The Regulation Mechanism of MUC5AC Secretion in Airway of Obese Asthma.

The purpose of this study was to establish a rat asthma model and extract MUC5AC to explore the mechanism of mucin 5AC (MUC5AC) signaling pathway regulating the function of asthmatic airway smooth muscle cells (ASMC) and participating in asthmatic airway remodeling. Western blot was used to detect ß-catenin (ß-catenin), glycogen synthase kinase-3ß (GSK-3ß), proto-oncogene MUC5AC and cyclin D1 (cyclin D1) in MUC5AC of asthmatic and normal groups. After inhibiting the interaction between ß-catenin and transcription cofactor p300 / CBP in ASMC of the asthma group and control group, the cell viability and cycle changes of ASMC were detected by the CCK-8 method and flow cytometry. After inhibiting the activity of P38 mitogen-activated protein kinase (MAPK), the protein expression changes of c-Myc and cyclin D1 were detected by Western blot. Results showed that comprehensive HE staining results of lung tissue sections indicate that the experimental rat model of asthma airway remodeling was successfully established. Compared with the control group, 100 fxmol and L1 Efaroxan promoted insulin secretion (P <0.01), and administration of the MUC5AC antagonist KU14R significantly inhibited the effect of MUC5AC.Western blot showed that the protein expression levels of ß-catenin, c-Myc and cyclin D1 in ASMC of the obese asthma group were significantly higher than those of the control group (P <0.05), while the protein expression level of GSK-3ß was lower than Control group (P <0.05). After inhibiting the interaction between ß-catenin and p300 / CBP, the decrease in cell viability and the degree of cell cycle change of ASMC in the asthma group were more obvious than those in the control group (P <0.05). After inhibiting the activity of P38 MAPK, the expressions of the target proteins c-Myc and cyclin D1 in the MUC5AC signaling pathway in ASMC model rats and control rats were down-regulated, and the difference was statistically significant (P <0.05). The conclusion was that the Wnt/ß-catenin signaling pathway can regulate the proliferation and differentiation of ASMC by up-regulating the expression level of cMyc. Cyclin D1 interacts with the MAPK signaling pathway, thereby affecting the function of ASMC and participating in asthma airway remodeling.

Protective effect of miR-138-5p inhibition modified human mesenchymal stem cell on ovalbumin-induced allergic rhinitis and asthma syndrome.

The objective of the study is to evaluate the protective effects of human mesenchymal stem cells (hMSCs) modified with miR-138-5p inhibitor against the allergic rhinitis and asthma syndrome (ARAS). MiR-138-5p or negative control was transfected into hMSCs, and fluorescence-activated cell sorting was used to evaluate hMSC surface markers. Quantitative real-time PCR (qRT-PCR) was used to evaluate miR-138-5p, SIRT1, caspase-3, IL-6, IL-1ß and TNF-α levels after TNF-α and IL-6 stimulations. hMSCs with or without miR-138-5p inhibition was intranasally administered into ARAS mice (n = 10 each group), followed by monitoring sneezing and nasal rubbing events to evaluate the allergic symptoms. Histamine, ovalbumin-specific IgE, IgG2a, IgG1 and LTC4 release were monitored in the serum and nasal lavage fluid using enzyme-linked immunosorbent assay. Expression of SIRT1 and HMGB1/TLR4 pathway in nasal mucosa was assessed. After miR-138-5p inhibitor transfection, the hMSC lineage was preserved. Binding between SIRT1 and miR-138-4p was observed, and miR-138-5p inhibition led to upregulation of SIRT1. Inhibition of miR-138-5p led to attenuated inflammatory responses of hMSCs upon TNF-α and IL-6 stimulation, and allergic symptoms in mice, as well as histamine and ovalbumin-specific IgG release. hMSCs with miR-138-5p inhibition showed characteristics of activated SIRT1 and inhibited HMGB1/TLR4 pathway. Inhibition of miR-138-5p in hMSCs enhanced its effects in attenuating inflammatory responses and allergic reaction in the ARAS model, which is presumably regulated by SIRT1 and the HMGB1/TLR4 pathway.

Investigation of LINC00342 as a poor prognostic biomarker for human patients with non-small cell lung cancer.

Cyclic long noncoding RNAs have recently become major players in cancer biology and can serve as biomarkers for cancer diagnosis and prognosis, and as potential therapeutic targets. We explored circulating LINC00342 as a predictor of non-small cell lung cancer (NSCLC). The expression of LINC00342 in tissues, serum, PBMC, and NSCLC cell lines were screened by reverse transcription quantitative polymerase chain reaction. A multistage validation and risk score formula detection analysis was used. The effect of LINC00342 on proliferation was assessed by MTT, p53, and PTEN pathways, which were analyzed by Western blot analysis. We found that LINC00342 was upregulated in the tissues, serum, and PBMC of patients with NSCLC. In addition, patients with higher LINC00342 expression levels were associated with poor overall survival. For the diagnosis of NSCLC, the specificity and sensitivity of LINC00342 were significantly higher than that of CYFRA 21-1. Moreover, LINC00342 promoted proliferation by inhibiting the expression of p53 and PTEN proteins in NSCLC cell lines. Our study demonstrates that LINC00342 is involved in the development, and LINC00342 may be a potential diagnostic factor and a target for new therapies for future patients with NSCLC.

Unsupervised Modality-Transferable Video Highlight Detection With Representation Activation Sequence Learning.

Identifying highlight moments of raw video materials is crucial for improving the efficiency of editing videos that are pervasive on internet platforms. However, the extensive work of manually labeling footage has created obstacles to applying supervised methods to videos of unseen categories. The absence of an audio modality that contains valuable cues for highlight detection in many videos also makes it difficult to use multimodal strategies. In this paper, we propose a novel model with cross-modal perception for unsupervised highlight detection. The proposed model learns representations with visual-audio level semantics from image-audio pair data via a self-reconstruction task. To achieve unsupervised highlight detection, we investigate the latent representations of the network and propose the representation activation sequence learning (RASL) module with k-point contrastive learning to learn significant representation activations. To connect the visual modality with the audio modality, we use the symmetric contrastive learning (SCL) module to learn the paired visual and audio representations. Furthermore, an auxiliary task of masked feature vector sequence (FVS) reconstruction is simultaneously conducted during pretraining for representation enhancement. During inference, the cross-modal pretrained model can generate representations with paired visual-audio semantics given only the visual modality. The RASL module is used to output the highlight scores. The experimental results show that the proposed framework achieves superior performance compared to other state-of-the-art approaches.

A novel inflammatory nutrient index for predicting survival outcomes in patients with non-small cell lung cancer.

Background: Lung cancer is one of the most common contributors to cancer-related deaths worldwide. This study aimed to develop a new blood index on the basis of the patient's systemic inflammation and nutritional status, which can be used to predict the prognosis of patients with non-small cell lung cancer (NSCLC). Methods: Pre-treatment blood markers were analyzed in 556 NSCLC patients from 2010 to 2019. A least absolute shrinkage and selection operator (LASSO) method was used to select indicators to establish a new integrated biomarker (PNAGR). Kaplan-Meier survival curves were used to assess the prognostic impact of platelet-to-lymphocyte ratio (PLR), albumin (ALB), and the PNAGR. The prognostic value was verified using univariate and multivariate Cox analyses. Results: We used four biomarkers including PLR, ALB, 1/albumin-to-globulin ratio (1/AGR), and neutrophil/albumin-to-globulin ratio (N/AGR) were used to screen for the PNAGR using LASSO. Patients with high PNAGR demonstrated lower overall survival (OS) compared to those with low PNAGR. In both univariate and multivariate analyses, PNAGR was revealed as an independent prognostic factor for OS. The predictive power of PNAGR [area under the curve (AUC): 0.753] was higher than that of the metrics alone. Conclusions: PNAGR is a novel and effective clinical prognostic tool with good clinical predictive value for NSCLC patients.

Improved Multiple-Image-Based Reflection Removal Algorithm Using Deep Neural Networks.

When imaging through a semi-reflective medium such as glass, the reflection of another scene can often be found in the captured images. It degrades the quality of the images and affects their subsequent analyses. In this paper, a novel deep neural network approach for solving the reflection problem in imaging is presented. Traditional reflection removal methods not only require long computation time for solving different optimization functions, their performance is also not guaranteed. As array cameras are readily available in nowadays imaging devices, we first suggest in this paper a multiple-image based depth estimation method using a convolutional neural network (CNN). The proposed network avoids the depth ambiguity problem due to the reflection in the image, and directly estimates the depths along the image edges. They are then used to classify the edges as belonging to the background or reflection. Since edges having similar depth values are error prone in the classification, they are removed from the reflection removal process. We suggest a generative adversarial network (GAN) to regenerate the removed background edges. Finally, the estimated background edge map is fed to another auto-encoder network to assist the extraction of the background from the original image. Experimental results show that the proposed reflection removal algorithm achieves superior performance both quantitatively and qualitatively as compared to the state-of-the-art methods. The proposed algorithm also shows much faster speed compared to the existing approaches using the traditional optimization methods.

TLR4 antagonist ameliorates combined allergic rhinitis and asthma syndrome (CARAS) by reducing inflammatory monocytes infiltration in mice model.

The present study aims to investigate the effects of toll-like receptor 4 (TLR4) antagonist in an ovalbumin (OVA)-induced mouse model of combined allergic rhinitis and asthma syndrome (CARAS). An OVA-induced mouse model of CARAS was established and TLR4 antagonist, TAK-242, was administrated intranasally or intraperitoneally. The number of sneezing and nasal rubbing was counted. The frequency of different cell types in the bronchoalveolar lavage fluid (BALF) and nasal lavage fluid (NLF) was analyzed using flow cytometry. Expressions of protein in nasal mucosa and lungs were determined using western blotting. Levels of interleukin (IL)-4, IL-5, and IL-13 were determined using Enzyme-linked Immunosorbent Assay (ELISA). Histological scores were applied for the assessment of lung injury. Treatment of TAK-242 downregulated CCL2 expression and reduced monocyte infiltration in nasal mucosa and lung tissues. Additionally, treatment of TAK-242 ameliorated upper airway symptoms including the sneezing and nasal rubbing by the regulation of cytokines including IL-4, IL-5, and IL-13. Furthermore, treatment of TAK-242 ameliorated lower airway symptoms including decreasing the frequency of CD45+SiglecF+CD11b+CD11c- eosinophils in BALF and IL13+ Th2 cells in the lungs. In conclusion, treatment of TAK-242 ameliorated CARAS-related lung injury by inhibiting lymphocyte infiltration, reducing monocytes infiltration, as well as regulating the frequency of eosinophils and Th2 cells.

Linc00221 modulates cisplatin resistance in non-small-cell lung cancer via sponging miR-519a.

Cisplatin resistance has been long considered an obstacle to the efficacy of chemotherapy in non-small-cell lung cancer (NSCLC). Long non-coding RNAs (lncRNAs) have been widely reported to participate in the various biological process including cancer. In the present study, we aim to explore the functions of Linc00221 and miR-519a in the sensitivity and the resistance of NSCLC to cisplatin. The levels of Linc00221, miR-519a, and zinc finger and BTB domain-containing five (ZBTB5) in NSCLC tissues were detected by qRT-PCR and Western blot. Colony formation and MTT assays were applied to detect the viability of cells after cisplatin treatment. Dual luciferase reporter assays were used to detect the inhibitory effect of miR-519a on ZBTB5 and Linc00221, and pull down experiments were employed to determine the direct interaction between Linc00221 and miR-519a. Our results showed that Linc00221 was highly expressed in cisplatin-resistant NSCLC tissues and cells and closely associated with poor prognosis. Linc00221 promoted the cisplatin resistance of NSCLC and miR-519a was a direct target of Linc00221. In addition, miR-519a could promote cisplatin sensitivity in NSCLC cells by targeting ZBTB5. Linc00221 could mediate the cisplatin sensitivity in NSCLC by adsorbing miR-519a to prevent its down-regulation of ZBTB5. In conclusion, Linc00221 promotes cisplatin resistance in NSCLC through the downstream miR-519a/ZBTB5 signaling axis, which could be used as a potential diagnostic and therapeutic target for clinical cisplatin-resistant NSCLC patients.

Robust Reflection Removal Based on Light Field Imaging.

In daily photography, it is common to capture images in the reflection of an unwanted scene. This circumstance arises frequently when imaging through a semi-reflecting material such as glass. The unwanted reflection will affect the visibility of the background image and introduce ambiguity that perturbs the subsequent analysis on the image. It is a very challenging task to remove the reflection of an image since the problem is severely ill-posed. In this paper, we propose a novel algorithm to solve the reflection removal problem based on light field (LF) imaging. For the proposed algorithm, we first show that the strong gradient points of an LF epipolar plane image (EPI) are preserved after adding to the EPI of another LF image. We can then make use of these strong gradient points to give a rough estimation of the background and reflection. Rather than assuming that the background and reflection have absolutely different disparity ranges, we propose a sandwich layer model to allow them to have common disparities, which is more realistic in practical situations. Then, the background image is refined by recovering the components in the shared disparity range using an iterative enhancement process. Our experimental results show that the proposed algorithm achieves superior performance over traditional approaches both qualitatively and quantitatively. These results verify the robustness of the proposed algorithm when working with images captured from real-life scenes.