OBJECTIVE: To investigate the infection of leptospirosa of host animals and the immune level of healthy population in flood areas. METHODS: Korth culture was used to culture leptospira for rodent kidney and oxen urine sample. The serogroups of leptospira and leptospira antibody were tested by microscopic agglutination test (MAT). RESULTS: In flood regions, draw-near-flood region, and new migration region, rodent density was 6.95%, 6.28%, and 8.67%, respectively. The positive rates of rodent with leptospira was 4.63%, 1.35%, and 3.13%, respectively. Leptospira positive rates of oxen urine were 5.88%, 5.98%, and 1.75%, respectively. The main serogroup of leptospira was Icterhamorrhagic and Canicola serogroup. The positive rates of leptospirosa antibody in healthy population was 45.91%, 62.30%, and 58.67%in these 3 regions respectively, which was significantly higher than the average level in China. The dominant serogroups of leptospira in health population were icterhamorrhagic, autumnalis, canicola, pomona and bataviae. The positive rate of antibody had no difference among different age groups. CONCLUSION The main host animals are rodents and oxen infected with leptospira and the positive rate of leptospira antibody is high in healthy population in the study area. The dominant serogroups in host animals are similar to that in healthy population, which is mostly icterhaemorrhagic.
Animals ; Antibodies, Bacterial ; blood ; urine ; Cattle ; China ; epidemiology ; Disasters ; Floods ; Humans ; Leptospira interrogans ; immunology ; isolation & purification ; Leptospirosis ; epidemiology ; Prevalence ; Rats ; Seroepidemiologic Studies

Background::Pulmonary embolism (PE) is a severe and acute cardiovascular syndrome with high mortality among patients with autoimmune inflammatory rheumatic diseases (AIIRDs). Accurate prediction and timely intervention play a pivotal role in enhancing survival rates. However, there is a notable scarcity of practical early prediction and risk assessment systems of PE in patients with AIIRD.Methods::In the training cohort, 60 AIIRD with PE cases and 180 age-, gender-, and disease-matched AIIRD non-PE cases were identified from 7254 AIIRD cases in Tongji Hospital from 2014 to 2022. Univariable logistic regression (LR) and least absolute shrinkage and selection operator (LASSO) were used to select the clinical features for further training with machine learning (ML) methods, including random forest (RF), support vector machines (SVM), neural network (NN), logistic regression (LR), gradient boosted decision tree (GBDT), classification and regression trees (CART), and C5.0 models. The performances of these models were subsequently validated using a multicenter validation cohort.Results::In the training cohort, 24 and 13 clinical features were selected by univariable LR and LASSO strategies, respectively. The five ML models (RF, SVM, NN, LR, and GBDT) showed promising performances, with an area under the receiver operating characteristic (ROC) curve (AUC) of 0.962-1.000 in the training cohort and 0.969-0.999 in the validation cohort. CART and C5.0 models achieved AUCs of 0.850 and 0.932, respectively, in the training cohort. Using D-dimer as a pre-screening index, the refined C5.0 model achieved an AUC exceeding 0.948 in the training cohort and an AUC above 0.925 in the validation cohort. These results markedly outperformed the use of D-dimer levels alone.Conclusion::ML-based models are proven to be precise for predicting the onset of PE in patients with AIIRD exhibiting clinical suspicion of PE.Trial Registration::Chictr.org.cn: ChiCTR2200059599.

Humans ; Lupus Vasculitis, Central Nervous System/pathology* ; Magnetic Resonance Imaging/methods* ; Diffusion Tensor Imaging/methods* ; Patients ; Lupus Erythematosus, Systemic/pathology* ; Brain/pathology*