Associations of PM2.5 and aspergillosis: ambient fine particulate air pollution and population-based big data linkage analyses

Liu, Po‐Yu; Tsan, Yu‐Tse; Chan, Yu‐Wei; Chan, Wei-Chen; Shi, Zhi-Yuan; Yang, Chao-Tung; Lou, Ben-Shen

doi:10.1007/s12652-018-0852-x

Cited by 16 publications

(17 citation statements)

References 51 publications

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“…Liu and colleagues investigated in Taiwan the association between ambient fine particulate PM2.5 air pollution and invasive aspergillosis at a population level. 9 They had access to a collection of long-term and nationwide databases of daily ambient PM2.5 levels and invasive aspergillosis incidence. A total of 1000000 patients during the study period (1999–2009) were included in the data set.…”

Section: Introductionmentioning

confidence: 99%

The human lung and Aspergillus: You are what you breathe in?

2019

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The diversity of fungal species comprising the lung mycobiome is a reflection of exposure to environmental and endogenous filamentous fungi and yeasts. Most lung mycobiome studies have been culture-based. A few have utilized next generation sequencing (NGS). Despite the low number of published NGS studies, several themes emerge from the literature: (1) moulds and yeasts are present in the human respiratory tract, even during health; (2) the fungi present in the respiratory tract are highly variable between individuals; and (3) many diseases are accompanied by decreased diversity of fungi in the lungs. Even in patients with the same disease, different patients have been shown to harbor distinct fungal communities. Those fungal species present in any one individual may represent a patient's unique environmental exposure(s), either to species restricted to the indoor environment, for example, Penicillium , or species found in the outdoor environment such as Aspergillus , wood and vegetation colonizing fungi and plant pathogens. In addition to causing clinical fungal infections, the lung mycobiome may have inflammatory effects that can cause or worsen lung disease. Most respiratory diseases that have been studied, have been associated with decreases in fungal diversity. However, none of these diversity studies distinguish between accidental, transient fungal colonizers and true residents of the respiratory tract. Where does Aspergillus feature in the mycobiomes of the respiratory tract? Do these mycobiomes reflect the diversity of fungi in outdoor and internal environments? These intriguing questions are explored here.

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Section: Introductionmentioning

confidence: 99%

The human lung and Aspergillus: You are what you breathe in?

2019

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“…Candida y Aureobasidium se encontraron en sujetos sanos; en contraste, Chrysonilia y Stemphylium en individuos alérgicos. Muchos hongos ambientales son causantes de micosis alérgicas broncopulmonares, algunos se encuentran asociados a material particulado en el aire (13,14) .…”

Section: Tracto Respiratoriounclassified

Micobioma: diversidad fúngica en el cuerpo humano

Restrepo-Rivera¹,

Cardona-Castro

2021

CES Med

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Introducción: los hongos hacen parte de los microorganismos que se encuentran en el ser humano y que interactúan con bacterias, virus y archaeas. El equilibrio inter e intra-especies es importante para mantener la salud en los seres humanos. La mayoría de los estudios sobre la micobioma se han relacionado con estados de enfermedad causados por hongos, siendo de relevancia la exploración de las comunidades comensales en individuos sanos. Métodos: se realizó una búsqueda bibliográfica en PubMed, ScienceDirect, Scopus y Google Scholar, usando los términos mycobiome, intestinal fungi, skin mycobiome, vaginal mycobiome, fungal microbiome. Se incluyeron artículos desde 1996 hasta 2020, de revisión y de resultados de investigación en todos los idiomas. Resultados y discusión: existe variabilidad en las comunidades fúngicas en los sitios corporales según sus características intrínsecas y la relación con el medio ambiente. El estado de salud en los seres humanos puede estar influenciado por la densidad y diversidad fúngica, a diferencia de los individuos enfermos en quienes se evidencia una disminución en la diversidad y que se asocia con el oportunismo de agentes patógenos.

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“…Liu et al [28] focused on the association between invasive aspergillosis and ambient fine particulate air pollution. Two data sets were leveraged for this study: the National Health Insurance Research Database (NHIRD) was used to define invasive aspergillosis, while the Taiwan air quality index (AQI) monitoring network was used to profile the PM 2.5 concentration in Taiwan.…”

Section: Related Workmentioning

confidence: 99%

“…Figure 3 depicts the system design. • Step 1: National Health Insurance Research Database and Air Quality Monitoring Network The diagnostic data for Aspergillus infection was analyzed by Taichung Veteran General Hospital [28] with the National Health Insurance Research Database (NHIRD). AQI and PM 2.5 data obtained from the Environmental Protection Agency Air Quality Monitoring Network in Taiwan, combined with the above long-term big data, was used to prove the correlation between air pollution and respiratory diseases.…”

Section: System Designmentioning

confidence: 99%

Influenza-like illness prediction using a long short-term memory deep learning model with multiple open data sources

et al. 2020

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The influenza problem has always been an important global issue. It not only affects people's health problems but is also an essential topic of governments and health care facilities. Early prediction and response is the most effective control method for flu epidemics. It can effectively predict the influenza-like illness morbidity, and provide reliable information to the relevant facilities. For social facilities, it is possible to strengthen epidemic prevention and care for highly sick groups. It can also be used as a reminder for the public. This study collects information on the influenzalike illness emergency department visits to the Taiwan Centers for Disease Control, and the PM 2.5 open-source data from the Taiwan Environmental Protection Administration's air quality monitoring network. By using deep learning techniques, the relevance of short-term estimates and the outbreak calculation method can be determined. The techniques are published by the WHO to determine whether the influenza-like illness situation is still in a stage of reasonable control. Finally, historical data and future forecasted data are integrated on the web page for visual presentation, to show the actual regional air quality situation and influenza-like illness data and to predict whether there is an outbreak of influenza in the region.

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Associations of PM2.5 and aspergillosis: ambient fine particulate air pollution and population-based big data linkage analyses

Cited by 16 publications

References 51 publications

The human lung and Aspergillus: You are what you breathe in?

The human lung and Aspergillus: You are what you breathe in?

Micobioma: diversidad fúngica en el cuerpo humano

Influenza-like illness prediction using a long short-term memory deep learning model with multiple open data sources

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