MATERIALS AND METHODS: This cross-sectional study included 400 dairy cattle from 72 household farms in eight subdistricts. Fecal samples (n=400) were examined using the Flukefinder® kit and the simple sedimentation technique was the gold standard for fasciolosis. In-person interviews using questionnaires collected data on farmers, farms, and animal characteristics. Chi-square and logistic regression analyses were performed to evaluate the associated risk factors for fasciolosis, and p < 0.05 was considered statistically significant.

RESULTS: The overall prevalence of fasciolosis in dairy cattle in Boyolali, Indonesia, was 16.50% (95% confidence interval [CI] 12.85-20.15) at the animal level (n = 400), whereas 40.28% at household farms (n = 72) level (95% CI 18.67-51.88). The relative sensitivity and specificity of the Flukefinder® kit compared with those of the gold standard were 79.49% and 92.52%, respectively, with a moderate agreement (kappa=0.59; p < 0.001). Fasciolosis was more likely in cattle originating from the Mojosongo subdistrict than from other subdistricts (odds ratio (OR)=5.28, 95% CI 1.22-22.94); from farms that did not process manure versus from those that did (OR = 3.03, 95% CI 1.43-4.71); and with farmers that had never attended extension programs compared with those who had (OR = 4.72, 95% CI 1.99-11.19). Studied cattle were mostly affected by light Fasciola spp. infections (92.4%, 95% CI 77.8-100%) followed by moderate (6.1%, 95% CI 0-22.2%) and heavy (1.5%, 95% CI 0-5.6%) infections.

OBJECTIVE: To formulate strategies for public health planning and the control of diabetes, this study aimed to develop a personalized ML model that predicts the blood glucose level of urban corporate workers in Bangladesh.

METHODS: Based on the basic noninvasive health checkup test results, dietary information, and sociodemographic characteristics of 271 employees of the Bangladeshi Grameen Bank complex, 5 well-known ML models, namely, linear regression, boosted decision tree regression, neural network, decision forest regression, and Bayesian linear regression, were used to predict blood glucose levels. Continuous blood glucose data were used in this study to train the model, which then used the trained data to predict new blood glucose values.

RESULTS: Boosted decision tree regression demonstrated the greatest predictive performance of all evaluated models (root mean squared error=2.30). This means that, on average, our model's predicted blood glucose level deviated from the actual blood glucose level by around 2.30 mg/dL. The mean blood glucose value of the population studied was 128.02 mg/dL (SD 56.92), indicating a borderline result for the majority of the samples (normal value: 140 mg/dL). This suggests that the individuals should be monitoring their blood glucose levels regularly.