Search

Cycling has gained increasing popularity worldwide as a healthy and sustainable mode of travel. This study aims to investigate the factors affecting the cycle count at the road segment level based on a detailed survey dataset of 794 road segments in London from 2015 to 2019. A spatial regression model was employed to control for possible spatial correlations among neighboring count points. The influencing factors at three spatial levels were considered in this study, including the characteristics of the target road segment, adjacent road segments, and adjacent areas. In addition, we investigated the differences in cycle counts between private and rental cycles during different time periods (morning peak, evening peak, and off-peak hours). The results indicated that private cycle count was positively correlated with cycle facility, public transit, minor road, network continuity, and connectivity. A similar positive effect was found for rental cycles, although the magnitude of this effect was smaller. In addition, parking facilities (i.e., docking stations and cycle parking) had significant impacts on cycle counts for both private and rental cyclists. This study provides several practical suggestions for improving cycling environments. [ABSTRACT FROM AUTHOR]

The analyses of the safety effects of the road network features can help to understand the risk factors in road crashes and provide useful policy implications. In macro-level analyses, adjacent traffic analysis zones (TAZs) could influence each other on road safety. Therefore, it is necessary to move beyond the common practice of just examining the road network of the TAZ itself and analyze how and to what extent the interrelated factors of the objective TAZ and its adjacent TAZs interact to affect crash frequency. This study aims to investigate the safety impacts of the discrepancies and accesses between adjacent TAZs. Based on data from London at the level of Lower Layer Super Output Area (LSOA), the Bayesian model was employed to analyze the safety effects of various factors related to the discrepancies and accesses (i.e., access number, discrepancies of road network density). A spatial weight based on the access number was applied when establishing the Bayesian spatial models. Results suggest that TAZs with greater discrepancies and more accesses from adjacent TAZs experience more crashes. [ABSTRACT FROM AUTHOR]

Gramicidin is a well-known antibiotic and recently was reported to induced tumour cell death, however, little is understood about the molecular mechanism of gramicidin as a therapeutic agent for solid tumours. Here, we investigated the role of gramicidin in cholangiocarcinoma cells. We found that gramicidin A inhibits cholangiocarcinoma cell growth and induced the necrotic cell death. We used next generation sequencing to analyse gene expression profiles of cholangiocarcinoma cells treated with gramicidin. We identified 265 differentially expressed genes in cholangiocarcinoma cells between PBS treatment and gramicidin treatment. EGR4 was confirmed to be a target of gramicidin-induced cell growth inhibition. Furthermore, we demonstrated that downregulation of EGR4 in cholangiocarcinoma cells leads to restraining tumour cell growth. Of note, EGR4 was expressed at highest levels in cholangiocarcinoma tissues among 17 types of human cancers, and EGR4 expression positively correlated with several growth factors associated with cholangiocarcinoma. Our findings ascertain that EGR4 is a potential target in cholangiocarcinoma and suppressing EGR4 by gramicidin establish an essential mechanism for bile duct carcinoma progression. [ABSTRACT FROM AUTHOR]

Objective: To analyze the differentially expressed genes (DEGs) between lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) with bioinformatics analysis and search for potential biomarkers for clinical diagnosis of nonsmall cell lung cancer (NSCLC)., Methods: The gene expression profiling datasets of LUAD and LUSC were acquired. The transcriptome differences between LUAD and LUSC were identified using R language processing and t-test analysis. The differential expressions of the genes were shown by Venn diagram. The DEGs identified by GEO2R were analyzed with DAVID and Ingenuity Pathway Analysis (IPA) to identify the signaling pathways and biomarkers that could be used for differential diagnosis of LUAD and LUSC. The TCGA data and the biomarker expression data from clinical lung cancer samples were used to verify the differential expressions of the Osteoarthritis pathway and LXR/RXR between LUAD and LUSC. We further examined the differential expressions of miR-181 and its two target genes, WNT5A and MBD2 , in 23 clinical specimens of lung squamous cell carcinoma and the paired adjacent tissues., Results: GEO data analysis identified 851 DEGs (including 276 up-regulated and 575 down-regulated genes) in LUAD and 885 DEGs (including 406 up-regulated and 479 down-regulated genes) in LUSC. DAVID and IPA analysis revealed that leukocyte migration and inflammatory responses were more abundant in LUAD than in LUSC. Osteoarthritis pathway was inhibited in LUAD and activated in LUSC. IPA analysis showed that transcription factors (GATA4, RELA, YBX1, TP63 and MBD2), cytokines (WNT5A and IL1A) and microRNAs (miR-34a, miR-181b and miR-15a) differed significantly between LUAD and LUSC. miR-34a with IL-1A, miR-15a with YBX1, and miR-181b with WNT5A and MBD2 could serve as the paired microRNA and mRNA targets for differential diagnosis of NSCLC subtypes. Analysis of the clinical samples showed an increased expression of miR-181b-5p and the down-regulation of WNT5A, which could be used as molecular markers for the diagnosis of LUSC., Conclusions: Through transcriptome analysis, we identified candidate genes, paired microRNAs and pathways for differentiating LUAD and LUSC, and they can provide novel differential diagnosis and therapeutic strategies for LUAD and LUSC.