Community- and home-based elderly care services， as an important service model designed to meet the living and medical needs of the elderly， hold significant importance in improving their quality of life and alleviating the pressure on the traditional healthcare systems through their policies and implementation effects. This paper analyzed the necessity of community- and home-based medical and elderly care services in China， identified primary issues in the implementation of current policies， and explored pathways for system optimization， to build a more efficient and fair service system. It found that the content of the community- and home-based integrated medical and elderly care services lacked breadth and depth， the service quality and effectiveness were poorly assessed， resource investment and allocation efficiency were uneven， and the supervision and policy support were insufficient. Therefore， it was suggested to construct a demand-driven policy framework， optimize resource allocation to shift towards balanced regional coverage， establish a diversified home-based integrated medical and elderly care service system based on demand， and create a sustainable supervision and evaluation mechanism for the implementation effect of integrated medical and elderly care， with a view to providing theoretical support and practical guidance for the reform and optimization of community- and home-based elderly care services policies in China.

OBJECTIVE:Nowadays,machine learning algorithms are gradually being applied to predict stroke and cardiovascular disease.Compared with traditional regression models,machine learning can learn from data to achieve high prediction accuracy by exploring the flexible relationship between a large number of predictive features and outcome variables,providing a new method for the formulation of individualized treatment and rehabilitation programs.This study aims to systematically evaluate stroke functional recovery and prognosis prediction models based on machine learning,comprehensively assessing their predictive performance and clinical application potential to provide references for the development,application,and promotion of related predictive models.METHODS:This review was conducted following the PRISMA(Preferred Reporting Items for Systematic Reviews and Meta-Analyses)guidelines.Relevant literature on stroke prognosis prediction using machine learning methods was selected by searching PubMed,EMbase,Web of Science Core Collection,CNKI,WanFang,and the China Biomedical Literature Database,with the search period from January 1,2014,to July 1,2024.Two researchers independently screened the literature and extracted data based on inclusion and exclusion criteria,using the Prediction model Risk Of Bias ASsessment Tool(PROBAST)to assess model quality.RESULTS:(1)A total of 3 126 articles were obtained in the preliminary search.After screening and exclusion,18 articles were finally included.150 prediction models were constructed using 13 machine learning methods.The three most frequently used methods are Logistic Regression,Random Forest,and Extreme Gradient Boosting(XGBoost).Only one study was externally validated.Eight studies reported how the missing data were handled.(2)In terms of outcome indicators,8 studies used the combination of clinical data and imaging data to build models,9 studies only used clinical data to build models,and 1 study only used imaging data to build models.(3)Each of the 18 studies gave the most important characteristics of the study,with the most mentioned being the National Institute of Health Stroke Scale and age.All studies reported area under curve values ranging from 0.74 to 0.96,with the highest area under curve being 0.96.The overall risk of bias in all models was high.The high risk of bias in the field of model analysis was the main reason for the high risk of overall bias in all models.(4)The results of meta-analysis showed that age and National Institute of Health Stroke Scale score had significant influence on stroke prognosis,with age[MD=8.49,95％CI(6.24,10.75),P＜0.01]and National Institute of Health Stroke Scale score[MD=4.78,95％CI(2.56,7.00),P＜0.01].CONCLUSION:This study systematically evaluated the predictive model of functional recovery and prognosis of stroke based on machine learning,and all the models have good predictive potential.However,future studies should increase the sample size of the included model,adopt prospective studies,and add external validation of the model to improve the stability and prediction accuracy of the model,control the risk of bias,and contribute to the validation and promotion of the model in practical clinical applications.At the same time,the interpolation of missing values is more transparent and accurate.Although existing machine learning models show good predictive performance,it is also important to focus on the functionality and usability of the model,and the inclusion of features will reduce ease of use.We should develop easy to use model interfaces and user-friendly clinical tools to enable medical staff to better apply the model for clinical decision.

Shared Decision-Making （SDM） is a crucial concept in modern medicine， emphasizing the joint participation of doctors and patients in the medical decision-making process. However， the authoritative position of doctors and the passive role of patients in traditional medical models often overlook the personal wishes and needs of patients， leading to tense doctor-patient relationships and medical disputes. By listening to and understanding their stories， narrative medicine helps doctors gain a more comprehensive understanding of patients’ situations and balances medical advice with patient needs in the decision-making process. Through systematic literature analysis and theoretical exploration， this paper investigated the application effects and mechanisms of narrative medicine in different medical contexts， as well as analyzed its specific role in the process of SDM. The aim is to explore the value of narrative medicine in bridging differences in SDM， revealing its role in promoting doctor-patient communication， enhancing decision-making participation， and improving medical outcomes. Researches had found that narrative medicine enhanced doctors’ “narrative ability”， promoted emotional communication and trust between doctors and patients， reduced conflicts and misunderstandings in decision-making， and improved patients’ sense of participation and trust， thus playing an important role in SDM. Therefore， by enhancing doctor-patient communication and understanding， promoting SDM and treatment selection between doctors and patients， personalized care and treatment optimization， advocating for doctor-patient co-construction， improving consultation efficiency， restoring the patient’s subject position， and other methods， it can bridge doctor-patient differences， promote communication and enhance decision-making participation， and improve medical outcomes and patient satisfaction.

OBJECTIVE:Nowadays,machine learning algorithms are gradually being applied to predict stroke and cardiovascular disease.Compared with traditional regression models,machine learning can learn from data to achieve high prediction accuracy by exploring the flexible relationship between a large number of predictive features and outcome variables,providing a new method for the formulation of individualized treatment and rehabilitation programs.This study aims to systematically evaluate stroke functional recovery and prognosis prediction models based on machine learning,comprehensively assessing their predictive performance and clinical application potential to provide references for the development,application,and promotion of related predictive models.METHODS:This review was conducted following the PRISMA(Preferred Reporting Items for Systematic Reviews and Meta-Analyses)guidelines.Relevant literature on stroke prognosis prediction using machine learning methods was selected by searching PubMed,EMbase,Web of Science Core Collection,CNKI,WanFang,and the China Biomedical Literature Database,with the search period from January 1,2014,to July 1,2024.Two researchers independently screened the literature and extracted data based on inclusion and exclusion criteria,using the Prediction model Risk Of Bias ASsessment Tool(PROBAST)to assess model quality.RESULTS:(1)A total of 3 126 articles were obtained in the preliminary search.After screening and exclusion,18 articles were finally included.150 prediction models were constructed using 13 machine learning methods.The three most frequently used methods are Logistic Regression,Random Forest,and Extreme Gradient Boosting(XGBoost).Only one study was externally validated.Eight studies reported how the missing data were handled.(2)In terms of outcome indicators,8 studies used the combination of clinical data and imaging data to build models,9 studies only used clinical data to build models,and 1 study only used imaging data to build models.(3)Each of the 18 studies gave the most important characteristics of the study,with the most mentioned being the National Institute of Health Stroke Scale and age.All studies reported area under curve values ranging from 0.74 to 0.96,with the highest area under curve being 0.96.The overall risk of bias in all models was high.The high risk of bias in the field of model analysis was the main reason for the high risk of overall bias in all models.(4)The results of meta-analysis showed that age and National Institute of Health Stroke Scale score had significant influence on stroke prognosis,with age[MD=8.49,95％CI(6.24,10.75),P＜0.01]and National Institute of Health Stroke Scale score[MD=4.78,95％CI(2.56,7.00),P＜0.01].CONCLUSION:This study systematically evaluated the predictive model of functional recovery and prognosis of stroke based on machine learning,and all the models have good predictive potential.However,future studies should increase the sample size of the included model,adopt prospective studies,and add external validation of the model to improve the stability and prediction accuracy of the model,control the risk of bias,and contribute to the validation and promotion of the model in practical clinical applications.At the same time,the interpolation of missing values is more transparent and accurate.Although existing machine learning models show good predictive performance,it is also important to focus on the functionality and usability of the model,and the inclusion of features will reduce ease of use.We should develop easy to use model interfaces and user-friendly clinical tools to enable medical staff to better apply the model for clinical decision.

The biological clock(also known as the circadian rhythm)is the fundamental reliance for all organisms on Earth to adapt and survive in the Earth's rotation environment.Circadian rhythm is the most basic regulatory mechanism of life activities,and plays a key role in maintaining normal physiological and biochemical homeostasis,disease occurrence and treatment.Recent studies have shown that the biologi-cal clock plays an important role in the development of oral tissues and in the occurrence and treatment of oral diseases.Since there is cur-rently no guiding literature on the research methods of biological clock in stomatology,researchers mainly conduct research based on pub-lished references,which has led to controversy about the research methods of biological clock in stomatology,and there are many confusions about how to rationally apply the research methods of circadia rhythms.In view of this,this expert consensus summarizes the characteristics of the biological clock and analyzes the shortcomings of the current biological clock research in stomatology,and organizes relevant experts to summarize and recommend 10 principles as a reference for the rational implementation of the biological clock in stomatology research.

Objective In the context of expanding in size and improving the quality of medical consumables,the"one product,one policy"approach has brought challenges to the management of consumables in public hospitals.This study investi-gated the potential management problems in the process of centralized procurement with volume of medical consumables in a pub-lic tertiary hospital in Guangdong Province,and accordingly worked out the corresponding management countermeasures and then assessed their implementation effects.Methods The fishbone diagrams was applied to systematically analyze the potential prob-lems in the implementation process of centralized procurement with volume of medical consumables.Targeted measures were worked out from May to July 2023 in the hospital.The time series data of centralized procurement of coronary stents,pacemak-ers,tubular/end-to-end anastomoses,and spinal products from December 2022 to December 2023 were analyzed.Changes in the monthly implementation rate and contract completion volume pre-and post-intervention were measured to evaluate the effectiveness of the intervention.Results The fishbone diagrams analysis revealed that the primary factors impeding procurement implementa-tion were internal system flaws,personnel management inadequacy,supply issues,and an absence of an information system.Post-intervention,the monthly implementation rate(t=-4.19,P＜0.05)and contract completion(t=-2.38,P＜0.05)significantly improved.Conclusion The implementation of intervention management for centralized procurement with volume of medical consumables can effectively promote the related implementation effect in the department.In the context of centralized pro-curement,clinical and health management personnel need to bolster their professionalism to ensure the procurement management efficiency and quality.It is crucial to deepen policy understanding and implementation,enhance production and distribution process supervision and disciplinary mechanisms,ensure multi-departmental coordination for supply security,and improve hospi-tal information systems and procurement platforms.

Effective cost control and structure optimization of medical consumables in public hospitals can facilitate a shift from extensive cost control to scientific and refine management.On the premise of ensuring medical quality,reducing the burden of patients'diagnosis and treatment and meeting the actual needs of hospital management,this approach aims to realize valuable healthcare outcomes.This study was conducted in a tertiary hospital,which balanced both the medical and economic val-ue of medical consumables.Using an integrated approach to specialty capacity building and disease structure optimization,the hospital restructured the use of medical consumables in Transcatheter Arterial Embolization(TAE)procedures.It developed standardized pathways and usage protocols tailored to specific diseases and surgical requirements.A targeted consumable usage policy framework was introduced,comprising"one department,one policy;one surgery type,one policy;and one consumable,one policy."This included initiatives such as validating the use of drug-loaded embolic microspheres,conducting multi-depart-mental review meetings,strictly regulating indications for these microspheres,limiting personnel involvement,and negotiating re-duced pricing on imported microspheres.Following implementation,the average case-mix index(CMI)for discharged patients undergoing TAE increased from 2.23 to 2.34(P＜0.001),while the average per-case cost of consumables decreased from 19 600 to 15 600(P＜0.001).These measures offer valuable decision-making and operational reference for hospitals nation-wide,supporting efficient,quality-focused consumables management.

【Objective】 To investigate the role of LIF/LIFR/STAT3 pathway in endometrial receptivity in rats with polycystic ovary syndrome (PCOS). 【Methods】 Forty 21-day-old SD female rats were divided into normal (control) group, model group, sham-operation group, and LIF group with 10 rats in each. The rat model of PCOS was constructed by subcutaneous injection of prasterone sodium sulfate at the back of the neck. The serum levels of testosterone (T), glucose and insulin in each group were detected. The morphological changes of the uterus in each group were observed by HE staining, and the morphological changes of endometrium were measured. Immunohistochemistry, Western blotting, and Real-time fluorescence quantitative PCR(qRT-PCR) were used to determine the protein expression and mRNA expression of LIF and STAT3 in rat endometrium. 【Results】 Compared with control group, the levels of integrin avb3, serum T, insulin and glucose in PCOS rats were significantly increased (P=0.000, P=0.000, P=0.001). Supplementation of exogenous LIF could significantly reduce the levels of integrin avb3, serum T, glucose and insulin in PCOS rats (P=0.000, P=0.002, P=0.003, P=0.007). HE results showed that exogenous LIF could reduce uterine cavity and glandular morphology in PCOS rats and increase the equivalent diameter (P=0.000, P=0.000) and area (P=0.000, P=0.000) of uterine glands and glandular cavity, the ratio of glandular interstitial area (P=0.000), and the average endometrial thickness (P=0.006), with statistically significant differences. Immunohistochemistry, Western blotting, and qRT-PCR results showed that the expression levels of LIF and p-STAT3 protein and mRNA in model group were significantly decreased compared with control group. Compared with model group, the protein and mRNA expressions of LIF and p-STAT3 in LIF group were significantly increased (P<0.05). 【Conclusion】 Exogenous LIF supplementation can improve endometrial receptivity in PCOS rats, and its mechanism is related to the LIF/LIFR/STAT3 pathway.

Under the background of the new medical reform, the performance evaluation of public hospitals is become increasingly strict and the operating pressure as well. Refined management of public hospitals is an effective way to improve the operation and management efficiency of public hospitals and promote its high-quality development. In view of the main problems faced in operation and management, a tertiary hospital launched the practice of refined management in May 2019. Through the implementation of the DRG-PPS reform, construction of information-based operation management system, reconstruction of the performance evaluation index system and promotion of Internet hospital diagnosis and treatment services, the hospital effectively improved its medical service quality, clinical path management capacity and department operation and management efficiency, comprehensively advancing high-quality development of the hospital.

A complete hospital security system is essential for the life and property safety of medical workers, patients and their families alike. The successful operation of the system depends on a reasonable staffing of the security department.From 2019, a tertiary hospital has explored and practiced the staffing management of security guards based on the actual needs of the hospital. The first job was to classify its security posts setup, and determine the staffing requirements of front-line security posts based on such indicators as scale, risk and people flow. The management range theory was called into play, to set the staffing coefficient of front-line security posts and their corresponding frontline administrators as 7∶1, and set that each management post needs one middle manager. The next job was to calculate the number of security guards per workload/post. The calculations estimated that the number of guards required for the four front-line security posts, namely, gate keeper post, public area patrol post, security and fire control post, and emergency response unit post, was 37, 46, 26 and 26 respectively. The corresponding management posts, namely, the office of the security department, the security management team, the fire management team, and the order maintenance team, required 7, 8, 5 and 5 management personnel respectively, totaling 160. Based on the calculations, the hospital optimized its staffing, and increased its total security personnel from 150 to 160, including the number of gate keeper posts were reduced by 17, and the number of emergency response unit posts was increased by 22. This optimization has effectively empowered the security department in dealing with medical disputes and emergencies, as well as identifying fire hazards, which serves a reference for the rational staffing of hospital security human resources.