"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:To analyze the efficacy, influencing factors, and risk warning of multi-plane low-temperature plasma radiofrequency ablation（MLT-RFA） in the treatment of obstructive sleep apnea hypopnea syndrome（OSAHS）. Methods:A total of 118 OSAHS patients admitted from October 2022 to June 2024 were selected as the research subjects. They were divided into mild group（n=46）, moderate group（n=52）, and severe group（n=20） according to the severity of their condition. MLT-RFA treatment was used for all patients. After surgery, the results of polysomnography（PSG） and the changes in the Calier Sleep Apnea Quality of Life Index（SAQLI） were observed before and after treatment. The incidence of complications after treatment was recorded, and the clinical efficacy of the patients was evaluated. At the same time, they were divided into a treatment effective group（n=106） and an ineffective group（n=12） according to their effects. The general clinical data of the two groups were compared, and binary logistics regression analysis was conducted to identify independent factors that affect treatment efficacy and construct a model. ROC curve analysis was used to evaluate the diagnostic efficacy of the model. Results:The treatment effectiveness rate of the mild group was 93.48%, the moderate group was 90.38%, and the severe group was 80.00%. There was no statistically significant difference in the treatment effectiveness rate among the three groups（P>0.05）. The AHI of the mild group, moderate group, and severe group increased sequentially, while the LSaO2and SAQLI scores decreased sequentially. After treatment, the AHI of all three groups decreased compared to before treatment, while the LSaO2and SAQLI scores increased compared to before treatment, and the differences were statistically significant（P<0.05）. The pre-treatment AHI of the effective group was lower than that of the ineffective group, and the pre-treatment LSaO2and SAQLI were higher than those of the ineffective group, with statistically significant differences（P<0.05）. Pre-treatment LSaO2and pre-treatment SAQLI are independent factors affecting the efficacy of MLT-RFA（P<0.05）. The AUC of pre-treatment LSaO2, pre-treatment SAQLI, and combined prediction were 0.907, 0.763, and 0.947, respectively, with sensitivities of 0.896, 0.840, and 0.917, and specificities of 0.833, 0.667, and 0.887, respectively. Conclusion:MLT-RFA has a significant effect on the treatment of OSAHS, and the AHI, LSaO2, and SAQLI of patients before treatment can predict the treatment effect, with LSaO2 and SAQLI being independent influencing factors. The combinerd prediction model exhibits high diagnostic efficiency, sensitivity, and specificity.
Radiofrequency Ablation/methods* ; Plasma Gases ; Sleep Apnea, Obstructive/surgery* ; Polysomnography ; Postoperative Complications/epidemiology* ; Quality of Life ; Severity of Illness Index ; Treatment Outcome ; Humans

The neurophysiological mechanisms by which exercise improves cognitive function have not been fully elucidated. A comprehensive and systematic review of current domestic and international neurophysiological evidence on exercise improving cognitive function was conducted from multiple perspectives. At the molecular level, exercise promotes nerve cell regeneration and synaptogenesis and maintains cellular development and homeostasis through the modulation of a variety of neurotrophic factors, receptor activity, neuropeptides, and monoamine neurotransmitters, and by decreasing the levels of inflammatory factors and other modulators of neuroplasticity. At the cellular level, exercise enhances neural activation and control and improves brain structure through nerve regeneration, synaptogenesis, improved glial cell function and angiogenesis. At the structural level of the brain, exercise promotes cognitive function by affecting white and gray matter volumes, neural activation and brain region connectivity, as well as increasing cerebral blood flow. This review elucidates how exercise improves the internal environment at the molecular level, promotes cell regeneration and functional differentiation, and enhances the brain structure and neural efficiency. It provides a comprehensive, multi-dimensional explanation of the neurophysiological mechanisms through which exercise promotes cognitive function.
Animals ; Humans ; Brain/physiology* ; Cognition/physiology* ; Exercise/physiology* ; Nerve Regeneration/physiology* ; Neuronal Plasticity/physiology*

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.

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 To analyze the research status,hotspots,and trends of wearable devices in nursing applications both domestically and internationally.Methods Using China National Knowledge Infrastructure,Wanfang Data Knowledge Service Platform,and Embase core databases as data sources,VOSviewer was used to visualize and analyze the publication time,keywords,and other relevant literature.Results Total of 428 articles were included,including 196 Chinese articles and 232 English articles.The overall publication volume showed an upward trend.Domestic research focuses on chronic diseases,artificial intelligence,and nursing,with the main research subjects being the elderly;Foreign research focuses on smart devices,self-monitoring,and quality of life,with the main research subjects being adults.Conclusion Currently,the number of publications on the application of wearable devices in nursing is relatively small,but the overall research heat is on the rise,mainly used for chronic diseases and self-monitoring.In the future,the application scope of wearable devices should be expanded and their potential value should be explored to promote the innovation and progress of nursing models.

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 observe the clinical efficacy of unilateral biportal endoscopy (UBE) assisted bilateral decompression in the treatment of lumbar disc herniation with imaging herniation on one side and clinical symptoms on the contralateral side.Methods:A total of 20 patients with lumbar disc herniation with imaging herniation on one side and clinical symptoms on the contralateral side treated with UBE from January 2022 to January 2024 in the Affiliated Hospital of Binzhou Medical University were retrospectively analyzed. There were 9 males and 11 females, aged 50.4±14.0 years (range, 23-72 years). The intervertebral disc herniation level included L 3-4 in 1 case, L 4-5 in 15 cases, and L 5S 1 in 4 cases. There were 10 cases on the left side and 10 cases on the right side. The duration of symptoms was 24.1±33.7 months (range, 1-120 months). Visual analogue scale (VAS) and Oswestry disability index (ODI) were used to evaluate postoperative pain relief and functional recovery. The clinical efficacy was evaluated by modified MacNab criteria at 6 months after operation. Results:All patients successfully completed the operation. The operation time was 90.3±24.6 min (range, 55-134 mins). The VAS scores of patients at 3 days, 1 month, 3 months and 6 months after operation were 3.6±0.9, 2.2±0.7, 1.2±0.5 and 1.0±0.6, respectively, which were lower than those before operation (6.4±0.8), and the differences were statistically significant ( F=668.728, P<0.001). The ODI at 3 days, 1 month, 3 months and 6 months after operation were 34.2%±4.7%, 28.7%±2.8%, 24.3%±2.1% and 20.5%±2.0%, respectively, which were lower than 69.4%±5.2% before operation, and the differences were statistically significant ( F=515.578, P<0.001). The clinical efficacy was evaluated by modified MacNab criteria at 6 months after operation. Among 20 patients, 18 cases were excellent, 1 case was good, and 1 case was fair. All patients were followed up for 9.1±2.1 months (range, 6-14 months). One patient had a dural tear during the operation, but the range was small and there was no defect, and no further treatment was performed. Numbness of the lower limbs occurred 1 day after operation, and the symptoms disappeared after symptomatic treatment. There was no recurrence of lower limb symptoms, lumbar instability, intervertebral space infection or other complications at the last follow-up. Conclusion:Bilateral decompression with UBE is effective in the treatment of lumbar disc herniation with imaging herniation on one side and clinical symptoms on the contralateral side, which can improve the lumbar pain and function of patients.