Background Eosinophilic inflammation is a major phenotype associated with poorly controlled disease in nasal polyp patients. The difference between systemic and local eosinophilia in relation to disease control is poorly understood. Objective To explore whether blood and polyp tissue eosinophil numbers are independent risk factors for poor disease control in patients with nasal polyp. Methods By using the electronic medical records database and manual evaluation, 183 nasal polyp patients who had undergone endoscopic sinus surgery at least one year prior to the study with complete data of tissue specimens, baseline blood routine test, nasal endoscopy and sinus computed tomography, were identified and recruited to assess disease control based on the criteria of a European position paper on rhinosinusitis and nasal polyps 2012 (EPOS 2012). Multiple logistic regression model was used to determine the association between blood and tissue eosinophil numbers and risk of poor disease control by adjusting for demographics and comorbidities. Results We broke down the cohort into 4 groups according to blood (0.3 ​× ​10 9 /L) and tissue (10%) eosinophils. The patients without eosinophilic inflammation represented the largest group (41.5%). The group with concordant blood and tissue eosinophilia represented the second largest (31.2%), and the patients with isolated tissue (15.3%) or blood (12.0%) eosinophilia were relatively rare. Multiple logistic regression models found blood eosinophil count and tissue eosinophil percentage were independently associated with increased risk for poor disease control after adjustments for covariates related to poor treatment outcome. Furthermore, subjects with concordant blood and tissue eosinophilia had a higher risk for poor disease control than those with isolated blood or tissue eosinophilia. Conclusion Concordant blood and tissue eosinophilia relates to a higher likelihood of poor disease control than isolated blood or tissue eosinophilia after adjustment of potential confounders in nasal polyp patients.

Eosinophils play a critical role in the pathogenesis of allergic airway inflammation. However, the relative importance of eosinophil activation and pathogenicity in driving the progression of disease severity of allergic rhinitis (AR) remains to be defined. We aimed to assess the relation of activated and pathogenic eosinophils with disease severity of patients with AR. Peripheral blood and nasal samples were collected from patients with mild (n=10) and moderate-severe (n=21) house dust mite AR and healthy control subjects (n=10) recruited prospectively. Expressions of activation and pathogenic markers on eosinophils in the blood and nose were analyzed by flow cytometry. The eosinophilic cation protein- (ECP-) releasing potential and the pro-Th2 function of blood eosinophils were compared between the mild and moderate-severe patients and healthy controls. Our results showed that the numbers of activated (CD44+ and CD69+) and pathogenic (CD101+CD274+) eosinophils in the blood and nose as well as blood eosinophil progenitors were increased in moderate-severe AR compared with the mild patients and healthy controls. In addition, the levels of activated and pathogenic eosinophils in the blood were positively correlated with the total nasal symptom score and serum ECP and eosinophil peroxidase (EPX) levels in patients with AR. Furthermore, the blood eosinophils obtained from the moderate-severe patients exhibited a higher potential of releasing ECP and EPX induced by CCL11 and of promoting Th2 responses than those from the mild patients and healthy controls. In conclusion, patients with moderate-severe AR are characterized by elevated levels of activated and pathogenic eosinophils, which are associated with higher production of ECP, EPX, and IL-4 in the peripheral blood.

Objectives To assess the impact of risk factors on the disease control among chronic rhinosinusitis (CRS) patients, following 1 year of functional endoscopic sinus surgery (FESS), and combining the risk factors to formulate a convenient, visualised prediction model. Design A retrospective and nonconcurrent cohort study. Setting and Participants A total of 325 patients with CRS from June 2018 to July 2020 at the First Affiliated Hospital of Sun Yat‐sen University, the Third Affliated Hospital of Sun Yat‐sen University, the Seventh Affiliated Hospital of Sun Yat‐sen University. Main Outcomes Measures Outcomes were time to event measures: the disease control of CRS after surgery 1 year. The presence of nasal polyps, smoking habits, allergic rhinitis (AR), the ratio of tissue eosinophil (TER) and peripheral blood eosinophil count (PBEC) and asthma was assessed. The logistic regression models were used to conduct multivariate and univariate analyses. Asthma, TER, AR, PBEC were also included in the nomogram. The calibration curve and area under curve (AUC) were used to evaluate the forecast performance of the model. Results In univariate analyses, most of the covariates had significant associations with the endpoints, except for age, gender and smoking. The nomogram showed the highest accuracy with an AUC of 0.760 (95% CI, 0.688–0.830) in the training cohort. Conclusions In this cohort study that included the asthma, AR, TER, PBEC, which had significantly affected the disease control of CRS after surgery. The model provided relatively accurate prediction in the disease control of CRS after FESS and served as a visualised reference for daily diagnosis and treatment.

Background Histopathology of nasal polyps contains rich prognostic information, which is difficult to extract objectively. In the present study, we aimed to develop a prognostic indicator of patient outcomes by analyzing scanned conventional hematoxylin and eosin (H&E)‐stained slides alone using deep learning. Methods An interpretable supervised deep learning model was developed using 185 H&E‐stained whole‐slide images (WSIs) of nasal polyps, each from a patient randomly selected from the pool of 232 patients who underwent endoscopic sinus surgery at the First Affiliated Hospital of Sun Yat‐Sen University (internal cohort). We internally validated the model on a holdout dataset from the internal cohort (47 H&E‐stained WSIs) and externally validated the model on 122 H&E‐stained WSIs from the Seventh Affiliated Hospital of Sun Yat‐Sen University and the University of Hong Kong‐Shenzhen Hospital (external cohort). A poor prognosis score (PPS) was established to evaluate patient outcomes, and then risk activation mapping was applied to visualize the histopathological features underlying PPS. Results The model yielded a patient‐level sensitivity of 79.5%, and specificity of 92.3%, with areas under the receiver operating characteristic curve of 0.943, on the multicenter external cohort. The predictive ability of PPS was superior to that of conventional tissue eosinophil number. Notably, eosinophil infiltration, goblet cell hyperplasia, glandular hyperplasia, squamous metaplasia, and fibrin deposition were identified as the main underlying features of PPS. Conclusions Our deep learning model is an effective method for decoding pathological images of nasal polyps, providing a valuable solution for disease prognosis prediction and precise patient treatment.