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RISS 인기검색어
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- 저자 : Gao Ziang (검색결과 2 건)
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Liu Peiqiang,Qin Danxue,Lv Hao,Fan Wenjun,Zhou Fangwei,Gao Ziang,Tao Zezhang,Xu Yu 대한천식알레르기학회 2021 Allergy, Asthma & Immunology Research Vol.13 No.6
Purpose: Allergic rhinitis (AR) is a common otolaryngology disease and one of the clinical causes of olfactory dysfunction (OD). The olfactory bulb serves as a transfer station for olfactory information transmission, and alleviating its neuroinflammation may be expected to improve AR-induced OD. Recent studies have suggested that the dopamine D2 receptor acts as a key target in regulating immune functions and neuroinflammatory reaction. However, the effect of dopamine D2 receptor on AR-induced neuroinflammation is still unknown. Methods: An AR mouse model with OD induced by ovalbumin were constructed. The buried food pellet test was to evaluate the olfactory function of the mice. Immunofluorescence staining, hematoxylin and eosin staining, enzyme-linked immunosorbent assay and western blotting were also used to investigate the molecular mechanisms underlying the antiinflammatory effects of the dopamine D2 receptor in AR-induced OD. Results: We found that AR-induced OD has a relationship with inflammatory responses in the olfactory bulb. Nasal administration of quinpirole (Quin, a dopamine D2 receptor agonist, 3 mg/kg) improved olfactory function in mice, inhibited the expression of toll-like receptor 4 (TLR4)/nuclear factor-κB (NF-κB) signalings and the levels of tumor necrosis factor-α, interleukin (IL)-1β and IL-6 in the olfactory bulb. In vitro, Quin (20 μmol/L) inhibited the release of TLR4/NF-κB signalings-dependent inflammatory cytokines in cultured microglia. Conclusions: Activation of the dopamine D2 receptor inhibits the release of inflammatory cytokines through TLR4/NF-κB signaling in the olfactory bulb microglia, and protects olfactory function.
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Zhou Huiqin,Fan Wenjun,Qin Danxue,Liu Peiqiang,Gao Ziang,Lv Hao,Zhang Wei,Xiang Rong,Xu Yu 대한천식알레르기학회 2023 Allergy, Asthma & Immunology Research Vol.15 No.1
Purpose: Chronic rhinosinusitis with nasal polyps (CRSwNP) can be classified into eosinophilic CRSwNP (eCRSwNP) and non-eosinophilic CRSwNP (non-eCRSwNP) by tissue biopsy, which is difficult to perform preoperatively. Clinical biomarkers have predictive value for the classification of CRSwNP. We aimed to evaluate the application of artificial neural network (ANN) modeling in distinguishing different endotypes of CRSwNP based on clinical biomarkers. Methods: Clinical parameters were collected from 109 CRSwNP patients, and their predictive ability was analyzed. ANN and logistic regression (LR) models were developed in the training group (72 patients) and further tested in the test group (37 patients). The output variable was the diagnosis of eCRSwNP, defined as tissue eosinophil count > 10 per high-power field. The receiver operating characteristics curve was used to assess model performance. Results: A total of 15 clinical features from 60 healthy controls, 60 eCRSwNP and 49 non-eCRSwNP were selected as candidate predictors. Nasal nitric oxide levels, peripheral eosinophil absolute count, total immunoglobulin E, and ratio of bilateral computed tomography scores for the ethmoid sinus and maxillary sinus were identified as important features for modeling. Two ANN models based on 4 and 15 clinical features were developed to predict eCRSwNP, which showed better performance, with the area under the receiver operator characteristics significantly higher than those from the respective LR models (0.976 vs. 0.902, P = 0.048; 0.970 vs. 0.845, P = 0.011). All ANN models had better fits than single variable prediction models (all P < 0.05), and ANN model 1 had the best predictive performance among all models. Conclusions: Machine learning models assist clinicians in predicting endotypes of nasal polyps before invasive detection. The ANN model has the potential to predict eCRSwNP with high sensitivity and specificity, and is superior to the LR model. ANNs are valuable for optimizing personalized patient management.
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