Objective:To evaluate the clinical utility of machine learning model based radiomics in predicting high-risk molecular subtypes of lower-grade gliomas(LrGGs).Methods:This was a cross-sectional study. A total of 287 patients diagnosed with LrGGs in the First Hospital of Shanxi Medical University, Shanxi Provincial People′s Hospital, and the Third Hospital of Shanxi Medical University from January 2011 to September 2023 were retrospectively collected, including 166 males and 121 females; 114 cases of high-risk molecular subtypes and 173 cases of non-high-risk molecular subtypes. All patients were divided into 201 cases in the training set and 86 cases in the test set according to 7∶3 in simple randomized grouping method. All patients underwent contrast-enhanced T 1WI (CE-T 1WI) and T 2-weighted fluid-attenuated inversion recovery sequence imaging (T 2-FLAIR), and the imaging features of high-risk and non-high-risk molecular subtypes were analyzed. Analysis of variance, recursive feature elimination, and Kruskal-Wallis were used for radiomics feature screening, and a support vector machine (SVM) classifier was used to construct a radiomics-based classifier model. Univariate and multivariate logistic regression were used to analyze clinical variables independently influencing high-risk molecular subtypes of LrGGs to construct a clinical model; a combined model was developed by integrating radiomics labels and clinical variables. Receiver operating characteristic curve and area under the curve (AUC), calibration curve, and decision curve were used to compare the predictive performance of different models. Results:The patient′s age ( OR=1.042, 95% CI 1.018-1.068, P=0.001), pathological grade ( OR=2.270, 95% CI 1.212-4.311, P=0.011), MGMT methylation status ( OR=0.456, 95% CI 0.238-0.866, P=0.017), and ependymal involvement ( OR=7.335, 95% CI 2.929-18.370, P<0.001) were independent influencing factors for the high-risk molecular subtype of LrGGs, and a clinical model was developed based on these factors. An SVM model was constructed based on 12 radiomics features (3 radiomics features based on CE-T 1WI and 9 radiomics features based on T 2-FLAIR). The radiomics score of the probability output by the SVM model was combined with age, pathological grade, MGMT methylation status, and ependymal involvement to develop a combined model. The AUC values of the SVM model for predicting the high-risk molecular subtype of LrGGs were 0.824 and 0.859 in the training set and test set, respectively; the AUC values of the clinical model in the training set and test set were 0.759 and 0.721, respectively; and the AUC values of the combined model in the training set and test set were 0.823 and 0.815, respectively. The combined model had a high clinical net benefit. Conclusion:The machine learning MRI radiomics model can preoperatively predict high risk molecular subtypes of LGGrs, assist in individualized treatment decisions.

Objective:To assess the efficacy and safety profile of antaitasvir phosphate combined with yiqibuvir in the treatment of chronic hepatitis C (CHC) of various genotypes, without cirrhosis or with compensated cirrhosis.Methods:394 cases with CHC from 22 centers were collected from October 2021 to April 2023. They were randomly assigned to receive either the experimental drugs (antaitasvir phosphate 100 mg+yiqibuvir 600 mg) or placebo treatment in a 3∶1 ratio. The patients were administered drugs once a day for 12 consecutive weeks, and then followed up for 24 weeks after treatment cessation. All subjects were unblinded at the four-week follow-up following drug discontinuation, with the experimental drug group continuing to complete subsequent post-discontinuation follow-up. The placebo group was switched to receive the experimental drugs for a repeated 12-week treatment period and followed up for another 24 weeks after discontinuation of the drug (placebo delayed treatment phase).The sustained virologic response rate (SVR12) was observed for subjects in the double-blind phase and the placebo delayed-treatment phase at 12 weeks after treatment cessation.Virological resistance analysis was performed on subjects who failed treatment. The primary efficacy endpoint was SVR12. The number and percentage of subjects who achieved "HCV RNA

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.

Objective:To investigate the efficacy and safety of bendamustine combined with anti-CD20 monoclonal antibody in the first-line treatment of older patients with indolent B-cell non-Hodgkin lymphoma (B-iNHL) .Methods:The clinical data of 159 patients with B-iNHL enrolled in 16 hospitals from Jiangsu Cooperative Lymphoma Group from December 1, 2019, to April 20, 2024, were analyzed for regimen efficacy and safety. Bendamustine plus rituximab (BR) and bendamustine plus obinutuzumab (BG) were administered to 139 (87.4% ) and 20 (12.6% ) patients, respectively.Results:Among the 159 patients, 101 (63.5% ) were male and 58 (36.5% ) were female, with a median age of 69 years (range: 60–84). Efficacy could be assessed in 138 (86.8% ) patients. The efficacy assessment demonstrated that the overall response rate was 92.0% with complete and partial remissions in 75 (54.3% ) and 52 (37.7% ) cases, respectively. With a median follow-up of 24 months (range: 4–64), the progression-free survival rate was (87.5 ± 3.0) % and the overall survival rate was (83.2 ± 3.3) %. Of the 27 patients who died, 6 (22.2% ) died due to disease progression. The mean applied dose of bendamustine per cycle was 73.0 (50.8–89.7) mg/m 2 per day, administered on days 1 and 2. Adverse events of grade 3 or higher were reported in 53 (33.3% ) patients, with infection (30 cases,18.9% ) and neutropenia (24 cases, 15.1% ) demonstrating the highest incidence. Conclusion:Bendamustine combined with anti-CD20 monoclonal antibody demonstrated good efficacy and is well-tolerated in the first-line treatment of elderly patients with B-iNHL.

Objective:To analyze the impact of wound closure in knee flexion versus extension on postoperative outcomes after total knee arthroplasty (TKA).Methods:Randomized controlled trials (RCTs) comparing the effects of knee flexion versus extension wound closure on TKA outcomes were retrieved from databases including CNKI, WanFang Data, Chinese Medical Journal Full-text Database, PubMed, Medline, Cochrane Library, and Embase, from inception to October 1, 2024. Outcome measures include knee range of motion (ROM), Knee Society score (KSS), visual analogue scale (VAS), and incidence of postoperative complications at different time points. Meta-analysis was performed using Stata 18.0. The methodological quality of included RCTs was assessed using the modified Jadad scale. A fixed-effects model was applied when heterogeneity was low, while a random-effects model was used when heterogeneity was high.Results:A total of 467 patients from 7 RCTs were included (233 in flexion group, 234 in extension group). The mean age was 66.4 years in the flexion group and 66.7 years in the extension group, with a follow-up ranging from 1 to 12 months. All studies were of high quality. The meta-analysis revealed that the flexion group had significantly greater knee ROM at 1 month [ WMD=3.72, 95% CI(3.12, 4.33), P<0.001] and 3 months [ WMD=5.31, 95% CI(0.79, 9.84), P=0.020] postoperatively compared to the extension group. At 6 months postoperatively, the flexion group showed significantly higher KSS [ WMD=-1.25, 95% CI(-1.51, -0.99), P<0.001]. No significant differences were found in ROM at 6 months [ WMD=0.89, 95% CI(-0.99, 2.77), P=0.350], VAS at 3 months [ WMD=-0.28, 95% CI(-1.59, -0.03), P=0.075], or complication rates [ RD=0.03, 95% CI(-0.01,0.07), P=0.198]. Conclusion:Wound closure in knee flexion can improve early knee range of motion within 3 months and functional outcomes at 6 months after TKA.

OBJECTIVE To provide help for further exploring the mechanism of action of traditional Chinese herb by constructing a complex network of traditional Chinese herb-gene-protein,optimizing the mining method of potential associated genes of traditional Chinese herb and improving the mining efficiency of traditional Chinese herb system biology information.METHODS A graph neural network model HERBGAT with an attention mechanism was proposed.A small amount of traditional Chinese herb-related gene data in the public data platform was used as input,and deep mining was performed in the traditional Chinese herb-gene-protein complex net-work to output potential traditional Chinese herb-related genes.The prediction results were analyzed by disease association analysis and KEGG signaling pathway analysis on the bioinformatics platform to clarify their mechanism of action,and the prediction results were verified by the literature retrieval platform.RESULTS The training results showed that the average prediction accuracy of the HERB-GAT model could reach 94%.Compared with the other two advanced complex network mining methods,HERBGAT showed better per-formance in the three indicators of ACC,AUC and AUPR.In the literature verification stage,the model prediction results were verified by TCM clinical literature and modern pharmacology literature,showing the good effect of HERBGAT in practical application.At the end of this paper,taking the HERBGAT model and the improved EMOGI model to explore the mechanism of action of Pinellia ternata in treating lung cancer as an example,199 potential associated genes of Pinellia ternata in treating lung cancer were found,and these potential associated genes were preliminarily analyzed and discussed with the help of bioinformatics methods.CONCLUSION The HERBGAT model can effectively mine potential traditional Chinese herb-associated genes,improve the mining efficiency of traditional Chinese herb-gene-protein complex networks,provide new ideas and references for the optimization of traditional Chinese herb system biology information mining methods,and provide data basis and experimental direction for exploring the mechanism of action of tradi-tional Chinese herb.

Objective:To analyze the impact of wound closure in knee flexion versus extension on postoperative outcomes after total knee arthroplasty (TKA).Methods:Randomized controlled trials (RCTs) comparing the effects of knee flexion versus extension wound closure on TKA outcomes were retrieved from databases including CNKI, WanFang Data, Chinese Medical Journal Full-text Database, PubMed, Medline, Cochrane Library, and Embase, from inception to October 1, 2024. Outcome measures include knee range of motion (ROM), Knee Society score (KSS), visual analogue scale (VAS), and incidence of postoperative complications at different time points. Meta-analysis was performed using Stata 18.0. The methodological quality of included RCTs was assessed using the modified Jadad scale. A fixed-effects model was applied when heterogeneity was low, while a random-effects model was used when heterogeneity was high.Results:A total of 467 patients from 7 RCTs were included (233 in flexion group, 234 in extension group). The mean age was 66.4 years in the flexion group and 66.7 years in the extension group, with a follow-up ranging from 1 to 12 months. All studies were of high quality. The meta-analysis revealed that the flexion group had significantly greater knee ROM at 1 month [ WMD=3.72, 95% CI(3.12, 4.33), P<0.001] and 3 months [ WMD=5.31, 95% CI(0.79, 9.84), P=0.020] postoperatively compared to the extension group. At 6 months postoperatively, the flexion group showed significantly higher KSS [ WMD=-1.25, 95% CI(-1.51, -0.99), P<0.001]. No significant differences were found in ROM at 6 months [ WMD=0.89, 95% CI(-0.99, 2.77), P=0.350], VAS at 3 months [ WMD=-0.28, 95% CI(-1.59, -0.03), P=0.075], or complication rates [ RD=0.03, 95% CI(-0.01,0.07), P=0.198]. Conclusion:Wound closure in knee flexion can improve early knee range of motion within 3 months and functional outcomes at 6 months after TKA.

OBJECTIVE To provide help for further exploring the mechanism of action of traditional Chinese herb by constructing a complex network of traditional Chinese herb-gene-protein,optimizing the mining method of potential associated genes of traditional Chinese herb and improving the mining efficiency of traditional Chinese herb system biology information.METHODS A graph neural network model HERBGAT with an attention mechanism was proposed.A small amount of traditional Chinese herb-related gene data in the public data platform was used as input,and deep mining was performed in the traditional Chinese herb-gene-protein complex net-work to output potential traditional Chinese herb-related genes.The prediction results were analyzed by disease association analysis and KEGG signaling pathway analysis on the bioinformatics platform to clarify their mechanism of action,and the prediction results were verified by the literature retrieval platform.RESULTS The training results showed that the average prediction accuracy of the HERB-GAT model could reach 94%.Compared with the other two advanced complex network mining methods,HERBGAT showed better per-formance in the three indicators of ACC,AUC and AUPR.In the literature verification stage,the model prediction results were verified by TCM clinical literature and modern pharmacology literature,showing the good effect of HERBGAT in practical application.At the end of this paper,taking the HERBGAT model and the improved EMOGI model to explore the mechanism of action of Pinellia ternata in treating lung cancer as an example,199 potential associated genes of Pinellia ternata in treating lung cancer were found,and these potential associated genes were preliminarily analyzed and discussed with the help of bioinformatics methods.CONCLUSION The HERBGAT model can effectively mine potential traditional Chinese herb-associated genes,improve the mining efficiency of traditional Chinese herb-gene-protein complex networks,provide new ideas and references for the optimization of traditional Chinese herb system biology information mining methods,and provide data basis and experimental direction for exploring the mechanism of action of tradi-tional Chinese herb.

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.

Objective:To evaluate the clinical utility of machine learning model based radiomics in predicting high-risk molecular subtypes of lower-grade gliomas(LrGGs).Methods:This was a cross-sectional study. A total of 287 patients diagnosed with LrGGs in the First Hospital of Shanxi Medical University, Shanxi Provincial People′s Hospital, and the Third Hospital of Shanxi Medical University from January 2011 to September 2023 were retrospectively collected, including 166 males and 121 females; 114 cases of high-risk molecular subtypes and 173 cases of non-high-risk molecular subtypes. All patients were divided into 201 cases in the training set and 86 cases in the test set according to 7∶3 in simple randomized grouping method. All patients underwent contrast-enhanced T 1WI (CE-T 1WI) and T 2-weighted fluid-attenuated inversion recovery sequence imaging (T 2-FLAIR), and the imaging features of high-risk and non-high-risk molecular subtypes were analyzed. Analysis of variance, recursive feature elimination, and Kruskal-Wallis were used for radiomics feature screening, and a support vector machine (SVM) classifier was used to construct a radiomics-based classifier model. Univariate and multivariate logistic regression were used to analyze clinical variables independently influencing high-risk molecular subtypes of LrGGs to construct a clinical model; a combined model was developed by integrating radiomics labels and clinical variables. Receiver operating characteristic curve and area under the curve (AUC), calibration curve, and decision curve were used to compare the predictive performance of different models. Results:The patient′s age ( OR=1.042, 95% CI 1.018-1.068, P=0.001), pathological grade ( OR=2.270, 95% CI 1.212-4.311, P=0.011), MGMT methylation status ( OR=0.456, 95% CI 0.238-0.866, P=0.017), and ependymal involvement ( OR=7.335, 95% CI 2.929-18.370, P<0.001) were independent influencing factors for the high-risk molecular subtype of LrGGs, and a clinical model was developed based on these factors. An SVM model was constructed based on 12 radiomics features (3 radiomics features based on CE-T 1WI and 9 radiomics features based on T 2-FLAIR). The radiomics score of the probability output by the SVM model was combined with age, pathological grade, MGMT methylation status, and ependymal involvement to develop a combined model. The AUC values of the SVM model for predicting the high-risk molecular subtype of LrGGs were 0.824 and 0.859 in the training set and test set, respectively; the AUC values of the clinical model in the training set and test set were 0.759 and 0.721, respectively; and the AUC values of the combined model in the training set and test set were 0.823 and 0.815, respectively. The combined model had a high clinical net benefit. Conclusion:The machine learning MRI radiomics model can preoperatively predict high risk molecular subtypes of LGGrs, assist in individualized treatment decisions.