment was recorded simultaneously.Results ERK-positive neurons were mainly distributed in the suprachiasmatic nucleus(SCN),arcuate nucleus(ARC),and locus coeruleus(LC),while ERK and CTR co-expressing positive neurons were specifically enriched in the PVN(P=0.0012).Lesion of PVN neurons led to a decrease in the number of ERK-positive neurons(P＜0.001)and blocked the anorectic effect of amylin(P＜0.001).Amylin activated the expression of the c-fos gene in the PVN region(P＜0.05),and this effect was inhibited by U0126(P＜0.05).After intracerebroventricular injection of amylin,the number of triple-positive neurons for p-ERK,nNOS,and CTR in the PVN increased(P＜0.05).Addition of AC 187 blocked the activation effect of amylin(P＜0.01)and reversed the anorectic effect of amylin(1 h,P=0.2218;2 h,P=0.2218;3 h,P=0.6974).Conclusion P-ERK neurons in the PVN regulate feeding behavior through an amylin receptor-dependent ERK signaling pathway.

Objective To propose a dynamic early warning model based on machine learning methods and validate its predi-ctive efficacy so as to achieve precise assessment and early warning of mortality risk in patients with traumatic hypothermia.Methods Firstly,a total of 480 patients who met inclusion criteria were retrospectively selected from the eICU database and randomly divided into training and test sets at an 8∶2 ratio.Secondly,physiological parameters were extracted from these patients,and five machine learning algorithms including XGBoost,AdaBoost,LightGBM,logistic regression(LR)and random forest(RF)were employed respectively to develop dynamic mortality risk warning models for traumatic hypothermia patients,utilizing a 1-hour observation window.Thirdly,receiver operating characteristic curves(ROC)were plotted using the test set data and the effects of different warning windows on the model performance were analyzed by calculating the AUC.Finally,the interpretability of the models was analyzed using the SHapley Additive exPlanations(SHAP)algorithm to elucidate the contribution of each feature to predictive performance.Results The optimal warning window for the dynamic warning model constructed using the eICU database was 12 hours,and in case of 12-hour warning window the logistic regression model achieved the highest AUC of 0.935 and showed optimal predictive performance.The results of the interpretability analysis by the SHAP algorithm showed that body temperature was the feature that had the greatest impact on the model results,and its reduction was positively correlated with the increased risk of death.Conclusion The machine learning-based dynamic warning model for mortality risk in traumatic hypothermia patients enables real-time dynamic risk assessment,providing robust support for clinicians to identify the patient's condition changes at an early stage and references for the adjustment of clinical treatment programs.[Chinese Medical Equipment Journal,2025,46(3):9-14]

Under the background of forensic medicine becoming a first-level discipline,the opportuni-ties and challenges of discipline development coexist.Starting from the actual situation and characteris-tics of local medical colleges and universities,this paper discusses the problems and solutions for the development of forensic medicine discipline from the perspective of building a regional high-level medical university.Combined with the experiences of carrying out forensic medicine education in our college,this paper supplies our thoughts and practices on improving the discipline system,enhancing the ability to serve society,perfecting the talent cultivation model and promoting forensic culture,to provide reference and inspiration for the development of forensic medicine in other universities,jointly promote the advancement of forensic medicine in China to a new stage,and contribute the wisdom and strength of forensic medical experts to the construction of a law-based China,a safe China and a healthy China.

Objective To propose a dynamic early warning model based on machine learning methods and validate its predi-ctive efficacy so as to achieve precise assessment and early warning of mortality risk in patients with traumatic hypothermia.Methods Firstly,a total of 480 patients who met inclusion criteria were retrospectively selected from the eICU database and randomly divided into training and test sets at an 8∶2 ratio.Secondly,physiological parameters were extracted from these patients,and five machine learning algorithms including XGBoost,AdaBoost,LightGBM,logistic regression(LR)and random forest(RF)were employed respectively to develop dynamic mortality risk warning models for traumatic hypothermia patients,utilizing a 1-hour observation window.Thirdly,receiver operating characteristic curves(ROC)were plotted using the test set data and the effects of different warning windows on the model performance were analyzed by calculating the AUC.Finally,the interpretability of the models was analyzed using the SHapley Additive exPlanations(SHAP)algorithm to elucidate the contribution of each feature to predictive performance.Results The optimal warning window for the dynamic warning model constructed using the eICU database was 12 hours,and in case of 12-hour warning window the logistic regression model achieved the highest AUC of 0.935 and showed optimal predictive performance.The results of the interpretability analysis by the SHAP algorithm showed that body temperature was the feature that had the greatest impact on the model results,and its reduction was positively correlated with the increased risk of death.Conclusion The machine learning-based dynamic warning model for mortality risk in traumatic hypothermia patients enables real-time dynamic risk assessment,providing robust support for clinicians to identify the patient's condition changes at an early stage and references for the adjustment of clinical treatment programs.[Chinese Medical Equipment Journal,2025,46(3):9-14]

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 investigate the clinical efficacy and safety of facilitated percutaneous coronary intervention(PCI)with half-dose recombinant staphylokinase(r-SAK)in patients with ST-segment elevation myocardial infarction(STEMI)who are expected to undergo PCI within 120 minutes.Methods From October 2021 to August 2022,a total of 200 STEMI patients in eight centers were included and randomly assigned in a 1﹕1 ratio to either r-SAK group or control group.Patients received loading doses of aspirin and ticagrelor and intravenous heparin and were randomized to receive an intravenous bolus of either 5 mg r-SAK or normal saline prior to PCI.The outcomes were set as ST-segment resolution(STR)at 60-90 minutes after PCI,the proportion and transition of pathological Q waves on the 5th day after PCI,and the proportion of high-sensitivity cardiac troponin T(hs-cTnT)peaking within 12 hours of onset.The safety outcome was major bleeding events defined as Bleeding Academic Research Consortium(BARC)≥type 3 bleeding during hospitalization.Results Compared with the control group,the r-SAK group had a higher proportion of STR≥70%within 60-90 minutes after PCI(58.3%vs.40.3%,P=0.009);a lower proportion of pathological Q waves(59.1%vs.74.1%,P=0.040);a lower rate of Q wave progression(14.8%vs.43.2%,P＜0.001);a higher rate of Q wave disappearance(12.5%vs.3.7%,P=0.027);and a higher proportion of hs-cTnT peaking within 12 hours of symptom onset[31/40(77.5%)vs.17/33(51.5%),P=0.027].Regarding the safety outcome,no significant difference in BARC≥type 3 bleeding was found between the two groups during hospitalization(P＞0.05).Conclusions For STEMI patients who were expected to undergo primary PCI within 120 minutes of symptom onset,the facilitated PCI with half-dose r-SAK significantly increased the proportion of STR≥70%at 60-90 minutes after PCI,reduced the formation of pathological Q waves,and shortened the time to peak hs-cTnT,without increasing the risk of bleeding,which should be an alternative reperfusion strategy worthy of further study.

Objective To propose a residual convolutional feedforward network-based high-resolution medical image generation model to enhance the quality for generating high-resolution medical images.Methods Firstly,a vector quantized generative adversarial network(VQGAN)was selected as the benchmark model;secondly,a residual convolutional feedfor-ward network composed of a residual module and a multi-scale convolutional feedforward module was integrated into the the encoder and decoder of VQGAN to form a new architecture;finally,the model propsed was compared with denoising diffusion model(DDM),StyleSwin model,VQGAN moel and SinGAN model to verify its performance,and the ablation experiment was carried out.Results The quantitative evaluation comparison showed that the model proposed behaved well generally with the lowest frechet inception distance(FID)(145.64),the lowest learned perceptual image patch similarity(LPIPS)(0.46)and the peak signal-to-noise ratio(PSNR)(62.63)only lower than that of DDM.Visualized comparison indicated the model proposed had the image generated gaining advantages over the other models in the texture,edges and sharpness of kidney stones and similarity to the real lesion images.The ablation experiment proved that the model proposed behaved better than the VQGAN benchmark model in convergence rate and training stability.Conclusion The proposed model has significant advantages in learning multi-scale texture features and retaining the information on low-dimensional anatomical structures,which can be used for generating high-quality high-resolution medical images.[Chinese Medical Equipment Journal,2025,46(9):1-8]

Objective:To analyze the multi antigen distribution characteristics of Rh blood group system in Shunde area of Guangdong Province,explore the feasibility of Rh phenotype compatible blood transfusion for blood recipients in this area,and formulate the precise blood transfusion strategies.Methods:From June 2022 to December 2024,113 226 hospitalized patients scheduled for blood transfusion in Shunde Hospital of Southern Medical University and 30 832 blood donors'blood samples provided by Shunde central blood station in the same period were detected for ABO blood group and Rh phenotype by microcolumn gel method,and the Rh phenotype data of the recipients and blood donors were compared and analyzed.Results:Among 113 226 blood samples,112 963 cases(99.77％)were RhD positive,with CCDee(54.96％)and CcDEe(28.21％)phenotypes being the main phenotypes;263 cases(0.23％)were RhD negative,with ccDee(50.19％)and CcDee(36.88％)as the main phenotype.Among 30 832 blood donor samples,CCDee(54.65％)and CcDEe(27.93％)were the main phenotypes of Rh phenotype with RhD positive.The positive rates of D,C,c,E and e antigens of blood recipients and blood donors were in the same order from high to low D＞e＞C＞c＞E.The frequency of e antigen in RhD positive recipients with different ABO blood groups was statistically different from that of blood donors(P＜0.05),while there was no statistical difference in the other four antigens(P＞0.05),the distribution of ccDEe phenotypes was statistically different(P＜0.05),but there was no statistical difference in the distribution of other eight phenotypes(P＞0.05).In RhD positive recipients,the probability of finding compatible blood for phenotype CcDEe was 100％,the probability of finding compatible blood for phenotype CCDee,CCDEe and DcDee was 54％-65％,and the probability of finding compatible blood for other phenotypes was less than 10％.Providing blood with CCDee and ccDEE phenotypes according to Rh blood matching scheme can meet the needs of more than 99％of patients with 9 Rh phenotype compatible blood transfusion in this region.Conclusion:Rh phenotype detection should be carried out for hospitalized patients to be transfused,and the precise transfusion strategy of Rh phenotype isotype or compatibility should be implemented to make the transfusion treatment of patients more safe and reliable.