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 explore the association between sleep quality and dry eye symptoms in adolescents,so as to provide the evidence for reducing the prevalence of dry eye symptoms. Methods: The study population was adolescents aged 12-24 years from the Psychology and Behavior Investigation of Chinese Residents (PBICR) survey, which was conducted from 20 June to 31 August 2022. A stratified random sampling and quota sampling method was used to select 6 456 adolescents within mainland China. The Ocular Surface Disease Index (OSDI) and Brief version of the Pittsburgh Sleep Quality Index (B-PSQI) were used to assess dry eye symptoms and sleep quality. Multiple Logistic regression model was used to explore the relationship between sleep quality and dry eye symptoms in adolescents. The influence of gender on the association was explored by using interaction terms. Results: A total of 2 815 adolescents reported having dry eye symptoms, with a prevalence of 43.6%. Logistic regression analysis results showed an increased risk of exacerbation of dry eye symptoms in adolescents with poor sleep quality. The OR (95% CI ) for mild, moderate, and severe dry eye symptoms groups were 1.39(1.16-1.67), 1.52(1.28-1.81), and 2.35(2.02-2.72), respectively, compared with the ocularly normal group ( P <0.05). There was a significant interaction between sleep quality and gender on dry eye symptoms in adolescents ( P <0.01). Conclusions Sleep quality is associated with dry eye symptoms in adolescents, and those with poor sleep quality have a higher risk of dry eye symptoms. The effect of sleep quality on dry eye symptoms is greater in boys.

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 prepare mouse monoclonal antibodies against the ectodomain of E2 (E2ecto) glycoprotein of Western equine encephalitis virus (WEEV). Methods A prokaryotic expression plasmid pET-28a-WEEV E2ecto was constructed and transformed into BL21 (DE3) competent cells. E2ecto protein was expressed by IPTG induction and presented mainly as inclusion bodies. Then the purified E2ecto protein was prepared by denaturation, renaturation and ultrafiltration. BALB/c mice were immunized with the formulated E2ecto protein using QuickAntibody-Mouse5W as an adjuvant via intramuscular route, boosted once at an interval of 21 days. At 35 days post-immunization, mice with antibody titer above 1×10⁴ were inoculated with E2ecto intraperitoneally, and spleen cells were fused with SP2/0 cells three days later. Hybridoma cells secreting specific monoclonal antibodies were screened by the limited dilution method, and ascites were prepared after intraperitoneal inoculation of hybridoma cells. The subtypes and titers of the antibodies in ascites were assayed by ELISA. The biological activity of the mAb was identified by immunofluorescence assay(IFA) on BHK-21 cells which were transfected with eukaryotic expression plasmid pCAGGS-WEEV-CE3E2E1. The specificity of the antibodies were evaluated with E2ecto proteins from EEEV and VEEV. Results Purified WEEV E2ecto protein was successfully expressed and obtained. Four monoclonal antibodies, 3G6G10, 3D7G2, 3B9E8 and 3D5B7, were prepared, and their subtypes were IgG2c(κ), IgM(κ), IgM(κ) and IgG1(κ), respectively. The titers of ascites antibodies 3G6G10, 3B9E8 and 3D7G2 were 10⁵, and 3D5B7 reached 10⁷. None of the four antibody strains cross-reacted with other encephalitis alphavirus such as VEEV and EEEV. Conclusion Four strains of mouse mAb specifically binding WEEV E2ecto are successfully prepared.
Horses ; Animals ; Mice ; Encephalitis Virus, Western Equine ; Ascites ; Immunosuppressive Agents ; Antibodies, Monoclonal ; Immunoglobulin M

The aim is to evaluate the effect of therapeutic communication on cervical cancer patients’ preoperative anxiety and hope level. The convenience sampling method was used to select the inpatients who will receive radical surgery for cervical cancer in the department of obstetrics and gynecology of the Second Affiliated Hospital of Xi’an Jiaotong University from November 2016 to November 2019 as the research object. 50 patients were grouped by the random number table method: 25 patients were in the intervention group, and 25 patients were in the control group. Patients in the intervention group were given therapeutic communication on the basis of routine nursing, and patients in the control group were given routine nursing. Both groups were investigated with the Self-Rating Anxiety Scale (SAS) and Herth Hope Index (HHI) on the first day of admission and the day before surgery. Before the intervention, there was no statistically significant difference between the two groups (P>0.05) . After the intervention, the anxiety level of the intervention group was lower than that of the control group (P<0.05), and the hope level was higher than that of the control group (P<0.05). It can be seen that therapeutic communication can alleviate preoperative anxiety of cervical cancer patients, improve their hope level, promote patient recovery, and ease tense medical relationship.

OBJECTIVE To systematically evaluate the real-world effectiveness and safety of belimumab in the treatment of lupus nephritis （LN） in Chinese adult patients. METHODS Retrieved from PubMed， Embase， Web of Science， Cochrane Library， Wanfang data， CNKI， VIP and CBM， real-world studies on belimumab in the treatment of LN in Chinese adult patients were collected from the inception to July 7th， 2023. Two reviewers independently screened the literature， extracted data， and assessed the quality of the included studies. Meta-analysis was then performed using RevMan 5.3 software. RESULTS A total of 10 real- world studies were included， involving 253 Chinese adult patients with LN. The results of the meta-analysis demonstrated that the complete renal response rate， partial renal response rate， and the incidence of adverse reaction rate in Chinese adult patients with LN treated with belimumab were 61% （95%CI was 46%-76%， P＜0.000 01）， 23%（95%CI was 2%-44%， P＝0.03）， and 30% （95%CI was 16%-43%， P＜0.000 01）， respectively. Belimumab could reduce the 24-hour urinary protein （MD＝－1.71， 95%CI was －3.02-－0.40， P＝0.01）， urine protein-creatinine ratio （MD＝－1.76，95%CI was －2.06-－1.46，P＜0.000 01）， the systemic lupus erythematosus disease activity index （MD＝－8.63， 95%CI was －12.12-－5.13， P＜0.000 01）， and glucocorticoids dosage （MD＝－18.65， 95%CI was －31.82-－5.48， P＝0.006）. In addition， it could elevate the levels of complement C3 （MD＝0.19， 95%CI was 0.08-0.30， P＝0.000 6） and complement C4 （MD＝0.06， 95%CI was 0.02-0.09， P＝0.001）. However， belimumab could not improve the levels of serum creatinine and estimated glomerular filtration rate （P＞0.05）. CONCLUSIONS Belimumab has good efficacy and safety in Chinese adult patients with LN.

Aim To evaluate the hypolipidemic effect of the total phenylpropanoid glycosides extracted from Ligustrum robustum (Roxb.) Blume (LRTPG) on hyperlipidemic golden hamsters and explore its regulatory effect on intestinal flora. Methods Sixty hamsters were randomly divided into a control group, a model group, a positive drug group, LRTPG-L group, LRTPG-M group, and LRTPG-H group. After the successful induction of the model by high-fat diet, the animals were continuously administered for four weeks, and their blood lipids and liver lipids were detected. The formed feces from the colorectal region of the hamsters in the control group, model group and LRTPG-H group were collected for 16S rDNA sequencing. Results LRTPG reduced serum TG, TC, LDL-C and liver TG, TC concentrations significantly in hyperlipidemic hamsters. The results of the intestinal microbiota sequencing showed that compared to the control group, LRTPG significantly decreased the relative abundance of the phylum Firmicutes and increased the relative abundance of the phylum Bacteroidetes and Verrucomicrobia (P < 0.01) at the phylum level. At the family level, LRTPG significantly increased the relative abundance of Christensenellaceae, Peptococcaceae, and Verrucomicrobiaceae (P < 0.05 or P < 0.01). At the genus level, LRTPG significantly increased the relative abundance of Oscillospira, Oscillibacter, Flavonifractor and Akkermansiaceae (P < 0.05 or P < 0.01). These changes in the flora were beneficial to the hypolipidemic effect of LRTPG. Conclusion LRTPG may exert its hypolipidemic effect by improving the intestinal flora disorder caused by a high-fat diet in golden hamsters.