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.

OBJECTIVE: To compare the therapeutic efficacy and safety of local anesthesia and spinal anesthesia for the patients with varicocele (VC) who underwent microsurgical varicocelectomy (MV). METHODS: We retrospectively analyzed the data of VC patients who underwent MV treatment at the Andrology Department of the Affiliated Ruikang Hospital of Guangxi University of Chinese Medicine from May 2020 to March 2023. Cases with complete clinical data and follow-up evaluation were selected and divided into a control group (spinal anesthesia) and an observation group (local anesthesia) according to different anesthesia methods. The surgical time (including anesthesia time), visual analogue scale (VAS) score for pain, hospital stay, treatment cost, sperm concentration, forward motile sperm rate, and normal sperm morphology rate after three months of surgery, as well as postoperative complications and recurrence rate were compared between the two groups. RESULTS: A total of 107 eligible cases were included, with 56 cases in the control group and 51 cases in the observation group. There was no significant difference in the VAS score for pain during and after four hours of surgery, as well as postoperative complications, and recurrence rate between the two groups (P> 0.05). There was an significant increase in sperm concentration, forward motile sperm rate, and normal sperm morphology rate in both of two groups after three months of surgery (P<0.05). However, there was no significant difference between the two groups three months after surgery (P>0.05). The surgical time and hospital stay were shorter than those of the control group (P<0.05). And the treatment cost in observation group was lower than that of the control group (P<0.05). CONCLUSION Both local anesthesia and lumbar anesthesia for MV treatment of VC have good efficacy and safety. However, patients treated with MV under local anesthesia for VC have obvious advantages in terms of operation time (including anesthesia time), hospital stay, and treatment cost, which is worthy of clinical promotion and application.
Humans ; Male ; Varicocele/surgery* ; Retrospective Studies ; Microsurgery ; Anesthesia, Spinal ; Adult ; Treatment Outcome ; Anesthesia, Local

Objective:To compare the short-term effect and adverse reaction of venetoclax(VEN)combined with azacitidine(AZA)versus"7+3"regimen in newly diagnosed elder patients with acute myeloid leukemia(AML).Methods:From January 2021 to January 2022,the clinical data of seventy-nine newly diagnosed elder patients with AML at the Second Hospital of Shanxi Medical University and the Shanxi Bethune Hospital were retrospectively analyzed,including VEN+AZA group(41 cases)and"7+3"group(38 cases).The propensity score matching(PSM)method was used to balance confounding factors,then response,overall survival(OS),progression-free survival(PFS)and adverse reactions between the two groups were compared.Results:The ORR of VEN+AZA group and"7+3"group was 68％and 84％,respectively,and the CRc was 64％and 72％,respectively,the differents were not statistically significant(P＞0.05).In the VEN+AZA group,there were 5 non-remission(NR)patients,4 with chromosome 7 abnormality(7q-/-7),and 1 with ETV6 gene mutation.Median followed-up time between the two groups was 8 months and 12 months,respectively,and the 6-months OS was 84％vs 92％(P=0.389),while 6-months PFS was 84％vs 92％(P=0.258).The main hematological adverse reactions in two groups were stage Ⅲ-Ⅳmyelosuppression,and the incidence rate was not statistically different(P＞0.05).The median time of neutrophil recovery in two groups was 27(11-70)d,25(14-61)d(P=0.161),and platelet recovery was 27(11-75)d,25(16-50)d(P=0.270),respectively.The infection rate of VEN+AZA group was lower than that of"7+3"group(56％vs 88％,P=0.012).The rate of lung infections of two groups was 36％and 64％,respectively,the difference was statistically significant(P=0.048).Conclusion:The short-term effect of VEN+AZA group and"7+3"regimens in eldrly AML patients are similar,but the VEN+AZA regimen had a lower incidence of infection.The presence of chromosome 7 abnormality(7q-/-7)may be a poor prognostic factor for elderly AML patients treated with VEN+AZA.

Objective:To evaluate the clinical and prognostic value of prothrombin time(PT)and activated partial thromboplastin time(APTT)in newly diagnosed patients with multiple myeloma(MM).Methods:The clinical data of 116 newly diagnosed MM patients in the Second Hospital and Third Hospital of Shanxi Medical University from October 2014 to March 2022 were analyzed retrospectively,and the patients were divided into two groups:normal PT and APTT group and prolonged PT or APTT group.The differences in sex,age,classification,staging,bleeding events,laboratory indicators[including hemoglobin(Hb),platelet count(PLT),serum calcium,serum albumin(ALB),lactate dehydrogenase(LDH),serum creatinine and β 2-microglobulin],and cytogenetic characteristics between the two groups of patients were compared.The effect of prolonged PT or APTT on survival of patients with MM was analyzed.Results:Compared with patients in normal PT and APTT group,patients in prolonged PT or APTT group were more likely to experience bleeding events(x2=5.087,P=0.024),with lower ALB levels(x2=4.962,P=0.026)and PLT levels(x2=4.309,P=0.038),and higher serum calcium levels(x2=5.056,P=0.025).The positive rates of del17p,del13q and 1q21+in prolonged PT or APTT group were higher than those in normal PT and APTT group,but the difference was not statistically significant(P＞0.05).K-M survival analysis showed that the prolonged PT or APTT group had a shorter median progression-free survival(PFS)(P=0.032)and overall survival(OS)(P=0.032).Multivariate Cox analysis showed that prolonged PT or APTT(HR=2.1 16,95％CI:1.025-4.372,P=0.043)and age ≥65 years(HR=2.403,95％CI:1.195-4.836,P=0.014)were independent risk factor for OS in newly diagnosed MM patients.However,prolonged PT or APTT had no significant effect on PFS of newly diagnosed MM patients(HR=1.162,95％CI:0.666-2.026,P=0.597).Conclusion:Newly diagnosed MM patients with prolonged PT or APTT have worse clinical indicators,shorter PFS and OS.Prolonged PT or APTT is an independent risk factor for OS in MM patients.

Objective:To investigate the clinical phenotype and molecular pathogenic mechanism of a hereditary coagulation factor Ⅴ deficiency (FⅤD)family.Methods:A phase I assay was used to measure coagulation factors Ⅱ,Ⅴ,Ⅶ,Ⅷ,Ⅸ,Ⅹ,Ⅺ,Ⅻ(FⅡ:C,FⅤ:C,FⅦ:C,FⅧ:C,FⅨ:C,FⅩ:C,FⅪ:C,FⅫ:C),activated partial thromboplastin time (APTT)and prothrombin time (PT)to determine the clinical phenotype and molecular pathogenesis of F VD.Prothrombin time (PT)were used for phenotypic identification;high-throughput exome sequencing was applied to screen the whole gene variants,and Sanger sequencing was used to verify the suspected variants in F5 gene;MutationTaster,PolyPhen-2 bioinformatics software was used to predict the pathogenicity of the variants,ClustalX software was used to analyze the amino acid conservatism,and PyMol software was used to simulate the model of the mutant protein.Results:The pre-documented patient had significantly prolonged PT and APTT,FⅤ:C was only 5. 45％,and there was no significant abnormality in TT,FIB and the rest of the coagulation factors.The mother,father and sister of the proband had prolonged PT and APTT,and FⅤ:C was reduced to different degrees.Genetic testing revealed the presence of a c.286G>C (p.Asp96His)pure missense variant in exon 3 of F5 in the prior witness,and a c.286G>C (p.Asp96His)heterozygous missense variant in father,mother,and sister of the proband.Bioinformatics analysis suggested that p.Asp96His was a pathogenic variant,and the associated amino acid site was highly conserved among 10 species.Protein simulation showed that the mutation of Asp96 to His96 could lead to the disappearance of the original hydrogen bond and the change of the distance,destroying the original hydrogen bond interaction force and affecting the stability of the protein structure.Conclusion:The F5 exon 3 c.286G>C (p.Asp96His)missense variant may have contributed to the reduction of FⅤ:C in the preexisting individual and family members,as well as being the genetic etiology of coagulation factor Ⅴ deficiency.

italic>Salmonella has emerged as a promising tumor-targeting strategy in recent years due to its good tumor targeting ability and certain safety. In order to further optimize its therapeutic effect, scientists have tried to modify Salmonella, including its attenuation and drug loading. This paper summarizes the mechanism and research progress of Salmonella-mediated targeted tumor therapy, and introduces the strategies and related progress of its modification and optimization. At the same time, the advantages, current challenges and future development directions of Salmonella-mediated tumor therapy are summarized.