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 study aims to examine the effects and potential mechanisms of disulfiram (DSF) on cardiac hypertrophic injury, focusing on the role of transforming growth factor-β-activated kinase 1 (TAK1)-mediated pan-apoptosis (PANoptosis). H9C2 cardiomyocytes were treated with angiotensin II (Ang II, 1 µmol/L) to establish an in vitro model of myocardial hypertrophy. DSF (40 µmol/L) was used to treat cardiomyocyte hypertrophic injury models, either along or in combination with the TAK1 inhibitor, 5z-7-oxozeaenol (5z-7, 0.1 µmol/L). We assessed cell damage using propidium iodide (PI) staining, measured cell viability with CCK8 assay, quantified inflammatory factor levels in cell culture media via ELISA, detected TAK1 and RIPK1 binding rates using immunoprecipitation, and analyzed the protein expression levels of key proteins in the TAK1-mediated PANoptosis pathway using Western blot. In addition, the surface area of cardiomyocytes was measured with Phalloidin staining. The results showed that Ang II significantly reduced the cellular viability of H9C2 cardiomyocytes and the binding rate of TAK1 and RIPK1, significantly increased the surface area of H9C2 cardiomyocytes, PI staining positive rate, levels of inflammatory factors [interleukin-1β (IL-1β), IL-18, and tumor necrosis factor α (TNF-α)] in cell culture media and p-TAK1/TAK1 ratio, and significantly up-regulated key proteins in the PANoptosis pathway [pyroptosis-related proteins NLRP3, Caspase-1 (p20), and GSDMD-N (p30), apoptosis-related proteins Caspase-3 (p17), Caspase-7 (p20), and Caspase-8 (p18), as well as necroptosis-related proteins p-MLKL, RIPK1, and RIPK3]. DSF significantly reversed the above changes induced by Ang II. Both 5z-7 and exogenous IL-1β weakened these cardioprotective effects of DSF. These results suggest that DSF may alleviate cardiac hypertrophic injury by inhibiting TAK1-mediated PANoptosis.
Animals ; MAP Kinase Kinase Kinases/physiology* ; Rats ; Myocytes, Cardiac/pathology* ; Disulfiram/pharmacology* ; Cardiomegaly ; Apoptosis/drug effects* ; Cell Line ; Angiotensin II ; Necroptosis/drug effects* ; Interleukin-1beta/metabolism* ; Receptor-Interacting Protein Serine-Threonine Kinases/metabolism* ; Lactones ; Resorcinols ; Zearalenone/administration & dosage*

To improve the quality evaluation system of Hibisci Mutabilis Folium, this study established high performance liquid chromatography(HPLC) fingerprints of Hibisci Mutabilis Folium and evaluated the quality differences of medicinal materials from different places of production by chemometrics. Furthermore, a content measurement method of differential components was established based on quantitative analysis of multi-components by single-marker(QAMS). The fingerprints of 17 batches of Hibisci Mutabilis Folium from different places of production were constructed, with a total of 19 common peaks marked and seven components confirmed. The similarity between the sample fingerprints and the reference fingerprints ranged from 0.890 to 0.974. By utilizing principal component analysis(PCA), hierarchical cluster analysis(HCA), and orthogonal partial least squares-discriminant analysis(OPLS-DA), the chemical patterns of fingerprints were identified. Five components that could be used to evaluate the quality differences of Hibisci Mutabilis Folium were screened, namely peak 6(quercetin 3-O-β-robinobioside), peak 7(rutin), peak 9(kaempferol-3-O-β-robinobioside), peak 10(kaempferol-3-O-rutinoside), and peak 14(tiliroside). The relative correction factors of isoquercitrin, kaempferol-3-O-β-robinobioside, kaempferol-3-O-rutinoside, kaempferol-3-O-β-D-glucoside, and tiliroside were measured with rutin as the internal reference. The QAMS method was established for the content measurement of six flavonoids, and the results showed there was no significant difference compared to the results obtained by an external standard method. In summary, the HPLC fingerprints and QAMS method established in the study, demonstrating stability and accuracy, can provide a reference for the overall quality evaluation of Hibisci Mutabilis Folium.
Chromatography, High Pressure Liquid/methods* ; Drugs, Chinese Herbal/chemistry* ; Quality Control ; Principal Component Analysis

Although metal blocks have been widely used for reconstructing uncontained tibial bone defects, the influence of their elastic modulus on the stability of tibial prosthesis fixation remains unclear. Based on this, a finite element model incorporating constrained condylar knee (CCK) prosthesis, tibia, and metal block was established. Considering the influence of the post-restraint structure of the prosthesis, the effects of variations in the elastic modulus of the block on the von Mises stress distribution in the tibia and the block, as well as on the micromotion at the bone-prosthesis fixation interface, were investigated. Results demonstrated that collision between the insert post and femoral prosthesis during tibial internal rotation increased tibial von Mises stress, significantly influencing the prediction of block elastic modulus variation. A decrease in the elastic modulus of the metal block resulted in increased von Mises stress in the proximal tibia, significantly reduced von Mises stress in the distal tibia, decreased von Mises stress of the block, and increased micromotion at the bone-prosthesis fixation interface. When the elastic modulus of the metal block fell below that of bone cement, inadequate block support substantially increased the risk of stress shielding in the distal tibia and fixation interface loosening. Therefore, this study recommends that biomechanical investigations of CCK prostheses must consider the post-constraint effect, and the elastic modulus of metal blocks for bone reconstruction should not be lower than 3 600 MPa.
Knee Prosthesis ; Humans ; Finite Element Analysis ; Tibia/surgery* ; Elastic Modulus ; Arthroplasty, Replacement, Knee/methods* ; Stress, Mechanical ; Metals ; Prosthesis Design ; Knee Joint/surgery* ; Biomechanical Phenomena

OBJECTIVE: To explore prevalence, incidence and possible factors of immediate herniated discs after posterior cervical expansive open-door laminoplasty (EODL). METHODS: Totally 29 patients with cervical spinal stenosis and intervertebral disc herniation who underwent EODL from October 2020 to December 2021 were collected, including 24 males and 5 females, aged from 43 to 81 years old with an average of (61.3±9.0) years old;the courses of disease ranged from 1 to 120 months with an average of (36.4±37.0) months. Three or more intervertebral discs on C₃-C₇ were observed. The clinical efficacy was evaluated according to Japanese Orthopaedic Association (JOA) score before operation, 3 days and 1, 3, 6 and 12 months after operation, respectively. The changes of herniated disc before and after operation were measured by multipoint area method and two-dimensional distance method, and incidence and percentage of herniated disc regression were further calculated. Cervical imaging parameters such as Cobb angle (C₃-C₇), intervertebral angle, T₁ slope (T₁S), spinal canal sagittal diameter, K-line angle, dural sac sagittal diameter were measured and compared before and after operation. Pearson correlation was used to analyze correlation between cervical sagittal imaging parameters and disc herniation changes before and after operation. RESULTS: All patients obtained grade A wound healing, and 14 of them were followed up for 3(1.00, 5.25) months. There were no immediate or long-term postoperative complications. Totally 101 herniated intervertebral discs were measured, of which 79 regression numbers were obtained by area measurement. The number of intervertebral disc regressions by distance measurement was 77. There was no statistically significant difference in Cobb angle, intervertebral angle, T₁S and K-line angle of C₃-C₇ (P>0.05), however, there were statistically significant differences in sagittal diameter of spinal canal, sagittal diameter of dural sac, and JOA score before and after operation(P<0.05). The regression ratio of disc herniation ranged from 5% to 50%, and regression ratio of disc herniation was greater than 25% in 45.57%(36/79). Disc herniation in C_4,5 was positively correlated with sagittal plane diameter in C₅(r=0.423, P=0.028). There was a negative correlation between changes of C_3,4 and C_3,4 intervertebral angle (r=-0.450, P=0.041). The improvement rate of cervical JOA score immediately after operation was (59.54±15.07) %, and postoperative follow-up improved to (76.57±14.66) %. CONCLUSION Herniated disc regression immediately after EODL is a common occurrence, and EODL should be selected as far as possible under the premise of satisfying surgical indications. The regression of disc herniation is positively correlated with spinal canal sagittal diameter, and spinal canal should be enlarged as far as possible in the appropriate scope during EODL, so as to create more opportunities and conditions for disc regression and achieve better clinical results.
Humans ; Female ; Male ; Intervertebral Disc Displacement/diagnostic imaging* ; Spinal Stenosis/diagnostic imaging* ; Laminoplasty/methods* ; Middle Aged ; Aged ; Cervical Vertebrae/diagnostic imaging* ; Adult ; Aged, 80 and over ; Intervertebral Disc/surgery*

Objective： To systematically evaluate the clinical efficacy and safety of Tonifying kidney and activating blood therapy for the treatment of diabetic mellitus erectile dysfunction. Methods： China National Knowledge Infrastructure(CNKI), Wanfang Data, VIP, Chinese Biomedical Database(CBM), PubMed, Cochrane Library, Embase and Web of Science were searched from inception until October 20th of 2024，for randomized controlled trials of Tonifying kidney and activating blood therapy for the treatment of diabetic erectile dysfunction. Literature screening, quality evaluation, and data extraction were carried out in accordance with relevant standards. The software of RevMan5.4 was used for the analysis of publication bias. And meta-analysis was conducted to assess the impact of this therapy on IIEF-5, total effective rate, adverse reactions. The evidence levels according to the analysis results were evaluated. Results: Totally 19 RCTs were included, involving 1 612 patients. The result of meta-analysis indicated that Tonifying kidney and activating blood therapy had advantages on the improvement of IIEF-5 scores (MD=3.59，95%CI［2.14，5.03］，P<0.01)，total effective rate (OR=4.30，95%CI［3.29，5.32］，P<0.000 01）. However, there was no statistically significant difference in the incidence of adverse reactions(OR=0.98，95%CI［0.48，2.01］，P=0.96) between the two groups. Conclusions： Tonifying kidney and activating blood therapy can improve the clinical efficacy and IIEF-5 score for the patients with diabetic erectile dysfunction. But considering the limited quantity of included studies, more high-quality studies still be needed to validate the therapeutic effect.
Humans ; Male ; Erectile Dysfunction/therapy* ; Randomized Controlled Trials as Topic ; Kidney ; Medicine, Chinese Traditional ; Diabetes Complications/therapy*