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The emergence of new SARS-CoV-2 variants with antibody-evading mutations raises concerns about variable levels of protection against infection after prior infection or vaccination (1). The Omicron variant is genetically divergent [...]

This study examines the pivotal role of COVID-19 testing in mitigating disease spread, particularly in underserved rural communities facing health care access challenges. The Southern Nevada Health District successfully implemented a vending program in Clark County, offering free COVID-19 antigen test kits. Strategically located based on health equity indices and featuring a user-friendly, multilingual registration process, these machines proved effective in reaching rural populations. The cost-effective model suggests potential adoption for broader public health interventions and services in other regions. (Am J Public Health. 2024;114(S7):S558–S561. https://doi.org/10.2105/AJPH.2024.307718) [ABSTRACT FROM AUTHOR]

Two rural settled Fulani villages, northeastern Nigeria, were surveyed for dietary practices and use of edible wild plants (n = 100 households). Commonly consumed species of edible wild barks, fruits, leaves, nuts, seeds, and tubers were analyzed for protein, fat, and carbohydrate and for minerals. Kuka bark (Adansonia digitata) given to infants to increase weight gain was high in fat, calcium, copper, iron, and zinc. Cediya (Ficus thonningii), dorowa (Parkia biglobosa) and zogale (Moringa oleifera) were good sources of protein and fat and excellent sources of calcium and iron or copper and zinc. Fruits, leaves, and nuts of aduwa (Balanites aegyptiaca) were widely used during the dry season and during drought. Edible wild species available during the wet season generally were inferior in energy and micronutrient mineral content compared to dry season plants. Fruits commonly eaten by children were poor sources of protein and minerals but rich in carbohydrate and fiber. Tsamiya seeds (Tamarindus indica) were good sources of zinc and used to make dawwa (porridge) commonly consumed during pregnancy. Kirya seeds (Prosopos africana) contained the highest zinc concentrations. Shiwaka leaves (Veronia colorate) consumed by pregnant women to increase breastmilk production and to expel intestinal worms, were high in fiber, phosphorus, magnesium, manganese, and were adequate sources of calcium., Introduction Throughout West Africa, especially in areas susceptible to drought, previous studies demonstrate the importance of edible wild plants as food sources (Grivetti et al., 1987; Humphry et al., 1993; [...]

Two rural settled Fulani villages in northeast Nigeria were surveyed for dietary practices and use of edible wild plants (n = 100 adult subjects). Dietary patterns and medical data were obtained for children under 5, pregnant and lactating women and the elderly. A diversified diet was maintained at both geographical locations through hunting, gathering, agriculture, horticulture, animal husbandry, food exchanges and cash purchases. Edible wild plants associated with children included fruits of baure (Ficus sycomorus), faru (Lannea schiniperi), giginya (Gardenia aqualla), kokiya (Strychnos spinosa) and nunu (Parinari curatellitolia). Leaves of shiwaka (Veronia colorate) were consumed by lactating women to stimulate breastmilk production. Generally, fruits of baure (Ficus sycomorus) were eaten to counter stomach pain; fruits of kisni (Bridelia ferruginea) were eaten to treat diarrhea; and bark of kuka (Adansonia digitata) was consumed for weight gain. Food storage was more important during wet seasons than dry because of local and regional flooding. Adult Fulani men rode to distant markets on bicycles, while women walked to market and in some instances expended 3200 kcal/day engaging in this activity., Introduction Edible wild plants are common food resources among rural populations. Barks, flowers, fruits, leaves, nuts, roots, seeds and tubers can be primary foods or secondary condiments to dishes prepared [...]

Numerous studies have addressed risk factors associated with high body-mass index (BMI) in the Hispanic population; however, there is a dearth of current literature exploring the obesity epidemic in Puerto Rican women residing on the U.S. mainland. Through a convenience sample of women, this study examined risk factors that predispose Puerto Rican women to self-reported high pre-pregnancy BMI, indicated by overweight status. This cross-sectional quantitative research study, using the transtheoretical model (TTM), was conducted with a sample of 128 Puerto Rican women at a healthcare center in New Haven, Connecticut. The TTM indicated that a majority of women were in the least healthy stages of change, and thus displayed unhealthy behaviors. This study has social-change implications that may assist childbirth educators, community hospitals and government agencies in formulating health-based initiatives in assessing issues related to unhealthy weight statuses, thereby addressing the obesity epidemic. Additionally, this study may assist healthcare professionals in developing culturally relevant interventions targeting health inequalities that disproportionally affect Puerto Rican women living on the U.S. mainland before pregnancy. [ABSTRACT FROM AUTHOR]

Genome sequencing from wastewater has emerged as an accurate and cost-effective tool for identifying SARS-CoV-2 variants. However, existing methods for analyzing wastewater sequencing data are not designed to detect novel variants that have not been characterized in humans. Here, we present an unsupervised learning approach that clusters co-varying and time-evolving mutation patterns leading to the identification of SARS-CoV-2 variants. To build our model, we sequenced 3,659 wastewater samples collected over a span of more than two years from urban and rural locations in Southern Nevada. We then developed a multivariate independent component analysis (ICA)-based pipeline to transform mutation frequencies into independent sources with co-varying and time-evolving patterns and compared variant predictions to >5,000 SARS-CoV-2 clinical genomes isolated from Nevadans. Using the source patterns as data-driven reference "barcodes", we demonstrated the model's accuracy by successfully detecting the Delta variant in late 2021, Omicron variants in 2022, and emerging recombinant XBB variants in 2023. Our approach revealed the spatial and temporal dynamics of variants in both urban and rural regions; achieved earlier detection of most variants compared to other computational tools; and uncovered unique co-varying mutation patterns not associated with any known variant. The multivariate nature of our pipeline boosts statistical power and can support accurate and early detection of SARS-CoV-2 variants. This feature offers a unique opportunity for novel variant and pathogen detection, even in the absence of clinical testing.