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Objective:To analyze the serology and molecular typing of Listeriamonocytogenes isolated from patients in Henan, and to explore the epidemic situation of listeriosis, construct the molecular traceability database of patient isolates, so as to provide laboratory basis for listeriosis traceability. Methods:From January 2015 to July 2020, 71 positive Listeriamonocytogenes cases were monitored in 16 sentinel hospitals in Henan. Eighty samples of positive cases were collected for detection, and 71 positive strains were obtained for molecular typing. According to the Entry-exit Inspection and Quarantine Industry Standard of China (SN/T 2521-2010) and the instructions for the use of diagnostic serum of Listeriamonocytogenes, 80 strains of Listeriamonocytogenes were serotyped, and PFGE cluster analysis was performed according to the User Manual of National Foodborne Disease Molecular Traceability Network. Results:A total of 71 positive listeriosis cases were detected, of which 38 cases were perinatal cases and 33 cases were non-perinatal cases. Among the 80 positive cases of listeriosis, 58.75% (47/80) were from perinatal cases, 20.00% (16/80) were from non-perinatal cases with underlying diseases, the proportion of>1 month-≤5 years old,>5-≤60 years old and >60 years old was 7.50% (6/80), 12.50% (10/80) and 1.25% (1/80), respectively, in non-perinatal age group. There were 5 types of specimens, 73.75% (59/80) were blood, 15.00% (12/80) were cerebrospinal fluid, and 3.75% (3/80) were stool, intrauterine swab and sputum. 80 strains of Listeriamonocytogenes were classified into three serotypes, Type 1/2b, Type 1/2a and Type 4b accounted for 61.25% (49/80), 35.00% (28/80) and 3.75% (3/80) respectively. The 71 strains of Listeriamonocytogenes were digested by AscⅠ, and 58 bands were obtained. Each band type included 1-4 strains, and the similarity was 60.8%-100%. GX6A16HA0005, GX6A16HA0011, GX6A16HA0030, GX6A16HA0023, GX6A16HA0029 and GX6A16HA0054 were dominant bands, including 4, 4, 4, 3, 2 and 2 strains respectively. Four strains of GX6A16HA0005 from 2016, 2018 and 2020 were isolated. One strain from 2016 and one strain from 2018 were from Puyang City. Four strains of GX6A16HA0011 were isolated from samples of 2016, 2018 and 2020, including two strains of 2020 from Luoyang City. Four strains of GX6A16HA0030 were isolated from 2018 samples from Luoyang City, Shangqiu City and Zhengzhou City, respectively. Three strains of GX6A16HA0023 were isolated from 2017 and 2018 samples, of which one strain from 2017 and one strain from 2018 were from Luoyang City. Two strains of GX6A16HA0029 were isolated from 2018 samples, from Kaifeng City and Puyang City respectively. Two strains of GX6A16HA0054 were isolated from 2020 from Pingdingshan City and Anyang City, respectively. The PFGE patterns of 4 strains with different serotypes were the same. Conclusion:Listeriosis cases in Henan are mainly found in patients during the perinatal period, and in elderly, new-born, and low immunity population. The infection type is mainly invasive infection; the serotypes of epidemic strains are 1/2a, 1/2b and 4b, and the results of PFGE typing of strains are diverse. There is a consistent phenomenon of cross-year or different regions in the same year, different time zones in the same year and the same region; phenotyping and genotyping or different genotyping techniques should be combined in the traceability analysis.
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Background@#The influencing factors of diabetic kidney disease (DKD) in Chinese patients with type 2 diabetes mellitus (T2DM) were explored to develop and validate a DKD diagnostic tool based on nomogram approach for patients with T2DM. @*Methods@#A total of 2,163 in-hospital patients with diabetes diagnosed from March 2015 to March 2017 were enrolled. Specified logistic regression models were used to screen the factors and establish four different diagnostic tools based on nomogram according to the final included variables. Discrimination and calibration were used to assess the performance of screening tools. @*Results@#Among the 2,163 participants with diabetes (1,227 men and 949 women), 313 patients (194 men and 120 women) were diagnosed with DKD. Four different screening equations (full model, laboratory-based model 1 [LBM1], laboratory-based model 2 [LBM2], and simplified model) showed good discriminations and calibrations. The C-indexes were 0.8450 (95% confidence interval [CI], 0.8202 to 0.8690) for full model, 0.8149 (95% CI, 0.7892 to 0.8405) for LBM1, 0.8171 (95% CI, 0.7912 to 0.8430) for LBM2, and 0.8083 (95% CI, 0.7824 to 0.8342) for simplified model. According to Hosmer-Lemeshow goodness-of-fit test, good agreement between the predicted and observed DKD events in patients with diabetes was observed for full model (χ2=3.2756, P=0.9159), LBM1 (χ2=7.749, P=0.4584), LBM2 (χ2=10.023, P=0.2634), and simplified model (χ2=12.294, P=0.1387). @*Conclusion@#LBM1, LBM2, and simplified model exhibited excellent predictive performance and availability and could be recommended for screening DKD cases among Chinese patients with diabetes.
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Background@#The influencing factors of diabetic kidney disease (DKD) in Chinese patients with type 2 diabetes mellitus (T2DM) were explored to develop and validate a DKD diagnostic tool based on nomogram approach for patients with T2DM. @*Methods@#A total of 2,163 in-hospital patients with diabetes diagnosed from March 2015 to March 2017 were enrolled. Specified logistic regression models were used to screen the factors and establish four different diagnostic tools based on nomogram according to the final included variables. Discrimination and calibration were used to assess the performance of screening tools. @*Results@#Among the 2,163 participants with diabetes (1,227 men and 949 women), 313 patients (194 men and 120 women) were diagnosed with DKD. Four different screening equations (full model, laboratory-based model 1 [LBM1], laboratory-based model 2 [LBM2], and simplified model) showed good discriminations and calibrations. The C-indexes were 0.8450 (95% confidence interval [CI], 0.8202 to 0.8690) for full model, 0.8149 (95% CI, 0.7892 to 0.8405) for LBM1, 0.8171 (95% CI, 0.7912 to 0.8430) for LBM2, and 0.8083 (95% CI, 0.7824 to 0.8342) for simplified model. According to Hosmer-Lemeshow goodness-of-fit test, good agreement between the predicted and observed DKD events in patients with diabetes was observed for full model (χ2=3.2756, P=0.9159), LBM1 (χ2=7.749, P=0.4584), LBM2 (χ2=10.023, P=0.2634), and simplified model (χ2=12.294, P=0.1387). @*Conclusion@#LBM1, LBM2, and simplified model exhibited excellent predictive performance and availability and could be recommended for screening DKD cases among Chinese patients with diabetes.