Proteomic and metabolomic proof of concept for unified airways in chronic rhinosinusitis and asthma.

BACKGROUND: The pathogenesis of chronic rhinosinusitis with nasal polyps (CRSwNP) with comorbid asthma remains unclear. OBJECTIVE: To assess upper and lower airway unity and identify a possible common pathogenesis in CRSwNP with asthma. METHODS: This study analyzed the expression of proteins and metabolites in nasal lavage fluid cells (NLFCs) and induced sputum cells (ISCs). Differentially expressed proteins and their function-related metabolites in the upper and lower airways of patients having CRSwNP with or without asthma were identified; relevant signaling pathways were analyzed, and key pathway-related proteins were identified. Parallel reaction monitoring was used to verify these target proteins. RESULTS: Protein or metabolite expression between NLFCs and ISCs was highly correlated and conservative on the basis of expression profiles and weighted gene coexpression network analysis. There were 17 differentially coexpressed proteins and their function-related 13 metabolites that were identified in the NLFCs and ISCs of CRSwNP, whereas 11 proteins and 11 metabolites were identified in CRSwNP with asthma. An asthma pathway was involved in the copathogenesis of upper and lower airways in whether CRSwNP or CRSwNP with asthma. The asthma pathway-related proteins proteoglycan 2 and eosinophil peroxidase, as the core of the protein-metabolism interaction networks between the upper and lower airways, were both highly coexpressed in NLFCs and ISCs in patients having either CRSwNP or CRSwNP with asthma by parallel reaction monitoring validation. CONCLUSION: Proteomics and metabolomics reveal upper and lower airway unity. Asthma pathway-related proteins proteoglycan 2 and eosinophil peroxidase from the upper airway could be used to assess the potential risk of lower airway dysfunction in CRSwNP.

Study on the efficacy and safety of neoadjuvant immunotherapy combined with chemotherapy regimen for III-IVA esophageal squamous cell carcinoma post-surgery.

BACKGROUND AND AIMS: The treatment of esophageal squamous cell carcinoma is still controversial, and neoadjuvant chemotherapy combined with immunotherapy is a hot topic of current research. We investigated the recent efficacy and surgical safety of patients with III-IVA esophageal squamous cell carcinoma after neoadjuvant regimen of paclitaxel + cisplatin/nedaplatin/carboplatin + sindilizumab, to provide a theoretical basis for evaluating the feasibility of surgery after neoadjuvant therapy. METHODS: The clinical data of patients with stage III-IVA esophageal squamous cell carcinoma admitted from January 2022 to April 2023 at our hospital were collected for retrospective analysis. The patients were divided into the neoadjuvant combination surgery group (34 patients with the regimen of paclitaxel + cisplatin/nedaplatin/carboplatin + sintilimab two/three cycles of preoperative neoadjuvant therapy) and surgery-only group (36 patients). Statistical analysis was performed to compare the differences between both groups particularly for intraoperative bleeding, operative time, incidence of postoperative pulmonary complications, laryngeal recurrent nerve injury, thoracic duct injury, anastomotic fistula, and postoperative hospital days. Additionally, the pCR/MPR rates of the neoadjuvant group were analysed. RESULTS: Significant differences were present in the clinical and pathological staging before and after neoadjuvant treatment (P ≤ 0.001). The neoadjuvant group had a pCR rate of 26.47% and an ORR rate of 88.23%. No significant differences were discovered in R0 resection rate between both groups, as well as intraoperative bleeding, operative time, intraoperative laryngeal recurrent nerve injury rate, thoracic duct injury rate, postoperative anastomosis incidence, postoperative hospital days, and postoperative lung infection incidence (P > 0.05). CONCLUSIONS: The neoadjuvant immune combination chemotherapy regimen had considerable tumor regression and pathological remission benefits, without reducing the safety of surgery, possibly presenting as a new treatment plan.

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.

Risk Factors for Abnormal Small Airway Function Indicators in Nasal Polyp Patients with and without Asthma.

INTRODUCTION: Small airway dysfunction (SAD) is associated with type 2 inflammation in patients who have non-asthmatic chronic rhinosinusitis with nasal polyps (CRSwNPs); however, the risk factors for abnormal small airway function indicators in CRSwNP patients with and without asthma remain unclear. METHODS: We retrospectively analyzed 41 asthmatic and 109 non-asthmatic CRSwNP patients. Clinical characteristics were compared between groups, correlations between small airway function and clinical parameters were calculated, and independent risk factors for every small airway indicator were identified in each group. RESULTS: Asthmatic CRSwNP patients had significantly reduced small airway function, and the proportion of patients with SAD was higher in asthmatic CRSwNP patients (65.85%) than in patients without asthma (9.17%). With regard to specific airway function indicators, age and a patient's blood eosinophil (%) were identified as independent risk factors for lower FEF50% %pred and FEF25-75% pred, with age being an independent risk factor for FEF75% %pred in asthmatic CRSwNP patients. In non-asthmatic CRSwNP patients, allergic rhinitis comorbidity was found to be an independent risk factor for FEF50% %pred, FEF75% %pred, and FEF25-75% %pred. CONCLUSION: Physicians should pay greater attention to risk factors for abnormal small airway function indicators in patients with CRSwNPs to prevent the occurrence of SAD.

Total serum immunoglobulin E (IgE) as an effective predictor for identifying allergic asthma in childhood asthma.

Background: Allergic asthma accounts for the majority of childhood asthma and is characterized by elevated total serum immunoglobulin E (tIgE). However, whether tIgE can predict allergic asthma in childhood asthma remains unclear. Objective: The purpose of this study was to identify the potential of tIgE for predicting allergic asthma in childhood asthma and provide a reliable reference value. Methods: Clinical characteristics and the level of tIgE from children with asthma in 2008 (n = 280) and 2018 (n = 479) were retrospectively analyzed. Receiver operating characteristic (ROC) curves were generated to determine the optimal cutoff points and predictive values of tIgE for diagnosing allergic asthma in childhood asthma in 2008 and 2018, and the diagnosis efficiency of tIgE was validated in 491 children with asthma of 2019. Results: The level of tIgE was significantly lower in 2018 than that in 2008. Receiver operating characteristic curves showed cutoff values of tIgE were 142.50 IU/mL (area under the curve [AUC] = 0.864) and 96.25 IU/mL (AUC = 0.835) for diagnosing allergic asthma in 2008 and 2018, respectively. The level of tIgE from children with asthma in 2019 was similar to that in 2018 but was significantly lower than that in 2008. We further used the cutoff value of tIgE = 96.25 IU/mL to validate the diagnosis efficiency in children with asthma of 2019 and found that the diagnostic accuracy, sensitivity, specificity of allergic asthma, and the Youden index reached 76.78%, 76.10%, 78.03%, and 0.540, respectively. Conclusion: The tIgE value is an effective predictor for diagnosing allergic asthma in childhood asthma, with tIgE = 96.25 IU/mL being the recommended limit.

The Tissue Lymphocyte-to-Eosinophil Ratio Predicted Long-Term Recurrence of Eosinophilic CRSwNP.

BACKGROUND: Eosinophilic chronic rhinosinusitis with nasal polyps (Eos-CRSwNP) remains a recalcitrant disease with a high recurrence rate. OBJECTIVE: This study aimed to identify a predictor of long-term recurrence in patients with Eos-CRSwNP. METHODS: A total of 39 Eos-CRSwNP patients who had their initial and recurrent nasal polyps surgically removed were retrospectively included in this study, with 49 Eos-CRSwNP patients without recurrence and 32 patients with non-Eos-CRSwNP matched by randomly chosen. Clinical characteristics were compared among or between groups. Spearman correlation analyses and a backward stepwise multivariate logistic regression analysis were performed to find factors associated with the recurrence and recurrence time of Eos-CRSwNP. Furthermore, a receiver operating characteristic (ROC) curve was used to determine the predictor of long-term Eos-CRSwNP recurrence. RESULTS: The number and ratio of tissue eosinophils were highest in Eos-CRSwNP with recurrence and lowest in non-Eos-CRSwNP. The ratio of tissue lymphocytes was highest in non-Eos-CRSwNP and lowest in Eos-CRSwNP with recurrence, with the number of tissue lymphocytes higher in Eos-CRSwNP without recurrence than the other two groups. The numbers of tissue lymphocytes in the initial nasal polyps were lower and the numbers of tissue eosinophils were higher in the group of recurrent nasal polyps that recurred at >5 years after surgery than in the nasal polyps that recurred at <5 years after surgery. The tissue lymphocyte-to-eosinophil ratio (LER) showed a significant negative correlation with the recurrence and the recurrence time of Eos-CRSwNP. A ROC curve revealed that a tissue LER value < 0.67 predicted long-term Eos-CRSwNP recurrence with 72.73% sensitivity and 82.35% specificity (area under the curve = 0.789). CONCLUSION: Tissue LER is strongly associated with Eos-CRSwNP recurrence and may play a key role in predicting long-term Eos-CRSwNP recurrence.

CT Radiomics-Based Nomogram for Predicting the Lateral Neck Lymph Node Metastasis in Papillary Thyroid Carcinoma: A Prospective Multicenter Study.

RATIONALE AND OBJECTIVES: This study is based on multicenter cohorts and aims to utilize computed tomography (CT) images to construct a radiomics nomogram for predicting the lateral neck lymph node (LNLN) metastasis in the papillary thyroid carcinoma (PTC) and further explore the biological basis under its prediction. MATERIALS AND METHODS: In the multicenter study, 1213 lymph nodes from 409 patients with PTC who underwent CT examinations and received open surgery and lateral neck dissection were included. A prospective test cohort was used in validating the model. Radiomics features were extracted from the CT images of each patient's LNLNs. Selectkbest, maximum relevance and minimum redundancy and the least absolute shrinkage and selection operator (LASSO) algorithm were used in reducing the dimensionality of radiomics features in the training cohort. Then, a radiomics signature (Rad-score) was calculated as the sum of each feature multiplied by the nonzero coefficient from LASSO. A nomogram was generated using the clinical risk factors of the patients and Rad-score. The nomograms' performance was analyzed in terms of accuracy, sensitivity, specificity, confusion matrix, receiver operating characteristic curves, and areas under the receiver operating characteristic curve (AUCs). The clinical usefulness of the nomogram was evaluated by decision curve analysis. Moreover, three radiologists with different working experiences and nomogram were compared to one another. Whole transcriptomics sequencing was performed in 14 tumor samples; the correlation of biological functions and high and low LNLN samples predicted by the nomogram was further investigated. RESULTS: A total of 29 radiomics features were used in constructing the Rad-score. Rad-score and clinical risk factors (age, tumor diameter, location and number of suspected tumors) compose the nomogram. The nomogram exhibited good discrimination performance of the nomogram for predicting LNLN metastasis in the training cohort (AUC, 0.866), internal test cohort (0.845), external test cohort (0.725), and prospective test cohort (0.808) and showed diagnostic capability comparable to senior radiologists, significantly outperforming junior radiologists (p < 0.05). Functional enrichment analysis suggested that the nomogram can reflect the ribosome-related structures of cytoplasmic translation in patients with PTC. CONCLUSION: Our radiomics nomogram provides a noninvasive method that incorporates radiomics features and clinical risk factors for predicting LNLN metastasis in patients with PTC.

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.

Applying a nomogram based on preoperative CT to predict early recurrence of laryngeal squamous cell carcinoma after surgery.

PURPOSE: To identify the value of a computed tomography (CT)-based radiomics model to predict probability of early recurrence (ER) in patients diagnosed with laryngeal squamous cell carcinoma (LSCC) after surgery. MATERIALS AND METHOD: Pre-operative CT scans of 140 LSCC patients treated by surgery are reviewed and selected. These patients are randomly split into the training set (n = 97) and test set (n = 43). The regions of interest of each patient were delineated manually by two senior radiologists. Radiomics features are extracted from CT images acquired in non-enhanced, arterial, and venous phases. Variance threshold, one-way ANOVA, and least absolute shrinkage and selection operator algorithm are used for feature selection. Then, radiomics models are built with five algorithms namely, k-nearest neighbor (KNN), logistic regression (LR), linear support vector machine (LSVM), radial basis function SVM (RSVM), and polynomial SVM (PSVM). Clinical factors are selected using univariate and multivariate logistic regressions. Last, a radiomics nomogram incorporating the radiomics signature and clinical factors is built to predict ER and its efficiency is evaluated by receiver operating characteristic (ROC) curve and calibration curve. Decision curve analysis (DCA) is also used to evaluate clinical usefulness. RESULTS: Four features are remarkably associated with ER in patients with LSCC. Applying to test set, the area under the ROC curves (AUCs) of KNN, LR, LSVM, RSVM, and PSVM are 0.936, 0.855, 0.845, 0.829, and 0.794, respectively. The radiomics nomogram shows better discrimination (with AUC: 0.939, 95% CI: 0.867-0.989) than the best radiomics model and the clinical model. Predicted and actual ERs in the calibration curves are in good agreement. DCA shows that the radiomics nomogram is clinically useful. CONCLUSION: The radiomics nomogram, as a noninvasive prediction tool, exhibits favorable performance for ER prediction of LSCC patients after surgery.

Indolent CD30-Positive Extranodal NK/T Cell Lymphoma with Large Cell Transformation: Case Report and Literature Review.

The nasal type of extranodal natural killer (NK)/T-cell lymphoma (ENKL) is an aggressive form of non-Hodgkin's lymphoma. Although ENKL is most commonly seen in the midline of the nose, face and Waldeyer's ring, it can also occur in the skin, gastrointestinal tract, soft tissues and other parts of the body. Severe ENKL cases are accompanied by hemophagocytosis, with clinical manifestations such as high fever, hepatosplenomegaly, and decreased blood cell count. ENKL at different locations exhibits similar histological features and immunophenotypes, such as a strong affinity for T cell markers CD2 and CD56, cytotoxic molecules, as well as a strong positive for EBER after in situ hybridization. Although indolent ENKL is extremely rare, we hereby present a case study of primary NK/T cell lymphoma in the spinal canal with the initial manifestation of a diffuse growth of small cells, and the survival and recurrence details after 11 years, accompanied by CD30-positive large cell transformation. The patient's condition after treatment has improved and is currently in good health.

Radiomics Analysis of Computed Tomography for Prediction of Thyroid Capsule Invasion in Papillary Thyroid Carcinoma: A Multi-Classifier and Two-Center Study.

Objective: To investigate the application of computed tomography (CT)-based radiomics model for prediction of thyroid capsule invasion (TCI) in papillary thyroid carcinoma (PTC). Methods: This retrospective study recruited 412 consecutive PTC patients from two independent institutions and randomly assigned to training (n=265), internal test (n=114) and external test (n=33) cohorts. Radiomics features were extracted from non-contrast (NC) and artery phase (AP) CT scans. We also calculated delta radiomics features, which are defined as the absolute differences between the extracted radiomics features. One-way analysis of variance and least absolute shrinkage and selection operator were used to select optimal radiomics features. Then, six supervised machine learning radiomics models (k-nearest neighbor, logistic regression, decision tree, linear support vector machine [L-SVM], Gaussian-SVM, and polynomial-SVM) were constructed. Univariate was used to select clinicoradiological risk factors. Combined models including optimal radiomics features and clinicoradiological risk factors were constructed by these six classifiers. The prediction performance was evaluated using the receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA). Results: In the internal test cohort, the best combined model (L-SVM, AUC=0.820 [95% CI 0.758-0.888]) performed better than the best radiomics model (L-SVM, AUC = 0.733 [95% CI 0.654-0.812]) and the clinical model (AUC = 0.709 [95% CI 0.649-0.783]). Combined-L-SVM model combines 23 radiomics features and 1 clinicoradiological risk factor (CT-reported TCI). In the external test cohort, the AUC was 0.776 (0.625-0.904) in the combined-L-SVM model, showing that the model is stable. DCA demonstrated that the combined model was clinically useful. Conclusions: Our combined model based on machine learning incorporated with CT radiomics features and the clinicoradiological risk factor shows good predictive ability for TCI in PTC.

Identification of a lncRNA AC011511.5- Mediated Competitive Endogenous RNA Network Involved in the Pathogenesis of Allergic Rhinitis.

LncRNA-miRNA-mRNA competing endogenous RNA (ceRNA) networks are thought to be involved in regulating the development of various inflammatory diseases. Up to now, the mechanism of such a network in allergic rhinitis (AR) remains unclear. In the study, we investigated the differential expression of lncRNAs (DElncRNAs) and mRNAs (DEmRNAs) by performing a microarray analysis of peripheral blood obtained from AR patients and healthy control subjects. StarBase 2.0 was used to predict miRNAs that might interact with various DElncRNAs and DEmRNAs. We constructed a ceRNA network based on potential lncRNA-miRNA-mRNA interactions. The Cluster Profiler R package was used to perform a functional enrichment analysis of the hub-ceRNA, and Molecular Complex Detection (MCODE) was used for further identification of the hub-ceRNA network. The expression levels of genes contained in the hub-ceRNA network were validated by RT-PCR. In total, 247 DEmRNAs and 18 DelncRNAs were aberrantly expressed in the PBMCs of AR patients. A ceRNA network consisting of 3 lncRNAs, 45 miRNAs, and 75 mRNAs was constructed. A GO analysis showed that negative regulation of immune response, response to interferon-beta, and response to interferon-alpha were important terms. A KEGG pathway analysis showed that 75 mRNAs were significantly enriched in "NOD-like receptor signaling pathway" and "tryptophan metabolism". Ultimately, a hub-ceRNA network was constructed based on 1 lncRNA (AC011511.5), 5 miRNAs (hsa-miR-576-5p, hsa-miR-520c-5p, hsa-miR-519b-5p, hsa-miR-519c-5p, and hsa-miR-518d-5p), and 2 mRNAs (ZFP36L1 and SNX27). Following further verification, we found that overexpression of lncRNA AC011511.5 or inhibitor of miR-576-5p upregulated SNX27 expression. The expression of SNX27 in the lncRNA AC011511.5 overexpression & miR-576-5p inhibitor group was not different from that in the miR-576-5p inhibitor group or lncRNA AC011511.5 overexpression group, indicating that overexpression of lncRNA AC011511.5 could not further upregulate the expression of SNX27 in miR-576-5p inhibitor Jurkat cells. This network may provide new insights to search for biomarkers that can be used for the diagnosis and clinical treatment of AR.

Extrathyroidal Extension Prediction of Papillary Thyroid Cancer With Computed Tomography Based Radiomics Nomogram: A Multicenter Study.

Objectives: To develop and validate a Computed Tomography (CT) based radiomics nomogram for preoperative predicting of extrathyroidal extension (ETE) in papillary thyroid cancer (PTC) patients. Methods: A total of 153 patients were randomly assigned to training and internal test sets (7:3). 46 patients were recruited to serve as an external test set. A radiologist with 8 years of experience segmented the images. Radiomics features were extracted from each image and Delta-radiomics features were calculated. Features were selected by using one way analysis of variance and the least absolute shrinkage and selection operator in the training set. K-nearest neighbor, logistic regression, decision tree, linear-support vector machine (linear -SVM), gaussian-SVM, and polynomial-SVM were used to build 6 radiomics models. Next, a radiomics signature score (Rad-score) was constructed by using the linear combination of selected features weighted by their corresponding coefficients. Finally, a nomogram was constructed combining the clinical risk factors with Rad-scores. Receiver operating characteristic (ROC) curve, decision curve analysis (DCA), and calibration curve were performed on the three sets to evaluate the nomogram's performance. Results: 4 radiomics features were selected. The six models showed the certain value of radiomics, with area under the curves (AUCs) from 0.642 to 0.701. The nomogram combining the Rad-score and clinical risk factors (radiologists' interpretation) showed good performance (internal test set: AUC 0.750; external test set: AUC 0.797). Calibration curve and DCA demonstrated good performance of the nomogram. Conclusion: Our radiomics nomogram incorporating the radiomics and radiologists' interpretation has utility in the identification of ETE in PTC patients.

Asthma does not influence the severity of COVID-19: a meta-analysis.

OBJECTIVE: Previous studies have reported a correlation between coronavirus disease-2019 (COVID-19) and asthma. However, data on whether asthma constitutes a risk factor for COVID-19 and the prevalence of asthma in COVID-19 cases still remain scant. Here, we interrogated and analyzed the association between COVID-19 and asthma. METHODS: In this study, we systematically searched PubMed, Embase, and Web of Science databases for studies published between January 1 and August 28, 2020. We included studies that reported the epidemiological and clinical features of COVID-19 and its prevalence in asthma patients. We excluded reviews, animal trials, single case reports, small case series and studies evaluating other coronavirus-related illnesses. Raw data from the studies were pooled into a meta-analysis. RESULTS: We analyzed findings from 18 studies, including asthma patients with COVID-19. The pooled prevalence of asthma in COVID-19 cases was 0.08 (95% CI, 0.06-0.11), with an overall I2 of 99.07%, p < 0.005. The data indicated that asthma did not increase the risk of developing severe COVID-19 (odds ratio [OR] 1.04 (95% CI, 0.75-1.46) p = 0.28; I2=20%). In addition, there was no significant difference in the incidence of asthma with age in COVID-19 infections [OR] 0.77(95% CI, 0.59-1.00) p = 0.24; I2=29%). CONCLUSION: Taken together, our data suggested that asthma is not a significant risk factor for the development of severe COVID-19.

KIF22 promotes progress of esophageal squamous cell carcinoma cells and is negatively regulated by miR-122.

Esophageal squamous cell carcinoma (ESCC) increases at fast rate of all cancer types in China, which urges the investigations of its potential mechanism. In this research, a highly expressed kinesin superfamily protein 22 (KIF22) was founded both in ESCC tissues and cancer cell lines. The following experiments pointed out that down-regulation of KIF22 remarkably restrained the malignant progression of ESCC cells. Besides, KIF22 knockdown promoted ESCC cells apoptosis and arrested cells in G0/G1 phase, while KIF22 also regulated the expression of cell cycle- and EMT-related proteins. Previous research revealed that the aberrant expressions of microRNAs (miRNAs) are related to tumors development. Based on the predict result, KIF22 was considered as the target of miR-122, which was demonstrated by luciferase reporter assay. miR-122 inhibitor could significantly reverse the function of KIF22 knockdown, including cell proliferation, migration and invasion. Furthermore, down-expressed miR-122 altered the function of KIF22 knockdown on cell cycle- and EMT-related proteins. In a word, this work illustrated the regulatory function of KIF22/miR-122 axis in ESSC and provided potential targets for potential targets for ESSC treatment.

Transcriptomic and microRNA Expression Profiles Identify Biomarkers for Predicting Neo-Chemoradiotherapy Response in Esophageal Squamous Cell Carcinomas (ESCC).

Neo-chemoradiotherapy (nCRT) before surgery is a standard treatment for locally advanced esophageal cancers. However, the treatment outcome of nCRT varied with different patients. This study aimed to identify potential biomarkers for prediction of nCRT-response in patients with esophageal squamous cell carcinoma (ESCC). Microarray datasets of nCRT responder and non-responder samples (access number GSE45670 and GSE59974) of patients with ESCC were downloaded from Gene Expression Omnibus (GEO) database. The mRNA expression profiles of cancer biopsies from four ESCC patients were analyzed before and after nCRT. Differentially expressed genes (DEGs) and miRNAs were screened between nCRT responder and non-responder ESCC samples. Functional enrichment analysis was conducted for these DEGs followed by construction of protein-protein interaction (PPI) network and miRNA-mRNA regulatory network. Finally, univariate survival analysis was performed to identify candidate biomarkers with prognostic values in ESCC. We identified numerous DEGs and differentially expressed miRNAs from nCRT responder group. GO and KEGG analysis showed that the dysregulated genes were mainly involved in biological processes and pathways, including "response to stimulus", "cellular response to organic substance", "regulation of signal transduction", "AGE-RAGE signaling pathway in diabetic complications", and "steroid hormone biosynthesis". After integration of PPI network and miRNA-mRNA network analysis, we found eight genes, TNF, AKR1C1, AKR1C2, ICAM1, GPR68, GNB4, SERPINE1 and MMP12, could be candidate genes associated with disease progression. Univariate cox regression analysis showed that there was no significant correlation between dysregulated miRNAs (such as hsa-miR-34b-3p, hsa-miR-127-5p, hsa-miR-144-3p, and hsa-miR-486-5p, et al.) and overall survival of ESCC patients. Moreover, abnormal expression of MMP12 was significantly correlated with pathological degree, TNM stage, lymph nodes metastasis, and overall survival of ESCC patients (p < 0.05). Taken together, our study identified that MMP12 might be a useful tumor biomarker and therapeutic target for ESCC.

PSMB4 inhibits cardiomyocyte apoptosis via activating NF-κB signaling pathway during myocardial ischemia/reperfusion injury.

Myocardial ischemia/reperfusion (I/R) injury induces cardiomyocyte apoptosis to deteriorate heart function. Thus, how to inhibit cardiomyocyte apoptosis is the focus of recent researches. Proteasome family member PSMB4 (proteasome subunit beta type-4) promotes cell survival. The relationship between PSMB4 and cardiomyocyte apoptosis during myocardial I/R is unknown. In this study, PSMB4 expression increased in rat myocardial I/R model, positively correlated with cleaved caspase-3 expression, negatively correlated with Bcl-2 expression. In vitro, neonatal ventricle cardiomyocyte hypoxia/reoxygenation (H/R) model was constructed to mimic myocardial I/R. PSMB4 silence promoted cardiomyocyte apoptosis and IκBα expression, inhibited the activation of NF-κB. On the contrary, PSMB4 overexpession inhibited cardiomyocyte apoptosis and IκBα expression, promoted the activation of NF-κB. Additionally, PSMB4-IκBα interaction was identified, suggesting that PSMB4 might participate in the proteasome dependent degradation of IκBα. The data indicates that PSMB4 inhibits cardiomyocyte apoptosis via activating NF-κB signaling pathway during myocardial I/R, which can supply novel molecular target for the treatment of ischemic heart disease.

[Expression and clinical significance of IL-33 in patients with non-small cell lung cancer].

Objective To investigate the expression of interleukin 33 (IL-33) in cancer and adjacent non-cancerous tissues from patients with non-small cell lung cancer (NSCLC). Methods Real-time quantitative PCR was performed to detect the mRNA expression of IL-33 in 61 pairs of cancerous and adjacent non-cancerous tissues. In addition, immunohistochemistry was used to evaluate the expression and location of IL-33 on paraffin sections in selected 12 cases with different pathological types, to analyze the correlation of IL-33 expression with clinicopathological variables and overall survival. Results The expression of IL-33 mRNA in cancer tissues was significantly lower than that in adjacent non-cancerous tissues. The immunohistochemical results showed that IL-33 protein was mainly localized in the nucleus, and was obviously down-regulated in NSCLC. Besides, the expression of IL-33 was associated with histological type, but was not associated with age, gender, smoking history, differentiation status and pathological TNM stage. According to the Kaplan-Meier analysis, the expression of IL-33 mRNA was evidently associated with post-operative overall survival of patients suffering from NSCLC. Conclusion IL-33 may play an important role in the development of NSCLC and the targeted therapy.