Determinants of career aspirations of medical students in southern China

<p>Abstract</p> <p>Background</p> <p>With recent changes in both the Chinese medical system and compensation of medical doctors, the career aspirations of Chinese medical students have become more diverse. Shantou University Medical College has conducted evaluations and instituted programs to enhance student preparedness to enter a variety of medical careers.</p> <p>Methods</p> <p>A survey was conducted with 85 students to evaluate medical career aspirations and their association with family background, personal skills, English language proficiency, and interest in biomedical research, which were considered as possible factors affecting their career interest.</p> <p>Results</p> <p>Chinese students aspire to traditional as well as nontraditional medical careers. A significant minority of students are now interested in nontraditional careers such as medical teaching or research. However, poor proficiency in the English language and lack of computer skills may limit their academic and career opportunities.</p> <p>Conclusion</p> <p>Career aspirations have changed among medical undergraduates. Although many wish to pursue a traditional clinical doctor career, many are interested in research and teaching careers. Factors such as family background, personal characteristics, school mentoring, and extracurricular support may play a role.</p

PPI Network Analysis of mRNA Expression Profile of Ezrin Knockdown in Esophageal Squamous Cell Carcinoma

Ezrin, coding protein EZR which cross-links actin filaments, overexpresses and involves invasion, metastasis, and poor prognosis in various cancers including esophageal squamous cell carcinoma (ESCC). In our previous study, Ezrin was knock down and analyzed by mRNA expression profile which has not been fully mined. In this study, we applied protein-protein interactions (PPI) network knowledge and methods to explore our understanding of these differentially expressed genes (DEGs). PPI subnetworks showed that hundreds of DEGs interact with thousands of other proteins. Subcellular localization analyses found that the DEGs and their directly or indirectly interacting proteins distribute in multiple layers, which was applied to analyze the shortest paths between EZR and other DEGs. Gene ontology annotation generated a functional annotation map and found hundreds of significant terms, especially those associated with cytoskeleton organization of Ezrin protein, such as “cytoskeleton organization,” “regulation of actin filament-based process,” and “regulation of actin cytoskeleton organization.” The algorithm of Random Walk with Restart was applied to prioritize the DEGs and identified several cancer related DEGs ranked closest to EZR. These analyses based on PPI network have greatly expanded our comprehension of the mRNA expression profile of Ezrin knockdown for future examination of the roles and mechanisms of Ezrin

The k = 12 clique from the downregulated miRNA-miRNA network and its co-regulated subpathways.

<p>Green nodes represent downregulated miRNAs, while upregulated miRNA is colored red. The size of the miRNA nodes corresponds to the node degree. <i>P</i>-value strength is represented by edge line width, with darker edges representing more significant interactions.</p

ESCC patient clusters and survival analysis.

<p>(<b>A</b>) The cluster of miR-31 and miR-338-3p in 89 ESCC patients. The prefix 0 represents deceased ESCC patients, while the prefix 1 represents living ESCC patients. (<b>B</b>) Survival of grouped ESCC patients is analyzed by Kaplan-Meier analysis and the log-rank test.</p

Power law of node degree distribution for the miRNA-subpathway networks.

<p>(<b>A</b>) Degree distribution of the downregulated miRNA-subpathway network. (<b>B</b>) Degree distribution of the upregulated miRNA-subpathway network. (<b>C</b>) Degree distribution of the total miRNA-subpathway network.</p

Network parameters of miRNA-subpathway and subpathway-subpathway networks.

<p>Network parameters of miRNA-subpathway and subpathway-subpathway networks.</p

The k = 6 clique from total miRNA-miRNA and its co-regulated subpathways.

<p>Red nodes represent upregulated miRNAs, while blue nodes are downregulated miRNAs. The size of the miRNA nodes corresponds to the node degree. <i>P</i>-value strength is represented by edge line width, with wider edges representing more significant interactions.</p

Graphic representation of three miRNA-subpathway networks.

<p>(<b>A</b>) Downregulated miRNA-subpathway network. (<b>B</b>) Upregulated miRNA-subpathway network. (<b>C</b>) Total miRNA-subpathway network. Nodes colored in green are downregulated miRNA, and red nodes are upregulated miRNAs. Blue nodes represent the subpathways. The size of the miRNA nodes correspond to the node degree (the number of subpathways that miRNA connected). <i>P</i>-value strength is represented by edge line width, with wider edges representing more significant interactions. Hsa-miR-320b and hsa-miR-1248 had the biggest degree are shaded in yellow.</p

Summary of differentially-expressed ESCC miRNAs applied in this study.

<p>Summary of differentially-expressed ESCC miRNAs applied in this study.</p

Schematic presentation of the analysis workflow to construct the miRNA-subpathway, miRNA-miRNA and subpathway-subpathway sub-networks.

<p>miRNA target genes were subjected to subpathway enrichment analysis to generate a miRNA-subpathway network. Then two sub-networks were constructed by the hypergeometric test.</p