Cumulative Pollen Concentration Curves for Pollen Allergy Diagnosis

Hoffmann, Tara Maria; Travaglini, Alessandro; Brighetti, Mariantonia; Şahin, Aydan Acar; Arasi, Stefania; Bregu, Blerina; Caeiro, Elsa; Sözmen, Şule Çağlayan; Charpin, D; Delgado, Luís; Dimou, Maria; Fiorilli, Massimo; Fonseca, João; Göksel, Özlem; Kalpaklioglu, F.; Lame, Blerta; Mazon, Angel; Mesonjësi, Eris; Nieto, Alma; Öztürk, Ayşe Bilge; Pajno, G.B.; Papadopoulos, N; Pellegrini, E.; Pereira, Ana Margarida; Pereira, Mariana; Pınar, Nur Münevver; Pinter, Elena; Priftanji, Alfred; Saçkesen, Cansın; Sfika, Ifigenia; Suárez, Javier; Thibaudon, M.; Tripodi, Salvatore; Ugus, U; Villella, Valeria; Matricardi, Paolo Maria; Dramburg, Stephanie; team, It. study

doi:10.18176/jiaci.0646

Cited by 5 publications

(7 citation statements)

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“…), Oleaceae, Cupressaceae, Poaceae, Asteraceae (Compositae), and many other allergenic species, with internal variations depending on the region and country [56]. The chosen taxa were the subject of recent multicenter studies on airborne pollen allergy in the Mediterranean environment [42], to which the plane tree (Platanus), a species that is very abundant in Rome and characteristic of street trees (especially in the city center), was added.…”

Section: Methodsmentioning

confidence: 99%

“…Aerobiological monitoring is an important tool for avoiding exposure to allergens and thus for preventing allergic symptoms [42]; therefore, since the 1980s, in the most important cities of Italy, Europe, and other continents, it has been possible to know the pollen concentration in the air through aerobiological monitoring activities conducted by monitoring centers. These centers soon built national and international networks; this made it possible to establish minimum requirements for each center and to standardize sampling methods [43][44][45].…”

Section: Pollen Flora and Aerobiological Monitoring Stationsmentioning

confidence: 99%

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Multiple Monitoring Stations in Big Cities: First Example of Three Spore Traps in Rome

et al. 2023

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(1) Background: Rome is a municipality with an area of 1287 km2 and presents floristic-vegetational complexity that is reflected in the composition of aerospora, which are responsible for pollinosis. The presence of airborne pollen can be detected by pollen monitoring. The large extent of the city’s territory makes it possible to verify possible changes in pollen composition in different sites of the city. With this in mind, a study was conducted to assess the differences in airborne pollen concentration, considering phenological and production indicators at three different sites in the city. (2) Methods: Pollen data of eight taxa were considered, Alnus spp., Castanea sativa Miller, Cupressaceae-Taxaceae, Olea europaea L., Platanaceae, Poaceae, Quercus spp., and Urticaceae, during 2020 and 2021, using three monitoring samplers. The airborne pollen concentration and the seasons of the three centers were calculated and compared with each other. (3) Results: The diversity between the three samplers shows a phenological succession in accordance with the microclimatic diversity present in the city. The heterogeneity of the airborne pollen concentration reflects the floristic-vegetational diversity, while qualitative and quantitative parameters indicate a homogeneous flowering trend reflecting the seasonality of the various species. (4) Conclusions: The present work and the Italian geographic context suggest the need for a greater number of sampling points to guarantee a true localization of the data. Having several sampling stations also contributes to the protection of health and green areas, which are difficult to manage, conserve, and maintain.

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Section: Methodsmentioning

confidence: 99%

Section: Pollen Flora and Aerobiological Monitoring Stationsmentioning

confidence: 99%

Multiple Monitoring Stations in Big Cities: First Example of Three Spore Traps in Rome

et al. 2023

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“…This problem may be solved by using curves based on the cumulative transformation of pollen data using AllergyMonitor ® . 105 …”

Section: Optimisation Of the Identification Of The Pollen Season And ...mentioning

confidence: 99%

“…Unfortunately, diagrams illustrating daily pollen concentrations from many sources in parallel make the interpretation of each of these curves difficult. This problem may be solved by using curves based on the cumulative transformation of pollen data using AllergyMonitor ® 105 …”

Section: Optimisation Of the Identification Of The Pollen Season And ...mentioning

confidence: 99%

Real‐world data using mHealth apps in rhinitis, rhinosinusitis and their multimorbidities

Sousa‐Pinto

Antó²,

Berger

et al. 2022

Clinical & Translational All

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Digital health is an umbrella term which encompasses eHealth and benefits from areas such as advanced computer sciences. eHealth includes mHealth apps, which offer the potential to redesign aspects of healthcare delivery. The capacity of apps to collect large amounts of longitudinal, real‐time, real‐world data enables the progression of biomedical knowledge. Apps for rhinitis and rhinosinusitis were searched for in the Google Play and Apple App stores, via an automatic market research tool recently developed using JavaScript. Over 1500 apps for allergic rhinitis and rhinosinusitis were identified, some dealing with multimorbidity. However, only six apps for rhinitis (AirRater, AllergyMonitor, AllerSearch, Husteblume, MASK‐air and Pollen App) and one for rhinosinusitis (Galenus Health) have so far published results in the scientific literature. These apps were reviewed for their validation, discovery of novel allergy phenotypes, optimisation of identifying the pollen season, novel approaches in diagnosis and management (pharmacotherapy and allergen immunotherapy) as well as adherence to treatment. Published evidence demonstrates the potential of mobile health apps to advance in the characterisation, diagnosis and management of rhinitis and rhinosinusitis patients.

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“…According to the previous researches [1][2][3], high concentration of allergenic pollen in the air may undermine the health of those who are pollen-hypersensitive. Stimulation by pollen-containing allergen can cause allergic reaction or exacerbate some related diseases.…”

Section: Introductionmentioning

confidence: 99%

Automatic 3D Pollen Recognition Based on Convolutional Neural Network

Wang

2021

Scientific Programming

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The importance of automatic pollen recognition has been examined in several areas ranging from paleoclimate studies to some daily practice such as pollen hypersensitivity forecasting. This paper attempts to present an automatic 3D pollen image recognition method based on convolutional neural network. To achieve this purpose, high feature dimensions and complex posture transformation should be taken into account. Therefore, this work focuses on a three-part novel approach: constructing spatial local key points to obtain local stable points of pollen images, computing orientational local binary pattern using local stable points as the inputs, and identifying the pollen grains using convolutional neural network as the classifier. Experiments are performed on two standard pollen image datasets: Confocal-E dataset and Pollenmonitor dataset. It is concluded that the proposed approach can effectively extract the features of pollen images and is robust to posture transformation, slight occlusion, and pollution.

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Cumulative Pollen Concentration Curves for Pollen Allergy Diagnosis

Cited by 5 publications

References 0 publications

Multiple Monitoring Stations in Big Cities: First Example of Three Spore Traps in Rome

Multiple Monitoring Stations in Big Cities: First Example of Three Spore Traps in Rome

Real‐world data using mHealth apps in rhinitis, rhinosinusitis and their multimorbidities

Automatic 3D Pollen Recognition Based on Convolutional Neural Network

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