Abstract. To efficiently prevent and treat bovine mastitis and minimize its effect on the dairy industry, a sensitive, rapid, and specific test is required for identifying the mastitis-causing pathogens. In this study, a biochip capable of detecting 7 common species of mastitis-causing pathogens, including Corynebacterium bovis, Mycoplasma bovis, Staphylococcus aureus, and the Streptococcus spp. S. agalactiae, S. bovis, S. dysgalactiae, and S. uberis, within 6 hr was developed. The technique is based on DNA amplification of genes specific to the target pathogens and consists of 4 basic steps: DNA extraction of bacteria, polymerase chain reaction, DNA hybridization, and colorimetric reaction. To examine the accuracy and specificity of this biochip, a preliminary test with 82 random quarter milk samples were analyzed and compared with results from conventional microbiological methods conducted simultaneously. Results from all but 1 sample analyzed by the biochip were in agreement with those analyzed by bacteriology. The biochip could be a feasible tool for rapidly diagnosing mastitis-causing pathogens in milk and providing information for a more effective treatment to cure mastitis.

Aims Physical activity has a protective effect against mortality and cardiovascular events in chronic kidney disease (CKD) patients. Nonetheless, how different levels of physical activity affect the health benefits in CKD remains unclear. This study aimed to investigate the dose–response effects of physical activity on mortality and major cardiorenal events in CKD. Methods and results We evaluated a longitudinal cohort of 4508 Taiwanese CKD patients between 2004 and 2017. Physical activity was assessed by the NHANES questionnaire and quantified in metabolic equivalent-hours per week (MET-hour/week). Patients were categorized into highly active (≥7.5 MET-h/week), low-active (0.1 to <7.5 MET-h/week), or inactive (0 MET-h/week) groups. Cox regression and restricted cubic spline models were utilized to explore the association between physical activity and the risks of study outcomes, including all-cause mortality, end-stage renal disease (ESRD), and major adverse cardiovascular events (MACE, a composite of cardiovascular death, myocardial infarction, ischaemic stroke, and hospitalized heart failure). During a median follow-up of 686 days, 739 death, 1059 ESRD, and 521 MACE events occurred. Highly active group had the lowest chance of all study outcomes, followed by low-active and inactive groups (P < 0.001). Multivariable Cox regression showed that only highly active group was independently associated with lower risks for all-cause mortality [hazard ratio (HR) 0.62; 95% confidence interval (CI) 0.53–0.74], ESRD (HR 0.83, 95% CI 0.72–0.96), and MACE (HR 0.63, 95% CI 0.51–0.76) compared to the inactive group. The risks of MACE did not further decrease once physical activity surpassed 15 MET-h/week, indicating a U-shaped association. The results were consistent in the subgroup and sensitivity analyses. Conclusion Physical activity of 7.5 to <15 MET-h/week is associated with lower risks of adverse cardiorenal outcomes and should be integrated into the care of CKD.

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The NEP-AKI-D study enrols a large number of representative AKI patients throughout Taiwan. The results of the current study are expected to provide more insight into the risk and prognostic factors of AKI and further attenuated further chronic kidney disease transition.

Objectives Type 2 diabetes mellitus (T2DM) imposes a great burden on healthcare systems, and these patients experience higher long-term risks for developing end-stage renal disease (ESRD). Managing diabetic nephropathy becomes more challenging when kidney function starts declining. Therefore, developing predictive models for the risk of developing ESRD in newly diagnosed T2DM patients may be helpful in clinical settings. Methods We established machine learning models constructed from a subset of clinical features collected from 53,477 newly diagnosed T2DM patients from January 2008 to December 2018 and then selected the best model. The cohort was divided, with 70% and 30% of patients randomly assigned to the training and testing sets, respectively. Results The discriminative ability of our machine learning models, including logistic regression, extra tree classifier, random forest, gradient boosting decision tree (GBDT), extreme gradient boosting (XGBoost), and light gradient boosting machine were evaluated across the cohort. XGBoost yielded the highest area under the receiver operating characteristic curve (AUC) of 0.953, followed by extra tree and GBDT, with AUC values of 0.952 and 0.938 on the testing dataset. The SHapley Additive explanation summary plot in the XGBoost model illustrated that the top five important features included baseline serum creatinine, mean serum creatine within 1 year before the diagnosis of T2DM, high-sensitivity C-reactive protein, spot urine protein-to-creatinine ratio and female gender. Conclusions Because our machine learning prediction models were based on routinely collected clinical features, they can be used as risk assessment tools for developing ESRD. By identifying high-risk patients, intervention strategies may be provided at an early stage.