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.

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*

This study investigated the effects of total flavonoids of Dracocephalum moldavica(TFDM) on apoptosis in rat H9c2 cells induced by endoplasmic reticulum stress(ERS) established by oxygen-glucose deprivation and reoxygenation(OGD/R) injury and tunicamycin(TM), and explored the potential mechanisms. After successful modeling, the following groups were set in this experiment: control group, model(OGD/R or TM) group, and TFDM low-, medium-, and high-dose groups(12.5, 25, and 50 μg·mL~(-1)). The OGD/R injury model was constructed in vitro. Cell proliferation was assessed using the cell counting kit-8(CCK-8) method. The levels of lactate dehydrogenase(LDH) and creatine kinase MB isoenzyme(CKMB) in the cell supernatant were detected. Western blot was used to assess the expression of ERS-related proteins, including glucose regulatory protein 78(GRP78), C/EBP homologous protein(CHOP), activating transcription factor 6(ATF6), and apoptotic proteins B-cell lymphoma 2(Bcl-2) and Bcl-2-associated X protein(Bax). Apoptosis was detected using the terminal deoxynucleotidyl transferase-mediated dUTP nick-end labeling(TUNEL) method. In the TM-induced ERS model, Western blot was used to measure the expression of ERS pathway-related proteins GRP78, CHOP, inositol-requiring enzyme 1(IRE1), X-box binding protein 1(XBP1), protein kinase RNA-like endoplasmic reticulum kinase(PERK), eukaryotic initiation factor 2α(eIF2α), ATF6, p-ATF6, and apoptotic proteins Bcl-2, Bax, cysteinyl aspartate specific proteinase-12(caspase-12), and cleaved caspase-12. Gene expression of GRP78, CHOP, PERK, and ATF6 was detected by real-time fluorescence quantitative PCR(RT-qPCR). Apoptosis was again detected using the TUNEL method. The results showed that in the OGD/R model, compared with the control group, the levels of LDH and CKMB in the cell supernatant were significantly increased in the OGD/R group. Compared with the OGD/R group, the levels of LDH and CKMB in the TFDM group were significantly reduced. Western blot results revealed that compared with the control group, the expression of ERS-related proteins and Bax in the OGD/R group was significantly increased, while the expression of Bcl-2 was significantly decreased. Compared with the OGD/R group, the expression of ERS-related proteins and Bax in the TFDM groups was significantly reduced, and the expression of Bcl-2 was significantly increased. TUNEL assay showed that apoptosis was significantly decreased after TFDM treatment. In the TM-induced ERS experiment, compared with the control group, the expression of ERS-related genes, ERS-related proteins, and apoptotic proteins in the TM group was significantly increased, while the expression of Bcl-2 was significantly decreased. Compared with the TM group, the expression of ERS-related genes, ERS-related proteins, and apoptotic proteins in the TFDM group was significantly reduced, and the expression of Bcl-2 was significantly increased. These results suggest that ERS exists in the OGD/R-injured H9c2 cell model, and TFDM can effectively inhibit ERS-induced apoptosis. The mechanism may be related to the downregulation of ERS pathway-related proteins and apoptotic proteins.
Animals ; Endoplasmic Reticulum Stress/drug effects* ; Apoptosis/drug effects* ; Rats ; Flavonoids/pharmacology* ; Glucose/metabolism* ; Cell Line ; Lamiaceae/chemistry* ; Drugs, Chinese Herbal/pharmacology* ; Oxygen/metabolism* ; Reperfusion Injury/physiopathology* ; Myocytes, Cardiac/cytology*

Guided by the concept of quality by design(QbD), this study optimizes the calcination and quenching process of calcined Magnetitum and establishes the XRD characteristic spectra of calcined Magnetitum, providing a scientific basis for the formulation of quality standards. Based on the processing methods and quality requirements of Magnetitum in the Chinese Pharmacopoeia, the critical process parameters(CPPs) identified were calcination temperature, calcination time, particle size, laying thickness, and the number of vinegar quenching cycles. The critical quality attributes(CQAs) included Fe mass fraction, Fe~(2+) dissolution, and surface color. The weight coefficients were determined by combining Analytic Hierarchy Process(AHP) and the criteria importance though intercrieria correlation(CRITIC) method, and the calcination process was optimized using orthogonal experimentation. Surface color was selected as a CQA, and based on the principle of color value, the surface color of calcined Magnetitum was objectively quantified. The vinegar quenching process was then optimized to determine the best processing conditions. X-ray diffraction(XRD) was used to establish the characteristic spectra of calcined Magnetitum, and methods such as similarity evaluation, cluster analysis, and orthogonal partial least squares-discriminant analysis(OPLS-DA) were used to evaluate the quality of the spectra. The optimized calcined Magnetitum preparation process was found to be calcination at 750 ℃ for 1 h, with a laying thickness of 4 cm, a particle size of 0.4-0.8 cm, and one vinegar quenching cycle(Magnetitum-vinegar ratio 10∶3), which was stable and feasible. The XRD characteristic spectra analysis method, featuring 9 common peaks as fingerprint information, was established. The average correlation coefficient ranged from 0.839 5-0.988 1, and the average angle cosine ranged from 0.914 4 to 0.995 6, indicating good similarity. Cluster analysis results showed that Magnetitum and calcined Magnetitum could be grouped together, with similar compositions. OPLS-DA discriminant analysis identified three key characteristic peaks, with Fe_2O_3 being the distinguishing component between the two. The final optimized processing method is stable and feasible, and the XRD characteristic spectra of calcined Magnetitum was initially established, providing a reference for subsequent quality control and the formulation of quality standards for calcined Magnetitum.
X-Ray Diffraction/methods* ; Drugs, Chinese Herbal/chemistry* ; Quality Control ; Particle Size

Incomplete debridement of chronic osteomyelitis is the main factor leading to recurrence. For the treatment of chronic osteomyelitis, the complete elimination of the source of infection is the key to preventing recurrence. This process includes not only the complete removal of infected lesions, dead bone, accreted scar tissue and granulation tissue, but also the elimination of dead space and improved local blood circulation. In these steps, debridement is a core procedure, and judging the scope of debridement is the premise of whether it could be completely debridement. This article systematically reviewed the application of different imaging techniques in evaluating the scope of chronic osteomyelitis infection, and discusses its future development trend. Although traditional plain X-ray film could preliminarily indicate osteomyelitis, it is difficult to determine the infection scope. CT scan has the function of accurate anatomic localization, which is important for preoperative assessment of the scope of bone infection, but the recognition of soft tissue information is limited. MRI, with its high sensitivity, clearly distinguishes between infected bone and soft tissue, which plays an important role in the evaluation of soft tissue infection, but may overestimate the extent of bone infection. Nuclide techniques such as 18F-FDG PET/CT and SPECT/CT show great potential for accurately assessing the extent of infection before surgery. In the future, by optimizing the combination of different imaging technologies, combining clinical symptoms, intraoperative conditions and pathological results, and developing an image analysis platform based on artificial intelligence, it will be able to more accurately assess the scope of infection, provide more effective and personalized treatment plans for patients with chronic osteomyelitis, enhance treatment effects, and significantly improve quality of life of patients.
Humans ; Osteomyelitis/diagnosis* ; Chronic Disease ; Magnetic Resonance Imaging ; Tomography, X-Ray Computed

OBJECTIVE: To investigate the clinical efficacy and safety of ultrasound-guided closed reduction and internal fixation using Kirschner wire for the treatment of olecranon fractures of the ulna in children. METHODS: Between January 2019 and January 2021, 13 children with olecranon fracture were treated with ultrasound-guided closed reduction and percutaneous Kirschner wire internal fixation, including 10 males and 3 females. The age ranged from 3 to 14 years old. Children with ulnar olecranon fractures were evaluated using the Gicquel scoring system. The clinical evaluation encompassed postoperative pain, functional status, and range of motion, with a maximum score of 15 points. The radiological assessment contributed an additional 4 points. A cumulative score of more than 18 scores was classified as excellent, more than 17 scores as good, more than16 scores as fair, and less than 16 scores as poor. Clinical assessment:A score of 14 indicates excellent performance, a score of 13 reflects good performance, a score of 12 denotes fair performance, and a score of less than 11 signifies poor performance. RESULTS: A total of 13 patients were followed up, with a duration ranging from 6 to 12 months. According to the Gicquel scoring criteria, the comprehensive evaluation of clinical and radiographic findings yielded 10 excellent and 3 good outcomes. Evaluation based solely on clinical findings resulted in 13 excellent outcomes. CONCLUSION Ultrasound-guided percutaneous cross Kirschner wire fixation for children's olecranon fracture has the advantages of less trauma, rapid recovery, less fluoroscopy, and good recovery of elbow function. The clinical effect is satisfactory.
Humans ; Child ; Male ; Female ; Fracture Fixation, Internal/instrumentation* ; Ulna Fractures/physiopathology* ; Bone Wires ; Child, Preschool ; Adolescent ; Olecranon Process/surgery* ; Ultrasonography ; Closed Fracture Reduction/methods* ; Olecranon Fracture