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Wyświetlanie 1-10 z 28
Tytuł:
Nasal versus bronchial and nasal response to oral aspirin challenge : clinical and biochemical differences between patients with aspirin-induced asthma/rhinitis
Autorzy:
Niżankowska-Mogilnicka, Ewa
Szczeklik, Andrzej
Gielicz, Anna
Świerczyńska-Krępa, Monika
Zarychta, Jacek
Opis:
Background: Aspirin-induced asthma/rhinitis (AIAR) is characterized by the altered metabolism of leukotrienes and proinflammatory prostaglandins. The basal and postchallenge levels of eicosanoids might reflect the clinical and biochemical characteristics of patients with distinct types of hypersensitive responses to aspirin. Objective: We compared clinical and eicosanoid profiles of patients with AIAR showing both bronchial and nasal versus isolated nasal responses to aspirin challenge. Methods: Twenty-three patients with AIAR underwent the single-blind, oral, placebo-controlled aspirin challenge. The bronchial response (BR) was evidenced by dyspnea and spirometry, whereas the nasal response (NR) was evidenced by nasal symptoms and acoustic rhinometry and/or rhinomanometry. Urinary leukotriene E4 (uLTE4), serum and urinary stable prostaglandin D2 metabolite, and 9α,11β-prostaglandin F2 (9α,11β-PGF2), were determined at baseline and after the aspirin challenge. Results: Fifteen subjects showed BR and NR (BNR), whereas 8 showed NR only. Basal uLTE4 in the BNR group was significantly higher than in the NR group. After aspirin challenge, it increased significantly in both groups. Serum 9α,11β-PGF2 increased after aspirin challenge in the BNR group only. The patients with BNR had more severe AIAR. Conclusions: BNR to aspirin in AIAR indicates a more advanced disease and more profound underlying eicosanoid metabolism disturbances.
Dostawca treści:
Repozytorium Uniwersytetu Jagiellońskiego
Artykuł
Tytuł:
Targeted eicosanoid lipidomics of exhaled breath condensate provide a distinct pattern in the aspirin-intolerant asthma phenotype
Autorzy:
Niżankowska-Mogilnicka, Ewa
Kaszuba, Marek
Szczeklik, Andrzej
Gielicz, Anna
Sanak, Marek
Bochenek, Grażyna
Opis:
Background: Eicosanoids, important signaling and inflammatory molecules, are present in exhaled breath condensate (EBC) in very low concentrations, requiring highly sensitive analytic methods for their quantification. Objective: We sought to assess a vast platform of eicosanoids in different asthma phenotypes, including aspirin-intolerant asthma, by means of a recently developed analytic approach based on mass spectrometry. Methods: EBC from 115 adult asthmatic subjects (62 with aspirin intolerance) and 38 healthy control subjects were assessed quantitatively for 19 eicosanoids by using complementary HPLC, gas chromatography–mass spectrometry, or both. Palmitic acid concentrations were used as a marker for dilution of condensate samples. Results: Asthma was characterized by an increase in arachidonate lipoxygenase products and cysteinyl leukotrienes. The COX pathway was also significantly upregulated in asthmatic subjects. Subjects with aspirin-intolerant asthma were distinguished by a sharp increase in the level of prostaglandin D2 and E2 metabolites; their 5- and 15- hydroxyeicosateraenoic acid levels were also higher than in aspirin-tolerant subjects. A classical discriminant analysis permitted us to classify correctly 99% of asthmatic subjects within the study population; the specificity of the analysis was 97%. The eicosanoid profiling allowed for 92% correct classification of aspirin-intolerant subjects. Conclusions: The highly sensitive eicosanoid profiling in EBC makes it possible to detect alterations in asthma, especially in its distinct phenotype characterized by hypersensitivity to aspirin and other nonsteroidal anti-inflammatory drugs. This permits us to discriminate asthmatic subjects from healthy subjects, as well as to distinguish the 2 asthma phenotypes based on the presence or absence of aspirin hypersensitivity.
Dostawca treści:
Repozytorium Uniwersytetu Jagiellońskiego
Artykuł
Tytuł:
Machine learning in the diagnosis of asthma phenotypes during coronavirus disease 2019 pandemic
Autorzy:
Celejewska-Wójcik, Natalia
Mastalerz, Lucyna
Gielicz, Anna
Sanak, Marek
Gawlewicz-Mroczka, Agnieszka
Ćmiel, Adam
Pytlewski, Adam
Opis:
Background: During the coronavirus disease 2019 (COVID-19) pandemic, it has become a pressing need to be able to diagnose aspirin hypersensitivity in patients with asthma without the need to use oral aspirin challenge (OAC) testing. OAC is time consuming and is associated with the risk of severe hypersensitive reactions. In this study, we sought to investigate whether machine learning (ML) based on some clinical and laboratory procedures performed during the pandemic might be used for discriminating between patients with aspirin hypersensitivity and those with aspirin-tolerant asthma. Methods: We used a prospective database of 135 patients with non-steroidal anti-inflammatory drug (NSAID)–exacerbated respiratory disease (NERD) and 81 NSAID-tolerant (NTA) patients with asthma who underwent OAC. Clinical characteristics, inflammatory phenotypes based on sputum cells, as well as eicosanoid levels in induced sputum supernatant and urine were extracted for the purpose of applying ML techniques. Results: The overall best ML model, neural network (NN), trained on a set of best features, achieved a sensitivity of 95% and a specificity of 76% for diagnosing NERD. The 3 promising models (i.e., multiple logistic regression, support vector machine, and NN) trained on a set of easy-to-obtain features including only clinical characteristics and laboratory data achieved a sensitivity of 97% and a specificity of 67%. Conclusions ML techniques are becoming a promising tool for discriminating between patients with NERD and NTA. The models are easy to use, safe, and achieve very good results, which is particularly important during the COVID-19 pandemic.
Dostawca treści:
Repozytorium Uniwersytetu Jagiellońskiego
Artykuł
Wyświetlanie 1-10 z 28

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