China’s private institutions for the education of health professionals: a time-series analysis from 1998 to 2012

Abstract Background Public institutions have been the major provider of education for health professionals in China for most of the twentieth century. In the 1990s, the Chinese government began to encourage the establishment of private education institutions, which have been steadily increasing in numbers over the past decade. However, there is a lack of authoritative data on these institutions and little has been published in international journals on the current status of private education of health professionals in China. In light of this knowledge gap, we performed a quantitative analysis of private institutions in China that offer higher education of health professionals. Methods Using previously unreleased national data provided by the Ministry of Education of China, we conducted time-series and descriptive analyses to study the scale, structure and educational resources from 1998 to 2012 of private institutions for health professional education. Results The number of private institutions that educate health professionals increased from two in 1999 to 123 in 2012. Private institutions displayed an average annual growth rate of 44.2% for enrolment, 59.0% for the number of students and 53.3% for the number of graduates. In 2012, nursing, clinical medicine and traditional Chinese medicine had the most students (37.2%, 32.8% and 8.9% respectively), representing 78.9% of all students in these institutions. Ninety-seven private institutions located in the more economically advantaged eastern and central China and only 26 ones were in the less economically advantaged western China, respectively turning out 85.2% and 14.8% of health professional graduates. There were less educational resources, such as the number of faculty members, physical space and assets, at private institutions than at public institutions. Conclusions Private institutions for the education of health professionals have emerged quickly in China, contributing to the demand for health professionals that exceeds what public institutions are able to offer. At the same time, the imbalance of geographical distribution and poor educational resources of private institutions are of concern. It may be of utmost importance to enhance administration and supervision to better regulate private institutions and their development plans. Future studies may be needed to better examine the effects of private institutions on the production and allocation of health workers.https://deepblue.lib.umich.edu/bitstream/2027.42/145446/1/12960_2018_Article_308.pd

Identification of Novel SNPs by Next-Generation Sequencing of the Genomic Region Containing the APC Gene in Colorectal Cancer Patients in China

We described an approach of identifying single nucleotide polymorphisms (SNPs) in complete genomic regions of key genes including promoters, exons, introns, and downstream sequences by combining long-range polymerase chain reaction (PCR) or NimbleGen sequence capture with next-generation sequencing. Using the adenomatous polyposis coli (APC) gene as an example, we identified 210 highly reliable SNPs by next-generation sequencing analysis program MAQ and Samtools, of which 69 were novel ones, in the 123-kb APC genomic region in 27 pair of colorectal cancers and normal adjacent tissues. We confirmed all of the eight randomly selected high-quality SNPs by allele-specific PCR, suggesting that our false discovery rate is negligible. We identified 11 SNPs in the exonic region, including one novel SNP that was not previously reported. Although 10 of them are synonymous, they were predicted to affect splicing by creating or removing exonic splicing enhancers or exonic splicing silencers. We also identified seven SNPs in the upstream region of the APC gene, three of which were only identified in the cancer tissues. Six of these upstream SNPs were predicted to affect transcription factor binding. We also observed that long-range PCR was better in capturing GC-rich regions than the NimbleGen sequence capture technique.MOST, Chin