Objective To explore the advantages and disadvantages of da Vinci robotic-assisted thoracoscopic resection of pediatric posterior mediastinal tumors compared to traditional thoracoscopic surgery.Methods A total of 79 pediatric patients who underwent posterior mediastinal tumor resection at Union Hospital,Tongji Medical College,Huazhong University of Sci-ence and Technology,between January 2018 and October 2021 were included.Among them,47 patients underwent da Vinci ro-botic-assisted thoracoscopic surgery(Group A),and 32 patients underwent traditional thoracoscopic surgery(Group B).The pa-tients'age,gender,weight,tumor type,size,surgical time,intraoperative and postoperative complications,and other factors were compared and analyzed.Results There were 44 male and 35 female patients,with an average age of 4.5 years[(4.7±2.4)vs.(4.4±1.8),P=0.19]and an average weight of 21.7 kg[(23.6±7.5)vs.(20.9±6.1),P=0.10].Tumor sizes were(5.2±3.1)cm vs.(4.5±2.3)cm(P=0.07).All surgeries were completed safely with no intraoperative deaths.Pathological re-sults showed 25 cases of ganglioneuroma,19 cases of ganglioneuroblastoma,16 cases of neuroblastoma,10 cases of lymphangio-ma,8 cases of foregut cyst,and 1 case of lipoblastoma.One patient required conversion to thoracotomy due to intraoperative bleeding.The average anesthesia time[(127±25)vs.(124±30)min,P=0.42]and the operative time[(84±17)vs.(102±27)min,P=0.02)were significantly different.Estimated intraoperative blood loss[(14.6±4.4)vs.(15.4±5.3)mL,P=0.38]and the chest drainage time[(3.7±2.5)vs.(4.1±3.0)days,P=0.09]varied between groups.The average lengths of hospital stay were different[(7.2±1.9)vs.(7.4±2.3)days,P=0.40].Postoperative complications included 1 case of chylo-thorax,3 cases of pneumothorax,and 1 case of Horner's syndrome in Group A.There were 1 case of hemothorax,4 cases of pneumothorax,and 1 case of Horner's syndrome in Group B.Patients with ganglioneuroblastoma and neuroblastoma received postoperative chemotherapy.Follow-up for 2 to 5 years showed that all children recovered well,with no recurrence.Conclusion Both da Vinci robotic-assisted surgery and traditional thoracoscopic surgery are safe and feasible for pediatric mediastinal tumor surgery.The optimized instruments of the da Vinci robotic surgical system offer advantages in surgeries involving larger tumors or complex anatomical structures.

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 analyze the risk factors for increased drainage volume after open transforaminal lumbar interbody fusion(TLIF),and to establish a predictive model and then validate it.Methods The clinical data of 680 patients who underwent open TLIF at the General Hospital of Northern Theater Command from January 2016 to December 2019 were collected and the patients were randomly divided into the training group(n=476)and the validation group(n=204).Taking the predictive factors screened out by LASSO regression analysis as independent variables,a multivariate Logistic regression predictive model was constructed.The model was internally validated through the receiver operating characteristic(ROC)curve,Hosmer-Lemeshow goodness-of-fit test,and calibration curve,and its clinical utility was assessed via decision curve analysis(DCA).Results LASSO regression analysis screened out four predictive variables:age,number of surgical segments,operative duration,and intraoperative blood loss.The multivariate Logistic regression predictive model demonstrated that age≥60 years,number of surgical segments≥4,operative duration≥2 hours,and intraoperative blood loss≥200 mL were independent influencing factors for the increased postoperative drainage volume in patients undergoing TLIF(P<0.05).ROC curve analysis revealed an area under the curve(AUC)of 0.816(95%CI:0.798 to 0.867)in the training group and 0.783(95%CI:0.685 to 0.823)in the validation group,indicating that the predictive model had good discriminatory ability.Additionally,the Hosmer-Lemeshow goodness-of-fit test and calibration curve indicated that the predictive model had a good degree of fit,and the predicted probability was basically consistent with the actual probability,demonstrating a good calibration.The DCA results confirmed that this predictive model could be applied in clinical practice.Conclusion The risk factors for increased drainage volume after open TLIF include age,number of surgical segments,operative duration,and intraoperative blood loss.The predictive model established based on these factors demonstrates good performance,and it can be applied in clinical guidance for the selection of drainage tube removal time after TLIF.

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 explore the advantages and disadvantages of da Vinci robotic-assisted thoracoscopic resection of pediatric posterior mediastinal tumors compared to traditional thoracoscopic surgery.Methods A total of 79 pediatric patients who underwent posterior mediastinal tumor resection at Union Hospital,Tongji Medical College,Huazhong University of Sci-ence and Technology,between January 2018 and October 2021 were included.Among them,47 patients underwent da Vinci ro-botic-assisted thoracoscopic surgery(Group A),and 32 patients underwent traditional thoracoscopic surgery(Group B).The pa-tients'age,gender,weight,tumor type,size,surgical time,intraoperative and postoperative complications,and other factors were compared and analyzed.Results There were 44 male and 35 female patients,with an average age of 4.5 years[(4.7±2.4)vs.(4.4±1.8),P=0.19]and an average weight of 21.7 kg[(23.6±7.5)vs.(20.9±6.1),P=0.10].Tumor sizes were(5.2±3.1)cm vs.(4.5±2.3)cm(P=0.07).All surgeries were completed safely with no intraoperative deaths.Pathological re-sults showed 25 cases of ganglioneuroma,19 cases of ganglioneuroblastoma,16 cases of neuroblastoma,10 cases of lymphangio-ma,8 cases of foregut cyst,and 1 case of lipoblastoma.One patient required conversion to thoracotomy due to intraoperative bleeding.The average anesthesia time[(127±25)vs.(124±30)min,P=0.42]and the operative time[(84±17)vs.(102±27)min,P=0.02)were significantly different.Estimated intraoperative blood loss[(14.6±4.4)vs.(15.4±5.3)mL,P=0.38]and the chest drainage time[(3.7±2.5)vs.(4.1±3.0)days,P=0.09]varied between groups.The average lengths of hospital stay were different[(7.2±1.9)vs.(7.4±2.3)days,P=0.40].Postoperative complications included 1 case of chylo-thorax,3 cases of pneumothorax,and 1 case of Horner's syndrome in Group A.There were 1 case of hemothorax,4 cases of pneumothorax,and 1 case of Horner's syndrome in Group B.Patients with ganglioneuroblastoma and neuroblastoma received postoperative chemotherapy.Follow-up for 2 to 5 years showed that all children recovered well,with no recurrence.Conclusion Both da Vinci robotic-assisted surgery and traditional thoracoscopic surgery are safe and feasible for pediatric mediastinal tumor surgery.The optimized instruments of the da Vinci robotic surgical system offer advantages in surgeries involving larger tumors or complex anatomical structures.

Objective To analyze the risk factors for increased drainage volume after open transforaminal lumbar interbody fusion(TLIF),and to establish a predictive model and then validate it.Methods The clinical data of 680 patients who underwent open TLIF at the General Hospital of Northern Theater Command from January 2016 to December 2019 were collected and the patients were randomly divided into the training group(n=476)and the validation group(n=204).Taking the predictive factors screened out by LASSO regression analysis as independent variables,a multivariate Logistic regression predictive model was constructed.The model was internally validated through the receiver operating characteristic(ROC)curve,Hosmer-Lemeshow goodness-of-fit test,and calibration curve,and its clinical utility was assessed via decision curve analysis(DCA).Results LASSO regression analysis screened out four predictive variables:age,number of surgical segments,operative duration,and intraoperative blood loss.The multivariate Logistic regression predictive model demonstrated that age≥60 years,number of surgical segments≥4,operative duration≥2 hours,and intraoperative blood loss≥200 mL were independent influencing factors for the increased postoperative drainage volume in patients undergoing TLIF(P<0.05).ROC curve analysis revealed an area under the curve(AUC)of 0.816(95%CI:0.798 to 0.867)in the training group and 0.783(95%CI:0.685 to 0.823)in the validation group,indicating that the predictive model had good discriminatory ability.Additionally,the Hosmer-Lemeshow goodness-of-fit test and calibration curve indicated that the predictive model had a good degree of fit,and the predicted probability was basically consistent with the actual probability,demonstrating a good calibration.The DCA results confirmed that this predictive model could be applied in clinical practice.Conclusion The risk factors for increased drainage volume after open TLIF include age,number of surgical segments,operative duration,and intraoperative blood loss.The predictive model established based on these factors demonstrates good performance,and it can be applied in clinical guidance for the selection of drainage tube removal time after TLIF.

The working principle and development of flexible semiconductor devices based on organic field effect transistor(OFET)technology were introduced.The current research status of OFET-based wearable flexible monitoring devices were reviewed,including biomechanical monitoring devices,tattoo biomonitoring devices and cellular detection devices and etc.The deficiencies of OFET-based wearable flexible monitoring devices were analyzed,and it's pointed out that miniaturization,personalization and diversification were the directions for the development of the future OFET-based wearable flexible moni-toring devices.[Chinese Medical Equipment Journal,2024,45(1):93-100]

Objective To achieve intelligent recognition and segmentation of common craniocerebral inju-ries(hereinafter referred to as"segmentation")by training convolutional neural network DeepLabV3+model based on CT images of blunt craniocerebral injury(BCI),and to explore the value of deep learning in automated diagnosis of BCI in forensic medicine.Methods A total of 5 486 CT images of BCI from living persons were collected as the training set,validation set and test set for model training and performance evaluation.Another 255 CT images of BCI and 156 normal craniocerebral CT images from living persons were collected as the blind test set to evaluate the ability of the model to seg-ment the five types of craniocerebral injuries including scalp hematoma,skull fracture,epidural hema-toma,subdural hematoma,and brain contusion.Another 340 BCI and 120 normal craniocerebral CT images from cadavers were collected as the new blind test set to explore the application value of the model trained by living CT images in the segmentation of BCI in cadavers.The five types CT images of all BCI except the blind test set were manually labeled;then,each dataset was inputted into the model to train the model.The performance of the model was evaluated and optimized based on the loss function and accuracy curves of the training set and validation set,and the generalization ability was evaluated based on the Dice value of the test set.According to the accuracy,precision and F1 value of the blind test set,the segmentation performance of the model for five types of BCI was evaluated.Results After training and optimizing the model,the average Dice values of the final optimal model to scalp hematoma,skull fracture,epidural hematoma,subdural hematoma and brain contusion segmen-tation were 0.766 4,0.812 3,0.938 7,0.782 7 and 0.858 1,respectively,all greater than 0.75,meeting the expected requirements.External validation showed that the F1 values were 93.02%,89.80%,87.80%,92.93%and 86.57%in living CT images,respectively;83.92%,44.90%,76.47%,64.29%and 48.89%in cadaveric CT images,respectively.The above suggested that the model was able to accu-rately segment various types of craniocerebral injury on living CT images,while its segmentation ability was relatively poor on cadaveric CT images,but still able to accurately segment scalp hematoma,epidu-ral hematoma and subdural hematoma.Conclusion Deep learning model trained on CT images can be used for BCI segmentation.However,the direct use of living persons'BCI models for the identifica-tion of cadaveric BCI has some limitations.This study provides a new approach for intelligent segmen-tation of virtual anatomical data for BCI.

Renal fibrosis is a common pathological manifestation of all chronic kidney diseases.Ferroptosis is closely related to the pathogenesis of renal fibrosis and can influence the onset of renal fibrosis,and it is the most critical step in the development of renal fibrosis.The paper describes the relationship between ferroptosis and renal fibrosis,discusses the research progress of ferroptosis on renal fibrosis,and further summarizes,analyzes,and describes the effective and highly targeted natural active ingredients of traditional Chinese medicines against ferroptosis,and concludes that the reversal of renal fibrosis is achieved through the regulation of the key targets of ferroptosis,with a view to providing a broad new direction for its prospects in the field of renal fibrotic disease prevention and treatment;and to provide a scientific guide for clinical treatment and basis for clinical treatment.

Aprocitentan is a dual endothelin receptor antagonist.Based on the effective evidence of Ⅲ phases of clinical trials,the drug was approved for marketing by the U.S.Food and Drug Administration on March 19,2024 for the treatment of refractory hypertension.At present,multiple clinical studies have confirmed that Aprocitentan has excellent antihypertensive effects and good tolerability.This article reviews the pharmacological effects,preclinical.