The impact of the intensity of media use on potential tourists’ risk perception and travel protective behavioral intentions in COVID-19

Introduction: In light of the COVID-19 pandemic, there is an increased need for potential travelers to gather information about their trips to mitigate perceived risks. This study aims to understand the relationship between the intensity of media use (both new and traditional), epidemic risk perception, and tourism protection behavior intention among potential tourists. Methods: A total of 491 valid questionnaires were collected in Shanghai, China. Factor analysis, path analysis, and effect analysis were conducted using SPSS and AMOS to examine the impact of different media types on epidemic risk perception and tourism protection behavior. Results: The findings indicate a positive association between new media use intensity and epidemic risk perception, as well as an intention to adopt safety-conscious tourism behaviors. In contrast, traditional media usage is inversely associated with risk perception but has no significant influence on protective behavior. The results also highlight the role of demographic factors, such as age, education level, occupation, and income, in modulating the relationship between media usage and risk perception. Discussion: The contrasting effects of new and traditional media suggest the need for a tailored approach in epidemic communication strategies. Public health officials should leverage new media to enhance risk perception and safety-oriented behaviors, while recognizing the role of traditional media in managing lower risk perceptions and assuaging panic. The study emphasizes the importance of personalized messaging based on demographic disparities in media usage and perception. The mediating role of risk perception in shaping protective behaviors offers insights for promoting adherence to safety protocols. Conclusion: This study contributes to a comprehensive understanding of media influences during health crises, emphasizing the responsibility of media platforms in transmitting accurate information. The findings call for a nuanced approach to epidemic communication, considering the strengths and weaknesses of different media types. Segmented and personalized messaging strategies can cater to demographic variations in media usage and perception. Enhancing risk perception through tailored messaging can promote protective behaviors and effectively manage public sentiment during health crises

A two-dimensional hybrid with molybdenum disulfide nanocrystals strongly coupled on nitrogen-enriched graphene via mild temperature pyrolysis for high performance lithium storage

A novel 2D hybrid with MoS₂ nanocrystals strongly coupled on nitrogen-enriched graphene (MoS₂/NGg-C₃N₄) is realized by mild temperature pyrolysis (550 °C) of a self-assembled precursor (MoS₃/g-C₃N₄–H⁺/GO). With rich active sites, the boosted electronic conductivity and the coupled structure, MoS₂/NGg₋C₃N₄ achieves superior lithium storage performance

Tumor biology experimental course design based on the integration of molecular biology and metabolomics

In recent years, single-subject experimental courses have not been able to meet the demands of the comprehensive development of life sciences. This study is based on two fundamental disciplines, molecular biology and metabolomics, with the analysis and intervention of tumor-related genes as the starting point. A comprehensive experimental course has been set up to analyze oncological genetic abnormal induced molecular signature and metabolome feature alterations through in vitro and in vivo experimental validations. This newly designed course combines guide theories and practical instructions. Through database screening, in vitro research and in vivo research, student’s research interest and knowledge reserves are greatly enhanced and a foundation is laid for further scientific research in the future

Oxymatrine Ameliorates Memory Impairment in Diabetic Rats by Regulating Oxidative Stress and Apoptosis: Involvement of NOX2/NOX4

Oxymatrine (OMT) is the major quinolizidine alkaloid extracted from the root of Sophora flavescens Ait and has been shown to exhibit a diverse range of pharmacological properties. The aim of the present study was to investigate the role of OMT in diabetic brain injury in vivo and in vitro. Diabetic rats were induced by intraperitoneal injection of a single dose of 65 mg/kg streptozotocin (STZ) and fed a high-fat and high-cholesterol diet. Memory function was assessed using a Morris water maze test. A SH-SY5Y cell injury model was induced by incubation with glucose (30 mM/l) to simulate damage in vitro. The serum fasting blood glucose, insulin, serum S100B, malondialdehyde (MDA), and superoxide dismutase (SOD) levels were analyzed using commercial kits. Morphological changes were observed using Nissl staining and electron microscopy. Cell apoptosis was assessed using Hoechst staining and TUNEL staining. NADPH oxidase (NOX) and caspase-3 activities were determined. The effects of NOX2 and NOX4 knockdown were assessed using small interfering RNA. The expression levels of NOX1, NOX2, and NOX4 were detected using reverse transcription-quantitative PCR and western blotting, and the levels of caspase-3 were detected using western blotting. The diabetic rats exhibited significantly increased plasma glucose, insulin, reactive oxygen species (ROS), S-100B, and MDA levels and decreased SOD levels. Memory function was determined by assessing the percentage of time spent in the target quadrant, the number of times the platform was crossed, escape latency, and mean path length and was found to be significantly reduced in the diabetic rats. Hyperglycemia resulted in notable brain injury, including histological changes and apoptosis in the cortex and hippocampus. The expression levels of NOX2 and NOX4 were significantly upregulated at the protein and mRNA levels, and NOX1 expression was not altered in the diabetic rats. NOX and caspase-3 activities were increased, and caspase-3 expression was upregulated in the brain tissue of diabetic rats. OMT treatment dose-dependently reversed behavioral, biochemical, and molecular changes in the diabetic rats. In vitro, high glucose resulted in increases in reactive oxygen species (ROS), MDA levels, apoptosis, and the expressions of NOX2, NOX4, and caspase-3. siRNA-mediated knockdown of NOX2 and NOX4 decreased NOX2 and NOX4 expression levels, respectively, and reduced ROS levels and apoptosis. The results of the present study suggest that OMT alleviates diabetes-associated cognitive decline, oxidative stress, and apoptosis via NOX2 and NOX4 inhibition

Drug repositioning with adaptive graph convolutional networks

Drug repositioning is an effective strategy to identify new indications for existing drugs, providing the quickest possible transition from bench to bedside. With the rapid development of deep learning, graph convolutional networks (GCNs) have been widely adopted for drug repositioning tasks. However, prior GCNs based methods exist limitations in deeply integrating node features and topological structures, which may hinder the capability of GCNs.In this study, we propose an adaptive graph convolutional networks approach, termed AdaDR, for drug repositioning by deeply integrating node features and topological structures. Distinct from conventional graph convolution networks, AdaDR models interactive information between them with adaptive graph convolution operation, which enhances the expression of model. Concretely, AdaDR simultaneously extracts embeddings from node features and topological structures and then uses the attention mechanism to learn adaptive importance weights of the embeddings. Experimental results show that AdaDR achieves better performance than multiple baselines for drug repositioning. Moreover, in the case study, exploratory analyses are offered for finding novel drug-disease associations.The implementation of AdaDR and the preprocessed data is available at: https://github.com/xinliangSun/AdaDR.Supplementary data are available at Bioinformatics online.Peer reviewe

Identification of a circadian gene signature that predicts overall survival in lung adenocarcinoma

Background Lung adenocarcinoma (LUAD) is one of the most common subtypes of lung cancer which is the leading cause of death in cancer patients. Circadian clock disruption has been listed as a likely carcinogen. However, whether the expression of circadian genes affects overall survival (OS) in LUAD patients remains unknown. In this article, we identified a circadian gene signature to predict overall survival in LUAD. Methods RNA sequencing (HTSeq-FPKM) data and clinical characteristics were obtained for a cohort of LUAD patients from The Cancer Genome Atlas (TCGA). A multigene signature based on differentially expressed circadian clock-related genes was generated for the prediction of OS using Least Absolute Shrinkage and Selection Operator (LASSO)-penalized Cox regression analysis, and externally validated using the GSE72094 dataset from the GEO database. Results Five differentially expressed genes (DEGs) were identified to be significantly associated with OS using univariate Cox proportional regression analysis (P 1, P < 0.001). Receiver operating characteristic (ROC) curves confirmed its prognostic value. Gene set enrichment analysis (GSEA) showed that Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways related to cell proliferation, gene damage repair, proteasomes, and immune and autoimmune diseases were significantly enriched. Conclusion A novel circadian gene signature for OS in LUAD was found to be predictive in both the derivation and validation cohorts. Targeting circadian genes is a potential therapeutic option in LUAD

Autoencoder Networks Decipher the Association between Lung Cancer and Alzheimer’s Disease

Lung cancer is the most common malignancy and is responsible for the largest cancer-related mortality worldwide. Alzheimer’s disease is a degenerative neurological disease that burdens healthcare worldwide. While the two diseases are distinct, several transcriptomic studies have demonstrated they are linked. However, no concordant conclusion on how they are associated has been drawn. Since these studies utilized conventional bioinformatics methods, such as the differentially expressed gene (DEG) analysis, it is naturally expected that the proportion of DEGs having either the same or inverse directions in lung cancer and Alzheimer’s disease is substantial. This raises the inconsistency. Therefore, a novel bioinformatics method capable of determining the direction of association is desirable. In this study, the moderated t-tests were first used to identify DEGs that are shared by the two diseases. For the shared DEGs, separate autoencoder (AE) networks were trained to extract a one-dimensional representation (pseudogene) for each disease. Based on these pseudogenes, the association direction between lung cancer and Alzheimer’s disease was inferred. AE networks based on 266 shared DEGs revealed a comorbidity relationship between Alzheimer’s disease and lung cancer. Specifically, Spearman’s correlation coefficient between the predicted values using the two AE networks for the Alzheimer’s disease test set was 0.825 and for the lung cancer test set was 0.316. Novel bioinformatics methods such as an AE network may help decipher how distinct diseases are associated by providing the refined representations of dysregulated genes