The integrated medical and elderly care model provides comprehensive medical and elderly care services by establishing medical facilities in elderly care institutions or forming close cooperative relationships with surrounding medical institutions. Currently， there are 87，000 paired partnerships established nationwide between medical and health institutions and elderly care service institutions， and more than 7，800 integrated medical and elderly care institutions have obtained the qualifications of medical and health institutions and completed elderly care service registration. This model not only meets the elderly’s healthcare needs but also provides life care， psychological support， and social activities， thereby improving their quality of life. However， the integrated medical and elderly care model also faces numerous ethical dilemmas in practice. This paper aimed to explore in depth the ethical dilemmas and countermeasures optimization in the work of elderly care institutions under this model， to promote the improvement of service quality， comprehensively guarantee the rights and interests of the elderly， and promote the healthy development of this model in practice. Under this model， the elderly care institutions not only bear the responsibility of providing long-term care and life services but also need to cooperate with medical institutions to provide healthcare and health management services for the elderly. By exploring the practical paths for elderly care institutions to address the ethical dilemmas faced with this model， feasible solutions were proposed to enhance the welfare of the elderly and promote the harmonious development of society.

Objective To explore the research status and emerging focus of virtual reality(VR)technology in the field of stroke.Methods The global literature of VR technology in the field of stroke from Jan.2014 to Aug.2024 was retrieved from the Web of Science Core Collection.Bibliometric software was used to draw a visual knowledge map of authors,institutions,key words,etc.Results After excluding proofreading notices,editorial materials,conference papers,etc.,a total of 785 articles were included.Over the past decade,the number of new publications has shown an upward trend.China was the country with the largest total number of publications(130 articles).Lamontagne Anouk(10 articles)and Calabro Rocco Salvatore(10 articles)were tied for the authors with the highest number of publications.The institution and journal with the most literature in this field were McGill University(Canada,27 articles)and Journal of Neuroengineering and Rehabilitation(60 articles),respectively.The key word analysis and the results of the strongest burst key words indicated that the research focused on upper limb,gait training,motor function,cognitive rehabilitation,post-stroke unilateral spatial neglect,stimulation,motor imagery,cortical reorganization,etc.Conclusion Over the past decade,the application of VR technology has gradually increased in both breadth and depth in the field of stroke,bringing new opportunities and thoughts to stroke rehabilitation.The new forms of VR technology combined with neuromodulation,neuroimaging,brain-computer interfaces,artificial intelligence,and telemedicine may be the future research topics and directions.

Objective:To evaluate the safety and complications of external cephalic version(ECV)for term breech presentation and to explore factors influencing the occurrence of ECV-related complications.Methods:Pregnant women with term breech presentation who underwent ECV(ECV group,n=751)and those who under-went direct cesarean section(CS)without ECV(CS group,n=706)at Guangzhou Women and Children's Medi-cal Center of Guangzhou Medical University,from January 1,2018,to July 31,2024,were enrolled.Differences in maternal clinical characteristics and neonatal outcomes were compared between the two groups.The ECV group was further divided into a successful ECV subgroup(n=537)and a failed ECV subgroup(n=214)to compare complication rates.Based on the presence or absence of complications,the ECV group was divided into a compli-cation subgroup(n=86)and a no-complication subgroup(n=665).Univariate analysis was performed on the clinical data of these subgroups.Statistically significant factors identified in the univariate analysis were subse-quently included in a multivariate Logistic regression analysis to identify high-risk factors for ECV complications.Results:①Among the 751 women undergoing ECV,the success rate was 71.50％(537/751).The vaginal deliv-ery rate following successful ECV was 57.26％(430/751).The overall complication rate was 11.45％(86/751),with a perinatal mortality rate of 0.13％(1/751).②There were no significant differences with regard to severe neonatal asphyxia and neonatal intensive care admission rate between ECV group and CS group(P＞0.05).③The total complication rate,incidence of cesarean delivery(CS)within 24 h,and incidence of uterine contrac-tions were significantly higher in the failed ECV group compared to the successful ECV group(P＜0.05).Howev-er,there was no statistically significant difference in the incidence of severe complications(fetal demise,placental abruption,emergency CS)between the two groups(P＞0.05).④Univariate and multivariate Logistic regression analyses revealed that three factors were associated with a reduced risk of ECV complications(P＜0.05):a high-er amniotic fluid index(AFI),non-engagement of the presenting part,and a palpable fetal head.Conversely,the use of anesthesia and the use of nifedipine as the tocolytic were associated with an increased risk of ECV compli-cations(P＜0.05).Conclusions:ECV does not increase the adverse outcomes of full-term neonates with breech presentation.But failed ECV can increase complications.Higher amniotic fluid index,not engaged of fetal presen-tation,touchable of fetal head and appropriate tocolytic agent application can reduce the complications while anes-thesia during ECV procedure can increase the complications of ECV.

Objective:To examine the validity and reliability of the DSM-5 Social Anxiety Disorder Severity Scale(SAD-D)in a Chinese adult population.Methods:The Chinese version of the DSM-5 Social Anxiety Disor-der Severity Scale was administered via online data collection platform Credamo to 300 adults(Sample 1,for item analysis,exploratory factor analysis and item selection of brief version of SAD-D)and 528 adults(Sample 2,for confirmatory factor analysis,criterion validity test,measurement invariance analysis and internal consistency reliabil-ity analysis for both SAD-D and its brief version).Criterion validity was tested with the Social Phobia Scale(SPIN)and Personal Report of Confidence as a Speaker(PRCS).A brief version of the scale was developed by u-sing the Ant Colony Optimization(ACO).A retest was conducted with 152 participants from Sample 2 after three weeks.Results:Exploratory factor analysis indicated that the SAD-D was a unidimensional scale with factor load-ings ranging from 0.49 to 0.82,and the results of the confirmatory factor analysis also supported the unidimension-al structure(x2/df=3.49,RMSEA=0.069,CFI=0.971,TLI=0.962,SRMR=0.028).The scores of Chinese version of the SAD-D were positively correlated with the SPIN scores(ICC=0.70,P＜0.001)and PRCS scores(ICC=0.73,P＜0.001).The Cronbach'α of the scale was 0.92,and the retest reliability was 0.85.The scale dem-onstrated cross-gender measurement invariance(△CFI＜0.01,△RMSEA＜0.01).The brief version of the SAD-D was selected as items 2,5,and 6,and its Cronbach'α coefficient was 0.86.Conclusion:The Chinese version of the SAD-D has satisfactoryvalidity andreliability,making it suitable for the assessment of social anxiety symptoms with Chinese adults.

Objective:To screen the optimal machine learning model for predicting the recurrence condition of hepatocellular carcinoma (HCC) at different time points post-surgery, based on the cutoff value of the Ki67 positive proliferation index condition calculated from recurrence-free survival and combined with various clinical features.Methods:retrospective study included initially treated patients with solitary HCC who underwent radical surgery at the Fifth Medical Center of the PLA General Hospital from January 2013 to March 2023. Data included general clinical data, preoperative laboratory parameters, and surgical pathology information about the subjects. The postoperative recurrence status was assessed by querying the medical record system or by telephone follow-up. The Ki67 positive index cutoff value was determined by the X-tile software based on the patient's recurrence-free survival status and time analysis. Survival rates were calculated using the Kaplan-Meier method, and survival curves were plotted. The study population was randomly divided into training and testing groups in a 7:3 ratio using a computer-generated random number method. The minimum redundancy maximum relevance (mRMR) method was used for feature variable selection. Predictive models for postoperative HCC recurrence conditions in patients with HCC were constructed using random forest, support vector machine, logistic regression, and gradient boosting decision tree machine learning algorithms. Inter-group comparisons for continuous data were performed using the t-test or Mann-Whitney U test. Inter-group comparisons of enumeration data were performed using the Pearson χ2 test, continuity-corrected χ2 test, or Fisher's exact test. Results:The cutoff values for the Ki67 positivity index were 0.3 and 0.5 in 510 cases, with a follow-up time ranging from 1.2 to 11.4 years (median: 6.2 years). The recurrence-free survival time was between 1 and 135 months (median: 32 months), with recurrence-free survival rates post-surgery at 1, 2, 3, and 5 years were 87.5%, 77.1%, 61.2%, and 54.5%, respectively. The top five variables predicted HCC recurrence and non-recurrence conditions following surgical follow-up at 6 months, 1 year, 2 years, and beyond 2 years, in accordance with information obtained by the mRMR screen out. The Ki67 positivity index screened a successfully constructed machine learning model to predict HCC recurrence and non-recurrence conditions following surgical follow-up at 6 months, 1 year, 2 years, and beyond 2 years. The machine learning model based on the gradient boosting decision tree algorithm had the best prediction performance among them (areas under the receiver operating characteristic curves for predicting HCC recurrence within six months in the training and validation sets were 0.996 and 0.946, and accuracies were 0.972 and 0.935, respectively).Conclusion:A machine learning model was successfully constructed using the Ki67 positivity index combined with four readily available clinical features to predict HCC recurrence. The machine learning model based on the gradient boosting decision tree algorithm demonstrated the best performance in terms of predicting HCC recurrence within six months after surgery.

Objective:To evaluate the safety and complications of external cephalic version(ECV)for term breech presentation and to explore factors influencing the occurrence of ECV-related complications.Methods:Pregnant women with term breech presentation who underwent ECV(ECV group,n=751)and those who under-went direct cesarean section(CS)without ECV(CS group,n=706)at Guangzhou Women and Children's Medi-cal Center of Guangzhou Medical University,from January 1,2018,to July 31,2024,were enrolled.Differences in maternal clinical characteristics and neonatal outcomes were compared between the two groups.The ECV group was further divided into a successful ECV subgroup(n=537)and a failed ECV subgroup(n=214)to compare complication rates.Based on the presence or absence of complications,the ECV group was divided into a compli-cation subgroup(n=86)and a no-complication subgroup(n=665).Univariate analysis was performed on the clinical data of these subgroups.Statistically significant factors identified in the univariate analysis were subse-quently included in a multivariate Logistic regression analysis to identify high-risk factors for ECV complications.Results:①Among the 751 women undergoing ECV,the success rate was 71.50％(537/751).The vaginal deliv-ery rate following successful ECV was 57.26％(430/751).The overall complication rate was 11.45％(86/751),with a perinatal mortality rate of 0.13％(1/751).②There were no significant differences with regard to severe neonatal asphyxia and neonatal intensive care admission rate between ECV group and CS group(P＞0.05).③The total complication rate,incidence of cesarean delivery(CS)within 24 h,and incidence of uterine contrac-tions were significantly higher in the failed ECV group compared to the successful ECV group(P＜0.05).Howev-er,there was no statistically significant difference in the incidence of severe complications(fetal demise,placental abruption,emergency CS)between the two groups(P＞0.05).④Univariate and multivariate Logistic regression analyses revealed that three factors were associated with a reduced risk of ECV complications(P＜0.05):a high-er amniotic fluid index(AFI),non-engagement of the presenting part,and a palpable fetal head.Conversely,the use of anesthesia and the use of nifedipine as the tocolytic were associated with an increased risk of ECV compli-cations(P＜0.05).Conclusions:ECV does not increase the adverse outcomes of full-term neonates with breech presentation.But failed ECV can increase complications.Higher amniotic fluid index,not engaged of fetal presen-tation,touchable of fetal head and appropriate tocolytic agent application can reduce the complications while anes-thesia during ECV procedure can increase the complications of ECV.

Objective:To examine the validity and reliability of the DSM-5 Social Anxiety Disorder Severity Scale(SAD-D)in a Chinese adult population.Methods:The Chinese version of the DSM-5 Social Anxiety Disor-der Severity Scale was administered via online data collection platform Credamo to 300 adults(Sample 1,for item analysis,exploratory factor analysis and item selection of brief version of SAD-D)and 528 adults(Sample 2,for confirmatory factor analysis,criterion validity test,measurement invariance analysis and internal consistency reliabil-ity analysis for both SAD-D and its brief version).Criterion validity was tested with the Social Phobia Scale(SPIN)and Personal Report of Confidence as a Speaker(PRCS).A brief version of the scale was developed by u-sing the Ant Colony Optimization(ACO).A retest was conducted with 152 participants from Sample 2 after three weeks.Results:Exploratory factor analysis indicated that the SAD-D was a unidimensional scale with factor load-ings ranging from 0.49 to 0.82,and the results of the confirmatory factor analysis also supported the unidimension-al structure(x2/df=3.49,RMSEA=0.069,CFI=0.971,TLI=0.962,SRMR=0.028).The scores of Chinese version of the SAD-D were positively correlated with the SPIN scores(ICC=0.70,P＜0.001)and PRCS scores(ICC=0.73,P＜0.001).The Cronbach'α of the scale was 0.92,and the retest reliability was 0.85.The scale dem-onstrated cross-gender measurement invariance(△CFI＜0.01,△RMSEA＜0.01).The brief version of the SAD-D was selected as items 2,5,and 6,and its Cronbach'α coefficient was 0.86.Conclusion:The Chinese version of the SAD-D has satisfactoryvalidity andreliability,making it suitable for the assessment of social anxiety symptoms with Chinese adults.

Objective:To screen the optimal machine learning model for predicting the recurrence condition of hepatocellular carcinoma (HCC) at different time points post-surgery, based on the cutoff value of the Ki67 positive proliferation index condition calculated from recurrence-free survival and combined with various clinical features.Methods:retrospective study included initially treated patients with solitary HCC who underwent radical surgery at the Fifth Medical Center of the PLA General Hospital from January 2013 to March 2023. Data included general clinical data, preoperative laboratory parameters, and surgical pathology information about the subjects. The postoperative recurrence status was assessed by querying the medical record system or by telephone follow-up. The Ki67 positive index cutoff value was determined by the X-tile software based on the patient's recurrence-free survival status and time analysis. Survival rates were calculated using the Kaplan-Meier method, and survival curves were plotted. The study population was randomly divided into training and testing groups in a 7:3 ratio using a computer-generated random number method. The minimum redundancy maximum relevance (mRMR) method was used for feature variable selection. Predictive models for postoperative HCC recurrence conditions in patients with HCC were constructed using random forest, support vector machine, logistic regression, and gradient boosting decision tree machine learning algorithms. Inter-group comparisons for continuous data were performed using the t-test or Mann-Whitney U test. Inter-group comparisons of enumeration data were performed using the Pearson χ2 test, continuity-corrected χ2 test, or Fisher's exact test. Results:The cutoff values for the Ki67 positivity index were 0.3 and 0.5 in 510 cases, with a follow-up time ranging from 1.2 to 11.4 years (median: 6.2 years). The recurrence-free survival time was between 1 and 135 months (median: 32 months), with recurrence-free survival rates post-surgery at 1, 2, 3, and 5 years were 87.5%, 77.1%, 61.2%, and 54.5%, respectively. The top five variables predicted HCC recurrence and non-recurrence conditions following surgical follow-up at 6 months, 1 year, 2 years, and beyond 2 years, in accordance with information obtained by the mRMR screen out. The Ki67 positivity index screened a successfully constructed machine learning model to predict HCC recurrence and non-recurrence conditions following surgical follow-up at 6 months, 1 year, 2 years, and beyond 2 years. The machine learning model based on the gradient boosting decision tree algorithm had the best prediction performance among them (areas under the receiver operating characteristic curves for predicting HCC recurrence within six months in the training and validation sets were 0.996 and 0.946, and accuracies were 0.972 and 0.935, respectively).Conclusion:A machine learning model was successfully constructed using the Ki67 positivity index combined with four readily available clinical features to predict HCC recurrence. The machine learning model based on the gradient boosting decision tree algorithm demonstrated the best performance in terms of predicting HCC recurrence within six months after surgery.

Efferocytosis refers to the process that phagocytes recognize and remove the apoptotic cells, which is essential for maintaining tissue homeostasis both in physiological and pathological conditions. Numerous studies have demonstrated that efferocytosis can prevent secondary necrosis and proinflammatory factor release, leading to the resolution of inflammation and tissue immunological tolerance in numerous diseases such as stroke. Stroke is a leading cause of death and morbidity for adults worldwide. Persistent inflammation triggered by the dead cells or cell debris is a major contributor to post-stroke brain damage. Effective efferocytosis might be an efficient strategy to minimize inflammation and restore brain homeostasis for neuronal regeneration and function recovery. In this review, we will discuss the phagocytes in the brain, the molecular mechanisms underlying efferocytosis, the role of efferocytosis in inflammation resolution, and the potential therapeutic applications targeting efferocytosis in stroke.
Humans ; Stroke ; Phagocytosis/physiology* ; Inflammation ; Apoptosis/physiology* ; Animals ; Phagocytes/physiology* ; Brain/metabolism* ; Efferocytosis

Artificial intelligence(AI)is a strategic technology leading a new round of technological revolution and industrial transformation.It is forward-looking,important and necessary to apply AI technology to the medical field.At present,the research and development of intelligent data monitoring equipment,intelligent medical instruments,disease auxiliary diagnosis and treatment platforms,auxiliary diagnosis and treatment integrated systems and other technologies have been widely carried out,and related products are gradually used in auxiliary medical prevention,diagnosis,treatment,and rehabilitation.Based on the recent development of AI technology in the medical field,the application status of intelligent auxiliary diagnosis and treatment equipment in three fields of intelligent monitoring equipment,virtual psychological diagnosis and treatment platforms,and traditional Chinese medicine auxiliary diagnosis and treatment instruments was summarized to provide reference for AI to connect disease and health,and realize the intersection of AI and medical disciplines.