High-dose vitamin D supplementation in patients with COVID-19: A meta-analysis of randomized controlled trials.

The efficacy of administering high doses of vitamin D to patients diagnosed with COVID-19 remains uncertain. We conducted a comprehensive search across multiple databases (PubMed, EMBASE, Cochrane Library, and ISI Web of Science) from inception until August 2022, with no limitations on language, to locate randomized controlled trials (RCTs) that investigated the impact of high-dose vitamin D supplementation (defined as a single dose of ≥100,000 IU or daily dose of ≥10,000 IU reaching a total dose of ≥100,000 IU) on COVID-19 patients. Risk ratios (RR) with 95% confidence intervals (CI) and weighted mean differences (WMD) with 95% CI were calculated. Our meta-analysis included 5 RCTs with a total of 834 patients. High-dose vitamin D supplementation did not show any significant benefits for mortality (I 2 = 0.0%, p = .670; RR 1.092, 95% CI 0.685-1.742, p = .711) or intensive care unit (ICU) admission (I 2 = 0.0%, p = .519; RR 0.707, 95% CI 0.454-1.102, p = .126) in COVID-19 patients compared to the control group. However, it was found to be safe and well-tolerated (I 2 = 0.0%, p = .887; RR 1.218, 95% CI 0.930-1.594, p = .151). Subgroup analysis also showed no benefits in overall mortality, including for patients with vitamin D deficiency (I 2 = 0.0%, p = .452; RR 2.441, 95% CI 0.448-13.312, p = .303) or compared to the placebo (I 2 = 0.0%, p = .673; RR 1.666, 95% CI 0.711-3.902, p = .240). Our research indicates that there is no evidence to support the efficacy of high-dose vitamin D supplementation in improving clinical outcomes among individuals with COVID-19, in line with previous studies focused on contexts including rickets. Considering the limitations of the study, additional research may be required.

Two-stage contextual transformer-based convolutional neural network for airway extraction from CT images.

Accurate airway segmentation from computed tomography (CT) images is critical for planning navigation bronchoscopy and realizing a quantitative assessment of airway-related chronic obstructive pulmonary disease (COPD). Existing methods face difficulty in airway segmentation, particularly for the small branches of the airway. These difficulties arise due to the constraints of limited labeling and failure to meet clinical use requirements in COPD. We propose a two-stage framework with a novel 3D contextual transformer for segmenting the overall airway and small airway branches using CT images. The method consists of two training stages sharing the same modified 3D U-Net network. The novel 3D contextual transformer block is integrated into both the encoder and decoder path of the network to effectively capture contextual and long-range information. In the first training stage, the proposed network segments the overall airway with the overall airway mask. To improve the performance of the segmentation result, we generate the intrapulmonary airway branch label, and train the network to focus on producing small airway branches in the second training stage. Extensive experiments were performed on in-house and multiple public datasets. Quantitative and qualitative analyses demonstrate that our proposed method extracts significantly more branches and longer lengths of the airway tree while accomplishing state-of-the-art airway segmentation performance. The code is available at https://github.com/zhaozsq/airway_segmentation.

Effects of doxofylline as an adjuvant on severe exacerbation and long-term prognosis for COPD with different clinical subtypes.

OBJECTIVE: This study aimed to investigate the effectiveness of doxofylline as an adjuvant in reducing severe exacerbation for different clinical subtypes of chronic obstructive pulmonary disease (COPD). METHODS: The clinical trial was an open-label non-randomized clinical trial that enrolled patients with COPD. The patients were divided into two groups (doxofylline group[DG] and non-doxofylline group[NDG]) according to whether the adjuvant was used. Based on the proportion of inflammatory cells present, the patients were divided into neutrophilic, eosinophilic, and mixed granulocytic subtypes. The rates of severe acute exacerbation, use of glucocorticoids, and clinical symptoms were followed up in the first month, the third month, and the sixth month after discharge. RESULTS: A total of 155 participants were included in the study. The average age of the participants was 71.2 ± 10.1 years, 52.3% of the patients were male, and 29.7% of the participants had extremely severe cases of COPD. In the third month after discharge the numbers of patients exhibiting severe exacerbation among the neutrophilic subtype were 5 (6.6%) in the DG versus 17 (22.4%) in the NDG (incidence rate ratio[IRR] = 0.4 [95% CI: 0.2-0.9] P = 0.024). In the sixth month after discharge, the numbers were 3 (3.9%) versus 13 (17.1%; IRR = 0.3 [95%; CI: 0.1-0.9], P = 0.045), and those for the eosinophilic subtype were 0 (0.0%) versus 4 (14.8%), P = 0.02. In the eosinophilic subtype, the results for forced expiratory volume in the first second and maximal mid-expiratory flow were significantly higher in the DG. The mean neutrophil and eosinophil levels were significantly lower than in the NDG among the neutrophilic subtype, and the neutrophil percentage was lower than in the NDG among the eosinophilic subtype. At the six-month follow-up, the dose adjustment rates of the neutrophilic and eosinophilic subtypes showed a significant difference (P< 0.05). CONCLUSIONS: As an adjuvant drug, doxofylline has a good therapeutic effect on patients with the neutrophilic and eosinophilic clinical subtypes of COPD. It can reduce the incidence of severe exacerbation, the use of glucocorticoids, and inflammatory reactions in the long term (when used for a minimum of 3 months).

Genome-wide association analyses identified novel susceptibility loci for pulmonary embolism among Han Chinese population.

BACKGROUND: A large proportion of pulmonary embolism (PE) heritability remains unexplained, particularly among the East Asian (EAS) population. Our study aims to expand the genetic architecture of PE and reveal more genetic determinants in Han Chinese. METHODS: We conducted the first genome-wide association study (GWAS) of PE in Han Chinese, then performed the GWAS meta-analysis based on the discovery and replication stages. To validate the effect of the risk allele, qPCR and Western blotting experiments were used to investigate possible changes in gene expression. Mendelian randomization (MR) analysis was employed to implicate pathogenic mechanisms, and a polygenic risk score (PRS) for PE risk prediction was generated. RESULTS: After meta-analysis of the discovery dataset (622 cases, 8853 controls) and replication dataset (646 cases, 8810 controls), GWAS identified 3 independent loci associated with PE, including the reported loci FGG rs2066865 (p-value = 3.81 × 10-14), ABO rs582094 (p-value = 1.16 × 10-10) and newly reported locus FABP2 rs1799883 (p-value = 7.59 × 10-17). Previously reported 10 variants were successfully replicated in our cohort. Functional experiments confirmed that FABP2-A163G(rs1799883) promoted the transcription and protein expression of FABP2. Meanwhile, MR analysis revealed that high LDL-C and TC levels were associated with an increased risk of PE. Individuals with the top 10% of PRS had over a fivefold increased risk for PE compared to the general population. CONCLUSIONS: We identified FABP2, related to the transport of long-chain fatty acids, contributing to the risk of PE and provided more evidence for the essential role of metabolic pathways in PE development.

Relationship Between ACSL4-Mediated Ferroptosis and Chronic Obstructive Pulmonary Disease.

Purpose: Although cigarette smoke exposure is the major risk factor for chronic obstructive pulmonary disease (COPD), the mechanism is not completely understood. The aim of the present study was to investigate whether ACSL4-mediated ferroptosis in lung epithelial cells plays a part in the COPD development process and its association. Patients and Methods: In this study, animal and cell models of COPD were modelled using cigarette smoke extracts (CSEs), and cell viability, lipid ROS, iron ion deposition, and ferroptosis-related markers were measured in lung tissue and lung epithelial cells following CSE exposure. Morphological changes in mitochondria were observed in lung tissue and epithelial cells of the lung by transmission electron microscope. The expression levels of ACSL4 mRNA and protein in lung tissue and epithelial cells were measured by real-time PCR and Western blotting. In addition, animal-interfering lentivirus and cell-interfering RNA against ACSL4 were constructed in this study, ferroptosis in lung tissue and lung epithelial cells after ACSL4 interference was detected, and ACSL4 mRNA and protein expression levels were detected. Results: CSE induced ferroptosis in lung tissues and lung epithelial cells, and the expression levels of ACSL4 were elevated in CSE-treated lung tissues and lung epithelial cells. After ACSL4 interference, the expression of ACSL4 decreased, mitochondrial morphology was restored, and ferroptosis in lung tissues and lung epithelial cells was alleviated. Both respiratory frequency and enhanced pause of COPD mice models decreased after ACSL4 interference. Conclusion: ACSL4-mediated ferroptosis in lung epithelial cells is associated with COPD and positively correlated with ferroptosis in epithelial cells.

Comparison of high flow nasal cannula and non-invasive positive pressure ventilation in children with bronchiolitis: A meta-analysis of randomized controlled trials.

Background: The effects of high-flow nasal cannula (HFNC) compared to non-invasive positive pressure ventilation (NIPPV) on children with bronchiolitis remain unclear. Methods: This meta-analysis was performed following the preferred reporting items for systematic reviews and meta-analysis (PRISMA) statement. Randomized controlled trials (RCTs) were identified from a comprehensive search in PubMed, EMBASE, Cochrane Library, and Web of Science without time and language limitations. Primary endpoints include the rate of treatment failure, the rate of need for intubation, and the pediatric intensive care unit (PICU) length of stay. Results: Five RCTs including 541 children of less than 24 months were enrolled in the meta-analysis. Compared to the NIPPV group, the rate of treatment failure was significantly higher in the HFNC treatment group (I 2 = 0.0%, P = 0.574; RR 1.523, 95% CI 1.205 to 1.924, P < 0.001). No significant difference was noted in the need for intubation (I 2 = 0.0%, P = 0.431; RR 0.874, 95% CI 0.598 to 1.276, P = 0.485) and the PICU length of stay (I 2 = 0.0%, P = 0.568; WMD = -0.097, 95% CI = -0.480 to 0.285, P = 0.618) between the HFNC group and the NIPPV treatment. Conclusion: Compared to the NIPPV group, HFNC therapy was associated with a significantly higher treatment failure rate in children suffering from bronchiolitis. The intubation rate and the PICU length of stay were comparable between the two approaches.

The Clinical Effects of Intravascular Ultrasound-Guided Percutaneous Coronary Intervention in Patients with Chronic Total Occlusion: A Meta-Analysis.

Background: The clinical effects of intravascular ultrasound (IVUS)-guided percutaneous coronary intervention (PCI) in patients with chronic total occlusion (CTO) lesions remain unclear. Methods: We identified all full-text published studies that compared the effects of IVUS-guided CTO-PCI with angiography-guided CTO-PCI by searching electric databases including PubMed, Embase, Cochrane Library, and ISI Web of Science from the establishment to Nov 2021. There was no language limitation. The endpoints included the incidence of major adverse cardiac events (MACE), cardiac death, all-cause death, myocardial infarction (MI), and target vessel revascularization (TVR). Results: Five studies involving a total of 2320 patients were included in this meta-analysis. Compared to the angiography-guided group, IVUS-guided PCI showed no significant reduction in the incidence of MACE (I 2 = 27.4%, P = 0.239; RR 0.929, 95% CI 0.765 to 1.128, P = 0.457), cardiac death (I 2 = 0.0%, P = 0.459; RR 0.574, 95% CI 0.299 to 1.103, P = 0.096), all-cause death (I 2 = 0.0%, P = 0.964; RR 0.677, 95% CI 0.395 to 1.163, P = 0.158), MI (I 2 = 46.7%, P = 0.131; RR0.836, 95% CI 0.508 to 1.377, P = 0.482), and TVR (I 2 = 21.2%, P = 0.279; RR 0.929, 95% CI 0.679 to 1.272, P = 0.648). Conclusions: IVUS-guided PCI demonstrated no significant benefit on MACE, cardiac death, all-cause death, MI, and TVR in patients with CTO lesions. However, given the study's limitations, additional high-quality RCTs are needed.

Fully automatic pipeline of convolutional neural networks and capsule networks to distinguish COVID-19 from community-acquired pneumonia via CT images.

BACKGROUND: Chest computed tomography (CT) is crucial in the diagnosis of coronavirus disease 2019 (COVID-19). However, the persistent pandemic and similar CT manifestations between COVID-19 and community-acquired pneumonia (CAP) raise methodological requirements. METHODS: A fully automatic pipeline of deep learning is proposed for distinguishing COVID-19 from CAP using CT images. Inspired by the diagnostic process of radiologists, the pipeline comprises four connected modules for lung segmentation, selection of slices with lesions, slice-level prediction, and patient-level prediction. The roles of the first and second modules and the effectiveness of the capsule network for slice-level prediction were investigated. A dataset of 326 CT scans was collected to train and test the pipeline. Another public dataset of 110 patients was used to evaluate the generalization capability. RESULTS: LinkNet exhibited the largest intersection over union (0.967) and Dice coefficient (0.983) for lung segmentation. For the selection of slices with lesions, the capsule network with the ResNet50 block achieved an accuracy of 92.5% and an area under the curve (AUC) of 0.933. The capsule network using the DenseNet121 block demonstrated better performance for slice-level prediction, with an accuracy of 97.1% and AUC of 0.992. For both datasets, the prediction accuracy of our pipeline was 100% at the patient level. CONCLUSIONS: The proposed fully automatic deep learning pipeline of deep learning can distinguish COVID-19 from CAP via CT images rapidly and accurately, thereby accelerating diagnosis and augmenting the performance of radiologists. This pipeline is convenient for use by radiologists and provides explainable predictions.

A vision transformer for emphysema classification using CT images.

Objective. Emphysema is characterized by the destruction and permanent enlargement of the alveoli in the lung. According to visual CT appearance, emphysema can be divided into three subtypes: centrilobular emphysema (CLE), panlobular emphysema (PLE), and paraseptal emphysema (PSE). Automating emphysema classification can help precisely determine the patterns of lung destruction and provide a quantitative evaluation.Approach. We propose a vision transformer (ViT) model to classify the emphysema subtypes via CT images. First, large patches (61×61) are cropped from CT images which contain the area of normal lung parenchyma, CLE, PLE, and PSE. After resizing, the large patch is divided into small patches and these small patches are converted to a sequence of patch embeddings by flattening and linear embedding. A class embedding is concatenated to the patch embedding, and the positional embedding is added to the resulting embeddings described above. Then, the obtained embedding is fed into the transformer encoder blocks to generate the final representation. Finally, the learnable class embedding is fed to a softmax layer to classify the emphysema.Main results. To overcome the lack of massive data, the transformer encoder blocks (pre-trained on ImageNet) are transferred and fine-tuned in our ViT model. The average accuracy of the pre-trained ViT model achieves 95.95% in our lab's own dataset, which is higher than that of AlexNet, Inception-V3, MobileNet-V2, ResNet34, and ResNet50. Meanwhile, the pre-trained ViT model outperforms the ViT model without the pre-training. The accuracy of our pre-trained ViT model is higher than or comparable to that by available methods for the public dataset.Significance. The results demonstrated that the proposed ViT model can accurately classify the subtypes of emphysema using CT images. The ViT model can help make an effective computer-aided diagnosis of emphysema, and the ViT method can be extended to other medical applications.

DR-MIL: deep represented multiple instance learning distinguishes COVID-19 from community-acquired pneumonia in CT images.

BACKGROUND AND OBJECTIVE: Given that the novel coronavirus disease 2019 (COVID-19) has become a pandemic, a method to accurately distinguish COVID-19 from community-acquired pneumonia (CAP) is urgently needed. However, the spatial uncertainty and morphological diversity of COVID-19 lesions in the lungs, and subtle differences with respect to CAP, make differential diagnosis non-trivial. METHODS: We propose a deep represented multiple instance learning (DR-MIL) method to fulfill this task. A 3D volumetric CT scan of one patient is treated as one bag and ten CT slices are selected as the initial instances. For each instance, deep features are extracted from the pre-trained ResNet-50 with fine-tuning and represented as one deep represented instance score (DRIS). Each bag with a DRIS for each initial instance is then input into a citation k-nearest neighbor search to generate the final prediction. A total of 141 COVID-19 and 100 CAP CT scans were used. The performance of DR-MIL is compared with other potential strategies and state-of-the-art models. RESULTS: DR-MIL displayed an accuracy of 95% and an area under curve of 0.943, which were superior to those observed for comparable methods. COVID-19 and CAP exhibited significant differences in both the DRIS and the spatial pattern of lesions (p<0.001). As a means of content-based image retrieval, DR-MIL can identify images used as key instances, references, and citers for visual interpretation. CONCLUSIONS: DR-MIL can effectively represent the deep characteristics of COVID-19 lesions in CT images and accurately distinguish COVID-19 from CAP in a weakly supervised manner. The resulting DRIS is a useful supplement to visual interpretation of the spatial pattern of lesions when screening for COVID-19.

Evaluating the Causal Association Between Educational Attainment and Asthma Using a Mendelian Randomization Design.

Asthma is a common chronic respiratory disease. In the past 10 years, genome-wide association study (GWAS) has been widely used to identify the common asthma genetic variants. Importantly, these publicly available asthma GWAS datasets provide important data support to investigate the causal association of kinds of risk factors with asthma by a Mendelian randomization (MR) design. It is known that socioeconomic status is associated with asthma. However, it remains unclear about the causal association between socioeconomic status and asthma. Here, we selected 162 independent educational attainment genetic variants as the potential instruments to evaluate the causal association between educational attainment and asthma using large-scale GWAS datasets of educational attainment (n = 405,072) and asthma (n = 30,810). We conducted a pleiotropy analysis using the MR-Egger intercept test and the MR pleiotropy residual sum and outlier (MR-PRESSO) test. We performed an MR analysis using inverse-variance weighted, weighted median, MR-Egger, and MR-PRESSO. The main analysis method inverse-variance weighted indicated that each 1 standard deviation increase in educational attainment (3.6 years) could reduce 35% asthma risk [odds ratio (OR) = 0.65, 95% confidence interval (CI) 0.51-0.85, P = 0.001]. Importantly, evidence from other MR methods further supported this finding, including weighted median (OR = 0.55, 95% CI 0.38-0.80, P = 0.001), MR-Egger (OR = 0.48, 95% CI 0.16-1.46, P = 0.198), and MR-PRESSO (OR = 0.65, 95% CI 0.51-0.85, P = 0.0015). Meanwhile, we provide evidence to support that educational attainment protects against asthma risk dependently on cognitive performance using multivariable MR analysis. In summary, we highlight the protective role of educational attainment against asthma. Our findings may have public health applications and deserve further investigation.

Effects of Liraglutide on Left Ventricular Function: A Meta-Analysis of Randomized, Placebo-Controlled Trials.

BACKGROUND: The effects of liraglutide treatment on the left ventricular systolic and diastolic function remain unclear. METHODS: This meta-analysis was conducted according to the preferred reporting items for systematic reviews and meta-analysis (PRISMA) statement. All relevant randomized, placebo-controlled trials (RCTs) were identified by searching PubMed, EMBASE, Cochrane Library, and ISI Web of Science from the establishment to January 2021 without language limitations. The weighted mean difference (WMD) with 95% confidence intervals (CIs) was calculated. RESULTS: Ten placebo-controlled RCTs involving a total of 732 cases were included in the meta-analysis. Compared with the placebo group, liraglutide therapy showed no beneficial effect on the left ventricular ejection fraction (LVEF) at the end of the study (WMD: 2.120, 95% CI: -0.688 to 4.929, P=0.139) and ΔLVEF during the trial period (WMD: -0.651, 95% CI: -1.649 to 0.348, P=0.202). Similarly, no statistical differences were noted in diastolic function parameters between the two groups, including the value early diastolic filling velocity (E)/the mitral annular early diastolic velocity (e') (WMD: -0.763, 95% CI: -2.157 to 0.630, P=0.283), Δe' (WMD: -0.069, 95% CI: -0.481 to 0.343, P=0.742), and ΔE/e' (WMD: -0.683, 95% CI: -1.663 to 0.298, P=0.172). CONCLUSIONS: Liraglutide treatment did not improve the left ventricular systolic and diastolic function. Given the study's limitations, further investigation may be warranted.

Atypical Pathogen Distribution in Chinese Hospitalized AECOPD Patients: A Multicenter Cross-Sectional Study.

Purpose: The proportion of atypical pathogens in patient with AECOPD within mainland China is unknown. The objectives of this study were to determine the distribution of atypical pathogens among Chinese patients with AECOPD, to evaluate the clinical characteristics of different atypical pathogen infections, and to compare different detection methods for atypical pathogens. Patients and Methods: Specimens were collected from patients with AECOPD from March 2016 to November 2018 at eleven medical institutions in eight cities in China. Double serum, sputum, and urine samples were obtained from 145 patients. Serological and nucleic acid tests were used to assess for Mycoplasma pneumonia and Chlamydia pneumoniae; serological, urinary antigen, and nucleic acid tests were applied to detect Legionella pneumophila. The clinical characteristics of atypical pathogen-positive and -negative groups were also compared. Results: The overall positivity rate for Mycoplasma pneumoniae was 20.69% (30/145), with the highest rate being 20.00% (29/145) when determined by passive agglutination.The overall positive rates for Chlamydia pneumoniae and Legionella pneumophila were 29.66% (43/145) and 10.34% (15/145), respectively. The most common serotype of Legionella pneumophila was type 6. The maximum hospitalized body temperature, ratio of eosinophils, C-reactive protein (CRP) level, and procalcitonin (PCT) level of the Mycoplasma pneumoniae-positive group were significantly higher than those of the Mycoplasma pneumoniae-negative group. Patients in the Chlamydia pneumoniae-positive group smoked more, had higher proportions of comorbidities and frequent aggravations in the previous two years than those in the Chlamydia pneumoniae-negative group. Furthermore, the forced expiratory volume in one second to forced vital capacity (FEV1/FVC) ratio assessment of lung function was higher, and the concentration of arterial blood bicarbonate (HCO3-) was lower in the Legionella pneumophila-positive group than in the Legionella pneumophila-negative group. Conclusion: Overall, atypical pathogens play an important role in AECOPD. Regarding the testing method, serological testing is a superior method to nucleic acid testing.

The Role of Fever Clinics in the Strategic Triage of Suspected Cases of Imported COVID-19.

INTRODUCTION: Novel coronavirus pneumonia (COVID-19) is an acute respiratory infectious disease, which has the characteristic of human-to-human transmission and is extremely contagious. Correctly standardizing the process of early screening of infection or suspected cases in the fever clinic has become a key part of the fight against the pandemic. METHODS: A retrospective analysis of patients in the fever clinic of Shenyang Medical College Affiliated Central Hospital from January 23 to March 1, 2020, was conducted in the present study. RESULTS: It was found that 16 suspected cases of COVID-19 in the fever clinic were diagnosed with respiratory infections, accounting for 0.59%. CONCLUSION: In case of a negative result in the second nucleic acid test, strategic triage and typing might be more conducive for the following nucleic acid tests for suspected cases in order to prevent the spread of the epidemic caused by missed diagnosis.

Long Non-Coding RNA CAR10 Facilitates Non-Small Cell Lung Cancer Cell Migration and Invasion by Modulating the miR-892a/GJB2 Pathway.

INTRODUCTION: Non-coding RNAs, including long non-coding (lnc)RNAs and microRNAs (miRs), play crucial roles in numerous malignant tumors, including non-small cell lung cancer (NSCLC). METHODS: The expression levels of chromatin-associated RNA Intergenic 10 (CAR10), gap junction protein beta 2 (GJB2) and miR-892a in NSCLC were evaluated by reanalyzing three Gene Expression Omnibus (GEO) datasets, and performing reverse transcription-quantitative PCR, immunohistochemistry staining and Western blot analysis, accordingly. Functionally, Transwell and Matrigel assays were performed to measure changes in the migration and invasion abilities of the A549 and H1299 cell lines. The targeted binding effects between CAR10 and miR-892a, as well as between miR-892a and GJB2 were confirmed by conducting dual-luciferase reporter and RNA pull-down assays, respectively. RESULTS: The present study demonstrated that CAR10 was upregulated in patients with NSCLC, which was also associated with a poor prognosis. Functionally, CAR10 was confirmed to be oncogenic and promoted NSCLC cell migration and invasion, using overexpression and knockdown Transwell assays. Furthermore, GJB2 expression was revealed to be upregulated and was positively correlated with CAR10 expression in NSCLC. A further mechanistic study revealed that GJB2 was a downstream target of CAR10, which induced the migration and invasive potential of the A549 and H1299 cell lines. More specifically, miR-892a was found to serve as a bridge between CAR10 and GJB2, via similar miRNA response elements. The RNA pull-down and luciferase assays indicated that miR-892a directly binds both CAR10 and GJB2. CONCLUSION: CAR10 promoted NSCLC cell migration and invasion by upregulating GJB2 and sponging miR-892a. These findings illustrated that the CAR10/miR-892a/GJB2 axis may be a novel molecular target for the treatment of NSCLC.

Emerging roles of circular RNAs in non­small cell lung cancer (Review).

Circular RNAs (circRNAs) are a class of novel endogenous transcripts with limited protein­coding abilities. CircRNAs have been demonstrated to function as critical regulators of tumor development and distant metastasis through binding to microRNAs (miRNAs) and interacting with RNA­binding proteins, thereby regulating transcription and translation. Emerging evidence has illustrated that certain circRNAs can serve as biomarkers for diagnosis and prognosis of cancer, and/or serve as potential therapeutic targets. Expression of functional circRNAs is commonly dysregulated in cancer and this is correlated with advanced Tumor­Node­Metastasis stage, lymph node status, distant metastasis, poor differentiation and shorter overall survival of cancer patients. Recently, an increasing number of studies have shown that circRNAs are closely associated with NSCLC. Functional experiments have revealed that circRNAs are intricately associated with the pathological progression of NSCLC. The present review provides an overview of the regulatory effect of circRNAs in the development and progression of NSCLC, taking into consideration various physiological and pathological processes, such as proliferation, apoptosis, invasion and migration, and their potential value as biomarkers and therapeutic targets.

PM2.5-induced pulmonary inflammation via activating of the NLRP3/caspase-1 signaling pathway.

Particulate matter 2.5 (PM2.5)-induced pulmonary inflammation has become a public concern in recent years. In which, the activation of the NLRP3/caspase-1 pathway was closely related to the inflammatory response of various diseases. However, the promotion effect of the NLRP3/caspase-1 pathway on PM2.5-induced pulmonary inflammation remains largely unclear. Here, our data showed that PM2.5 exposure caused lung injury in the mice by which inflammatory cell infiltration occurred in lung and alveolar structure disorder. Meanwhile, the exposure of human bronchial epithelial cells (16HBE) to PM2.5 resulted in suppressed cell viability, as well as elevated cell apoptosis. Moreover, a higher level of inflammatory cytokine and activation of the NLRP3/caspase-1 pathway in PM2.5-induced inflammation mice models and 16HBE cells. Mechanistically, pretreatment with MCC950, a NLRP3/caspase-1 pathway inhibitor, prevented PM2.5-induced lung injury, inflammatory response, and the number of inflammatory cells in BALFs, as well as promoted cell viability and decreased inflammatory cytokine secretion. Collectively, our findings indicated that the NLRP3/caspase-1 pathway serves a vital role in the pathological changes of pulmonary inflammation caused by PM2.5 exposure. MCC950 was expected to be the therapeutic target of PM2.5 inhalation mediated inflammatory diseases.

Apigenin Enhanced Antitumor Effect of Cisplatin in Lung Cancer via Inhibition of Cancer Stem Cells.

Cancer stem cell theory has been proposed to explain tumor heterogeneity and the carcinogenesis process. Highly tumorigenic lung cancer stem cells develop resistance to cisplatin (CDDP), a common chemotherapy drug. Herein, we attempted to clarify whether apigenin (API) can improve the antitumor efficiency of CDDP in lung cancer using cancer stem cells. Lung cancer stem cells were identified as CD 133 positive cancer cells in non-small cell lung cancer (NSCLC) A549, H1299 cells and CDDP-resistant NSCLC A549R cells. The cytotoxic effect of API was measured in CDDP-treated A549, H1299, and A549R cells. API repressed CD 133 positive cells and enhanced the antitumor effect of CDDP in A549, H1299, and A549R cells. The synergistic antitumor effect of API and CDDP was blocked by addition of the p53 inhibitor Pifithrin-α, and siRNA targeting the p53 gene in A549R cells. Furthermore, API eliminates CDDP-induced CSC via p53, since A549R cells lacking p53 and Pifithrin-α addition derepressed the decrease in CD 133 positive cells after API treatment in CDDP-treated A549 and A549R cells. The findings indicate that API might eliminate cancer stem cells and enhance the antitumor effects of CDDP in NSCLC via p53.

Thymoquinone ameliorates the PM2.5-induced lung injury in rats.

BACKGROUND: This study aims to explore the effect of thymoquinone (TQ) on particulate matter 2.5 (PM2.5)-induced lung injury. METHODS: The PM2.5 sample was provided by Shenyang Environment Monitor Central Station. Lung injury was established by intratracheal instillation PM2.5 (7.5 mg/kg) followed by TQ treatment (20 and 40 mg/kg) for 14 d in rats. Hematoxylin and eosin (HE) and Evans blue dye (EBD) staining were detected on lung tissues. ELISA, real-time PCR, western blotting and TUNEL assays were also performed. RESULTS: The data showed that TQ diminished lung injury and EBD accumulation. The number of macrophages, neutrophils, eosinophils, and lymphocytes was ameliorated after TQ treatment. In addition, TQ suppressed the inflammation reaction parameters (interleukin-1ß and -6, IL-1ß and IL-6; tumor necrosis factor-α, TNF-α) and oxidative stress in PM2.5-induced lung injury. The levels of nuclear factor erythroid 2-related factor 2 (Nrf2) and heme oxygenase (HO-1) were increased due to the treatment of TQ. The number of TUNEL-positive cells was prominently reduced in TQ-treated rats compared with that in PM2.5 group. Intratracheal instillation PM2.5 activated autophagy, whilst TQ blocked it in lung. CONCLUSIONS: Taken together, this study provides the first in vivo evidence that TQ suppresses inflammation, oxidative stress, apoptosis, and autophagy in PM2.5-induced lung injury.

DCT-MIL: Deep CNN transferred multiple instance learning for COPD identification using CT images.

While many pre-defined computed tomographic (CT) measures have been utilized to characterize chronic obstructive pulmonary disease (COPD), it is still challenging to represent pathological alternations of multiple dimensions and highly spatial heterogeneity. Deep CNN transferred multiple instance learning (DCT-MIL) is proposed to identify COPD via CT images. After the lung is divided into eight sections along the axial direction, one random axial CT image is taken out from each section as one instance. With one instance as the input, the activations of neural layers of AlexNet trained by natural images are extracted as features. After dimension reduction through principle component analysis, features of all instances are input into three MIL methods: Citation k-Nearest-Neighbor (Citation-KNN), multiple instance support vector machine, and expectation-maximization diverse density. Moreover, the performance dependence of the resulted models on the depth of the neural layer where activations are extracted and the number of features is investigated. The proposed DCT-MIL achieves an exceptional performance with an accuracy of 99.29% and area under curve of 0.9826 while using 100 principle components of features extracted from the fourth convolutional layer and Citation-KNN. It outperforms not only DCT-MIL models using other settings and the pre-trained AlexNet with fine-tuning by montages of eight lung CT images, but also other state-of-art methods. Deep CNN transferred multiple instance learning is suited for identification of COPD using CT images. It can help finding subgroups with high risk of COPD from large populations through CT scans ordered doing lung cancer screening.