A major challenge in biology is to understand how phylogeny, diet, and environment shape the mammalian gut microbiome. Yet most studies of nonhuman microbiomes have relied on relatively coarse dietary categorizations and have focused either on individual wild populations or on captive animals that are sheltered from environmental pressures, which may obscure the effects of dietary and environmental variation on microbiome composition in diverse natural communities. We analyzed plant and bacterial DNA in fecal samples from an assemblage of 33 sympatric large-herbivore species (27 native, 6 domesticated) in a semiarid East African savanna, which enabled high-resolution assessment of seasonal variation in both diet and microbiome composition. Phylogenetic relatedness strongly predicted microbiome composition (r = 0.91) and was weakly but significantly correlated with diet composition (r = 0.20). Dietary diversity did not significantly predict microbiome diversity across species or within any species except kudu; however, diet composition was significantly correlated with microbiome composition both across and within most species. We found a spectrum of seasonal sensitivity at the diet−microbiome nexus: Seasonal changes in diet composition explained 25% of seasonal variation in microbiome composition across species. Species’ positions on (and deviations from) this spectrum were not obviously driven by phylogeny, body size, digestive strategy, or diet composition; however, domesticated species tended to exhibit greater diet−microbiome turnover than wildlife. Our results reveal marked differences in the influence of environment on the degree of diet−microbiome covariation in free-ranging African megafauna, and this variation is not well explained by canonical predictors of nutritional ecology.

Background Health behavior is influenced by culture and social context. However, there are limited data evaluating the scope of these influences on COVID-19 response. Objective This study aimed to compare handwashing and social distancing practices in different countries and evaluate practice predictors using the health belief model (HBM). Methods From April 11 to May 1, 2020, we conducted an online, cross-sectional survey disseminated internationally via social media. Participants were adults aged 18 years or older from four different countries: the United States, Mexico, Hong Kong (China), and Taiwan. Primary outcomes were self-reported handwashing and social distancing practices during COVID-19. Predictors included constructs of the HBM: perceived susceptibility, perceived severity, perceived benefits, perceived barriers, self-efficacy, and cues to action. Associations of these constructs with behavioral outcomes were assessed by multivariable logistic regression. Results We analyzed a total of 71,851 participants, with 3070 from the United States, 3946 from Mexico, 1201 from Hong Kong (China), and 63,634 from Taiwan. Of these countries, respondents from the United States adhered to the most social distancing practices (χ23=2169.7, P<.001), while respondents from Taiwan performed the most handwashing (χ23=309.8, P<.001). Multivariable logistic regression analyses indicated that self-efficacy was a positive predictor for handwashing (odds ratio [OR]United States 1.58, 95% CI 1.21-2.07; ORMexico 1.5, 95% CI 1.21-1.96; ORHong Kong 2.48, 95% CI 1.80-3.44; ORTaiwan 2.30, 95% CI 2.21-2.39) and social distancing practices (ORUnited States 1.77, 95% CI 1.24-2.49; ORMexico 1.77, 95% CI 1.40-2.25; ORHong Kong 3.25, 95% CI 2.32-4.62; ORTaiwan 2.58, 95% CI 2.47-2.68) in all countries. Handwashing was positively associated with perceived susceptibility in Mexico, Hong Kong, and Taiwan, while social distancing was positively associated with perceived severity in the United States, Mexico, and Taiwan. Conclusions Social media recruitment strategies can be used to reach a large audience during a pandemic. Self-efficacy was the strongest predictor for handwashing and social distancing. Policies that address relevant health beliefs can facilitate adoption of necessary actions for preventing COVID-19. Our findings may be explained by the timing of government policies, the number of cases reported in each country, individual beliefs, and cultural context.

Nonalcoholic fatty liver disease (NAFLD) is one of the most common forms of liver disease worldwide and has emerged as a significant public health concern in China. A better understanding of the etiology of NAFLD can inform effective management strategies for this disease. We examined factors associated with NAFLD in two districts of Hangzhou, China, focusing on the relationship of regional body fat distribution, muscle mass, and NAFLD. We used baseline data to carry out a cross‐sectional analysis among 3,589 participants from the Wellness Living Laboratory (WELL) China study, a longitudinal population‐based study that aims to investigate and promote well‐being among the Chinese population. NAFLD was defined using the widely validated fatty liver index (FLI). Multivariate logistic regressions were performed to assess independent associations between NAFLD and metabolic risk factors (e.g., insulin resistance) and dual x‐ray absorptiometry (DXA)‐derived measures (e.g., android fat ratio [AFR] and skeletal muscle index [SMI]). Of the 3,589 participants, 476 (13.3%) were classified as having FLI‐defined NAFLD (FLI ≥60). Among those, 58.0% were men. According to our analysis, AFR (odds ratio [OR], 10.0; 95% confidence interval [CI], 5.8‐18.5), insulin resistance (OR, 4.0; 95% CI, 3.0‐5.3), high alanine aminotransferase levels (OR, 7.6; 95% CI, 5.8‐10.0), smoking (OR, 2.0; 95% CI, 1.4‐3.0), and male sex (OR, 2.9; 95% CI, 2.0‐4.2) were positively associated with NAFLD risk, while SMI (OR, 0.1; 95% CI, 0.07‐0.13) was inversely associated with NAFLD risk. Conclusion: In addition to known metabolic risk factors, DXA‐derived AFR and SMI may provide additional insights to the understanding of NAFLD. Interventions that aim to decrease AFR and increase SMI may be important to reduce the burden of NAFLD in this population.

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PURPOSE In a review of cancer incidence across continents (GLOBOCAN 2012), data sources from Ghana were classified as Frequencies, the lowest classification for inclusion, signifying the worst data quality for inclusion in the analysis. Recognizing this deficiency, the establishment of a population-based cancer registry was proposed as part of a broader cancer control plan. METHODS The registry was examined under the following headings: policy, data source, and administrative structure; external support and training; and definition of geographic coverage. RESULTS The registry was set up based on the Ghana policy document on the strategy for cancer control. The paradigm shift ensured subscription to one data collection software (CanReg 5) in the country. The current approach consists of trained registrars based in the registry who conduct active data abstraction at the departments and units of the hospital and pathologic services. To ensure good governance, an administrative structure was created, including an advisory board, a technical committee, and registry staff. External support for the establishment of the Accra Cancer Registry has come mainly from Stanford University and the African Cancer Registry Network, in collaboration with the University of Ghana. Unlike previous attempts, this registry has a well-defined population made up of nine municipal districts. CONCLUSION The Accra Cancer Registry was established as a result of the lessons learned from failed previous attempts and aim to provide a model for setting up other cancer registries in Ghana. It will eventually be the focal point where all the national data can be collated.

There were no significant differences between RFA and TE resection in intraoperative or postoperative outcome parameters.

IMPORTANCEThere is an urgent need to assess the feasibility of COVID-19 surveillance measures in educational settings. OBJECTIVETo assess whether young children can feasibly self-collect SARS-CoV-2 samples for surveillance testing over the course of an academic year.

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