Current diagnostic methods for wooden breast and white striping, common breast muscle myopathies of modern commercial broiler chickens, rely on subjective examinations of the pectoralis major muscle, time-consuming microscopy, or expensive imaging technologies. Further research on these disorders would benefit from more quantitative and objective measures of disease severity that can be used in live birds. To this end, we utilized untargeted metabolomics alongside two statistical approaches to evaluate plasma metabolites associated with wooden breast and white striping in 250 male commercial broiler chickens. First, mixed linear modeling was employed to identify metabolites with a significant association with these muscle disorders and found 98 metabolites associated with wooden breast and 44 metabolites associated with white striping (q-value < 0.05). Second, a support vector machine was constructed using stepwise feature selection to determine the smallest subset of metabolites with the highest categorization accuracy for wooden breast. The final support vector machine achieved 94% accuracy using only 6 metabolites. The metabolite 3-methylhistidine, which is often used as an index of myofibrillar breakdown in skeletal muscle, was the top metabolite for both wooden breast and white striping in our mixed linear model and was also the metabolite with highest marginal prediction accuracy (82%) for wooden breast in our support vector machine. Overall, this study identified a candidate set of metabolites for an objective measure of wooden breast or white striping severity in live birds and expanded our understanding of these muscle disorders.
Background: A key challenge in estimating epidemiological parameters for a pandemic such as the initial COVID-19 outbreak in Wuhan is the discrepancy between the officially reported number of infections and the true number of infections. A common approach to tackling the challenge is to use the number of infections exported from the originating city to infer the true number. This approach can only provide a static estimate of the epidemiological parameters before city lockdown because there are almost no exported cases thereafter.Methods: We propose a Bayesian estimation method that dynamically estimates the epidemiological parameters by recovering true numbers of infections from day-to-day official numbers. To illustrate the use of this method, we provide a comprehensive retrospection on how the COVID-19 had progressed in Wuhan from January 19 to March 5, 2020. Particularly, we estimate that the outbreak sizes by January 23 and March 5 were 11,239 [95% CI 4,794–22,372] and 124,506 [95% CI 69,526–265,113], respectively.Results: The effective reproduction number attained its maximum on January 24 (3.42 [95% CI 3.34–3.50]) and became less than 1 from February 7 (0.76 [95% CI 0.65–0.92]). We also estimate the effects of two major government interventions on the spread of COVID-19 in Wuhan.Conclusions: This case study by our proposed method affirms the believed importance and effectiveness of imposing tight non-essential travel restrictions and affirm the importance and effectiveness of government interventions (e.g., transportation suspension and large scale hospitalization) for effective mitigation of COVID-19 community spread.
A key challenge for estimating the epidemiological parameters of the COVID-19 outbreak in Wuhan is the discrepancy between the officially reported number of infections and the true number of infections. A common approach to tackling the challenge is to use the number of infections exported from Wuhan to infer the true number in the city. This approach can only provide a static estimate of the epidemiological parameters before Wuhan lockdown on January 23, 2020, because there are almost no exported cases thereafter. Here, we propose a method to dynamically estimate the epidemiological parameters of the COVID-19 outbreak in Wuhan by recovering true numbers of infections from day-to-day official numbers. Using the method, we provide a comprehensive retrospection on how the disease had progressed in Wuhan from January 19 to March 5, 2020. Particularly, we estimate that the outbreak sizes by January 23 and March 5 were 11,239 [95% CI 4,372] and 124,506 [95% CI 69,113], respectively. The effective reproduction number attained its maximum on January 24 (3.42 [95% CI 3.34-3.50]) and became less than 1 from February 7 (0.76 [95% CI 0.65-0.92]). We also estimate the effects of two major government interventions on the spread of COVID-19 in Wuhan. In particular, transportation suspension and large scale hospitalization respectively prevented 33,719 and 90,072 people from getting infected in the nine-day time period right after its implementation.Acknowledgements: We thank Huiran Li for her help with literature review and data processing.
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