The 2019 novel coronavirus is a non-segmented positive-sense RNA virus belonging to the Coronaviridae-Nidovirales family. We examined the swings in purchase behavior following the outbreak of the COVID-19 in Wuhan, China, and across the world based on the theory of fear appeal. We gathered published statistics (suspected, confirmed, and fatality) on the COVID-19 alongside the purchase of personal protective equipment to examine the swings in online purchase behavior. With a serial mediated analysis, we established that fear appeal is associated with the sharp dynamics in the online purchase as related to the COVID-19. The results confirmed that fear appeal promotes social presence in anticipation of seeking affection, acceptance, and social information. This feeling is a precondition for developing e-loyalty, which promotes purchase behavior. Even though our variables might not be conclusive enough, we believe the findings are fundamental to understanding the swings in the purchase trend in this and any similar situations.

Although the increasing mobile technology applies in our daily lives, there remain questions that clearly illustrate the experience inspired by the mobile technology environment. It is theoretically and practically meaningful to systematically reveal how mobile technology generates users' virtual experience and, subsequently, the behavioral response. To fill the gaps, drawing on the Stimulus-Organism-Response model, this paper regards flow as the core experience based on the features of the mobile users' experience. The framework is that mobile technology will promote users' virtual experience, and in turn, affect their behavioral response. This study uses online survey data from 452 respondents. We employ the structural equation model based on the partial least squares method and further mediation analysis by the Sobel test to validate the research hypotheses. Our results show that the mobile technology environment has a significantly positive effect on users' purchase intention/return mediated by the virtual experience. However, the direct relationship between ubiquity and flow, as well as telepresence and intention to purchase/return, are not significant. Finally, a few theoretical and managerial contributions are also discussed.

Triple-negative breast cancer (TNBC) is characterized by fast progression with high potential for metastasis, and poor prognosis. The dysregulation of microRNAs (miRNAs) occurring in the initiation or progression of cancers often leads to aberrant gene expression. The aim of the present study was to explore the function of miR-126 in TNBC cells. Expression levels of miR-126-3p were determined by quantitative real-time PCR. Then, the effects of miR-126-3p on migration, proliferation, invasion, and angiogenesis were assessed through in vitro experiments including Cell Counting Kit-8, colony formation, Transwell invasion and vasculogenic mimicry formation assays. One of the target genes for miR-126-3p predicted by TargetScan was confirmed by luciferase activity assay. Results indicated that miR-126-3p expression was reduced in TNBC cell lines. Functional assays revealed that miR-126-3p overexpression inhibited cell proliferation, migration, invasion, colony formation capacity and vasculogenesis by 1.2-, 1.8-, 2.3-, 2.0-and 3.3-fold, respectively, compared to the miRNA-negative control group of MDA-MB-231 cells (P<0.001, respectively). In addition, the regulator of G-protein signaling 3 (RGS3) was hypothesized and validated as a direct target of miR-126-3p in TNBC. The proliferation, migration, invasion, colony formation capacity and vasculogenesis of MDA-MB-231 cells were significantly increased by 1.4-, 2.0-, 1.8-, 1.4-and 3.2-fold, respectively, in cells transfected with pcDNA3.0-RGS3 compared to pcDNA3.0-negative control groups (P<0.001, respectively). The influence of miR-126-3p expression was reversed by RGS3 restoration. Collectively, the present study revealed that miR-126-3p plays a role as a tumor suppressor in regulating TNBC cell activities by targeting RGS3, indicating that the miR-126-3p/RGS3 axis may be a potential treatment target.

No abstract

No abstract

In the era of Web 2.0, there is a huge amount of user-generated content, but the huge amount of unstructured data makes it difficult for merchants to provide personalized services and for users to extract information efficiently, so it is necessary to perform sentiment analysis for restaurant reviews. The significant advantage of Bi-GRU is the guaranteed symmetry of the hidden layer weight update, to take into account the context in online restaurant reviews and to obtain better results with fewer parameters, so we combined Word2vec, Bi-GRU, and Attention method to build a sentiment analysis model for online restaurant reviews. Restaurant reviews from Dianping.com were used to train and validate the model. With F1-score greater than 89%, we can conclude that the comprehensive performance of the Word2vec+Bi-GRU+Attention sentiment analysis model is better than the commonly used sentiment analysis models. We applied deep learning methods to review sentiment analysis in online food ordering platforms to improve the performance of sentiment analysis in the restaurant review domain.

No abstract

Background: Breast cancer (BC), the leading cause of cancer-related deaths among women, remains a serious threat to human health worldwide. The biological function and prognostic value of disulfidptosis as a novel strategy for BC treatment via induction of cell death remain unknown.Methods: Gene mutations and copy number variations (CNVs) in 10 disulfidptosis genes were evaluated. Differential expression, prognostic, and univariate Cox analyses were then performed for 10 genes, and BC-specific disulfidptosis-related genes (DRGs) were screened. Unsupervised consensus clustering was used to identify different expression clusters. In addition, we screened the differentially expressed genes (DEGs) among different expression clusters and identified hub genes. Moreover, the expression level of DEGs was detected by RT-qPCR in cellular level. Finally, we used the least absolute shrinkage and selection operator (LASSO) regression algorithm to establish a prognostic feature based on DEGs, and verified the accuracy and sensitivity of its prediction through prognostic analysis and subject operating characteristic curve analysis. The correlation of the signature with the tumor immune microenvironment and tumor stemness was analyzed.Results: Disulfidptosis genes showed significant CNVs. Two clusters were identified based on three DRGs (DNUFS1, LRPPRC, SLC7A11). Cluster A was found to be associated with better survival outcomes(p &lt; 0.05) and higher levels of immune cell infiltration(p &lt; 0.05). A prognostic signature of four disulfidptosis-related DEGs (KIF21A, APOD, ALOX15B, ELOVL2) was developed by LASSO regression analysis. The signature showed a good prediction ability. In addition, the prognostic signature in this study were strongly related to the tumor microenvironment (TME), tumor immune cell infiltration, tumor mutation burden (TMB), tumor stemness, and drug sensitivity.Conclusion: The prognostic signature we constructed based on disulfidptosis-DEGs is a good predictor of prognosis in patients with BC. This prognostic signature is closely related to TME, and its potential correlation provides clues for further studies.