House dust mite (HDM) allergens are considered to be one of the most common causes of asthma and allergic rhinitis in the world. Cysteine proteases Der p 1 and Der f 1 (group 1) and also NPC 2 family proteins Der p 2 and Der f 2 (group 2) of D. pteronyssinus and D. farinae respectively are considered the main allergens of HDMs. The difference in the sensitivity of the population to these and other allergy causing components of HDM determines the treatment strategy. Thus, the purpose of this work was to determine the pattern of sensitization of the Ukrainian population to individual allergy causing molecular components of HDM in order to improve treatment strategies for the HDM allergy in various regions of Ukraine. To determine the molecular profile of sensitization to HDM, the data of multiplex allergy test Alex2 have been obtained from 10,651 patients. The sample included 57.86% children under the age of 18 and 42.14% adults. A Python language-based statistical analysis was performed, in order to group patients by sensitization to individual molecules and their combinations, regarding the age and geographical location of the patients. Simultaneous sensitization to Der f 2 and Der p 2 allergens was the most common among the entire group Simultaneous sensitization to 5 molecules—of group 1 (Der p 1 and Der f 1), group 2 (Der f 2 and Der p 2), and Der p 23—was the second most common for entire dataset and for the children group. This pattern differed in adults, where monosensitization to Der p 23 occupied the second position, suggesting that this molecule is an important factor of HDM allergy in Ukraine. Of the 16 analyzed regions, sensitization to Der p 23 prevailed in 2 Western regions of Ukraine. In the rest of the regions combination of Der p 2 and Der f 2 was the most prevalent. The established character of population sensitization to HDM in Ukraine is a good prognostic marker of allergen immunotherapy (AIT) efficacy.
Background As the process and nature of developing sensitivity to house dust mites (HDMs) remain not fully studied, our goal was to establish the pattern, nature and timeframe of house dust mite (HDM) sensitization development in patients in Ukraine as well as the period when treatment of such patients would be most effective. Methods The data of the multiplex allergy test Alex2 was collected from 20,033 patients. To determine age specifics of sensitization, descriptive statistics were used. Bayesian Network analysis was used to build probabilistic patterns of individual sensitization. Results Patients from Ukraine were most often sensitized to HDM allergens of group 1 (Der p 1, Der f 1) and group 2 (Der p 2, Der f 2) as well as to Der p 23 (55%). A considerable sensitivity to Der p 5, Der p 7 and Der p 21 allergens was also observed. The overall nature of sensitization to HDM allergens among the population of Ukraine is formed within the first year of life. By this time, there is a pronounced sensitization to HDM allergens of groups 1 and 2 as well as to Der p 23. Significance of sensitization to Der p 5, Der p 7 and Der p 21 allergens grows starting from the age of 3–6. Bayesian Network data analysis indicated the leading role of sensitization to Der p 1 and Der f 2. While developing the sensitivity to group 5 allergens, the leading role may belong to Der p 21 allergen. Conclusion The results obtained indicate the importance of determining the sensitization profile using the multi-component approach. A more detailed study of the optimal age for AIT prescription is required as the pattern of sensitization to HDMs is formed during the first year of life.
The daily pollen forecast provides crucial information for allergic patients to avoid exposure to specific pollens. Pollen counts are typically measured with air samplers and analyzed with microscopy by trained experts. Automated analyses of pollen extracts are being explored as an alternative to traditional pollen counting. METHODS: Extracts of ambient air-sampled pollen from Munich in 2016 and 2017 were lyophilized, rehydrated in optimal NMR buffers, and filtered to remove proteins. NMR spectra were analyzed for pollen associated metabolites. Regression and classification models, using traditional machine learning and deep learning algorithms, were trained to recognize patterns in the metabolites or NMR spectra, based on expertidentified pollen counts. RESULTS: Regression and decision-tree based algorithms using the concentration of metabolites, measured from the NMR spectra, outperformed using the NMR spectra themselves as input data for pollen identification. Categorical prediction algorithms trained for low, medium, high, and very high pollen count groups had accuracies of 74% for the tree, 82% for the grass, and 93% for the weed pollen count. Deep learning models performed better than regression models for NMR spectral input, and were the overall best method in terms of relative error and classification accuracy (86% for tree, 89% for grass, and 93% for weed pollen count). CONCLUSIONS: This study demonstrates that NMR spectra of airsampled pollen extracts could be used in an automated fashion to provide genus and type-specific measures of the pollen count. The classification algorithms can accurately differentiate the low/medium/high category standards of the National Allergy Board.
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