"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: 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 the feasibility of non-ECG-gated scan using wide-body detector in left atrial computed tomo-graphy angiography(CTA)and to assess its value in evaluating left atrial appendage closure(LAAC)outcomes.Methods Patients who underwent LAAC and completed left atrial CTA were prospectively selected.Participants were randomly divided into groups A and B.In group A,the heart rate was measured using a finger clip oximeter before the examination,and the heart rate was set as the simula-ted heart rate.The simulated signal was generated by the CT equipment to complete the examination.In group B,the conventional ECG-gated scan was adopted.The residual leakage of left atrial appendage occluder,image quality,patient comfort and radiation dose of the two groups were compared,and statistical analysis was performed for these indicators.Results Both groups completed the evaluation of the endothelialization degree of the left atrial appendage occluder,and there was no significant difference in the objective image quality evaluation or subjective image score between the two groups(P＞0.05).The total scores of the physiological dimension,psy-chological dimension,social cultural dimension and environmental dimension of patients in group A were higher than those in group B(P＜0.001),and the volume CT dose index(CTDIvol)and dose length product(DLP)decreased by 25％and 25.36％respectively,with statistically significant differences(P＜0.001).Conclusion During the left atrial CTA examination,the wide-body detector with non-ECG-gated scan mode can provide good image quality for clinical use and observe the embolization of the left atrial appendage after LAAC surgery.At the same time,it can effectively reduce the radiation dose for patients and improve patient comfort dur-ing the examination,showing high clinical feasibility and appli-cation value.

Background::An evidence gap still exists regarding the efficacy and safety of tegoprazan in patients with erosive esophagitis (EE) in China. This study aimed to verify the efficacy and safety of tegoprazan vs. esomeprazole in patients with EE in China. Methods::This study was a multicenter, randomized, double-blind, parallel, active-controlled, non-inferiority phase III trial of patients with EE randomized 1:1 to tegoprazan 50 mg/day vs. esomeprazole 40 mg/day. This study was conducted in 32 sites between October 24, 2018 and October 18, 2019. The primary endpoint was the cumulative endoscopic healing rate at week 8. The secondary endpoint included endoscopic healing rate at week 4, changes in the reflux disease questionnaire (RDQ) and gastroesophageal reflux disease health-related quality of life (GERD-HRQL) scores, and symptom improvement. Results::A total of 261 patients were randomized: 132 to the tegoprazan group and 129 to the esomeprazole group. The cumulative endoscopic healing rate at 8 weeks in the tegoprazan group was non-inferior to that of the esomeprazole group (91.1% vs. 92.8%, difference: -1.7%, 95% confidence interval [CI]: -8.5%, 5.0%, P = 0.008). There were no statistically significant differences in the changes in RDQ (total, severity, and frequency) and GERD-HRQL scores between the two groups (all P >0.05). The percentages of days without symptoms, including daytime and nighttime symptoms based on patients' diaries, were similar between the two groups (all P >0.05). In the tegoprazan and esomeprazole groups, 71.5% (93/130) and 61.7% (79/128) of the participants reported adverse events (AEs), 2.3% and 0 experienced serious AEs, while 70.0% and 60.2% had treatment-emergent AEs, respectively. Conclusion::Tegoprazan 50 mg/day demonstrated non-inferior efficacy in healing EE, symptom improvement, and quality of life, and it has similar tolerability compared with esomeprazole 40 mg/day.

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.

Exosomal circular RNA (circRNAs) are pivotal in cancer biology, and tumor pathophysiology. These stable, non-coding RNAs encapsulated in exosomes participated in cancer progression, tumor growth, metastasis, drug sensitivity and the tumor microenvironment (TME). Their presence in bodily fluids positions them as potential non-invasive biomarkers, revealing the molecular dynamics of cancers. Research in exosomal circRNAs is reshaping our understanding of neoplastic intercellular communication. Exploiting the natural properties of exosomes for targeted drug delivery and disrupting circRNA-mediated pro-tumorigenic signaling can develop new treatment modalities. Therefore, ongoing exploration of exosomal circRNAs in cancer research is poised to revolutionize clinical management of cancer. This emerging field offers hope for significant breakthroughs in cancer care. This review underscores the critical role of exosomal circRNAs in cancer biology and drug resistance, highlighting their potential as non-invasive biomarkers and therapeutic targets that could transform the clinical management of cancer.

OBJECTIVE: To investigate the association between long-term glycemic control and cerebral infarction risk in patients with diabetes through a large-scale cohort study. METHODS: This prospective, community-based cohort study included 12,054 patients with diabetes. From 2006 to 2012, 38,272 fasting blood glucose (FBG) measurements were obtained from these participants. FBG trajectory patterns were generated using latent mixture modelling. Cox proportional hazards models were applied to assess the subsequent risk of cerebral infarction associated with different FBG trajectory patterns. RESULTS: At baseline, the mean age of the participants was 55.2 years. Four distinct FBG trajectories were identified based on FBG concentrations and their changes over the 6-year follow-up period. After a median follow-up of 6.9 years, 786 cerebral infarction events were recorded. Different trajectory patterns were associated with significantly varied outcome risks (Log-Rank P < 0.001). Compared with the low-stability group, Hazard Ratio ( HR) adjusted for potential confounders were 1.37 for the moderate-increasing group, 1.23 for the elevated-decreasing group, and 2.08 for the elevated-stable group. CONCLUSION Sustained high FBG levels were found to play a critical role in the development of ischemic stroke among patients with diabetes. Controlling FBG levels may reduce the risk of cerebral infarction.
Humans ; Cerebral Infarction/blood* ; Middle Aged ; Male ; Female ; Blood Glucose/analysis* ; Fasting/blood* ; Aged ; Prospective Studies ; Risk Factors ; Diabetes Mellitus/blood* ; Adult ; Proportional Hazards Models