Risk Factors for Acute Rhinosinusitis in Childhood Asthma.

INTRODUCTION: Specific pathogen infections associated with acute rhinosinusitis (ARS) in infants are risk factors for allergic asthma in adolescents. However, the risk factors for ARS onset remain largely unknown in asthmatic children. In this study, we aim to investigate the risk factors for ARS in childhood asthma. METHODS: This study retrospectively compared and analyzed the clinical characteristics of asthmatic children with (n = 194) or without ARS (n = 799). Univariate regression analyses were performed to identify ARS-associated risk factors in asthmatic children, and subsequent multivariate backward stepwise logistic regression analyses were performed to identify independent risk factors. RESULTS: The onset age, values of blood eosinophils (EOS) (%), and total IgE were significantly lower in patients with ARS than in those without ARS. Moreover, the proportions of patients allergic to Dermatophagoides pteronyssinus (d1) and Dermatophagoides farinae (d2) were significantly smaller in children with ARS (all p values <0.05). Univariate analyses showed that an older onset age, a higher body mass index, a higher value of blood EOS (%) were protective factors, while a higher value of blood lymphocytes (%) and a higher degree of sensitization to d1 and d2 were risk factors for ARS. Further backward stepwise multivariate logistic regression analyses confirmed that a younger onset age and allergic sensitization to d1 were independent risk factors for ARS in childhood asthma. CONCLUSION: Younger onset age and allergic sensitization to d1 are risk factors for the onset of ARS in childhood asthma, so allergen intervention should be performed as early as possible in asthmatic children.

Artificial intelligence-based prediction of cervical lymph node metastasis in papillary thyroid cancer with CT.

OBJECTIVES: To develop an artificial intelligence (AI) system for predicting cervical lymph node metastasis (CLNM) preoperatively in patients with papillary thyroid cancer (PTC) based on CT images. METHODS: This multicenter retrospective study included the preoperative CT of PTC patients who were divided into the development, internal, and external test sets. The region of interest of the primary tumor was outlined manually on the CT images by a radiologist who has eight years of experience. With the use of the CT images and lesions masks, the deep learning (DL) signature was developed by the DenseNet combined with convolutional block attention module. One-way analysis of variance and least absolute shrinkage and selection operator were used to select features, and a support vector machine was used to construct the radiomics signature. Random forest was used to combine the DL, radiomics, and clinical signature to perform the final prediction. The receiver operating characteristic curve, sensitivity, specificity, and accuracy were used by two radiologists (R1 and R2) to evaluate and compare the AI system. RESULTS: For the internal and external test set, the AI system achieved excellent performance with AUCs of 0.84 and 0.81, higher than the DL (p = .03, .82), radiomics (p < .001, .04), and clinical model (p < .001, .006). With the aid of the AI system, the specificities of radiologists were improved by 9% and 15% for R1 and 13% and 9% for R2, respectively. CONCLUSIONS: The AI system can help predict CLNM in patients with PTC, and the radiologists' performance improved with AI assistance. CLINICAL RELEVANCE STATEMENT: This study developed an AI system for preoperative prediction of CLNM in PTC patients based on CT images, and the radiologists' performance improved with AI assistance, which could improve the effectiveness of individual clinical decision-making. KEY POINTS: • This multicenter retrospective study showed that the preoperative CT image-based AI system has the potential for predicting the CLNM of PTC. • The AI system was superior to the radiomics and clinical model in predicting the CLNM of PTC. • The radiologists' diagnostic performance improved when they received the AI system assistance.

Network Pharmacology-Based Approach for Investigating the Role of Xanthii Fructus in Treatment of Allergic Rhinitis.

Xanthii Fructus (XF) has been used for treatment of allergic rhinitis (AR), but its pharmacological mechanism of action remains unclear. We aimed to explore the potential mechanism of XF in treatment of AR by using a network pharmacology approach combined with in vivo verification experiments in this study. We identified 945 AR-related pathogenic genes, 11 active components in XF and 178 targets of those active components by corresponding databases. Finally, 54 targets of active components from XF in treatment of AR were identified by the Protein-protein interaction (PPI) network, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis, among which Tumor Necrosis Factor (TNF), Mitogen-activated Protein Kinase 3 (MAPK3), Prostaglandin G/H Synthase 2 (PTGS2), Epidermal Growth Factor Receptor (EGFR) showed strongest interactions. The molecular docking analysis showed that moupinamide could bind to EGFR at LEU704 and LEU703, and PTGS2 at TRP387; 24-Ethylcholest-4-en-3-one was identified to bind to MAPK3 at THR347. The validation of quantitative real-time reverse transcription PCR (RT-PCR) showed that XF decreased the levels of MAPK3, PTGS2, and EGFR expression in the nasal mucosa from AR mice gavaged with an XF water decoction. Meanwhile, the levels of interleukin (IL)-4, IL-5 and IL-13were also decreased after the treatment of XF by Enzyme-linked immunosorbent assay (ELISA). Our results provide the pharmacological mechanism and possible intervention targets of XF in treatment of AR.

Development and Validation of a Computed Tomography-Based Radiomics Nomogram for the Preoperative Prediction of Central Lymph Node Metastasis in Papillary Thyroid Microcarcinoma.

RATIONALE AND OBJECTIVES: This study aims to develop and validate a computed tomography (CT)-based radiomics nomogram for pre-operatively predicting central lymph node metastasis (CLNM) in patients with papillary thyroid microcarcinoma (PTMC) and explore the underlying biological basis by using RNA sequencing data. METHODS: This study trained 452 PTMC patients across two hospitals from January 2012 to December 2020. The sets were randomly divided into the training (n = 339), internal test (n = 86), external test (n = 27) sets. Radiomics features were extracted from primary lesion's pre-operative CT images for each patient. After screening for features, five algorithms such as K-nearest neighbor, logistics regression, linear-support vector machine (SVM), Gaussian SVM, and polynomial SVM were used to establish the radiomics models. The performance of these five algorithms was evaluated and compared directly to radiologist's interpretation (CT-reported lymph node status). The radiomics signature score (Rad-score) was generated using a linear combination of the selected features. By combining the clinical risk factors and Rad score, a radiomics nomogram was established and compared with Rad-score and clinical model. The performance of the nomogram was evaluated based on the receiver operating characteristic (ROC) curve, calibration curve, and the decision curve analysis (DCA). The potential biological basis of nomogram was revealed by performing genetic analysis based on the RNA sequencing data. RESULTS: A total of 25 radiomic features were ultimately selected to train the machine learning models, and the five machine learning models outperformed the radiologists' interpretation by achieving area under the ROC curves (AUCs) ranging from 0.606 to 0.730 in the internal test set. By incorporating the Rad score and clinical risk factors (sex, age, tumor-diameter, and CT-reported lymph node status), this nomogram achieved AUCs of 0.800 and 0.803 in the internal and external test set, which were higher than that of the Rad-score and clinical model, respectively. Calibration curves and DCA also showed that the nomogram had good performance. As for the biological basis exploration, in patients predicted by nomogram to be PTC patients with CLMN, 109 genes were dysregulated, and some of them were associated with pathways and biological processes such as tumor angiogenesis. CONCLUSION: This radiomics nomogram successfully identified CLNM on pretreatment imaging across multiple institutions, exceeding the diagnostic ability of radiologists and had the potential to be integrated into clinical decision making as a non-invasive pre-operative tool.

Network pharmacology analysis and experimental verification reveal the mechanism of the traditional Chinese medicine YU-Pingfeng San alleviating allergic rhinitis inflammatory responses.

YU-Pingfeng San (YPFS) can regulate inflammatory response to alleviate the symptoms of nasal congestion and runny rose in allergic rhinitis (AR). However, the mechanism of action remains unclear. In this study, 30 active ingredients of three effective herbs included in YPFS and 140 AR/YPFS-related genes were identified by database analysis. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis showed that the targets were mainly enriched in immune inflammatory-related biological processes and pathways. Finally, three hub gene targeting epidermal growth factor receptor (EGFR), mitogen-activated protein kinase 1 (MAPK1), and protein kinase B1 (AKT1) related to YPFS and AR were identified by network pharmacology analysis. YPFS treatment decreased the expression of EGFR, MAPK1, and AKT1 in ovalbumin (OVA)-induced AR mice and impaired the production of inflammatory factors interleukin (IL)-4, IL-5, and IL-13, thus alleviating immunoglobulin E (IgE) production and the symptoms of scratching nose in AR. Through molecular docking analysis, we found that the active ingredients decursin, anomalin, and wogonin of YPFS could bind to EGFR, MAPK1, and AKT1 proteins. Moreover, decursin treatment impaired the expression of IL-4 and IL-5 in human PBMCs. These results suggested that YPFS could alleviate the AR inflammatory responses by targeting EGFR, MAPK1, and AKT1, showing the mechanism of action of YPFS in AR treatment.