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https://www.selleckchem.com/products/cerdulatinib-prt062070-prt2070.html Tuberculosis remains a significant infectious disease of farm animals and humans worldwide. The objective of this study was to assess various risk factors associated with testing positive for bovine tuberculosis (bTB) in high-yielding Holstein cows in an intensive dry-lot dairy operation. In a retrospective observational study, 9312 records from Holstein cows from a large dairy herd in northern Mexico were used. The incidence rate of lactating cows reactor to bTB was 7.3 cases/100 cow years (95% CI = 6.7-7.9%). Multiple logistic regression models indicated that cows with total milk yield during the first lactation >10,200 kg were 1.3 times (95% confidence intervals (CI) for odds ratio (OR) = 1.2-1.6) more likely to be detected as bTB reactors than cows with total milk yield 48 kg were 1.9 times (95% CI for OR = 1.6-2.2) more likely to be reactor to bTB than cows with peak milk yield less then 48 kg (9.2 vs. 5.1%; P less then 0.01). Cows with either puerperal metritis (OR = 0.07, 95% CI = 0.5-0.9) or carrying twins (OR = 0.05, 95% CI = 0.01-0.19) had a protective role for being reactor to bTB. This study showed that increased milk production was associated with a higher risk of becoming positive to tuberculin skin test in high-yielding Holstein cows. COVID-19 pandemic has currently no vaccines. Thus, the only feasible solution for prevention relies on the detection of COVID-19-positive cases through quick and accurate testing. Since artificial intelligence (AI) offers the powerful mechanism to automatically extract the tissue features and characterise the disease, we therefore hypothesise that AI-based strategies can provide quick detection and classification, especially for radiological computed tomography (CT) lung scans. Six models, two traditional machine learning (ML)-based (k-NN and RF), two transfer learning (TL)-based (VGG19 and InceptionV3), and the last two were our custom-designed dee

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