Clinical practice guidelines represent the best recommendations for patient care. They are developed through systematically reviewing currently available clinical evidence and weighing the relative benefits and risks of various interventions. However, clinical practice guidelines have to go through a long translation cycle from development and revision to clinical promotion and application, facing problems such as scattered distribution, high duplication rate, and low actual utilization. At present, the clinical practice guideline information platform can directly or indirectly solve the problems related to the lengthy revision cycles, decentralized dissemination and limited application of clinical practice guidelines. Therefore, this paper systematically examines different types of clinical practice guideline information platforms and investigates their corresponding challenges and emerging trends in platform design, data integration, and practical implementation, with the aim of clarifying the current status of this field and providing valuable reference for future research on clinical practice guideline information platforms.

BACKGROUND:Exercise as a form of active rehabilitation can improve the dysfunction caused by peripheral nerve injury,and different exercise modalities target different lesion sites and recovery mechanisms. OBJECTIVE:To comprehensively analyze the application and mechanisms of different exercise modalities in functional recovery from peripheral nerve injury. METHODS:A computerized search was conducted in PubMed and CNKI databases for relevant literature published before January 2024.The search terms used were"peripheral nerve injury,spinal cord,exercise,cerebral cortex,muscle atrophy,mirror therapy,blood flow restriction training"in both English and Chinese.Finally,77 articles were included for review. RESULTS AND CONCLUSION:Peripheral nerve injury can cause systemic pathological changes such as skeletal muscle atrophy,corresponding spinal cord segmental lesions,and sensorimotor cortex remodeling.Aerobic exercise can improve dysfunction by enhancing the immune response,promoting glial cell polarization,and promoting the release of nerve growth factor.Blood flow restriction exercise can regulate the secretion of muscle growth factor,promote muscle growth and enhance muscle strength.Mirror movement has a good effect in activating the cerebral cortex and reducing cortical remodeling.Different exercise modalities have potential benefits in functional recovery after peripheral nerve injury;however,there are still some problems and challenges,such as the choice of exercise modalities,the control of exercise intensity and frequency,and the detailed analysis of mechanisms.

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.

ObjectiveTo evaluate the quality of research and evidence related to antihypertensive Chinese patent medicines combined with western medicines for the treatment of hypertension， synthesize and update the evidence， form expert consensus， and provide evidence for clinical decision-making. MethodThe databases of China National Knowledge Infrastructure （CNKI）， WanFang Data Knowledge Service Platform （WanFang）， Vip Chinese Science and Technology Journal Database （VIP）， Chinese Biomedical Literature Service System （Sinomed）， National Library of Medicine （PubMed）， Cochrane Library， Web of Science， and US Clinical Trials Registry were searched for randomized controlled trials of antihypertensive Chinese medicine combined with western medicine for the treatment of hypertension from database construction to July 31， 2022. The quality of the literature was evaluated using the bias risk assessment tool in Cochrane Handbook 6.3. Evidence synthesis of main outcome indicators was performed using R software. The Grading of Recommendations Assessment， Development， and Evaluation profiler （GRADEprofiler） 3.6 was employed to evaluate the quality of evidence. Expert consensus was formed based on the Delphi method after two rounds of voting. Result64 pieces of literature were included， and the results of literature quality evaluation and risk of bias showed that 70.31% （45/64） of the studies indicated some risks， and 29.69% （19/64） indicated high risks. Compared with conventional western medicines， the combination of Chinese patent medicines with western medicines can significantly lower systolic pressure （SBP） and diastolic pressure （DBP）， increase the effective rate of antihypertensive， reduce the incidence of adverse reactions， endothelin-1， and traditional Chinese medicine syndrome scores. Egger's test showed that Songling Xuemaikang capsules reduced SBP and DBP. Tianma Gouteng granules reduced SBP and DBP and increased the effective rate of antihypertensive， and Xinmaitong capsules reduced SBP and increased the effective rate of antihypertensive， without significant publication bias. Songling Xuemaikang capsules increased the effective rate of antihypertensive， and Xinmaitong capsules decreased DBP， with significant publication bias. The results of the GRADE evidence quality evaluation showed that most evidence was at grades B and C. Finally， four strong recommendations and 14 weak recommendations were formed. ConclusionCompared with conventional western medicines for the treatment of hypertension， antihypertensive Chinese patent medicines combined with western medicines have advantages in reducing blood pressure and improving drug use safety， but they are mostly weak recommendations in terms of efficacy， and more high-quality evidence is needed.

Objective To explore the effect of cannabinoid receptor 2(CB2)on orthodontic tooth movement(OTM)rate and periodontal tissue reconstruction of pressure area in mice.Methods Thirty CB2-/-male mice and thirty littermate control WT male mice were individually accepted the orthodontic appliance at their age of 6 weeks.The mice were respectively scarified at 3 days,7 days,14 days and 21 days after the operation.Then the tooth movement distance was examined through the stereomicroscope.Hematoxylin-eosin staining was performed to explore the biological responses of periodontium at the distal mesial root pressure area.Anti-tartrate acid phospha-tase staining was performed to calculate the number and distribution of osteoclasts at the distal mesial root pressure area,and MMP-9 was evaluated by immunohistochemistry to examine the number of MMP-9(+)monocytes and multinucleated cells in the same district as the TRAP staining.Results Compared with those WT mice at 3,7,14 and 21 days,OTM distance showed a gradual increased tendency according with experimental time over 21 days.The widths of periodontal ligament on the pressure side were markedly greater in CB2-/-mice than WT mice at 7,14 and 21 days(P＜0.000 1).The numbers of TRAP positive osteoclasts were significantly greater in CB2-/-mice than those in WT mice at 14 days of OTM(P＜0.001).MMP-9 immunohistochemical staining showed that the number of MMP-9(+)monocytes and multinucleated cells was more in CB2-/-mice than that in WT mice at 14 days of OTM(P＜0.05).Conclusion The absence of CB2 accelerates orthodontic tooth movement under or-thodontic force.The absence of CB2 reinforces bone resorption in orthodontic tooth movement compressive area dur-ing orthodontic tooth movement.