Objective This study aimed to provide an effective scientific basis for prevention and control of cockroaches on aircrafts by identifying cockroach-carried pathogens,and assess the insecticidal efficacy of gel bait mediated cockroach control on aircrafts,to provide technical guidance for aircraft disinsection.Methods Cassette-trapping was used to trap cockroaches,and the carried pathogens were detected using bacterial cultivation techniques.The gel bait mediated killing rate was calculated after 1,7,and 30 d by field application of gel bait.Results A total of 411 cockroaches were captured,and all were identified as Blattella germanica.26 strains of pathogenic bacteria were isolated from the trapped cockroaches.The killing rates of cockroaches were 58.8％-96.3％with 1-30 day application of gel bait.Statistically significant differences were observed in cockroach killing rates on different days(χ2=58.95,P<0.01).Conclusions B.germanica carry a large variety of pathogenic bacteria and opportunistic pathogens and are thus important infectious disease carriers.Gel bait agents have proven to be very effective against cockroaches on aircrafts.

AIM To analyze the dynamic change patterns of aflatoxin B1,aflatoxin B2,aflatoxin G1,aflatoxin G2,T-2 toxin and deoxynivalenol contents during the fermentation of Massa Medicata Fermentata.METHODS The analysis was performed on a 40 ℃ thermostatic Waters ACQUITY UPLC HSS T3 column(100 mm×2.1 mm,1.8 μm),with the mobile phase comprising of 0.01％formic acid-[acetonitrile-methanol(1∶1)]flowing at 0.3 mL/min,and electron spray ionization source was adopted in positive ion scanning with multiple reaction monitoring mode.RESULTS Six mycotoxins showed good linear relationships within their own ranges(R2＞0.998 0),whose average recoveries were 76.1％-119.3％with the RSDs of 0.49％-9.27％,and except for deoxynivalenol,their contents demonstrated the trends of growing out of nothing and gradually increasing.CONCLUSION The risk of mycotoxin infection exists in the fermentation of Massa Medicata Fermentata.This simple,efficient,rapid and sensitive method can provide a reference for whole-process monitoring the fermentation process for Massa Medicata Fermentata.

Objective To analyze the risk factors for increased drainage volume after open transforaminal lumbar interbody fusion(TLIF),and to establish a predictive model and then validate it.Methods The clinical data of 680 patients who underwent open TLIF at the General Hospital of Northern Theater Command from January 2016 to December 2019 were collected and the patients were randomly divided into the training group(n=476)and the validation group(n=204).Taking the predictive factors screened out by LASSO regression analysis as independent variables,a multivariate Logistic regression predictive model was constructed.The model was internally validated through the receiver operating characteristic(ROC)curve,Hosmer-Lemeshow goodness-of-fit test,and calibration curve,and its clinical utility was assessed via decision curve analysis(DCA).Results LASSO regression analysis screened out four predictive variables:age,number of surgical segments,operative duration,and intraoperative blood loss.The multivariate Logistic regression predictive model demonstrated that age≥60 years,number of surgical segments≥4,operative duration≥2 hours,and intraoperative blood loss≥200 mL were independent influencing factors for the increased postoperative drainage volume in patients undergoing TLIF(P<0.05).ROC curve analysis revealed an area under the curve(AUC)of 0.816(95%CI:0.798 to 0.867)in the training group and 0.783(95%CI:0.685 to 0.823)in the validation group,indicating that the predictive model had good discriminatory ability.Additionally,the Hosmer-Lemeshow goodness-of-fit test and calibration curve indicated that the predictive model had a good degree of fit,and the predicted probability was basically consistent with the actual probability,demonstrating a good calibration.The DCA results confirmed that this predictive model could be applied in clinical practice.Conclusion The risk factors for increased drainage volume after open TLIF include age,number of surgical segments,operative duration,and intraoperative blood loss.The predictive model established based on these factors demonstrates good performance,and it can be applied in clinical guidance for the selection of drainage tube removal time after TLIF.

AIM To analyze the dynamic change patterns of aflatoxin B1,aflatoxin B2,aflatoxin G1,aflatoxin G2,T-2 toxin and deoxynivalenol contents during the fermentation of Massa Medicata Fermentata.METHODS The analysis was performed on a 40 ℃ thermostatic Waters ACQUITY UPLC HSS T3 column(100 mm×2.1 mm,1.8 μm),with the mobile phase comprising of 0.01％formic acid-[acetonitrile-methanol(1∶1)]flowing at 0.3 mL/min,and electron spray ionization source was adopted in positive ion scanning with multiple reaction monitoring mode.RESULTS Six mycotoxins showed good linear relationships within their own ranges(R2＞0.998 0),whose average recoveries were 76.1％-119.3％with the RSDs of 0.49％-9.27％,and except for deoxynivalenol,their contents demonstrated the trends of growing out of nothing and gradually increasing.CONCLUSION The risk of mycotoxin infection exists in the fermentation of Massa Medicata Fermentata.This simple,efficient,rapid and sensitive method can provide a reference for whole-process monitoring the fermentation process for Massa Medicata Fermentata.

Objective To explore the risk factors for noncontiguous spinal fractures(NSFs)in the elderly.Methods The clinical data of 614 elderly patients with spinal fracture from January 2013 to December 2019 were analyzed retrospectively.Patients were divided into the NSFs group and the Non-NSFs group according to whether NSFs occurred or not.Univariate analysis and multivariate Logistic regression analysis were used to screen the risk factors of NSFs.Results Univariate analysis showed that female(P=0.003),high-energy violent injury(P=0.032),osteoporosis(P=0.004),fracture in spring(P=0.020),and previous spinal fracture history(P<0.001)were associated with the occurrence of NSFs.Multivariate Logistic regression analysis showed that fracture in spring(P=0.024),previous spinal fracture history(P<0.001)and high-energy violent injury(P=0.038)were the independent risk factors for the occurrence of NSFs in the elderly.Conclusion High-energy violent injury,fracture in spring and previous spinal fracture history are the independent risk factors for the occurrence of NSFs in the elderly.Therefore,elderly patients with the above risk factors should be examined more carefully and comprehensively to avoid missed diagnosis and delayed diagnosis.In order to reduce the incidence of this disease,corresponding measures should be taken according to the preventable risk factors.

Objective To analyze the risk factors for increased drainage volume after open transforaminal lumbar interbody fusion(TLIF),and to establish a predictive model and then validate it.Methods The clinical data of 680 patients who underwent open TLIF at the General Hospital of Northern Theater Command from January 2016 to December 2019 were collected and the patients were randomly divided into the training group(n=476)and the validation group(n=204).Taking the predictive factors screened out by LASSO regression analysis as independent variables,a multivariate Logistic regression predictive model was constructed.The model was internally validated through the receiver operating characteristic(ROC)curve,Hosmer-Lemeshow goodness-of-fit test,and calibration curve,and its clinical utility was assessed via decision curve analysis(DCA).Results LASSO regression analysis screened out four predictive variables:age,number of surgical segments,operative duration,and intraoperative blood loss.The multivariate Logistic regression predictive model demonstrated that age≥60 years,number of surgical segments≥4,operative duration≥2 hours,and intraoperative blood loss≥200 mL were independent influencing factors for the increased postoperative drainage volume in patients undergoing TLIF(P<0.05).ROC curve analysis revealed an area under the curve(AUC)of 0.816(95%CI:0.798 to 0.867)in the training group and 0.783(95%CI:0.685 to 0.823)in the validation group,indicating that the predictive model had good discriminatory ability.Additionally,the Hosmer-Lemeshow goodness-of-fit test and calibration curve indicated that the predictive model had a good degree of fit,and the predicted probability was basically consistent with the actual probability,demonstrating a good calibration.The DCA results confirmed that this predictive model could be applied in clinical practice.Conclusion The risk factors for increased drainage volume after open TLIF include age,number of surgical segments,operative duration,and intraoperative blood loss.The predictive model established based on these factors demonstrates good performance,and it can be applied in clinical guidance for the selection of drainage tube removal time after TLIF.

Objective To explore the risk factors for noncontiguous spinal fractures(NSFs)in the elderly.Methods The clinical data of 614 elderly patients with spinal fracture from January 2013 to December 2019 were analyzed retrospectively.Patients were divided into the NSFs group and the Non-NSFs group according to whether NSFs occurred or not.Univariate analysis and multivariate Logistic regression analysis were used to screen the risk factors of NSFs.Results Univariate analysis showed that female(P=0.003),high-energy violent injury(P=0.032),osteoporosis(P=0.004),fracture in spring(P=0.020),and previous spinal fracture history(P<0.001)were associated with the occurrence of NSFs.Multivariate Logistic regression analysis showed that fracture in spring(P=0.024),previous spinal fracture history(P<0.001)and high-energy violent injury(P=0.038)were the independent risk factors for the occurrence of NSFs in the elderly.Conclusion High-energy violent injury,fracture in spring and previous spinal fracture history are the independent risk factors for the occurrence of NSFs in the elderly.Therefore,elderly patients with the above risk factors should be examined more carefully and comprehensively to avoid missed diagnosis and delayed diagnosis.In order to reduce the incidence of this disease,corresponding measures should be taken according to the preventable risk factors.

Objective:To investigate the causal correlation between depression and stress urinary incontinence(SUI)using Mendelian randomization(MR)analysis.Methods:We searched the FinnGen Consortium database for genome-wide association studies(GWAS)on depression and obtained 23 424 case samples and 192 220 control samples,with the GWAS data on SUI provided by the UK Biobank,including 4 340 case samples and 458 670 control samples.We investigated the correlation between depression and SUI based on the depression data collected from the Psychiatric Genomics Consortium(PGC).We employed inverse-variance weighting as the main method for the MR study,and performed sensitivity analysis to verify the accuracy and stability of the findings.Results:Analysis of the data from the UK Biobank and FinnGen Consortium showed that depression was significantly correlated with an increased risk of SUI(P=0.005),but not SUI with the risk of depression(P=0.927).And analysis of the PGC data verified the correlation of depression with the increased risk of SUI(P=0.043).Conclusion:Depression is associated with an increased risk of SUI,while SUI does not increase the risk of depression.

Objective To explore the efficiency of artificial intelligence and radiomics-assisted X-ray in diagnosis of lumbar osteoporotic vertebral compression fractures(OVCF).Methods The clinical data of 455 patients diagnosed as lumbar OVCF by MRI in our hospital were selected.The patients were divided into the training group(n=364)and the validation group(n=91),X-ray films were extracted,the image delineation,feature extraction and data analysis were carried out,and the artificial intelligence radiomics deep learning was applied to establish a diagnostic model for OVCF.After verifying the effectiveness of the model by receiver operating characteristic(ROC)curve,area under the curve(AUC),calibration curve,and decision curve analysis(DCA),the efficiencies of manual reading,model reading,and model-assisted manual reading of X-ray in the early diagnosis of OVCF were compared.Results The ROC curve,AUC and calibration curve proved that the model had good discrimination and calibration,and excellent diagnostic performance.DCA demonstrated that the model had a higher clinical net benefit.The diagnostic efficiency of the manual reading group:the accuracy rate was 0.89,the recall rate was 0.62.The diagnostic efficiency of the model reading group:the accuracy rate was 0.93,the recall rate was 0.86,the model diagnosis showed good predictive performance,which was significantly better than the manual reading group.The diagnostic efficiency of the model-assisted manual reading group:the accuracy rate was 0.92,the recall rate was 0.72,and the recall rate of the model-assisted manual reading group was higher than that of the manual reading group,but lower than that of the model reading group,indicating the superiority of the model diagnosis.Conclusion The diagnostic model established based on artificial intelligence and radiomics in this study has reached an ideal level of efficacy,with better diagnostic efficacy compared with manual reading,and can be used to assist X-ray in the early diagnosis of OVCF.

Objective To develop a clinical prediction model for predicting risk factors for prolonged hospital stay after anterior cervical discectomy and fusion(ACDF).Methods The clinical data of 914 patients underwent ACDF treatment for cervical spondylotic myelopathy(CSM)were retrospectively analyzed.According to the screening criteria,800 eligible patients were eventually included,and the patients were divided into the development cohort(n=560)and the validation cohort(n=240).LASSO regression was used to screen variables,and multivariate Logistic regression analysis was used to establish a prediction model.The prediction model was evaluated from three aspects:differentiation,calibration and clinical effectiveness.The performance of the model was evaluated by area under the curve(AUC)and Hosmer-Lemeshow test.Decision curve analysis(DCA)was used to evaluate the clinical effectiveness of the model.Results In this study,the five factors that were significantly associated with prolonged hospital stay were male,abnormal BMI,mild-to-moderate anemia,stage of surgery(morning,afternoon,evening),and alcohol consumption history.The AUC of the development cohort was 0.778(95%CI:0.740 to 0.816),with a cutoff value of 0.337,and that of the validation cohort was 0.748(95%CI:0.687 to 0.809),with a cutoff value of 0.169,indicating that the prediction model had good differentiation.At the same time,the Hosmer-Lemeshow test showed that the model had a good calibration degree,and the DCA proved that it was effective in clinical application.Conclusion The prediction model established in this study has excellent comprehensive performance,which can better predict the risk of prolonged hospital stay,and can guide clinical intervention as soon as possible,so as to minimize the postoperative hospital stay and reduce the cost of hospitalization.