No abstract

Cancer-associated fibroblasts (CAFs), an activated subpopulation of fibroblasts, occupy a central position in the tumor microenvironment and have been shown to promote chemoresistance in multiple cancer types by secreting inflammatory cytokines. Herein, we report that tumor-secreted exosomal long non-coding RNAs (lncRNAs) can regulate cisplatin resistance in esophageal squamous cell carcinoma (ESCC) through transformation of normal fibroblasts (NFs) to CAFs. Primary CAFs and matched NFs were isolated from tumor tissues and matched normal esophageal epithelial tissues of ESCC patients. Fluorescence microscopy and qRT-PCR were used to investigate the transportation of exosomal lncRNAs from ESCC cells to NFs. To identify the specific lncRNAs involved, 14 ESCCrelated lncRNAs were measured in NFs after incubation with exosomes from ESCC cells. We demonstrated that lncRNA POU3F3 can be transferred from ESCC cells to NFs via exosomes and that it mediated fibroblast activation. Activated fibroblasts further promoted proliferation and cisplatin resistance of ESCC cells through secreting interleukin 6 (IL-6). Moreover, our clinical data showed that high levels of plasma exosomal lncRNA POU3F3 correlated significantly with lack of complete response and poor survival in ESCC patients. Therefore, these data demonstrate that lncRNA POU3F3 is involved in cisplatin resistance in ESCC and that this effect is mediated through exosomal lncRNA POU3F3-induced transformation of NFs to CAFs.

License Plate Recognition (LPR) is of great significance due to its wide range of applications in the Intelligent Transportation System (ITS). It is an important and challenging research topic in image recognition fields. However, many of the current methods are still not robust in real-world complex scenario. The main contribution of this paper is to propose a multi-task convolutional neural network for license plate detection and recognition (MTLPR) with better accuracy and lower computational cost, and introduce a comprehensive data set of Chinese license plate. First, we train a Multi-task Convolutional Neural Networks (MTCNN) to detect license plate. Then we introduce an end-to-end method to recognize license plate information, which further improves the recognition precision. Last, We compare the experimental result with other state-of-the-art methods. The experimental result shows that our method achieves up to 98% recognition precision and is superior to other methods in the precision and speed of detection and recognition. INDEX TERMS Object detection, optical character recognition, license plate recognition, convolutional neural network.

No abstract

Circular RNA (circRNA) circ-LRP6 was recently proven to be a pivotal player in various human diseases. Nevertheless, its role in esophageal squamous cell cancer (ESCC) remains unknown. In the current study, we investigated the expression level, biological function and mechanism of circ-LRP6 in ESCC. Circ-LRP6 was significantly upregulated in ESCC tissues and correlated with malignant clinicopathological characteristics and poor prognosis. Knockdown of circ-LRP6 evidently reduced ESCC cell viability, colony formation and invasion. Circ-LRP6 was mainly located in the cytoplasm and could sponge miR-182 to increase the expression of Myc, a well-documented proto-oncogene. Importantly, circ-LRP6 depletion significantly retarded tumor growth in vivo. And silencing of miR-182 or overexpression of Myc effectively rescued the attenuated aggressive phenotype of ESCC cells caused by circ-LRP6 knockdown. Therefore, our data indicate that circ-LRP6 is a novel oncogenic circRNA in ESCC, targeting the circ-LRP6/miR-182/ Myc signaling may be a promising therapeutic approach for ESCC patients.

microRNA-133a (miR-133a) and miR-133b, located on chromosome 18 in the same bicistronic unit, have been commonly identified as being downregulated in esophageal squamous cell carcinoma (ESCC). The aim of this study was to investigate the correlation of miR-133a/b expression with efficacy of paclitaxel-based chemotherapy and clinical outcome of ESCC patients. miR-133a expression and miR-133b expression were examined in 100 newly diagnosed ESCC patients prior to treatment by quantitative real-time PCR. Then, the patients received four cycles of paclitaxel-based chemotherapy, the short-term treatment efficacy was evaluated, and a 3-year follow-up was performed. Expression levels of miR-133a and miR-133b were both significantly lower in ESCC tissues compared to adjacent noncancerous tissues (both P < 0.001). In addition, combined miR-133a/b downregulation was found to be closely correlated with advanced tumor stage (P = 0.02) and poor differentiation (P = 0.01). Moreover, the response rate of ESCC patients to paclitaxel-based chemotherapy was significantly higher in combined miR-133a/b downregulation group compared with other groups (P = 0.02). Furthermore, univariate and multivariate Cox analyses revealed that tumor stage and combined expression of miR-133a/b were independent prognosis factors in ESCC patients. Our data offer the convincing evidence that combined expression of miR-133a and miR-133b may predict chemosensitivity of patients with ESCC undergoing paclitaxel-based chemotherapy, implying its importance in applying 'personalized cancer medicine' in the clinical treatment of ESCC. We also identified combined expression of miR-133a and miR-133b as an effective prognostic marker of this malignancy.

Purpose: In this multicenter phase 3 trial, the efficacy and safety of 60 Gy and 50 Gy doses delivered with modern radiotherapy technology for definitive concurrent chemoradiotherapy (CCRT) in patients with inoperable esophageal squamous cell carcinoma (ESCC) were evaluated. Patients and Methods: Patients with pathologically confirmed stage IIA‒IVA ESCC were randomized 1:1 to receive conventional fractionated 60 Gy or 50 Gy to the tumor and regional lymph nodes. Concurrent weekly chemotherapy (docetaxel 25 mg/m2; cisplatin 25 mg/m2) and two cycles of consolidation chemotherapy (docetaxel 70 mg/m2; cisplatin 25 mg/m2 days 1‒3) were administered. Results: A total of 319 patients were analyzed for survival, and the median follow-up was 34.0 months. The 1- and 3-year locoregional progression-free survival (PFS) rates for the 60 Gy group were 75.6% and 49.5% versus 72.1% and 48.4%, respectively, for the 50 Gy group [HR, 1.00; 95% confidence interval (CI), 0.75‒1.35; P = 0.98]. The overall survival rates were 83.7% and 53.1% versus 84.8% and 52.7%, respectively (HR, 0.99; 95% CI, 0.73‒1.35; P = 0.96), whereas the PFS rates were 71.2% and 46.4% versus 65.2% and 46.1%, respectively (HR, 0.97; 95% CI, 0.73‒1.30; P = 0.86). The incidence of grade 3+ radiotherapy pneumonitis was higher in the 60 Gy group (nominal P = 0.03) than in the 50 Gy group. Conclusions: The 60 Gy arm had similar survival endpoints but a higher severe pneumonitis rate compared with the 50 Gy arm. Fifty Gy should be considered as the recommended dose in CCRT for ESCC.

Convolutional neural networks (CNN) have a significant improvement in the accuracy of object detection. As networks become deeper, the precision of detection becomes obviously improved, and more floating-point calculations are also needed. Because of the great amount of calculation, it is inconvenient for mobile and embedded vision applications. Many researchers apply the knowledge distillation method to improve the precision of object detection by transferring knowledge from a deeper and larger teachers network to a small student one. Most methods of knowledge distillation are needed to design complex cost functions and mainly aim at the two-stage object detection algorithm. Therefore, we propose a clean and effective knowledge distillation method called Generative Adversarial Networks-Knowledge Distillation(GAN-KD) for the one-stage object detection. The feature maps generated by teacher network and student network are employed as true and fake samples respectively, and generating adversarial training for both of them to improve the performance of the student network in one-stage object detection. The experimental result shows that our approach achieves the performance gain of 5% mAP when compared with MobilenetV1 on COCO dataset. INDEX TERMS Object detection, generative adversarial networks, knowledge distillation.