Roles of the tumor microenvironment in the resistance to programmed cell death protein 1 inhibitors in patients with gastric cancer

Despite the continuous developments and advancements in the treatment of gastric cancer(GC),which is one of the most prevalent types of cancer in China,the overall survival is still poor for most patients with advanced GC.In recent years,with the progress in tumor immunology research,attention has shifted toward immunotherapy as a therapeutic approach for GC.Programmed cell death protein 1(PD-1)inhibitors,as novel immunosuppressive medications,have been widely utilized in the treatment of GC.However,many patients are still resistant to PD-1 inhibitors and experience recurrence in the advanced stages of PD-1 immunotherapy.To reduce the occurrence of drug resistance and recurrence in GC patients receiving PD-1 immunotherapy,to maximize the clinical activity of immunosuppressive drugs,and to elicit a lasting immune response,it is essential to research the tumor microenvironment mechanisms leading to PD-1 inhibitor resistance in GC patients.This article reviews the progress in studying the factors influencing the resistance to PD-1 inhibitors in the GC tumor microenvironment,aiming to provide insights and a basis for reducing resistance to PD-1 inhibitors for GC patients in the future.

Captioning Videos Using Large-Scale Image Corpus

Video captioning is the task of assigning complex high-level semantic descriptions （e.g., sentences or paragraphs） to video data. Different from previous video analysis techniques such as video annotation, video event detection and action recognition, video captioning is much closer to human cognition with smaller semantic gap. However, the scarcity of captioned video data severely limits the development of video captioning. In this paper, we propose a novel video captioning approach to describe videos by leveraging freely-available image corpus with abundant literal knowledge. There are two key aspects of our approach： 1） effective integration strategy bridging videos and images, and 2） high efficiency in handling ever-increasing training data. To achieve these goals, we adopt sophisticated visual hashing techniques to efficiently index and search large-scale images for relevant captions, which is of high extensibility to evolving data and the corresponding semantics. Extensive experimental results on various real-world visual datasets show the effectiveness of our approach with different hashing techniques, e.g., LSH （locality-sensitive hashing）, PCA-ITQ （principle component analysis iterative quantization） and supervised discrete hashing, as compared with the state-of-the-art methods. It is worth noting that the empirical computational cost of our approach is much lower than that of an existing method, i.e., it takes 1/256 of the memory requirement and 1/64 of the time cost of the method of Devlin et al.