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 effect of Huanglian Jiedu Decoction on the intestinal flora of type 2 diabetes mellitus(T2DM) was investigated using 16S rRNA sequencing technology. Sixty rats were randomly divided into a normal group(10 rats) and a modeling group(50 rats). After one week of adaptive feeding, a high-fat diet + streptozotocin was given for modeling, and fasting blood glucose >16.7 mmol·L~(-1) was considered a sign of successful modeling. The modeling group was randomly divided into the model group, high-, medium-, and low-dose groups of Huanglian Jiedu Decoction, and metformin group. After seven days of intragastric treatment, the feces, colon, and pancreatic tissue of each group of rats were collected, and the pathological changes of the colon and pancreatic tissue of each group were observed by hematoxylin-eosin staining. The changes in the intestinal flora structure of each group were observed by the 16S rRNA sequencing method. The results showed that compared with the model group, the high-, medium-, and low-dose of Huanglian Jiedu Decoction reduced fasting blood glucose levels to different degrees and showed no significant changes in body weight. The number of islet cells increased, and intestinal mucosal damage attenuated. Alpha diversity analysis revealed that Huanglian Jiedu Decoction reduced the abundance and diversity of intestinal flora in rats with T2DM; at the phylum level, low-and mediam-dose of Huanglian Jiedu Decoction reduced the abundance of Bacteroidota, Proteobacteria, and Desulfobacterota and increased the abundance of Firmicute and Bacteroidota/Firmicutes, while the high-dose of Huanglian Jiedu Decoction increased the relative abundance of Proteobacteria and Bacteroidota/Firmicutes ratio, and decreaseal the relative; abundance of Firmicute; at the genus level, Huanglian Jiedu Decoction increased the relative abundance of Allobaculum, Blautia, and Lactobacillus; LEfse analysis revealed that the biomarker of low-and medium-dose groups of Huanglian Jiedu Decoction was Lactobacillus, and the structure of the intestinal flora of the low-dose group of Huanglian Jiedu Decoction was highly similar to that of the metformin group. PICRUSt2 function prediction revealed that Huanglian Jiedu Decoction mainly affected carbohydrate and amino acid metabolic pathways. It suggested that Huanglian Jiedu Decoction could reduce fasting blood glucose and increase the number of islet cells in rats with T2DM, and its mechanism of action may be related to increasing the abundance of short-chain fatty acid-producing strains and Lactobacillus and affecting carbohydrate and amino acid metabolic pathways.
Animals ; Drugs, Chinese Herbal/administration & dosage* ; Diabetes Mellitus, Type 2/metabolism* ; Gastrointestinal Microbiome/drug effects* ; Rats ; Male ; Rats, Sprague-Dawley ; Humans ; Bacteria/drug effects* ; Blood Glucose/metabolism*

This study aims to explore the mechanism of Buyang Huanwu Decoction(BHD) in promoting angiogenesis after oxygen-glucose deprivation/reoxygenation(OGD/R) of mouse brain microvascular endothelial cell line(brain-derived Endothelial cells.3, bEnd.3) based on the caveolin-1(Cav1)/Yes-associated protein 1(YAP1)/hypoxia-inducible factor-1α(HIF-1α) signaling pathway. Ultra-high performance liquid chromatography-quadrupole-time-of-flight mass spectrometry(UPLC-Q-TOF-MS) was used to analyze the blood components of BHD. The cell counting kit-8(CCK-8) method was used to detect the optimal intervention concentration of drug-containing serum of BHD after OGD/R injury of bEnd.3. The lentiviral transfection method was used to construct a Cav1 silent stable strain, and Western blot and polymerase chain reaction(PCR) methods were used to verify the silencing efficiency. The control bEnd.3 cells were divided into a normal group(sh-NC control group), an OGD/R model + blank serum group(sh-NC OGD/R group), and an OGD/R model + drug-containing serum group(sh-NC BHD group). Cav1 silent cells were divided into an OGD/R model + blank serum group(sh-Cav1 OGD/R group) and an OGD/R model + drug-containing serum group(sh-Cav1 BHD group). The cell survival rate was detected by the CCK-8 method. The cell migration ability was detected by a cell migration assay. The lumen formation ability was detected by an angiogenesis assay. The apoptosis rate was detected by flow cytometry, and the expression of YAP1/HIF-1α signaling pathway-related proteins in each group was detected by Western blot. Finally, co-immunoprecipitation was used to verify the interaction between YAP1 and HIF-1α. The results showed astragaloside Ⅳ, formononetin, ferulic acid, and albiflorin in BHD can all enter the blood. The drug-containing serum of BHD at a mass fraction of 10% may be the optimal intervention concentration for OGD/R-induced injury of bEnd.3 cells. Compared with the sh-NC control group, the sh-NC OGD/R group showed significantly decreased cell survival rate, cell migration rate, mesh number, node number, and lumen length, significantly increased cell apoptotic rate, significantly lowered phosphorylation level of YAP1 at S127 site, and significantly elevated nuclear displacement level of YAP1 and protein expression of HIF-1α, vascular endothelial growth factor(VEGF), and vascular endothelial growth factor receptor 2(VEGFR2). Compared with the same type of OGD/R group, the sh-NC BHD group and sh-Cav1 BHD group had significantly increased cell survival rate, cell migration rate, mesh number, node number, and lumen length, a significantly decreased cell apoptotic rate, a further decreased phosphorylation level of YAP1 at S127 site, and significantly increased nuclear displacement level of YAP1 and protein expression of HIF-1α, VEGF, and VEGFR2. Compared with the sh-NC OGD/R group, the sh-Cav1 OGD/R group exhibited significantly decreased cell survival rate, cell migration rate, mesh number, node number, and lumen length, a significantly increased cell apoptotic rate, a significantly increased phosphorylation level of YAP1 at S127 site, and significantly decreased nuclear displacement level of YAP1 and protein expression of HIF-1α, VEGF, and VEGFR2. Compared with the sh-NC BHD group, the sh-Cav1 BHD group showed significantly decreased cell survival rate, cell migration rate, mesh number, node number, and lumen length, a significantly increased cell apoptotic rate, a significantly increased phosphorylation level of YAP1 at the S127 site, and significantly decreased nuclear displacement level of YAP1 and protein expression of HIF-1α, VEGF, and VEGFR2. YAP1 protein was present in the protein complex precipitated by the HIF-1α antibody, and HIF-1α protein was also present in the protein complex precipitated by the YAP1 antibody. The results confirmed that the drug-containing serum of BHD can increase the activity of YAP1/HIF-1α pathway in bEnd.3 cells damaged by OGD/R through Cav1 and promote angiogenesis in vitro.
Drugs, Chinese Herbal/pharmacology* ; Animals ; Mice ; Signal Transduction/drug effects* ; Glucose/metabolism* ; Caveolin 1/genetics* ; Hypoxia-Inducible Factor 1, alpha Subunit/genetics* ; YAP-Signaling Proteins ; Oxygen/metabolism* ; Endothelial Cells/metabolism* ; Cell Line ; Adaptor Proteins, Signal Transducing/genetics* ; Neovascularization, Physiologic/drug effects* ; Cell Hypoxia/drug effects* ; Angiogenesis

OBJECTIVE: To explore the effectiveness of arthroscopic All-inside single needle vertical suture technique in treatment of complete radial tear of the meniscus. METHODS: Between January 2019 and January 2022, 18 patients (18 knees) with complete radial tear of the meniscus were treated by using arthroscopic All-inside single needle vertical suture technique. Among them, there were 12 males and 6 females with an average age of 37.1 years (range, 16-50 years). The causes of radial meniscus tears included the sports injuries in 11 cases, sprains/falls in 4 cases, and traffic accident injuries in 3 cases. The interval between injury and operation was 3-25 days (mean, 11.7 days). All patients had knee joint pain. Knee joint tenderness and McMurray sign were both positive. MRI showed the 15 cases of lateral meniscus tear and 3 cases of medial meniscus tear; 15 cases of anterior cruciate ligament injury, 1 case of posterior cruciate ligament injury, and 15 cases of tibial plateau bone contusion. Six patients underwent isolated meniscus repair and 12 patients with cruciate ligament rupture underwent meniscus repair and simultaneous cruciate ligament reconstruction. The operation time and incidence of postoperative complications were recorded. At last follow-up, the meniscus healing was evaluated according to Barrett's criteria and knee joint MRI, respectively. Lysholm score and International Knee Documentation Committee (IKDC) score were used to evaluate the functional recovery of the knee joint. RESULTS: The operation time was 19-28 minutes (mean, 23.3 minutes) in 6 patients with isolated meniscus repair and 38-52 minutes (mean, 45.8 minutes) in 12 patients with meniscus repair and simultaneous cruciate ligament reconstruction. All incisions healed by first intention. After operation, 1 patient developed the deep vein thrombosis of lower limb, the other patients had no complication. All patients were followed up 12-18 months (mean, 15.2 months). At last follow-up, 16 cases of meniscus reached clinical healing according to Barrett's criteria, with a healing rate of 88.9%. MRI re-examination of the knee joint showed that 5 cases had complete healing of the meniscus, 11 cases had partial healing, and 2 cases did not heal. The total healing rate (complete healing and partial healing) was 88.9%. After operation, the Lysholm score and IKDC score of 18 patients increased compared to preoperative scores, and further improved with time. The differences between different time points were significant ( P<0.05). Six patients with isolated meniscus repair had the same changes in the above scores, and the differences between the different time points were significant ( P<0.05). CONCLUSION The arthroscopic All-inside single needle vertical suture technique can achieve good short-term effectiveness in the treatment of complete radial tears of the meniscus.
Humans ; Male ; Adult ; Female ; Arthroscopy/methods* ; Tibial Meniscus Injuries/surgery* ; Middle Aged ; Adolescent ; Young Adult ; Suture Techniques ; Treatment Outcome ; Magnetic Resonance Imaging ; Anterior Cruciate Ligament Injuries/surgery* ; Menisci, Tibial/surgery* ; Knee Joint/surgery*

Male factors contribute to 50% of infertility cases, with 20%-30% of cases being solely attributed to male infertility. Helicase for meiosis 1 ( HFM1 ) plays a crucial role in ensuring proper crossover formation and synapsis of homologous chromosomes during meiosis, an essential process in gametogenesis. HFM1 gene mutations are associated with male infertility, particularly in cases of non-obstructive azoospermia and severe oligozoospermia. However, the effects of intracytoplasmic sperm injection (ICSI) in HFM1 -related infertility cases remain inadequately explored. This study identified novel biallelic HFM1 variants through whole-exome sequencing (WES) in a Chinese patient with severe oligozoospermia, which was confirmed by Sanger sequencing. The pathogenicity of these variants was assessed using real-time quantitative polymerase chain reaction (RT-qPCR) and immunoblotting, which revealed a significant reduction in HFM1 mRNA and protein levels in spermatozoa compared to those in a healthy control. Transmission electron microscopy revealed morphological abnormalities in sperm cells, including defects in the head and flagellum. Despite these abnormalities, ICSI treatment resulted in a favorable fertility outcome for the patient, indicating that assisted reproductive techniques (ART) can be effective in managing HFM1 -related male infertility. These findings offer valuable insights into the management of such cases.
Humans ; Male ; Sperm Injections, Intracytoplasmic ; Oligospermia/therapy* ; Adult ; Spermatozoa/ultrastructure* ; Exome Sequencing ; Mutation

Objective： The aim of this study is to explore the predictive value of biparametric magnetic resonance imaging(bpMRI)for pelvic lymph node metastasis in prostate cancer patients with PSA≤20 μg/L and establish a nomogram. Methods： The imaging data and clinical data of 363 patients undergoing radical prostatectomy and pelvic lymph node dissection in the First Affiliated Hospital of Nanjing Medical University from July 2018 to December 2023 were retrospectively analyzed. Univariate analysis and multivariate logistic regression were used to screen independent risk factors for pelvic lymph node metastasis in prostate cancer, and a nomogram of the clinical prediction model was established. Calibration curves were drawn to evaluate the accuracy of the model. Results： Multivariate logistic regression analysis showed extrocapusular extension (OR=8.08,95%CI=2.62－24.97, P<0.01), enlargement of pelvic lymph nodes (OR=4.45,95%CI=1.16－17.11,P=0.030), and biopsy ISUP grade(OR=1.97,95%CI=1.12－3.46, P=0.018)were independent risk factors for pelvic lymph node metastasis. The C-index of the prediction model was 0.834, which indicated that the model had a good prediction ability. The actual value of the model calibration curve and the prediction probability of the model fitted well, indicating that the model had a good accuracy. Further analysis of DCA curve showed that the model had good clinical application value when the risk threshold ranged from 0.05 to 0.70.Conclusion： For prostate cancer patients with PSA≤20 μg/L, bpMRI has a good predictive value for the pelvic lymph node metastasis of prostate cancer with extrocapusular extension, enlargement of pelvic lymph nodes and ISUP grade≥4.
Humans ; Male ; Prostatic Neoplasms/diagnostic imaging* ; Lymphatic Metastasis ; Retrospective Studies ; Nomograms ; Prostate-Specific Antigen/blood* ; Lymph Nodes/pathology* ; Pelvis ; Predictive Value of Tests ; Prostatectomy ; Lymph Node Excision ; Risk Factors ; Magnetic Resonance Imaging ; Logistic Models ; Middle Aged ; Aged