Isolation, identification, molecular typing, and drug resistance of Escherichia coli from infected cattle and sheep in Xinjiang, China.

BACKGROUND: Escherichia coli infections are common in Xinjiang, a major region of cattle and sheep breeding in China. Therefore, strategies are required to control E. coli. The aim of this study was to investigate the phylogenetic groups, virulence genes, and antibiotic resistance characteristics of E. coli isolates. METHODS: In this study, 116 tissue samples were collected from the organs of cattle and sheep that were suspected of having E. coli infections between 2015 and 2019. Bacteria in the samples were identified using a biochemical identification system and amplification of 16S rRNA, and the phylogenetic groupings of E. coli isolates were determined by multiplex polymerase chain reactions. In addition, PCR detection and analysis of virulence factors, antibiotic resistance genes, and drug-resistant phenotypes of E. coli isolates were performed. RESULTS: A total of 116 pathogenic E. coli strains belonging to seven phylogenetic groups were isolated, with the majority of isolates in groups A and B1. Among the virulence genes, curli-encoding crl had the highest detection rate of 97.4%, followed by hemolysin-encoding hlyE with the detection rate of 94.82%. Antimicrobial susceptibility test results indicated that the isolates had the highest rates of resistance against streptomycin (81.9%). CONCLUSION: These characteristics complicate the prevention and treatment of E. coli-related diseases in Xinjiang.

Isolation, Identification, and Genetic Phylogenetic Analysis of Two Different Genotypes of Bovine Parainfluenza 3 Virus in China.

Bovine parainfluenza virus 3 (BPIV3) is one of several viruses that contribute to bovine respiratory disease complex (BRDC). During this study, isolation of BPIV3 was attempted from 20 PCR-positive swabs by Madin-Darby Bovine Kidney (MDBK) cells. Nine samples showed obvious cytopathic lesions identified as BPIV3 by reverse-transcription polymerase chain reaction amplification and sequencing. The genomes of isolates XJ21032-1 and XJ20055-3 were sequenced using Illumina sequencing technology and determined to have lengths of 15,512 bp and 15,479 bp, respectively. Phylogenetic analysis revealed that isolate XJ21032-1 was genotype B, and isolate XJ20055-3 was genotype C. In addition, the two isolates had multiple amino acid changes in nucleocapsid protein, fusion protein, and hemagglutinin/neuraminidase, major antigenic proteins. This allows the further recognition of the presence of BPIV3 type B in Chinese cattle herds. We hope this will help trace the origin of BPIV3, improve the understanding of differences between genotypes, and provide data support for vaccine development.

Physical Activity under Stress: A Perspective of HAPA and Individual Differences.

AIMS: Physical activity is an effective way for people to cope with stress. However, people often decrease their physical activity in response to stressors. Therefore, we aimed to understand the relationship between perceived stress and physical activity from an outcome expectancies perspective and investigated the moderating role of future orientation in this relationship. METHODS: This study recruited 425 students who completed a three-wave survey at six-week intervals. A moderated mediation model was used to examine the mediating effects of outcome expectancies and the moderating effects of future orientation. RESULTS: The results indicated that outcome expectancies mediated the relationship between perceived stress and physical activity. This relationship was moderated by future orientation. In particular, the mediating effects were significant for people with a high future orientation, but not for those with a low future orientation. CONCLUSION: Our results demonstrate the adaptive function of future orientation in response to general stress. Importantly, the link between perceived stress and reduced physical activity could be mitigated by encouraging people to focus on future consequences. Future studies should consider developing intervention strategies that help those struggling with stressful contexts.

Clinical impact and quality of randomized controlled trials involving interventions evaluating artificial intelligence prediction tools: a systematic review.

The evidence of the impact of traditional statistical (TS) and artificial intelligence (AI) tool interventions in clinical practice was limited. This study aimed to investigate the clinical impact and quality of randomized controlled trials (RCTs) involving interventions evaluating TS, machine learning (ML), and deep learning (DL) prediction tools. A systematic review on PubMed was conducted to identify RCTs involving TS/ML/DL tool interventions in the past decade. A total of 65 RCTs from 26,082 records were included. A majority of them had model development studies and generally good performance was achieved. The function of TS and ML tools in the RCTs mainly included assistive treatment decisions, assistive diagnosis, and risk stratification, but DL trials were only conducted for assistive diagnosis. Nearly two-fifths of the trial interventions showed no clinical benefit compared to standard care. Though DL and ML interventions achieved higher rates of positive results than TS in the RCTs, in trials with low risk of bias (17/65) the advantage of DL to TS was reduced while the advantage of ML to TS disappeared. The current applications of DL were not yet fully spread performed in medicine. It is predictable that DL will integrate more complex clinical problems than ML and TS tools in the future. Therefore, rigorous studies are required before the clinical application of these tools.

Radiomics using CT images for preoperative prediction of futile resection in intrahepatic cholangiocarcinoma.

OBJECTIVES: To investigate and compare radiomics and clinical information for preoperative prediction of futile resection in intrahepatic cholangiocarcinoma (ICC). METHODS: A total of 203 ICC patients from two centers were included and randomly allocated with a ratio of 7:3 into the training cohort and the validation cohort. Clinical characteristics and radiomics features were selected using random forest algorithm and logistic models to construct a clinical model and a radiomics model, respectively. A combined logistic model that incorporated the developed radiomics signature and clinical risk factors was then built. The performance of these models was evaluated and compared by plotting the receiver operating characteristic (ROC) curve and calculating the area under the curve (AUC). RESULTS: The radiomics model showed a higher AUC than the clinical model in the validation cohort (AUC: 0.804 (95% CI: 0.697, 0.912) vs. 0.590 (95% CI: 0.415, 0.765), p = 0.043) for predicting futile resection in ICC. The radiomics model reached a sensitivity of 0.846 (95% CI: 0.546, 0.981) and a specificity of 0.771 (95% CI: 0.627, 0.880) in the validation cohort. Moreover, the radiomics model had comparable AUCs with the combined model in training and validation cohorts. CONCLUSIONS: We presented an internally validated radiomics model for the prediction of futile resection in ICC patients. Compared with clinical information, radiomics using CT images had greater potential for predicting futile resection accurately before surgery. KEY POINTS: • Radiomics model using CT images could predict futile resection in intrahepatic cholangiocarcinoma preoperatively. • Radiomics model using CT images was superior to clinical information for predicting futile resection accurately before surgery.