Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

PURPOSE: The fixation method commonly employed worldwide for treating unstable fractures of the posterior pelvic ring is the percutaneous iliosacral screw technique. However, prolonged operation time and frequent fluoroscopies result in surgical risks. This study aimed to investigate whether a new triangulation method could reduce operative and fluoroscopy times and increase the accuracy of screw placement. METHODS: This study is a real-world retrospective cohort analysis that examined a patient cohort who underwent percutaneous iliosacral screw fixation between January 1, 2019 and December 31, 2022. Inclusion criteria were patients (1) diagnosed with posterior pelvic ring instability who underwent pelvic fracture closed reduction and percutaneous S1 transverse-penetrating iliosacral screw placement and (2) aged >18 years. Exclusion criteria were: (1) combined proximal femoral fractures, (2) severe soft tissue injury in the surgical area, (3) incomplete imaging data, and (4) declining to provide written informed consent by the patient. The patients were divided into 2 groups according to the screw insertion method: conventional and triangulation methods. Screw placement and fluoroscopy times recorded by the C-arm were compared between the 2 methods. The accuracy of screw placement was evaluated by Smith grading on postoperative CT. Normality tests were conducted to assess the distribution of the quantitative variables and the Chi-square test was used to compare the qualitative variables. RESULTS: The study included a total of 94 patients diagnosed with posterior pelvic ring instability, who underwent percutaneous iliosacral screw placement. The patients were divided into 2 groups: 46 patients treated with the conventional surgical method and 48 patients received the triangulation method. The operation time (61.13±9.69 vs. 35.77±6.27) min and fluoroscopy frequency times (52.15±9.29 vs. 24.40±4.04) of the triangulation method were significantly reduced (p<0.001). CONCLUSIONS The use of a triangular positioning technique for the surface positioning of percutaneous iliosacral screws could reduce the operative time and fluoroscopy frequency. And the screw placement accuracy using this new method was comparable to that using other conventional methods.
Humans ; Retrospective Studies ; Bone Screws ; Pelvic Bones/surgery* ; Male ; Female ; Fracture Fixation, Internal/methods* ; Fractures, Bone/surgery* ; Adult ; Middle Aged ; Fluoroscopy ; Aged ; Sacrum/surgery* ; Operative Time

OBJECTIVE: To evaluate the efficacy and safety of Shexiang Tongxin Dropping Pill (STDP) in treating stable angina patients with phlegm-heat and blood-stasis syndrome by exercise duration and metabolic equivalents. METHODS: This multicenter, randomized, double-blind, placebo-controlled clinical trial enrolled stable angina patients with phlegm-heat and blood-stasis syndrome from 22 hospitals. They were randomized 1:1 to STDP (35 mg/pill, 6 pills per day) or placebo for 56 days. The primary outcome was the exercise duration and metabolic equivalents (METs) assessed by the standard Bruce exercise treadmill test after 56 days of treatment. The secondary outcomes included the total angina symptom score, Chinese medicine (CM) symptom scores, Seattle Angina Questionnaire (SAQ) scores, changes in ST-T on electrocardiogram and adverse events (AEs). RESULTS: This trial enrolled 309 patients, including 155 and 154 in the STDP and placebo groups, respectively. STDP significantly prolonged exercise duration with an increase of 51.0 s, compared to a decrease of 12.0 s with placebo (change rate: -11.1% vs. 3.2%, P<0.01). The increase in METs was significantly greater in the STDP group than in the placebo group (change: -0.4 vs. 0.0, change rate: -5.0% vs. 0.0%, P<0.01). The improvement of total angina symptom scores (25.0% vs. 0.0%), CM symptom scores (38.7% vs. 11.8%), reduction of nitroglycerin consumption (100.0% vs. 11.3%), and all domains of SAQ, were significantly greater with STDP than placebo (all P<0.01). The changes in Q-T intervals at 28 and 56 days from baseline were similar between the two groups (both P>0.05). Twenty-five participants (16.3%) with STDP and 16 (10.5%) with placebo experienced AEs (P=0.131), with no serious AEs observed. CONCLUSION STDP could improve exercise tolerance in patients with stable angina and phlegm-heat and blood stasis syndrome, with a favorable safety profile. (Registration No. ChiCTR-IPR-15006020).
Humans ; Double-Blind Method ; Drugs, Chinese Herbal/adverse effects* ; Male ; Female ; Middle Aged ; Angina, Stable/physiopathology* ; Aged ; Syndrome ; Treatment Outcome ; Placebos ; Tablets

Numerous studies on the formation and consolidation of memory have shown that memory processes are characterized by phase-dependent and dynamic regulation. Memory retrieval, as the only representation of memory content and an active form of memory processing that induces memory reconsolidation, has attracted increasing attention in recent years. Although the molecular mechanisms specific to memory retrieval-induced reconsolidation have been gradually revealed, an understanding of the time-dependent regulatory mechanisms of this process is still lacking. In this study, we applied a transcriptome analysis of memory retrieval at different time points in the recent memory stage. Differential expression analysis and Short Time-series Expression Miner (STEM) depicting temporal gene expression patterns indicated that most differential gene expression occurred at 48 h, and the STEM cluster showing the greatest transcriptional upregulation at 48 h demonstrated the most significant difference. We then screened the differentially-expressed genes associated with that met the expression patterns of those cluster-identified genes that have been reported to be involved in learning and memory processes in addition to dipeptidyl peptidase 9 (DPP9). Further quantitative polymerase chain reaction verification and pharmacological intervention suggested that DPP9 is involved in 48-h fear memory retrieval and viral vector-mediated overexpression of DPP9 countered the 48-h retrieval-induced attenuation of fear memory. Taken together, our findings suggest that temporal gene expression patterns are induced by recent memory retrieval and provide hitherto undocumented evidence of the role of DPP9 in the retrieval-induced reconsolidation of fear memory.
Animals ; Fear/physiology* ; Male ; Dipeptidyl-Peptidases and Tripeptidyl-Peptidases/genetics* ; Memory Consolidation/physiology* ; Time Factors ; Mental Recall/drug effects* ; Mice ; Gene Expression Profiling

Objective This paper aims to investigate the application of intraoral scanning and cone beam computed tomography(CBCT)registration implant robot in dental implant surgery.Methods The data of 40 cases with dental de-fect of robot-assisted implantation from November 2023 to May 2024 were retrospectively analyzed.Before the opera-tion,the intraoral scan data and CBCT data of the posi-tioning markers were automatically fused with the initial CBCT images,and the registration error was calculated.The average registration error of positioning markers was determined during the operation,and the implantation ac-curacy was analyzed after the operation.Results The intraoral scan data and CBCT data of 40 patients with dental defect wearing positioning markers were successfully registered with the initial CBCT image,and the registration errors were(0.157±0.026)mm and(0.154±0.033)mm,respectively.Statistical analysis showed no statistical significance between them.The registration errors of the marker was(0.037 3±0.003 6)mm.A total of 55 implants were performed,and the total deviations of the implant point and the apical point were(0.78±0.41)and(0.89±0.28)mm,respectively.The transverse deviations of the implant point and the apical point were(0.44±0.36)and(0.58±0.25)mm,respectively.The depth deviations of the implant point and the apical point were(0.51±0.32)and(0.54±0.36)mm,respectively.The devia-tion of the implant angle was 1.24°±0.67°.Conclusion The fusion technology based on intraoral scanning and CBCT registration can meet the accuracy requirements of preoperative registration of oral implant robots.The technology in-creases the choice of registration methods before robot-assisted dental implant surgery and reduces the multiple radiation exposuresof the patient.

Objective To investigate the effect of reducing the image-guided X-ray field area on the accuracy of Cyberknife radiotherapy,in order to provide a feasible method for achieving patient protection optimization.Methods Firstly,the spine-tracking,fiducial tracking and lung-tracking radiotherapy plans were formulated for the simulation phantom,and then image-guided full-field localization and position pre-setting were carried out for the simulation phantom,and the spine-tracking,fiducial tracking and lung-tracking radiotherapy plans were executed for the simulation phantom using a reduced lead block field area,respectively.Secondly,the radiotherapy accuracy of different radiotherapy plans was verified by end-to-end(E2E)software using new EBT films of the same batch as the base film.Finally,the changes of the simulation phantom were compared in terms of position pre-presetting error,radiotherapy accuracy and lead block field area.Results The spine-tracking and fiducial tracking radiotherapy plans had the translation errors not higher than 0.1 mm and the rotation errors not higher than 0.1°,which were comparable to the fluctuated conventional Cyberknife image-guided locating;the spine-tracking,fiducial tracking and lung-tracking radiotherapy plans had the lead block field radiotherapy accu-racies being 0.71,0.18 and 1.06 mm,respectively,which met the clinical requirements for Cyberknife radiotherapy;the lead block field areas of the spine-tracking,fiducial tracking and lung-tracking radiotherapy plans were reduced to 19.75％,29.28％and 12.71％of the full field area,respectively,and the efficacy for field area optimization was significant.Conclusion It's feasible to involve a reduced image-guided X-ray field area in Cyberknife radiotherapy,which contributes to optimizing radiation protection for the patients.[Chinese Medical Equipment Journal,2024,45(11):49-53]