An animal's body mass is said to be indirectly related to its rate of heat loss; that is, smaller animals with higher surface area to volume tend to lose heat faster than larger animals. Thus, thermoregulation should be related to body size, however, generalizable patterns are still unclear. Domestic dogs are a diverse species of endothermic mammals, including a 44-fold difference in body size.
View Article and Find Full Text PDFDomestic dogs are a widely diverse species of endothermic mammals that show a positive correlation between body mass and whole-animal metabolic rate, but a negative correlation between body mass and lifespan, making them an interesting system for determining thermoregulatory patterns in relation to body mass, body morphology, and age within a single mammalian species. Though previous work has found differences in thermoregulation across seasons and with training in dogs of different sizes, we now seek to determine (1) whether sampling event-related temperature differences remained when dogs exercised intensely and acutely outdoors and (2) whether thermal differences were also expressed in short-term burst exercise in athletic dogs compared to long-term exercise in non-athletic dogs, as previously found. Here, we measured tympanic membrane temperature (T) as a correlate of core or internal body temperature (T).
View Article and Find Full Text PDFAntimicrob Steward Healthc Epidemiol
September 2024
Background: Antimicrobial resistance (AMR) is one of the greatest global health problems for humans, animals, and the environment. Although the association between various factors and AMR is being increasingly researched, the need to understand the contribution of social and ecological determinants, especially in developing nations, remains. This review fills these knowledge gaps by synthesizing existing evidence on the social and ecological determinants of AMR in Africa.
View Article and Find Full Text PDFBackground: Previous studies have implicated the role of H. pylori infection in developing the metabolic syndrome. However, findings remain contradictory, and data from developing countries are scarce.
View Article and Find Full Text PDFIntroduction: Previous studies have sought to identify risk factors for malnutrition in populations of schoolchildren, depending on traditional logistic regression methods. However, holistic machine learning (ML) approaches are emerging that may provide a more comprehensive analysis of risk factors.
Methods: This study employed feature selection and association rule learning ML methods in conjunction with logistic regression on epidemiological survey data from 1,036 Ethiopian school children.