Objective:To analyze the early risk factors of necrotizing pneumonia (NP) in children with Mycoplasma pneumoniae pneumonia (MPP) and construct a clinical prediction model.Methods:In this case-control study, the clinical data of MPP patients who were hospitalized at Wuhan Children′s Hospital, Tongji Medical College, Huazhong University of Science & Technology, from January 2021 to May 2024 were retrospectively analyzed.According to whether NP occurred, the children were divided into the NP group and the non-NP (NNP) group.A total of 62 and 124 children were included in the NP and NNP groups after nearest neighbor matching at a ratio of 1∶2 (with a caliper value of 0.02), respectively.LASSO regression was used to select the optimal factors, and the multivariate Logistic regression analysis was used to establish a clinical prediction model.Internal and external validation of the prediction model was then conducted.The receiver-operating characteristic (ROC) curve and calibration curve were used to evaluate the predictive ability and calibration of the prediction model.The clinical decision curve analysis (DCA) was used to evaluate its clinical predictive value.Results:The LASSO regression analysis showed that white blood cells (WBC), neutrophil percentage, C-reactive protein (CRP), procalcitonin, D-dimer, ferritin, fever duration, and lung consolidation were factors influencing the occurrence of NP in children with MPP ( P<0.05).The ROC analysis showed that the area under the curve (AUC) of the prediction model was 0.838 (95% CI: 0.765-0.911, P<0.001) in the training set, 0.834 (95% CI: 0.755-0.913, P<0.001) in the validation set, and 0.924 (95% CI: 0.902-0.981, P<0.001) in the external validation set.Bootstrap was used for repeated sampling for 1 000 times for internal validation, and the calibration curve showed that the model had good early consistency.The clinical DCA showed that the model had good clinical application value. Conclusions:WBC, CRP, D-dimer, ferritin, fever duration and lung consolidation have good value for the early prediction of MPNP in children.

Objective:To analyze the early risk factors of necrotizing pneumonia (NP) in children with Mycoplasma pneumoniae pneumonia (MPP) and construct a clinical prediction model.Methods:In this case-control study, the clinical data of MPP patients who were hospitalized at Wuhan Children′s Hospital, Tongji Medical College, Huazhong University of Science & Technology, from January 2021 to May 2024 were retrospectively analyzed.According to whether NP occurred, the children were divided into the NP group and the non-NP (NNP) group.A total of 62 and 124 children were included in the NP and NNP groups after nearest neighbor matching at a ratio of 1∶2 (with a caliper value of 0.02), respectively.LASSO regression was used to select the optimal factors, and the multivariate Logistic regression analysis was used to establish a clinical prediction model.Internal and external validation of the prediction model was then conducted.The receiver-operating characteristic (ROC) curve and calibration curve were used to evaluate the predictive ability and calibration of the prediction model.The clinical decision curve analysis (DCA) was used to evaluate its clinical predictive value.Results:The LASSO regression analysis showed that white blood cells (WBC), neutrophil percentage, C-reactive protein (CRP), procalcitonin, D-dimer, ferritin, fever duration, and lung consolidation were factors influencing the occurrence of NP in children with MPP ( P<0.05).The ROC analysis showed that the area under the curve (AUC) of the prediction model was 0.838 (95% CI: 0.765-0.911, P<0.001) in the training set, 0.834 (95% CI: 0.755-0.913, P<0.001) in the validation set, and 0.924 (95% CI: 0.902-0.981, P<0.001) in the external validation set.Bootstrap was used for repeated sampling for 1 000 times for internal validation, and the calibration curve showed that the model had good early consistency.The clinical DCA showed that the model had good clinical application value. Conclusions:WBC, CRP, D-dimer, ferritin, fever duration and lung consolidation have good value for the early prediction of MPNP in children.

This study was aimed to identify pET21b-HPV16E2/BL21(DE3) strain and to optimize the expression of human papillomavirus type 16 (HPV16) E2 protein by orthogonal analysis. Four influence factors on two levels were selected to increase the target protein quantity. The four factors were induction time, induction temperature, inductor concentration and cell density. The quantity of HPV16 E2 protein was used as the evaluation parameter. Induced by IPTG, HPV16 E2 protein was analyzed by SDS-PAGE and Western Blot. Target protein was analyzed by GIS imaging system to quantify the protein level. SPSS13. 0 software was applied to analyze the result. Data showed that the expression strain pET211rHPV16 E2/BL21(DE3) was identified correctly. HPV16 E2 protein expressed mainly at insoluble form. The 42KD protein band was identified by SDS-PAGE and Western blot. Orthogonal test was applied on influence factor analysis and expression optimization successfully. Main influence factors were inductor concentration and induction temperature. The optimimum condition of maximum expression quantity was 37 degrees C, 7h, 1.0 mmol/L IPTG and OD600 1.0. In this experiment, orthogonal test could not only be used to analyze the influential factors and promote the target protein expression, but also be used to provide a better experiment method for molecular biological study.
DNA-Binding Proteins ; biosynthesis ; genetics ; Genetic Vectors ; genetics ; Human papillomavirus 16 ; metabolism ; Humans ; Oncogene Proteins, Viral ; biosynthesis ; genetics ; Papillomavirus Infections ; virology ; Recombinant Proteins ; biosynthesis ; genetics