Faecal E. coli can act as reservoirs for resistance genes. Here, we analyzed prevalence of drug resistance in faecal E. coli isolated from healthy children at a single kindergarten in Beijing, China, then used whole genome sequencing to characterize fluoroquinolone-non-susceptible strains. Our results revealed high resistance to ampicillin (54.0%), trimethoprim/sulphurmethoxazole (47.5%) and tetracycline (58.9%) among 576 faecal E. coli isolates, 49.2% of which exhibited multidrug resistance. A total of 113 E. coli isolates were not susceptible to ciprofloxacin, with four sequence types, namely ST1193 (25.7%), ST773 (13.3%), ST648 (8.8%) and ST131 (7.1%) found to be the most prevalent (54.9%). With regards to resistance to quinolones, we detected chromosomal mutations in gyrA, parC, and parE in 111 (98.2%), 105 (92.9%), and 67 (61.1%) isolates, respectively. blaCTX-M (37.2%) was the major ESBL gene, whereas blaCTX-M-14 (12.4%) and blaCTX-M-27 (11.5%) were the most frequent subtypes. A total of 90 (79.6%) ExPEC and 65 (57.5%) UPEC isolates were classified. Overall, these findings revealed clonal spread of certain prevalent STs, namely ST1193, ST773, ST648 and ST131 E. coli isolates in healthy children within a single kindergarten in Beijing, China, affirming the seriousness of the multidrug resistance problem and potential pathogenicity of E. coli isolates in healthy children. Therefore, there is an urgent need for increased surveillance to enhance control of this problem.

Existing biomarkers for ovarian cancer lack sensitivity and specificity. We compared the diagnostic efficacy of nonlinear machine learning and linear statistical models for diagnosing ovarian cancer using a combination of conventional laboratory indicators. We divided 901 retrospective samples into an ovarian cancer group and a control group, comprising non-ovarian malignant gynecological tumor (NOMGT), benign gynecological disease (BGD), and healthy control subgroups. Cases were randomly assigned to training and internal validation sets. Two linear (logistic regression (LR) and Fisher’s linear discriminant (FLD)) and three nonlinear models (support vector machine (SVM), random forest (RF), and artificial neural network (ANN)) were constructed using 22 conventional laboratory indicators and three demographic characteristics. Model performance was compared. In an independent prospectively recruited validation set, the order of diagnostic efficiency was RF, SVM, ANN, FLD, LR, and carbohydrate antigen 125 (CA125)-only (AUC, accuracy: 0.989, 95.6%; 0.985, 94.4%; 0.974, 93.4%; 0.915, 82.1%; 0.859, 80.1%; and 0.732, 73.0%, respectively). RF maintained satisfactory classification performance for identifying different ovarian cancer stages and for discriminating it from NOMGT-, BGD-, or CA125-positive control. Nonlinear models outperformed linear models, indicating that nonlinear machine learning models can efficiently use conventional laboratory indicators for ovarian cancer diagnosis.

The proof-of-concept strategy based on ZIF67@Gr-PEG for simultaneously ablating tumors and relieve hypoxia has achieved good therapeutic effects towards salivary adenoid cystic carcinoma. The combination of MW sensitizer with self-sufficient...

The metabolism of plasticizing phthalates may correlate with an increased risk of benign prostatic hyperplasia in humans. Diethylphthalate (DEHP) and its metabolites interfere with sex hormone function, causing inflammation and oxidative stress at low doses. The effects may contribute to the development of benign prostate hyperplasia.

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