Parietaria Allergy: An Intriguing Challenge for the Allergist

Outdoor airborne allergens: Characterization, behavior and monitoring in Europe

Aeroallergens or inhalant allergens, are proteins dispersed through the air and have the potential to induce allergic conditions such as rhinitis, conjunctivitis, and asthma. Outdoor aeroallergens are found predominantly in pollen grains and fungal spores, which are allergen carriers. Aeroallergens from pollen and fungi have seasonal emission patterns that correlate with plant pollination and fungal sporulation and are strongly associated with atmospheric weather conditions. They are released when allergen carriers come in contact with the respiratory system, e.g. the nasal mucosa. In addition, due to the rupture of allergen carriers, airborne allergen molecules may be released directly into the air in the form of micronic and submicronic particles (cytoplasmic debris, cell wall fragments, droplets etc.) or adhered onto other airborne particulate matter. Therefore, aeroallergen detection strategies must consider, in addition to the allergen carriers, the allergen molecules themselves. This review article aims to present the current knowledge on inhalant allergens in the outdoor environment, their structure, localization, and factors affecting their production, transformation, release or degradation. In addition, methods for collecting and quantifying aeroallergens are listed and thoroughly discussed. Finally, the knowledge gaps, challenges and implications associated with aeroallergen analysis are described.

[A study of airborne pollen monitoring and its connection with allergic rhinitis visits in Taiyuan over the summer and autumn seasons]

Objective:To explore the distribution of airborne pollen in summer and autumn in Taiyuan, analyze the correlation between pollen characteristics, meteorological factors and allergic sensitization, and provide for the prevention and treatment of allergic diseases in this. Methods:The gravity sedimentation method was used to investigate the types, quantities and dispersion patterns of airborne pollen in Taiyuan City from July 21, 2022 to October 20, 2022. he meteorological and patient information was collected during the same period SPSS 26.0 software. Results:①A total of 17 118 pollen grains were collected, and identified as 14 families, 10 genera, and 4 species. The peak period for pollen dispersal in summer and autumn in Taiyuan City from late August to early September. airborne pollen Artemisia（66.62%）, Cannabis/Humulus（17.79%）, Sophora japonica（8.18%）, Chenopodiaceae/Amaranthaceae（2.83%）, Gramineae（2.11%）. ②The concentration of airborne pollen in Taiyuan City positively correlated with the average temperature（5-20℃） and maximum temperature（11-30℃） within a certain range（r=0.547, 0.315, P<0.05）. ③The content of airborne pollen in Taiyuan City positively correlated with the number of visits and allergen positive rate of patients with allergic rhinitis（AR） in our hospital（r=0.702, 0.747, P<0.05）. Conclusion:The peak period for airborne pollen dispersal during the summer and autumn seasons in Taiyuan City from late August to early September. The dominant pollen is Artemisia, Cannabis/Humulus, Sophora japonica, Chenopodiaceae/Amaranthaceae, Gramineae, and the absolute advantage pollen is Artemisia. Meteorological factors pollen content. Within a certain range, temperature the diffusion and transportation of pollen. The number of pollen grains the number of visits, which can serve as an environmental warning indicator for AR patients to take preventive, thereby reducing the risk of allergies.

Neural networks for increased accuracy of allergenic pollen monitoring

Monitoring of airborne pollen concentrations provides an important source of information for the globally increasing number of hay fever patients. Airborne pollen is traditionally counted under the microscope, but with the latest developments in image recognition methods, automating this process has become feasible. A challenge that persists, however, is that many pollen grains cannot be distinguished beyond the genus or family level using a microscope. Here, we assess the use of Convolutional Neural Networks (CNNs) to increase taxonomic accuracy for airborne pollen. As a case study we use the nettle family (Urticaceae), which contains two main genera (Urtica and Parietaria) common in European landscapes which pollen cannot be separated by trained specialists. While pollen from Urtica species has very low allergenic relevance, pollen from several species of Parietaria is severely allergenic. We collect pollen from both fresh as well as from herbarium specimens and use these without the often used acetolysis step to train the CNN model. The models show that unacetolyzed Urticaceae pollen grains can be distinguished with > 98% accuracy. We then apply our model on before unseen Urticaceae pollen collected from aerobiological samples and show that the genera can be confidently distinguished, despite the more challenging input images that are often overlain by debris. Our method can also be applied to other pollen families in the future and will thus help to make allergenic pollen monitoring more specific.