"Sinew prescription correspondence" is the principle of selecting prescriptions for channel sinew diseases. On the basis of the theory of syndrome differentiation and treatment， the pulse manifestation corresponds to the channel sinew syndrome， which can improve the flexibility and standardization of clinical prescriptions. From the perspective of systematic dialectical sphygmology， this paper explains the dialectical relationship between channel sinew theory and pulse body elements， pulse wall elements， pulse elements and blood flow elements， and clarifies the internal relationship between pulse manifestation and prescriptions at the level of channel sinew disease. The prescription is derived from the method， while the method is established with the syndrome， and the prescription is unified by the method. According to the theory of "sinew prescription correspondence"， the treatment ideas of channel sinew diseases were analyzed from the perspective of channel sinew distribution， functional characteristics and structural changes. On this basis， the diagnosis of channel sinew disease and the application of prescriptions are expanded， and the research on the internal treatment and diagnosis mode of "pulse manifestation-channel sinew-zang fu （脏腑）" is prospected， so as to expand the differentiation and treatment methods of channel sinew theory.

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 To analyze the relationships of serum microRNA(miR)-15,miR-16,miR-17-5p,and triglyceride-glucose index(TyG)with severity and prognosis of acute ischemic stroke(AIS).Methods A total of 136 AIS patients admitted to the Shijiazhuang Hospital of Traditional Chi-nese Medicine from April 2023 to April 2024 were enrolled.Prognosis and disease severity were as-sessed using modified Rankin Scale(mRS)and National Institutes of Health Stroke Scale(NIHSS),respectively.Based on mRS scores at 3 months post-onset,patients were divided into poor prognosis group(mRS Score＞2,n=42)and good prognosis group(mRS Score ≤ 2,n=94).Serum levels of miR-15,miR-16,miR-17-5p,TyG,and NIHSS scores were compared between two groups.Pearson correlation analysis was used to evaluate associations of these biomarkers with NIHSS scores.Logistic regression was used to identify risk factors for poor prognosis,while receiver operating characteristic(ROC)curves was applied to assess the predictive value of individual and combined biomarkers.Results The poor prognosis group exhibited significantly higher serum miR-15,miR-16,miR-17-5p,TyG,and NIHSS scores than the good prognosis group(t=8.634,13.171,29.018,2.687,26.432;P＜0.05).Positive correlations were observed between these biomarkers and NIHSS scores(r=0.472,0.449,0.492,0.437;P＜0.05).Logistic regression identified miR-15[OR(95％CI):3.526(2.628 to 5.859)],miR-16[1.976(1.226 to 3.017)],miR-17-5p[1.828(1.294 to 3.428)],NIHSS score[1.787(1.105 to 2.896)],TyG[1.886(1.233 to 3.284)],and homo-cysteine[1.906(1.252 to 3.794)]as independent risk factors for poor prognosis(P＜0.05).ROC analysis demonstrated that the combined model(miR-15,miR-16,miR-17-5p,TyG,and NIHSS)achieved superior predictive performance(AUC=0.877;95％CI,0.820 to 0.948)com-pared to individual biomarkers(Z＞2.527,P＜0.05).Conclusion Elevated serum miR-15,miR-16,miR-17-5p,and TyG are closely associated with AIS severity and poor prognosis.The above indicators combined with NIHSS scores can enhance predictive accuracy for unfavorable outcomes.

BACKGROUND: At present, biopsy is essential for the diagnosis of prostate cancer (PCa) before radical prostatectomy (RP). However, with the development of prostate-specific membrane antigen positron emission tomography/computed tomography (PSMA PET/CT) and multiparametric magnetic resonance imaging (mpMRI), it might be feasible to avoid biopsy before RP. Herein, we aimed to explore the feasibility of avoiding biopsy before RP in patients highly suspected of having PCa after assessment of PSMA PET/CT and mpMRI. METHODS: Between December 2017 and April 2022, 56 patients with maximum standardized uptake value (SUVmax) of ≥4 and Prostate Imaging Reporting and Data System (PI-RADS) ≥4 lesions who received RP without preoperative biopsy were enrolled from two tertiary hospitals. The consistency between clinical and pathological diagnoses was evaluated. Preoperative characteristics were compared among patients with different pathological types, T stages, International Society of Urological Pathology (ISUP) grades, and European Association of Urology (EAU) risk groups. RESULTS: Fifty-five (98%) patients were confirmed with PCa by pathology, including 49 (89%) with clinically significant prostate cancer (csPCa, defined as ISUP grade ≥2 malignancy). One patient was diagnosed with high-grade prostatic intraepithelial neoplasia (HGPIN). CsPCa patients, compared with clinically insignificant prostate cancer (cisPCa) and HGPIN patients, were associated with a higher level of prostate-specific antigen (22.9 ng/mL vs . 10.0 ng/mL, P = 0.032), a lower median prostate volume (32.2 mL vs . 65.0 mL, P = 0.001), and a higher median SUVmax (13.3 vs . 5.6, P <0.001). CONCLUSIONS It might be feasible to avoid biopsy before RP for patients with a high probability of PCa based on PSMA PET/CT and mpMRI. However, the diagnostic efficacy of csPCa with PI-RADS ≥4 and SUVmax of ≥4 is inadequate for performing a procedure such as RP. Further prospective multicenter studies with larger sample sizes are necessary to confirm our perspectives and establish predictive models with PSMA PET/CT and mpMRI.
Humans ; Male ; Prostatectomy/methods* ; Prostatic Neoplasms/diagnosis* ; Middle Aged ; Aged ; Positron Emission Tomography Computed Tomography/methods* ; Biopsy ; Multiparametric Magnetic Resonance Imaging ; Prostate-Specific Antigen/metabolism*

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 To investigate and analyze the current situation and problems of the advanced personnel en-gaging in the hospital-acquired infection control during their training period and explore the existing countermeas-ures and future development.METHODS The literatures regarding to the advanced study in China were retrieved from databases,the subjects of the literatures covered infection control-related advanced study practice,discipline construction,position competence,talent cultivation,scientific research innovation,professional title evaluation,laws,regulations and development plans.From Aug.2024 to Nov.2024,a questionnaire survey and face-to-face interviews were conducted among 36 advanced study personnel from 9 provinces of China who engaged in hos-pital-acquired infection control in the First Medical Center of Chinese PLA General Hospital.Eventually,36 ques-tionnaires were retrieved,all of which were valid with a questionnaire recovery rate of 100.00％.RESULTS Among the 36 advanced study personnel of hospital-acquired infection control,58.33％were medium-grade professional ti-tle;preventive medicine(41.67％),clinical medicine(25.00％)and nursing(16.67％)ranked the top 3 majors.The personnel engaged in the infection control for more than 6 years,and the duration of the advanced study was generally 3 or 6 months.In reality,the personnel faced the choices in terms of the purposes of further education,learning approaches and learning contents.The advanced study personnel also encountered the problems of challenges from promotion,improvement of position competency,integration with clinical training,supervision and practice,as well as physiological,psychological and family pressure.CONCLUSION Aiming at the problems that the advanced study personnel are generally concerned about,such as how to scientifically and effectively carry out hospital-acquired infection control advanced study and preset and solve the problems that may encounter,it is necessary to formulate targeted training programmes so as to provide bases and enlightenment for establishment of a long-term mechanism for advanced study of infection control in China.