How to Build CHD@ZJU

CHD related Articles were retrieved from Pubmed, by entering keywords "coronary heart disease" and constrict the publish date from 2000/1/1 to now (2013/1/23). As a result, totally 115898 articles were found and their abstracts were downloaded for text mining. Since some articles didn't contain abstracts, only 88396 abstracts remained.
The text-mining process to get CHD related genes could be divided in to 5 following steps:

  • 1) Extracting all keywords from abstracts and ignoring those keywords start with numbers. 101402 keywords were extracted.

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  • 2) Input these keywords into Gene library in ArrayTrack and find possible related genes. 4674 genes were then found.

  • 3) Put these 4674 genes again into pubmed abstracts to find related aticles. Only genes which offical name or there keyword description (such as prolactin for gene PRL) could be found in the abstract would be remained. As a result, 1247 genes were remained.

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  • 4) Manually examined on the 1247 genes to validate it was acutally related to CHD. Some genes would be filtered if it represents other meanings (such as gene CAD, Entrez ID:790, carbamoyl-phosphate synthetase 2, is mostly meant coronary arterial disease in articles). 681 genes were then validated with at least one reference.

  • 5) All genes was compared with 1078 CHD genes in RGD database, and 370 genes were overlapped. These 370 genes were labels as "RGD_Supported" and the other 293 genes were labels as "REFERED". All 663 genes had supported references in CHD@ZJU which were examined by step 4.

    • How To contact Us

    Collaboration Information: Prof. Xiaohui Fan (fanxh@zju.edu.cn)

    Website using assistance : Leihong Wu (11019004@zju.edu.cn)




    Health equity audits in general practice: a strategy to reduce health inequalities.
  • Author:"Badrick, Ellena;Hull, Sally;Mathur, Rohini;Shajahan, Shamin;Boomla, Kambiz;Bremner, Stephen;Robson, John"

  • Published Year:2013

  • Journal:Primary health care research & development

  • Abstract:"BACKGROUND: This quality improvement project was set in Tower Hamlets, east London, with the aim of reducing health inequalities by ethnicity, age and gender in the management of three common chronic diseases. METHODS: Routinely collected clinical data were extracted from practice computer systems using Morbidity Information Query and Export Syntax (MIQUEST) and Egton Medical Information Systems (EMIS) Web, between 2007 and 2010. Health equity audits for 38 practices in Tower Hamlets primary care trust (PCT) were constructed to cover key process and outcome measures for each of the three major chronic diseases: coronary heart disease (CHD), type 2 diabetes mellitus and chronic obstructive pulmonary disease (COPD). The equity audit was disseminated to practices along with facilitation sessions. RESULTS: We show evidence of baseline inequalities in each condition across the three east London PCTs. The intervention tracked four key indicators (cholesterol levels in CHD, blood pressure and haemoglobin A1c levels in diabetes and % smoking in COPD). Performance for physician-driven interventions improved, but smoking rates remained static. All ethnic groups showed improvement, but there was no evidence of a reduction in differences between ethnic groups. Reductions in gender and age group differences were noted in diabetes and CHD. CONCLUSIONS: Using routine clinical data, it is possible to develop practice-level health equity reports. These can unmask previously hidden inequalities between groups, and promote discussion with practice teams to stimulate strategies for improvements in performance. Steady improvements in chronic disease management were observed, however, systematic differences between ethnic groups remain. We are not able to attribute observed changes to the audits. These reports illustrate the importance of collecting ethnicity data at practice level. Tools such as this audit can be adapted to monitor inequalities in primary care settings."

  • 10.1017/S1463423612000606

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