Fungal spores are biological particles that are ubiquitous in the outdoor air. Spores of several very common fungal species are known allergens, with the potential to cause respiratory illnesses by exacerbating asthma and allergic rhinitis. The National Allergy Bureau typically has one monitoring station established per city to determine fungal spore counts for an entire metropolitan area. However, variations in fungal spore concentrations could occur among different locations. The objective of this study was to measure and compare airborne fungal spore concentrations in five locations in Las Vegas for the year 2015 to determine if there are differences among microenvironments in the city. Twenty-four-hour or 7-day air samples were collected from five sites across the Las Vegas Valley. Samples were analyzed with a light microscope for fungal spores and counts were converted to concentrations of spores per volume of air. Mixed-model methods were used to evaluate mean differences. Results showed that smuts (basidiomycetes) were the dominant spore type for all five sites during the spring season. Cladosporium species were responsible for the second most dominant spore type with the highest concentrations occurring during the summer and fall months. Results obtained from the five stations established in Las Vegas show that there are important variations among the sites regarding fungal spore concentrations. The data suggest that more sites and additional monitoring of outdoor allergens are needed to provide information necessary to inform the community of outdoor air quality conditions and their potential effects on public health. This study presents new outdoor fungal spore data for the southwest region of the USA, focused in the Las Vegas Valley.
The urbanization of the Las Vegas Valley has transformed this part of the Mohave Desert into a green oasis by introducing many non-native plant species, some of which are allergenic. Typically, one monitoring station is established per city to obtain pollen counts for an entire metropolitan area. However, variations in pollen concentrations could occur among different microenvironments. The objective of this study is to measure and compare pollen concentrations in five locations in Las Vegas to determine if there are significant differences between microenvironments within the city. Air samples were collected from five sites across the Las Vegas Valley over a 1-year period. Prepared slides were analyzed with a light microscope for pollen grains and converted into airborne pollen concentrations. Mixed model methods were used to determine mean differences. Tree pollen was the greatest contributor to the annual average airborne pollen concentrations (130 grains/m3) compared to weeds (6 grains/m3) and grass (3 grains/m3). The highest peak occurred in March 2016 (9589 total grains/m3). There were several differences among sites with respect to concentrations of individual tree species and for total weed and grass concentrations. We observed significant variations in concentration and composition among the five pollen collection stations that were established across the Las Vegas Valley. This study presented new outdoor pollen data for the southwest region of the USA, focused in Las Vegas. The results indicate that more sites and comprehensive monitoring of outdoor allergens are needed to provide accurate information to the community about outdoor air quality conditions.Electronic supplementary materialThe online version of this article (10.1007/s10661-018-6738-8) contains supplementary material, which is available to authorized users.
Artificial intelligence (AI) is used in an increasing number of areas, with recent interest in generative AI, such as using ChatGPT to generate programming code or DALL-E to make illustrations. We describe the use of generative AI in medical education. Specifically, we sought to determine whether generative AI could help train pediatric residents to better recognize genetic conditions. From publicly available images of individuals with genetic conditions, we used generative AI methods to create new images, which were checked for accuracy with an external classifier. We selected two conditions for study, Kabuki (KS) and Noonan (NS) syndromes, which are clinically important conditions that pediatricians may encounter. In this study, pediatric residents completed 208 surveys, where they each classified 20 images following exposure to one of 4 possible educational interventions, including with and without generative AI methods. Overall, we find that generative images perform similarly but appear to be slightly less helpful than real images. Most participants reported that images were useful, although real images were felt to be more helpful. We conclude that generative AI images may serve as an adjunctive educational tool, particularly for less familiar conditions, such as KS.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.