To satisfy the growing healthcare demands of the public, it is essential to develop a public service platform for vaccination. This initiative aligns with national policies, optimizes resource allocation, innovates service models, enhances service efficiency, and reduces service costs. Drawing on relevant national policies and regulatory requirements, as well as the notable achievements and practical experiences gained through the exploration and innovation of vaccination service models across various regions, this paper proposes expert recommendations. It defines the essential components and functional specifications for public service platforms, focusing on public needs such as electronic vaccination record management, appointment management, the promotion of electronic vaccination certificates, vaccination certificate verification for school enrollment, vaccination site navigation, and science communication and public engagement. The recommendations aim to serve as a reference for the development of vaccination public service platforms nationwide.

Objective To design a shared appointment pool system to realize shared appointment resources for on-line and off-line ways based on data synchronization and information sharing.Methods The system was designed with Internet Plus on-line and off-line intelligent medical appointment platform,which used Oracle 11g database for data storage and the front-end server and data center server for data exchange.PowerBuilder language and Java language were used for the development of the system,and there were five functional modules included in the system for appointment resource definition,appointment resource generation,appointment resource distribution,appointment list adjustment and outpatient consultation arrangement.Results The system developed contributed to unified managment of on-line and off-line appointment resources,and could be used for tracing,summarization and analysis of appointment resources.Conclusion The system developed realizes the synch-ronization of multi-way consultation data and the maximum sharing of appointment resources under the background of smart healthcare,which is of conducive for improving the utilization rate of medical resources.[Chinese Medical Equipment Journal,2025,46(3):42-47]

Objective:To evaluate the safety and efficacy of the TiRobot assisted screw placement in patients with lumbar degenerative diseases.Methods:The clinical data of 165 patients with lumbar degenerative diseases from January 2020 to December 2022 in Beijing Friendship Hospital, Capital Medical University were retrospectively analyze, and all patients were treated with posterior lumbar decompression and instrumentation. Among them, 46 patients were used the TiRobot assisted screw placement during surgery (robotic-assisted group), and 119 patients underwent freehand screw placement by C-arm X-ray machine fluoroscopy (freehand group). The operation time, intraoperative blood loss, complication and skelalgia visual analogue scale (VAS), Oswestry disability index (ODI) before and after operation were recorded. The accuracy of screw placement and rate of proximal facet joint violation were compared between two groups.Results:There were no statistical difference in operation time, intraoperative blood loss and incidence of complication between two groups ( P>0.05). In the two groups, the 3 d VAS and ODI after operation were significantly lower than those before operation, robot-assisted group: (3.33 ± 1.40) scores vs. (6.54 ± 2.00) scores and (16.96 ± 8.03) scores vs. (43.09 ± 5.48) scores; freehand group: (3.56 ± 1.29) scores vs. (6.55 ± 1.65) scores and (18.89 ± 6.74) scores vs. (44.91 ± 4.96) scores, and there were statistical differences ( P<0.01); there were no statistical difference in VAS and ODI before operation and 3 d after operation between two groups ( P>0.05). A total of 234 screws were implanted in robot-assisted group, and 590 screws were implanted in freehand group. The accuracy of screw placement in robot-assisted group was significantly higher than that in freehand group: 80.77% (189/234) vs. 74.58% (440/590), the rate of proximal facet joint violation was significantly lower than that in freehand group: 2.56% (6/234) vs. 7.29% (43/590), and there were statistical differences ( χ2 = 3.56 and 6.68, P<0.05). Conclusions:The TiRobot assisted screw placement for lumbar degenerative diseases is safe and effective. Compared to freehand technique, the TiRobot assisted method demonstrates higher screw placement accuracy and a lower rate of proximal facet joint violation.

Objective:To analyze the reoperation rate and causes during the early adoption phase of unilateral biportal endoscopy (UBE).Methods:The clinical data of 180 patients who underwent UBE performed by a single surgeon at Beijing Friendship Hospital, Capital Medical University from October 2021 to June 2023 were retrospectively analyzed. Clinical and imaging data of patients who underwent reoperation were collected to analyze the causes of reoperation, and the clinical efficacy of the reoperations was also followed up. Measurement data were expressed as mean ± standard deviation ( ± s), and t-test was used before and after treatment. Results:A total of 180 patients who underwent UBE were included in this study, of which 6 patients underwent reoperation, and the reoperation rate was 3.33%. Among them, 3 cases occurred in the first 90 surgeries and the other 3 occurred in the subsequent 90 surgeries. The causes of reoperation were as follows: recurrent lumbar disc herniation at the same segment postoperatively in 2 cases, insufficient decompression in 2 cases, disc herniation following isolated decompression in 1 case, and immediate postoperative perianal numbness in 1 case. The time between the initial surgery and reoperation ranged from 0 to 187 days, with an average of 63.3 days. The average follow-up time after reoperation was 18.3 months. The visual analogue scale (VAS) and Oswestry disability index (ODI) scores of the patients at the last follow-up were significantly improved compared with those before operation (VAS score of low back pain: 5.2 ± 1.7 before operation, 1.2 ± 0.8 at the last follow-up, P<0.001; VAS score of leg pain: 7.2 ± 1.5 before operation, 1.2 ± 1.2 at the last follow-up, P<0.001; ODI score: 67.3 ± 5.7 before operation, 20.2 ± 8.2 at the last follow-up, P<0.001). The postoperative modified MacNab scores were generally satisfactory (4 cases were rated as excellent, accounting for 66.7%; 2 cases were rated as good, accounting for 33.3%). Except for one patient who experienced dural injury during open revision surgery, there were no serious complications such as nerve damage. Conclusions:In the early stages of UBE surgery, recurrent lumbar disc herniation and inadequate decompression are the primary reasons for reoperation, typically occurring within the first three months postoperatively. Reoperation does not significantly increase the risk of nerve injury. Enhanced early postoperative follow-up is recommended. For symptomatic patients, a second surgery with thorough decompression can yield satisfactory treatment outcomes.

To satisfy the growing healthcare demands of the public, it is essential to develop a public service platform for vaccination. This initiative aligns with national policies, optimizes resource allocation, innovates service models, enhances service efficiency, and reduces service costs. Drawing on relevant national policies and regulatory requirements, as well as the notable achievements and practical experiences gained through the exploration and innovation of vaccination service models across various regions, this paper proposes expert recommendations. It defines the essential components and functional specifications for public service platforms, focusing on public needs such as electronic vaccination record management, appointment management, the promotion of electronic vaccination certificates, vaccination certificate verification for school enrollment, vaccination site navigation, and science communication and public engagement. The recommendations aim to serve as a reference for the development of vaccination public service platforms nationwide.

Objective:To analyze the learning curve of TiRobot-assisted lumbar pedicle screw fixation (LPSF) by cumulative sum (CUSUM) test method.Methods:The clinical data of 50 patients who underwent TiRobot-assisted LPSF from January 2020 to December 2022 in Beijing Friendship Hospital, Capital Medical University were retrospectively analyzed. CUSUM analysis and learning curve fitting were performed with robot usage time as the main indicator with the time for each step refined (robot registration time, path planning time and guide wire placement time), to select the best learning curve fitting model with the R2 value closest to 1. Using the turning point of the learning curve as the boundary, the learning curve was divided into two stages as learning stage and maturity stage, and then the observation indexes were compared between the two stages. Results:All 50 patients successfully completed the surgery without perioperative complications, with a total of 244 pedicle screws implanted. The total robot usage time and robot registration time showed a gradually decreasing trend with the increase of case number, and the learning curves were successfully fitted and reached their peaks at the seventeenth and thirteenth cases respectively. The entire learning process was divided into learning stage (17 cases) and maturity stage (33 cases) based on the turning point of the learning curve of total robot usage time. The path planning time and guide wire placement time did not show significant changes with the increase in the case number. The total robot usage time, robot registration time and the intraoperative blood loss in the learning stage were significantly higher than those in the maturity stage: (35.35 ± 1.58) min vs. (30.61 ± 0.43) min, (20.83 ± 1.56) min vs. (14.94 ± 0.29) min and 400 (150, 500) ml vs. 200 (110, 300) ml, the guide wire placement time of per screw was significantly lower than that in the maturity stage: 2.00 (1.83, 2.34) min/screw vs. 2.33 (2.13, 2.69) min/screw, and there were statistical differences ( P<0.05 or <0.01). There were no statistical difference in the path planning time, path planning time of per screw, guide wire placement time and the accuracy of screw placement between two stages ( P>0.05). Conclusions:TiRobot-assisted LPSF is a new technology with safety and effectiveness, and it has a relatively short learning curve. To achieve technological maturity, at least 17 surgeries are required with accumulated experience, and the robot registration is the main step of the learning process. After reaching maturity stage, the robot usage time is significantly shortened and intraoperative trauma is significantly reduced while the relatively high screw placement accuracy is ensured.

Aim To explore the mechanism of clar-ithromycin in treating inflammatory bowel disease(IBD)by inhibiting Kv1.3 channel protein in colonic epithelial cells.Methods A chronic IBD rat model was induced using dextran sulfate sodium(DSS)in vi-vo experiments,with clarithromycin intervention.The physical signs of each group of rats were observed,and the disease activity index(DAI)score and colonic mu-cosal damage index(CMDI)score were calculated.RT-qPCR was used to detect the levels of relevant cyto-kines in colonic tissue of rats.Flow cytometry was em-ployed to detect the relative proportions of immune cells in the peripheral blood and colonic tissue of each group of rats.Lipopolysaccharide(LPS)was used to establish an inflammation model of colon epithelial cells(NCM460)to clarify the inhibitory effect of clar-ithromycin on Kv1.3 channel protein.Results In vi-vo experiments:compared to the model group,the clar-ithromycin intervention group exhibited a reduced de-gree of weight loss(P＜0.01),and a significant de-crease in DAI scores(P＜0.01).There was an in-crease in colon length,a reduction in weight,and a de-crease in CMDI scores(P＜0.05).Levels of TNF-α,IL-1 β,and IL-6 in colon tissue were significantly re-duced(P＜0.01).The numbers of peripheral blood and colonic regulatory T lymphocytes(Th),cytotoxic T lymphocytes(CTL),natural killer cells(NK),B lym-phocytes(B),and dendritic cells(DC)were signifi-cantly decreased(P＜0.05).Clarithromycin reduced the expression of Kv1.3 channel protein in colon tissue(P＜0.05).In vitro experiments:compared to the model group,the clarithromycin group significantly pro-moted the proliferation of NCM460 cells(P＜0.01)and simultaneously significantly reduced the levels of TNF-α and IL-6 in cells(P＜0.05).Clarithromycin also reduced the expression of Kv1.3 channel protein in NCM460 cells(P＜0.05).Conclusions Clar-ithromycin may play an immunomodulatory role by in-hibiting the expression of Kv1.3 channel protein,re-ducing inflammation in the body,and playing a role in the treatment of IBD.

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.

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.

Objective To investigate the improvement effects of homogeneous fecal microbiota transplantation(FMT)on chemotherapy-induced diarrhea(CID)in mice.Methods Fifteen C57BL/6N mice were divided into control group,CID model group and CID+FMT group according to the random number distribution and remainder grouping method,with 5 mice per group.Control group received no intervention,and their feces were used to prepare fecal bacteria suspension.CID model group was injected intraperitoneally with fluorouracil(65 mg/kg)for 5 consecutive days to construct the CID mouse model,followed by gavage with 0.1 ml of saline on alternate days.CID+FMT group was given 0.1 ml fecal bacteria suspension gavage on alternate days for one week,followed by intraperitoneal injection of fluorouracil(65 mg/kg)for 5 consecutive days to construct the CID mouse model,with the experiment ending on the 14th day.During the experiment,the mice's food intake and body weight were recorded.At the end of the experiment,the mice were euthanized with deep carbon dioxide anesthesia,and the mice colonic specimens from cecum to anus were collected for hematoxylin and eosin(HE)staining and histopathological examination.Fecal samples were collected for 16S rRNA gene sequencing.Shannon index,Simpson index and Chao1 algorithm were used to analyze the α-diversity species of the intestinal flora in each group of mice.Similarity analysis(Anosim)was used to perform non-parametric on the inter-group differences of intestinal flora among the mice.Linear discriminate analysis size effect(LEfSe)and nonmetric multidimensional scaling(NMDS)were employed to analyze the intestinal dominant flora and the similarity classification relationships in each group of mice.Results The colonic specimen's length from cecum to anus in CID model group was significantly shorter than that in control group(P<0.05),while there was no significant difference between CID+FMT group and CID model group(P>0.05).The weight of mice in CID model group decreased by 42.04%,while control group mice gained 10.24%,with a significant difference between the two groups(P<0.05).The weight of mice in CID+FMT group decreased by 8.12%,which was significantly improved compared to CID model group(P<0.05).HE staining results revealed the intestinal mucosal structure in CID model group was severely damaged,with atrophy and deformation,accompanied by inflammatory cell infiltration,and the pathological score was higher than that of control group(P<0.05).Compared with CID model group,the intestinal mucosal integrity and crypt cells in the CID+FMT group were improved,with less damage,and the pathological score was lower than that of CID model group,but the difference was not statistically significant(P>0.05).The α-diversity analysis showed that there were significant differences in the Shannon,Simpson and Chao1 indices among the three groups(P<0.05).ANOSIM and NMDS analysis revealed that the intestinal flora in CID+FMT group was closer to the normal intestinal flora compared to CID model group.LEfSe analysis showed that the intestinal flora in CID model group was enriched in famliy_Bacteroidaceae,and the intestinal flora in CID+FMT group was similar to that of control group,with an enrichenment of familiy_Enterobacteriaceae.Conclusion Homogeneous FMT can improve the abundance of intestinal flora in CID mice,making it more similar to normal intestinal flora,thereby protecting intestinal mucosa,reducing damage and alleviating the severity of CID.