instruction:
In this task, you're given a paragraph from the research paper and your task is to generate a suitable title for the research paper based on the given paper. Under 100 words is a good title length.
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The primary objective of this randomized controlled trial was to determine whether anti-IL-10 egg yolk antibodies fed upon arrival to a calf ranch would lower the prevalence of Cryptosporidium parvum shedding in naturally challenged preweaned dairy calves. The secondary objectives included measuring the effect of anti-IL-10 antibodies on calf health, performance, and shedding of less common diarrheal pathogens. A total of 133 calves, enrolled at 24 to 72 h of age, received a daily dose of 0.96 g of egg yolk powder with anti-IL-10 antibodies (MAB, n = 71) or without anti-IL-10 antibodies (MEP, n = 62) split between 2 feedings for the first 11 d on feed at a calf ranch. Daily health evaluations were completed for 15 d after arrival and on d 56. Digital weights were collected at enrollment and d 56, and hipometer weights were collected at enrollment and d 7 and 56. Packed cell volume and serum total protein concentration were measured at enrollment and on d 7 and 14. Fecal pH was measured at enrollment and on d 5 and 14, and fecal pathogen (C. parvum, coronavirus, rotavirus, and Salmonella spp.) shedding was assessed at d 5 and 14. Continuous outcomes were compared between groups using a Student's t-test or Wilcoxon rank sum test. Fecal pathogen shedding at d 14, respiratory disease at d 56, and antibiotic usage were compared using relative risk (RR) and chi-squared test. Fecal pH (median and interquartile range) on d 14 was 6.65 (6.39-6.99) and 6.52 (5.97-6.81) for MAB and MEP, respectively. On d 56, the risk of respiratory disease was lower for MAB compared with MEP (RR = 0.40; confidence interval = 0.16-0.99). The risk for antibiotic treatment was lower for MAB-compared with MEP-treated calves (RR = 0.38; confidence interval = 0.17-0.88). The risk of shedding rotavirus was higher in MAB (RR = 1.38; confidence interval = 1.10-1.81) calves. After multivariable analyses, hipometer weights (least squares means standard error) were 1.7 0.8 kg greater on d 56 in MAB compared with MEP; however, ADG was 0.04 0.02 kg/d lower in MAB calves. Total health score, diarrhea days, average respiratory score, packed cell volume, and serum total protein were not affected by feeding anti-IL-10 egg antibodies. In summary, feeding anti-IL-10 antibodies was associated with increased fecal pH, reduced risk of respiratory disease later in the preweaning period, and decreased antibiotic usage despite higher rotavirus infection. These findings might be associated with improved mucosal immunity, enhanced host defenses, or reduced susceptibility and warrant further investigation.
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Health benefits of orally administered anti-IL-10 antibody in milk-fed dairy calves

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Background: Adverse events are associated with significant morbidity, mortality and cost in hospitalized patients. Measuring adverse events is necessary for quality improvement, but current detection methods are inaccurate, untimely and expensive. The advent of electronic health records and the development of automated methods for encoding and classifying electronic narrative data, such as natural language processing, offer an opportunity to identify potentially better methods. The objective of this study is to determine the accuracy of using automated methods for detecting three highly prevalent adverse events: a) hospital-acquired pneumonia, b) catheter-associated bloodstream infections, and c) in-hospital falls. Methods/design: This validation study will be conducted at two large Canadian academic health centres: the McGill University Health Centre (MUHC) and The Ottawa Hospital (TOH). The study population consists of all medical, surgical and intensive care unit patients admitted to these centres between 2008 and 2014. An automated detection algorithm will be developed and validated for each of the three adverse events using electronic data extracted from multiple clinical databases. A random sample of MUHC patients will be used to develop the automated detection algorithms (cohort 1, development set). The accuracy of these algorithms will be assessed using chart review as the reference standard. Then, receiver operating characteristic curves will be used to identify optimal cut points for each of the data sources. Multivariate logistic regression and the areas under curve (AUC) will be used to identify the optimal combination of data sources that maximize the accuracy of adverse event detection. The most accurate algorithms will then be validated on a second random sample of MUHC patients (cohort 1, validation set), and accuracy will be measured using chart review as the reference standard. The most accurate algorithms validated at the MUHC will then be applied to TOH data (cohort 2), and their accuracy will be assessed using a reference standard assessment of the medical chart. Discussion: There is a need for more accurate, timely and efficient measures of adverse events in acute care hospitals. This is a critical requirement for evaluating the effectiveness of preventive interventions and for tracking progress in patient safety through time.
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Accuracy of using automated methods for detecting adverse events from electronic health record data: a research protocol

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Coronavirus disease 2019 (COVID-2019) has been recognized as a global threat, and several studies are being conducted using various mathematical models to predict the probable evolution of this epidemic. These mathematical models based on various factors and analyses are subject to potential bias. Here, we propose a simple econometric model that could be useful to predict the spread of COVID-2019. We performed Auto Regressive Integrated Moving Average (ARIMA) model prediction on the Johns Hopkins epidemiological data to predict the epidemiological trend of the prevalence and incidence of COVID-2019. For further comparison or for future perspective, case definition and data collection have to be maintained in real time.
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Application of the ARIMA model on the COVID- 2019 epidemic dataset