Free living mites comprise a huge and various groups of tiny arthropods in the class Arachida, mainly of the Pyroglyphidae family. Exposure to allergens derived from house dust mite (HDM) feces is a postulated risk factor for allergic sensitization, asthma development and asthma morbidity. However, practical and effective method to mitigate these allergens in low-income, urban home environments remains elusive. It well known that (HDM) physiology is greatly affected by hydrothermal microclimatic condition. El Arish has subtropical climate and warm humid summer, such situation are favourable to proliferate house dust mites. As no valid data are available for house dust mites fauna of El Arish, this study was carried out to determine the prevalence and contamination rates of homes in El Arish city. Samples of house dust collected in 2008 from 50 houses in El Arish city were subjected to acarological examination. Acri were found in (34.6 %) of the samples collected from these homes. Results indicated that dust mites were present in all humid environments. Also, hypersensitivity to dust mites was common among patients with asthma.
Download full-text PDF |
Source |
|---|
Publication Analysis
Top Keywords
Similar Publications
Spatio-temporal variation of air quality and its driving factors in Jinan and Qingdao during 2014-2022.
J Hazard Mater
December 2024
School of Environmental and Municipal Engineering, Qingdao University of Technology, Qingdao 266520, China. Electronic address:
Over the past 20 years, urbanization of Shandong Province has strongly supported the rapid growth and sustained transformation of economy, however, this region has suffered from serious atmospheric pollution due to intense human activity. Identifying and qualifying the spatio-temporal variation of air pollution and its driving forces of Shandong Province would help in the formulation of effective mitigation policies. A deep understanding of the coupling relationship between air quality and socioeconomic drivers was essential for evaluating the quality of urbanization and long term sustainability.
View Article and Find Full Text PDFBasic Science and Pathogenesis.
Alzheimers Dement
December 2024
University of North Dakota, Grand Forks, ND, USA.
Background: Alzheimer's disease (AD) is an age-related neurodegenerative disorder affecting nearly 50 million individuals worldwide. Besides aging, various comorbidities can increase the risk of AD, such as asthma. However, the molecular mechanism(s) underlying this asthma-associated AD exacerbation is unknown.
View Article and Find Full Text PDFMechanistic modelling of allergen-induced airways disease in early life.
Sci Rep
January 2025
Department of Bioengineering, Imperial College London, London, SW7 2AZ, UK.
Asthma affects approximately 300 million individuals worldwide and the onset predominantly arises in childhood. Children are exposed to multiple environmental irritants, such as viruses and allergens, that are common triggers for asthma onset, whilst their immune systems are developing in early life. Understanding the impact of allergen exposures on the developing immune system and resulting alterations in lung function in early life will help prevent the onset and progression of allergic asthma in children.
View Article and Find Full Text PDFPM2.5 exposure deteriorates Th1/Th2 balance in pediatric asthma by downregulating ALKBH5 and enhancing SRSF1 m6A methylation.
Int J Biometeorol
January 2025
Department of Children Health, Changsha Hospital for Maternal & Child Health Care Affiliated to Hunan Normal University, No.416 of Chengnan East Road, Yuhua District, Changsha, Hunan, 410007, China.
Accumulating evidence has shown that long-term exposure to particulate matter with aerodynamic diameter of less than 2.5 μm (PM2.5) causes Th1/Th2 imbalance and increases the risk of allergic asthma (AA) in children.
View Article and Find Full Text PDFA deep learning method based on multi-scale fusion for noise-resistant coal-gangue recognition.
Sci Rep
January 2025
College of Intelligent Equipment, Shandong University of Science and Technology, Taian, 271000, Shandong, China.
Coal-gangue recognition technology plays an important role in the intelligent realization of integrated working faces and coal quality improvement. However, the existing methods are easily affected by high dust, noise, and other disturbances, resulting in unstable recognition results that make it difficult to meet the needs of industrial applications. To realize accurate recognition of coal-gangue in noisy environments, this paper proposes an end-to-end multi-scale feature fusion convolutional neural network (MCNN-BILSTM) based gangue recognition method, which can automatically learn and fuse complementary information from multiple signal components of vibration signals.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!