Transient receptor potential vanilloid 4 promotes the growth of non-small cell lung cancer by regulating Foxp3.

OBJECTIVE(S): Transient receptor potential vanilloid 4 (TRPV4) participates in malignant tumor. However, the role of TRPV4 in non-small cell lung cancer (NSCLC) remains unclear. In this study, we demonstrated TRPV4 was upregulated in NSCLC tissues and NSCLC cell lines. MATERIALS AND METHODS: TRPV4 level in the NSCLC patients and cell lines were detected, and its function was studied both in vivo and vitro. RESULTS: The level of TRPV4 showed a positive correlation with tumor size of NSCLC patients. Activation TRPV4 by agonist GSK1016790A promoted cell proliferation and decreased apoptosis in A549 cells, and these effects were enhanced when the cells have overexpressed TRPV4. Moreover, GSK1016790A induced inhibitory effects on apoptosis of A549 cells was impaired when GSK1016790A used together with TRPV4 selective antagonist HC-067047, or impaired when the cells have already downregulated TRPV4 expression by TRPV4 siRNA. In vivo study, pharmacological inhibition of TRPV4 prevented A549 cells transplanted tumor growth. It was showed Foxp3 level was significantly increased in the NSCLC tissues, and showed a positive correlation with the level of TRPV4. Deactivation of TRPV4 using TRPV4 siRNA or HC-067047 significantly reduced expression of Foxp3 in GSK1016790A treated NSCLC cells. Moreover, downregulation Foxp3 by transfection of Foxp3 siRNA significantly impaired TRPV4 induced NSCLC cells proliferations in vitro. CONCLUSIONS: Antitumor effects caused by TRPV4 inhibition in NSCLC might be attributed to the suppression of Foxp3 which induced subsequent cell apoptosis. Thus, pharmacological inhibition of TRPV4 may be a promising option for NSCLC treatment.

Octanoic acid-rich enteral nutrition prevented lipopolysaccharide-induced acute liver injury through c-Jun N-terminal kinase-dependent autophagy.

BACKGROUND: Acute liver injury (ALI) is an essential component of sepsis associated with poor outcomes. Octanoic acid (OA), a medium-chain fatty acid, has a protective effect on sepsis-induced organ damage, and autophagy is an adaptive response to sepsis. However, the underlying mechanism by which OA prevents ALI remains unknown. Therefore, we investigated whether OA-rich enteral nutrition (EN) prevented lipopolysaccharide (LPS)-induced ALI through the c-Jun N-terminal kinase (JNK)-dependent autophagy. METHODS: Firstly, Sprague Dawley rats were randomly divided into four groups (sham, LPS, LPS + EN, and LPS + EN + OA) to detect the effect of OA-rich EN on LPS-induced ALI. Then, rats were randomly divided into five groups (sham, LPS, LPS + EN + OA, LPS + EN + OA + anisomycin (AN), and LPS + SP600125) to explore the mechanism by which OA-rich EN prevented ALI. EN and OA-rich EN were conducted through gastric tubes for 3 days. The liver protective effects were measured by liver histopathological changes, enzymes, inflammatory cytokines of serum and liver, the levels of autophagy, and JNK activity. RESULTS: OA-rich EN inhibited JNK activity, up-regulated autophagy and prevented LPS-induced ALI. Inhibition of JNK activity conferred by SP promoted autophagy and prevented LPS-induced ALI. Moreover, the protective effect of autophagy and inhibition of JNK activity conferred by OA-rich EN on ALI was counteracted by AN. CONCLUSION: OA-rich EN prevented LPS-induced ALI through JNK-dependent autophagy. This result suggested that OA-rich EN may be a therapeutic potential for ALI in patients with sepsis.

High-Expression of Cytoplasmic Poly (A) Binding Protein 1 (PABPC1) as a Prognostic Biomarker for Early-Stage Esophageal Squamous Cell Carcinoma.

BACKGROUND AND OBJECTIVE: Poly (A) binding protein cytoplasmic 1 (PABPC1) plays a crucial role in the regulation of RNA polyadenylation, translation initiation, and mRNA stability and may be involved in tumorigenesis. Herein, we set out to identify the prognostic value of PABPC1 expression in esophageal squamous cell carcinoma (ESCC). METHODS: Using quantitative real-time PCR (qRT-PCR) and immunohistochemical analysis, the present study investigated mRNA and protein expressions of PABPC1 in 231 ESCCs and their paired adjacent normal epithelial tissues. RESULTS: We observed a reduction in the average mRNA expression of PABPC1 in ESCC tissue specimen, but the mRNA expression of PABPC1 was significantly higher (P<0.001) in ESCC tissues with high PABPC1 expression and lower (P=0.033) in tissues with low PABPC1 expression. In immunohistochemical analysis, positive expression of the PABPC1 protein was identified in 179 ESCC tissue specimens (179/231, 77.5%), while the percentage of ESCC tissue specimens with high expression of PABPC1 was found to be 41.1% (95/231). PABPC1 expression was found to be significantly correlated with lymph node metastasis (LNM) (P=0.011), pathological stage (P=0.021), tumor recurrence (P<0.001), and the outcome (P<0.001) of patients with ESCC. High expression of PABPC1 was associated with poor overall survival (OS) of ESCC patients (P<0.001) among all pathological stages, particularly in the early stages (pStage-I and -II), and identified to be an independent prognostic factor for OS of patients with ESCC in multivariate analysis (HR=2.622; 95% CI, 1.68-4.129). Comparatively, the expression of Ki-67, p53, and nm23 was not associated with OS. CONCLUSION: In this study, we discovered that PABPC1 is a prognostic biomarker and a therapeutic target for ESCC, particularly early-stage ESCC.

A novel immune-related gene signature predicts survival in esophageal squamous cell carcinoma.

BACKGROUND: Immune-related genes (IRGs) are highly relevant to the progression and prognosis of esophageal squamous cell carcinoma (ESCC). A prognostic signature could be reliable in stratifying ESCC patients according to the risk score, which may help manage systematic treatments. In this study, a systematic and reliable immune signature was developed to estimate the prognostic stratification in ESCC. METHODS: Ribonucleic acid (RNA) expression data of 79 ESCC samples from the Cancer Genome Atlas (TCGA) database and 269 normal esophageal mucosal samples from the Genotype-Tissue Expression (GTEx) project database were downloaded from the University of California, Santa Cruz (UCSC) website to form a TCGA-GTEx dataset. First, we screened differentially expressed genes (DEGs) and then filtered IRGs based on the Immunology Database and Analysis Portal (ImmPort) database to obtain immune-related DEGs (IRDEGs). Next, a novel prognostic signature based on IRDEGs was developed using multivariable Cox analysis. Immune infiltration status was evaluated via single-sample gene set enrichment analysis (ssGSEA). ESCC tissues were grouped into three clusters in terms of immune infiltration (Immunity-L, Immunity-M, and Immunity-H) by applying an unsupervised hierarchical clustering algorithm. Finally, the samples were divided into high- and low-risk groups using the median of the risk score scores for GSEA pathway enrichment analysis in the three clusters. RESULTS: The prognostic signature based on IRDEGs (FCER1G, ISG20, and EGFR) performed moderately in prognostic predictions, with a concordance index (C-index) value of 0.73 [95% (confidence interval) CI: 0.63-0.84, P=2.02E-05] and an area under the curve (AUC) value of 0.817. The xenobiotic metabolism pathway was significantly enriched and up-regulated both in the high-risk group of the immunity-M and immunity-H clusters. CONCLUSIONS: The novel immune-related prognostic signature we constructed has a good prognostic, predictive ability and can be used as an independent prognostic indicator. Our study provides clinicians with a quantitative tool to predict the probability of individual survival time and helps clinicians select targets for immunotherapies and individualized treatment strategies for ESCC patients.

Identification of RHEX as a novel biomarker related to progression and immunity of non-small cell lung carcinoma.

BACKGROUND: The therapeutic response and prognosis of patients with non-small cell lung carcinoma (NSCLC) are widely related to immunity. To improve the prognosis of patients and provide reliable information to guide appropriate personalized treatment strategies, it is necessary to identify reliable prognostic or predictive indicators closely related to tumor phenotype and immune traits in NSCLC. METHODS: Based on The Cancer Genome Atlas (TCGA)-NSCLC mRNA expression profile data, a novel approach combining differential gene expression analysis, single-sample gene set enrichment analysis (ssGSEA), and weighted gene co-expression network analysis (WGCNA) was used to screen hub genes. Subsequently, the regulator of hemoglobinization and erythroid cell expansion (RHEX) was identified as a key gene using the log-rank test and confirmed in the ArrayExpress database. The relationship between RHEX and clinicopathological parameters was analyzed using the Wilcoxon rank-sum test. More importantly, through gene set enrichment analysis (GSEA) and cell-type identification by estimating relative subsets of RNA transcripts (CIBERSORT) algorithms, and with reference to the Tumor IMmune Estimation Resource (TIMER) database, we explored the relevant pathways of RHEX and its relationship with tumor-infiltrating immune cells (TICs). Finally, we depicted the association between RHEX and immunomodulators in the TCGA and a web portal TISIDB. RESULTS: The RHEX mRNA expression levels in tumor tissues were lower than those in normal tissues and declined with the progression of NSCLC. Meanwhile, RHEX overexpression was associated with high immune infiltration levels and a favorable clinical prognosis. RHEX may participate in tumor microenvironment (TME) regulation through multiple tumor-immune related pathways, especially the JAK-STAT signaling pathway. Furthermore, RHEX expression affected the infiltrating abundance of multiple TICs and positively correlated with most of the immunomodulators in NSCLC. CONCLUSIONS: Our study is the first to propose that RHEX is an immune-related gene with prognostic value in NSCLC and reveals the underlying mechanism between RHEX and tumor-immune system interactions. These results ultimately provide guidance for prognosis and immunotherapy for NSCLC patients.

MiR-654-3p Suppresses Non-Small Cell Lung Cancer Tumourigenesis by Inhibiting PLK4.

PURPOSE: MiR-654-3p plays important roles in many types of malignant tumours. However, the biological function of miR-654-3p in non-small cell lung cancer (NSCLC) remains unknown. In this study, the role of miR-654-3p in NSCLC was investigated. METHODS: qRT-PCR was used to evaluate the level of miR-654-3p in NSCLC tissues and cell lines, while Cell Counting Kit-8, Annexin V/propidium iodide dual staining or TUNEL staining were used to investigate proliferation and apoptosis of NSCLC cells. Luciferase assays and Western blotting were performed to validate potential targets of miR-654-3p. RESULTS: MiR-654-3p levels were significantly decreased in NSCLC patients and cell lines and were significantly correlated with the tumour size and tumour node metastasis stage of NSCLC patients. In A549 cells, miR-654-3p overexpression significantly increased apoptosis and inhibited growth both in vivo and in vitro, while downregulation of miR-654-3p had the opposite effects. In addition, polo-like kinase 4 (PLK4) was shown to be a target gene of miR-654-3p that is negatively regulated by miR-654-3p in A549 cells. Furthermore, PLK4 was observed to be highly expressed in NSCLC tissues and cells, and PLK4 overexpression abolished the inhibitory effects of miR-654-3p overexpression on NSCLC cell proliferation. Finally, the animal experiment results further demonstrated that miR-654-3p inhibits tumour growth and regulates PLK4 expression. CONCLUSION: Our results demonstrate that miR-654-3p functions as a growth-suppressing miRNA by targeting PLK4 in NSCLC.

Polyimide@Ketjenblack Composite: A Porous Organic Cathode for Fast Rechargeable Potassium-Ion Batteries.

Potassium-ion batteries (PIBs) configurated by organic electrodes have been identified as a promising alternative to lithium-ion batteries. Here, a porous organic Polyimide@Ketjenblack is demonstrated in PIBs as a cathode, which exhibits excellent performance with a large reversible capacity (143 mAh g-1 at 100 mA g-1 ), high rate capability (125 and 105 mAh g-1 at 1000 and 5000 mA g-1 ), and long cycling stability (76% capacity retention at 2000 mA g-1 over 1000 cycles). The domination of fast capacitive-like reaction kinetics is verified, which benefits from the porous structure synthesized using in situ polymerization. Moreover, a renewable and low-cost full cell is demonstrated with superior rate behavior (106 mAh g-1 at 3200 mA g-1 ). This work proposes a strategy to design polymer electrodes for high-performance organic PIBs.

Mask R-CNN.

We present a conceptually simple, flexible, and general framework for object instance segmentation. Our approach efficiently detects objects in an image while simultaneously generating a high-quality segmentation mask for each instance. The method, called Mask R-CNN, extends Faster R-CNN by adding a branch for predicting an object mask in parallel with the existing branch for bounding box recognition. Mask R-CNN is simple to train and adds only a small overhead to Faster R-CNN, running at 5 fps. Moreover, Mask R-CNN is easy to generalize to other tasks, e.g., allowing us to estimate human poses in the same framework. We show top results in all three tracks of the COCO suite of challenges, including instance segmentation, bounding-box object detection, and person keypoint detection. Without bells and whistles, Mask R-CNN outperforms all existing, single-model entries on every task, including the COCO 2016 challenge winners. We hope our simple and effective approach will serve as a solid baseline and help ease future research in instance-level recognition. Code has been made available at: https://github.com/facebookresearch/Detectron.

Focal Loss for Dense Object Detection.

The highest accuracy object detectors to date are based on a two-stage approach popularized by R-CNN, where a classifier is applied to a sparse set of candidate object locations. In contrast, one-stage detectors that are applied over a regular, dense sampling of possible object locations have the potential to be faster and simpler, but have trailed the accuracy of two-stage detectors thus far. In this paper, we investigate why this is the case. We discover that the extreme foreground-background class imbalance encountered during training of dense detectors is the central cause. We propose to address this class imbalance by reshaping the standard cross entropy loss such that it down-weights the loss assigned to well-classified examples. Our novel Focal Loss focuses training on a sparse set of hard examples and prevents the vast number of easy negatives from overwhelming the detector during training. To evaluate the effectiveness of our loss, we design and train a simple dense detector we call RetinaNet. Our results show that when trained with the focal loss, RetinaNet is able to match the speed of previous one-stage detectors while surpassing the accuracy of all existing state-of-the-art two-stage detectors. Code is at: https://github.com/facebookresearch/Detectron.

Potential of the glasses-free three-dimensional display system in shortening the learning curve of video-assisted endoscopic surgery: a self-controlled ex-vivo study.

BACKGROUND: One of the largest challenges in endoscopic surgical training is adapting to a two-dimensional (2D) view. The glasses-free three-dimensional (GF-3D) display system was designed to integrate the merits of both 2D and conventional 3D (C-3D) displays, allowing surgeons to perform video-assisted endoscopic surgery under a stereoscopic view without heavy and cumbersome 3D glasses. METHODS: In this study, 15 junior thoracic surgeons were divided to test one routine and one complex task three times each via traditional high-definition 2D (HD-2D) and GF-3D to determine whether there was any advantage when using the GF-3D system to acquire endoscopic skills. The duration, numbers of stitches, and distance between every two stitches were recorded for every procedure. RESULTS: Seven participants were enrolled in the HD-2D group and eight participants were enrolled in the GF-3D group. All 15 participants successfully completed porcine skin continuous suture and tracheal continuous anastomosis procedures three times each. For skin continuous suture, there was no significant difference between the two groups in terms of the learning curve for speed (P=0.683) and accuracy (P=0.556). For tracheal continuous anastomosis, there was a significant difference between the two groups in terms of the learning curve for speed (P=0.001), but no significant difference was observed between the two groups in terms of the learning curve for accuracy (P=0.211). CONCLUSIONS: In summary, both HD-2D and GF-3D display systems are efficient for routine and complex endoscopic surgery. With the help of GF-3D, surgeons can acquire new complex endoscopic skills faster than HD-2D and be free from burdensome polarized glasses. More comparative studies in a clinical setting are needed to further explore the feasibility, necessity, and economic aspects of the GF-3D display system.

Phytofabrication of Nanoparticles as Novel Drugs for Anticancer Applications.

Cancer is one of the foremost causes of death globally and also the major stumbling block of increasing life expectancy. Although the primary treatment of surgical resection, chemotherapy, and radiotherapy have greatly reduced the mortality of cancer, the survival rate is still low because of the metastasis of tumor, a range of adverse drug reactions, and drug resistance. For all this, it is relevant to mention that a growing amount of research has shown the anticarcinogenic effect of phytochemicals which can modulate the molecular pathways and cellular events include apoptosis, cell proliferation, migration, and invasion. However, their pharmacological potential is hindered by their low water solubility, low stability, poor absorption, and rapid metabolism. In this scenario, the development of nanotechnology has created novel formulations to maximize the potential use of phytochemicals in anticancer treatment. Nanocarriers can enhance the solubility and stability of phytochemicals, prolong their half-life in blood and even achieve site-targeting delivery. This review summarizes the advances in utilizing nanoparticles in cancer therapy. In particular, we introduce several applications of nanoparticles combined with apigenin, resveratrol, curcumin, epigallocatechin-3-gallate, 6-gingerol, and quercetin in cancer treatment.

Is the Glasses-Free 3-Dimensional Display System More Effective for Complex Video-Assisted Thoracic Surgery? A Self-Controlled Study Ex Vivo.

Objective. Considering the demerits of a high-definition 2-dimensional (HD-2D) system, with its lack of stereopsis, and a conventional 3-dimensional (C-3D) system, which results in a dimmed image, we have recently developed a glasses-free 3-dimensional (GF-3D) display system for reconstruction surgeries such as video-assisted thoracic surgery (VATS) for tracheal reconstruction. Methods. Thoracic surgeons were invited to complete thoracoscopic continuous suture of a transected porcine trachea using the HD-2D, C-3D, and GF-3D systems on separate mornings in randomized order. The duration, numbers of stitches, and distance between every 2 stitches were recorded for every procedure. The surgeons' spontaneous eye blink rate was recorded for 5 minutes before the procedure and the last 5 minutes of the procedure. Results. Fifteen volunteers successfully completed the tracheal reconstruction procedures in this study. Both C-3D (0.403 ± 0.064 stitch/min, P < .001) and GF-3D (0.427 ± 0.079 stitch/min, P < .001) showed significant advantages in speed compared with HD-2D (0.289 ± 0.065 stitch/min). Both C-3D (2.536 ± 2.223 mm, P < .001) and GF-3D (2.603 ± 2.159 mm, P < .001) showed significant advantages in accuracy compared with HD-2D (3.473 ± 3.403 mm). Both HD-2D (1.240 ± 0.642, P < .001) and GF-3D (1.307 ± 0.894, P < .001) showed significant advantages in eye fatigue compared with C-3D (3.333 ± 1.44). Conclusions. All 3 available display systems are efficient for complex VATS. With the help of stereopsis, surgeons can achieve faster operation using C-3D and GF-3D systems in a thoracoscopic simulated setting. GF-3D may be a more effective display system for VATS reconstruction in terms of speed, accuracy, and eye fatigue during operations.

Matrine induces apoptosis via targeting CCR7 and enhances the effect of anticancer drugs in non-small cell lung cancer in vitro.

This study mainly investigated the effects of matrine on cell apoptosis and the effects of anticancer drugs in non-small cell lung cancer (NSCLC) cell lines (A549 and LK2 cells). The results showed that matrine (≥10 µM) caused a significant inhibition on cell viability and 10 and 100 µM matrine induced cell apoptosis via influencing p53, bax, casp3, and bcl-2 expressions in A549 cells. In addition, matrine significantly down-regulated C-C chemokine receptor type 7 (CCR7) expression, and blocking the down-regulation of CCR7 by exogenous chemokine ligand 21 (CCL21) treatment alleviated matrine-caused effects of apoptosis genes in A549 cells. The results were further validated in LK2 cells that matrine regulated apoptosis gene expressions, which were reversed by CCL21 treatment. Furthermore, matrine enhances the effects of cisplatin, 5-fluorouracil, and paclitaxel in A549 cells, and the anticancer effects exhibit a dosage-dependent manner. In summary, matrine induced cell apoptosis and enhanced the effects of anticancer drugs in NSCLC cells; the mechanism might be associated with the CCR7 signal.

Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks.

State-of-the-art object detection networks depend on region proposal algorithms to hypothesize object locations. Advances like SPPnet [1] and Fast R-CNN [2] have reduced the running time of these detection networks, exposing region proposal computation as a bottleneck. In this work, we introduce a Region Proposal Network (RPN) that shares full-image convolutional features with the detection network, thus enabling nearly cost-free region proposals. An RPN is a fully convolutional network that simultaneously predicts object bounds and objectness scores at each position. The RPN is trained end-to-end to generate high-quality region proposals, which are used by Fast R-CNN for detection. We further merge RPN and Fast R-CNN into a single network by sharing their convolutional features-using the recently popular terminology of neural networks with 'attention' mechanisms, the RPN component tells the unified network where to look. For the very deep VGG-16 model [3] , our detection system has a frame rate of 5 fps (including all steps) on a GPU, while achieving state-of-the-art object detection accuracy on PASCAL VOC 2007, 2012, and MS COCO datasets with only 300 proposals per image. In ILSVRC and COCO 2015 competitions, Faster R-CNN and RPN are the foundations of the 1st-place winning entries in several tracks. Code has been made publicly available.

Image Super-Resolution Using Deep Convolutional Networks.

We propose a deep learning method for single image super-resolution (SR). Our method directly learns an end-to-end mapping between the low/high-resolution images. The mapping is represented as a deep convolutional neural network (CNN) that takes the low-resolution image as the input and outputs the high-resolution one. We further show that traditional sparse-coding-based SR methods can also be viewed as a deep convolutional network. But unlike traditional methods that handle each component separately, our method jointly optimizes all layers. Our deep CNN has a lightweight structure, yet demonstrates state-of-the-art restoration quality, and achieves fast speed for practical on-line usage. We explore different network structures and parameter settings to achieve trade-offs between performance and speed. Moreover, we extend our network to cope with three color channels simultaneously, and show better overall reconstruction quality.

Accelerating Very Deep Convolutional Networks for Classification and Detection.

This paper aims to accelerate the test-time computation of convolutional neural networks (CNNs), especially very deep CNNs [1] that have substantially impacted the computer vision community. Unlike previous methods that are designed for approximating linear filters or linear responses, our method takes the nonlinear units into account. We develop an effective solution to the resulting nonlinear optimization problem without the need of stochastic gradient descent (SGD). More importantly, while previous methods mainly focus on optimizing one or two layers, our nonlinear method enables an asymmetric reconstruction that reduces the rapidly accumulated error when multiple (e.g., ≥ 10) layers are approximated. For the widely used very deep VGG-16 model [1] , our method achieves a whole-model speedup of 4 × with merely a 0.3 percent increase of top-5 error in ImageNet classification. Our 4 × accelerated VGG-16 model also shows a graceful accuracy degradation for object detection when plugged into the Fast R-CNN detector [2] .

Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition.

Existing deep convolutional neural networks (CNNs) require a fixed-size (e.g., 224 × 224) input image. This requirement is "artificial" and may reduce the recognition accuracy for the images or sub-images of an arbitrary size/scale. In this work, we equip the networks with another pooling strategy, "spatial pyramid pooling", to eliminate the above requirement. The new network structure, called SPP-net, can generate a fixed-length representation regardless of image size/scale. Pyramid pooling is also robust to object deformations. With these advantages, SPP-net should in general improve all CNN-based image classification methods. On the ImageNet 2012 dataset, we demonstrate that SPP-net boosts the accuracy of a variety of CNN architectures despite their different designs. On the Pascal VOC 2007 and Caltech101 datasets, SPP-net achieves state-of-the-art classification results using a single full-image representation and no fine-tuning. The power of SPP-net is also significant in object detection. Using SPP-net, we compute the feature maps from the entire image only once, and then pool features in arbitrary regions (sub-images) to generate fixed-length representations for training the detectors. This method avoids repeatedly computing the convolutional features. In processing test images, our method is 24-102 × faster than the R-CNN method, while achieving better or comparable accuracy on Pascal VOC 2007. In ImageNet Large Scale Visual Recognition Challenge (ILSVRC) 2014, our methods rank #2 in object detection and #3 in image classification among all 38 teams. This manuscript also introduces the improvement made for this competition.