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 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.

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 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]

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 explore the key factors affecting the selection and effectiveness of bladder management modalities in patients with spinal cord injury，so as to provide reference for the optimization of individualized bladder management strategies. Methods: The clinical and follow-up data of 78 patients with spinal cord injury treated in our hospital during Jan.1,2013 and Dec.31,2022 were retrospectively analyzed.The distribution of bladder management modalities among different grades of injuries was analyzed. Bowker symmetry test was used to evaluate the difference between bladder management modalities at discharge and at the end of follow-up. Multiple linear regression was used to explore the influencing factors of bladder management effects. Plotting Kaplan-Meier survival curves were adopted to calculate the median time of changes in bladder management. Results: At discharge，there were 9 cases of self-catheterization，19 cases of intermittent catheterization，22 cases of reflexive voiding，26 cases of long-term catheterization，and 2 cases using urinary collector.At the end of follow-up，there were 15 cases of self-catheterization，8 cases of intermittent catheterization，34 cases of reflexive voiding，14 cases of long-term catheterization，and 7 cases using urinary collector.There was a significant difference between the modalities of bladder management at discharge and at the end of follow-up (χ =21.43，P=0.018).Multiple linear regression showed a significant decrease of 8.60 in the total neurogenic bladder symptom score (NBSS) for grade D injuries compared with grade A injuries (P=0.026). The median time to bladder management change was 7.93 months (95%CI:5.44-9.44), with approximately 50% of patients experiencing a change in bladder management within 8 months after discharge. Conclusion: The modalities of bladder management changed significantly after discharge.The grade of injury was a key factor affecting the effectiveness of bladder management.Higher grade was associated with worse effectiveness of bladder management.

Objective: Previous observational studies have confirmed the correlation between gut microbiota and bladder cancer，but the causal relationship is still unclear.This study aimed to explore the causal relationship between them with Mendelian randomization. Methods: Genetic variation summary data of 211 gut microbiota and bladder cancer genome-wide association studies (GWAS) were obtained from the MiBioGen Consortium and Finngen database.Single nucleotide polymorphisms (SNPs) closely related to these studies were screened as instrumental variables.The causal relationship between gut microbiota and bladder cancer were analyzed with inverse variance weighting (IVW)，MR-Egger，weighted median，maximum likelihood，robust adjustment feature score and MR-PRESSO，with IVW as the primary analysis method.Additionally，sensitivity analysis was used to test the heterogeneity (Cochran Q) and horizontal pleiotropy (MR-Egger intercept term and global test from MR-PRESSO estimator) to ensure the robustness of the results. Results: The IVW results indicated that Lachnospiraceae UCG004 (OR：1.42)，Desulfovibrionales (Order) (OR：1.48)，Eubacterium ruminantium group (OR：1.33)，Olsenella (OR：1.24)，Ruminococcaceae UCG002 (OR：1.39)，Ruminococcaceae UCG005 (OR：1.42) and Ruminococcaceae UCG013 (OR：1.64) significantly increased the risk of bladder cancer.Conversely，Bacteroidetes (Phylum) (OR：0.61)，Eubacterium brachy group (OR：0.80)，Ruminococcaceae UCG004 (OR：0.73)，Rikenellaceae (Family) (OR：0.67)，Lachnospiraceae ND3007 group (OR：0.47), Adlercreutzia (OR：0.73) and an unknow genus (OR：0.75) were associated with a reduced risk of bladder cancer.Sensitivity analyses did not reveal any heterogeneity or horizontal pleiotropy. Conclusion: This study reveals the causal role of 14 gut microbiota in the pathogenesis of bladder cancer，among which Lachnospiraceae UCG004，Desulfovibrionales (Order)，Eubacterium ruminantium group，Olsenella，Ruminococcaceae UCG002，Ruminococcaceae UCG005 and Ruminococcaceae UCG013 are risk factors for bladder cancer，while Bacteroidetes (Phylum)，Eubacterium brachy group，Ruminococcaceae UCG004，Rikenellaceae (Family)，Lachnospiraceae ND3007 group，Adlercreutzia and an unknown genus are the protective factors.

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.