To date, two chimeric antigen receptors (CAR)-T cell products from autologous T cells have been approved by The United States Food and Drug Administration (FDA). The case-by-case autologous T cell generation setting is largely considered as a pivotal restraining cause for its large-scale clinical use because of the costly and prolonged manufacturing procedure. Further, activated CAR-T cells mainly express immune checkpoint molecules, including CTLA4, PD1, LAG3, abrogating CAR-T anti-tumor activity. In addition, CAR-T cell therapy potently results in some toxicity, such as cytokine releases syndrome (CRS). Therefore, the development of the universal allogeneic T cells with higher anti-tumor effects is of paramount importance. Thus, genome-editing technologies, in particular, clustered regularly interspaced short palindromic repeat (CRISPR)-Cas9 are currently being used to establish “off-the-shelf” CAR-T cells with robust resistance to immune cell-suppressive molecules. In fact, that simultaneous ablation of PD-1, T cell receptor alpha constant (TRAC or TCR), and also β-2 microglobulin (B2M) by CRISPR-Cas9 technique can support the manufacture of universal CAR-T cells with robust resistance to PD-L1. . Indeed, the ablation of β2M or TARC can severely hinder swift elimination of allogeneic T cells those express foreign HLA-I molecules, and thereby enables the generation of CAR-T cells from allogeneic healthy donors T cells with higher persistence in vivo. Herein, we will deliver a brief overview of the CAR-T cell application in the context of tumor immunotherapy. More importantly, we will discuss recent finding concerning the application of genome editing technologies for preparing universal CAR-T cells or cells that can effectively counter tumor escape, with a special focus on CRISPR-Cas9 technology.

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In this comparative study, we investigated different influential factors of mobile-assisted language learning (MALL) for improving productive and receptive language learning skills. To do this, 100 scientific research papers were selected from the top and high-quality journals based on the implications of MALL in higher education language learning. Eight papers were selected according to the specific criteria to categorize the results based on language skills interpretation and technological concepts. Therefore, after thoroughly understanding proposed methodologies and comparing them properly, underlying assumptions about this phenomenon are elaborated, and holistic and sustainable solutions are provided to address this idea. The results of this analysis indicated that Mobile devices are being utilized extensively in developing countries, with vocabulary being the primary language learning area assisted by technology and yielding satisfactory results. According to the findings of this comparative study, LINE and WhatsApp would be of interest among university students to improve receptively (listening and reading) and productivity (writing and speaking), respectively. Moreover, the technology acceptance model (TAM) would be a prominent option for teachers to adapt their current and future educational programs instead of only in-person teaching to improve students’ learning quality.

There has been quite a number of research efforts in extracting academic social network from on-line open sources such as the DBLP, ACM DL and IEEXplore. Extraction of such a network is usually based on the concept of co-occurrences. One of the issues in such efforts is actually involved extracting reliable and trusted network particularly when dealing with the heterogeneity of features in the Web. In this paper we demonstrate the use of association rule to enhance existing superficial method for extracting social network from online database such as the DBLP. The approach proposed has shown the capacity to extract social relation as well as the strength of these relations.