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Accurately predicting freeway accident severity is crucial for accident prevention, road safety, and emergency rescue services in intelligent freeway systems. However, current research lacks the required precision, hindering the effective implementation of freeway rescue. In this paper, we efficiently address this challenge by categorizing influencing factors into two levels: human and non-human, further subdivided into 6 and 36 categories, respectively. Furthermore, based on the above factors, an efficient and accurate Freeway Accident Severity Prediction (FASP) method is developed by using the two-level fuzzy comprehensive evaluation. The factor and evaluation sets are determined by calculating the fuzzy evaluation matrix of a single factor. The weight matrix is calculated through the entropy method to compute the final evaluation matrix. Based on the maximum membership principle, the severity of the freeway accident is predicted. Finally, based on the experiments conducted with the traffic accident datasets in China and the US, it is shown that FASP is able to accurately predict the severity of freeway traffic accidents with thorough considerations and low computational cost. It is noted that FASP is the first attempt to achieve freeway accident severity prediction using the two-level fuzzy comprehensive evaluation method to the best of our knowledge.

Background: Rheumatoid arthritis is a systemic inflammatory autoimmune disease that severely impacts physical and mental health. Autophagy is a cellular process involving the degradation of cellular components in lysosomes. However, from a bioinformatics perspective, autophagy-related genes have not been comprehensively elucidated in rheumatoid arthritis. Methods: In this study, we performed differential analysis of autophagy-related genes in rheumatoid arthritis patients using the GSE93272 dataset from the Gene Expression Omnibus database. Marker genes were screened by least absolute shrinkage and selection operator. Based on marker genes, we used unsupervised cluster analysis to elaborate different autophagy clusters, and further identified modules strongly associated with rheumatoid arthritis by weighted gene co-expression network analysis. In addition, we constructed four machine learning models, random forest model, support vector machine model, generalized linear model and extreme gradient boosting based on marker genes, and based on the optimal machine learning model, a nomogram model was constructed for distinguishing between normal individuals and rheumatoid arthritis patients. Finally, five external independent rheumatoid arthritis datasets were used for the validation of our results. Results: The results showed that autophagy-related genes had significant expression differences between normal individuals and osteoarthritis patients. Through least absolute shrinkage and selection operator screening, we identified 31 marker genes and found that they exhibited significant synergistic or antagonistic effects in rheumatoid arthritis, and immune cell infiltration analysis revealed significant changes in immune cell abundance. Subsequently, we elaborated different autophagy clusters (cluster 1 and cluster 2) using unsupervised cluster analysis. Next, further by weighted gene co-expression network analysis, we identified a brown module strongly associated with rheumatoid arthritis. In addition, we constructed a nomogram model for five marker genes (CDKN2A, TP53, ATG16L2, FKBP1A, and GABARAPL1) based on a generalized linear model (area under the curve = 1.000), and the predictive efficiency and accuracy of this nomogram model were demonstrated in the calibration curves, the decision curves and the five external independent datasets were validated. Conclusion: This study identified marker autophagy-related genes in rheumatoid arthritis and analyzed their impact on the disease, providing new perspectives for understanding the role of autophagy-related genes in rheumatoid arthritis and providing new directions for its individualized treatment.

Background: Bispecific antibody (BsAb)-based cancer immunotherapy has provided new avenues for the treatment of various malignancies. The approval of Blinatumomab has encouraged further investigation into these treatments, and a series of preclinical and clinical trials have been conducted, together with the publication of numerous articles. Here, the knowledge structure of BsAb-based cancer immunotherapy is summarized using bibliometric analysis to provide in-depth insight into current research trends and foci. Methods: The studies included in the bibliometric analysis of BsAbs in cancer immunotherapy were retrieved from the online Web of Science Core Collection (WOSCC) database on April 16th, 2023. Visualization analysis was performed with the help of CtieSpace (version 6.2.2.msi [64-bit]), VOSviewer (version 1.6.19), R (version 4.2.1), and the Bibliometric analysis platform (R-based online data processing tool). Results: A total of 1750 papers were identified. Analysis of annual publications and total citations indicated that publications have increased steadily over the past few decades. The USA, followed by Germany, had largest number of publications, making significant contributions to the field. The Memorial Sloan Kettering Cancer Center received the highest number of citations (n = 3769). However, its collaboration and cooperation with different institutions require further strengthening. MAbs and Clinical Cancer Research published the most papers, while Blood and Cancer Research were the most commonly co-cited journals. DM Goldenberg from the USA published the most articles with the highest H-index (34), and the most co-cited author (2137 citations) was PA Baeuerle; both these authors have distinguished achievements in this field. Analysis of co-cited references and keywords showed that the hotspots and research focus on the use of BsAbs for solid tumors have increased rapidly while the application of BsAb immunotherapy in hematologic malignancies has expanded significantly. The hot topics in the field included cytokine release syndrome, the efficacy and safety of BsAbs, resistance mechanisms, and the exploration and optimization of combination therapies. Conclusion: Cancer immunotherapies based on BsAbs are a hot topic in research. Current studies focus on the construction and optimization of BsAb structure, as well as their combination with other treatment modalities to improve their efficacy and overcome resistance. Furthermore, it is expected that the ongoing investigation of BsAb-based immunotherapy for solid tumors will bear fruit with significant clinical application prospects in the near future.

This study explores the Chinese media narrative of Thailand as a tourist destination after Thailand legalised cannabis use for medicinal purposes, using framing and social identity theories. A content analysis was performed by three coders on 128 posts obtained between 2019 and 2022 from four media houses on the Chinese social media platform Weibo: Taiguo.com, Taihuabbs, Vision Thai, and The Sing Sian Media. The first three are Chinese media houses, and the fourth is a Chinese-language newspaper in Bangkok. The results revealed that the Chinese media's framing of the legalisation of cannabis use in Thailand was mainly neutral. In contrast, Chinese netizens' comments showed a negative perception of Thailand as a travel destination after the legalisation of cannabis use. In addition, the study identified the different themes and aspects of narrating, news sources, and media types used to present the information. The implications of these findings highlight the need for the Thai government to develop effective policies and communication strategies to address negative perceptions and promote Thailand as a safe and attractive destination for tourists. The study also suggests the importance of considering cultural and social factors when crafting communication strategies, particularly for sensitive topics such as cannabis use.

Background: LINC02535 has gained much attention for its oncogenicity across several cancers, but the systematic pan-cancer analysis of LINC02535 has not been carried out before. Methods: Herein, we explored the expression level, prognostic value, and hallmark pathways of LINC02535 across multiple cancers using the Cancer Genome Atlas (TCGA) and Cancer Cell Line Encyclopedia (CCLE) databases. Moreover, the expression and biological features of LINC02535 in lung adenocarcinoma (LUAD) were confirmed by qRT-PCR, in vitro and in vivo experiments. Results: LINC02535 is differentially expressed in 10 of 17 human cancers and serves as a favorable or unfavorable biomarker in distinct cancer types. Gene set enrichment analysis (GSEA) indicated that key oncogenic pathways/phenotypes were remarkably activated in most cancers with intratumoral increased LINC02535, whereas these pathways/phenotypes were suppressed in other cancer types (colon adenocarcinoma, kidney renal clear cell carcinoma, rectal adenocarcinoma) with intratumoral decreased LINC02535. Of note, the epithelial-mesenchymal transition (EMT) phenotype was greatly enriched in LUAD patients with elevated LINC02535. Based on the TCGA and CCLE datasets, LINC02535 was positively correlated with the EMT-related gene CD73 (also named as NT5E, an immunosuppressive gene) in almost all cancer types (Spearman R > 0.5, P < 0.001) including LUAD. Most importantly, qRT-PCR confirmed that LINC02535 was upregulated in lung cancer cells or tissues as opposed to human bronchial epithelial cells or paratumor tissues. Knockdown of LINC02535 inhibited proliferation, migration of LUAD cells and retarded xenografted tumor growth. Moreover, silencing of LINC02535 induced apoptosis and cell cycle arrest at G1 phase. Conclusions: The findings from our pan-cancer analysis provide a relatively comprehensive understanding of the potential value of LINC02535 across multiple cancers, and the oncogenic role of LINC02535 in LUAD has been confirmed.