OMP reduced the risk of PTB between 28 and 31 weeks plus 6 days, NICU admissions, and neonatal morbidity and mortality in high risk patients.

BackgroundAlzheimer’s disease (AD) is one of the leading genetically complex and heterogeneous disorder that is influenced by both genetic and environmental factors. The underlying risk factors remain largely unclear for this heterogeneous disorder. In recent years, high throughput methodologies, such as genome-wide linkage analysis (GWL), genome-wide association (GWA) studies, and genome-wide expression profiling (GWE), have led to the identification of several candidate genes associated with AD. However, due to lack of consistency within their findings, an integrative approach is warranted. Here, we have designed a rank based gene prioritization approach involving convergent analysis of multi-dimensional data and protein-protein interaction (PPI) network modelling.ResultsOur approach employs integration of three different AD datasets- GWL,GWA and GWE to identify overlapping candidate genes ranked using a novel cumulative rank score (SR) based method followed by prioritization using clusters derived from PPI network. SR for each gene is calculated by addition of rank assigned to individual gene based on either p value or score in three datasets. This analysis yielded 108 plausible AD genes. Network modelling by creating PPI using proteins encoded by these genes and their direct interactors resulted in a layered network of 640 proteins. Clustering of these proteins further helped us in identifying 6 significant clusters with 7 proteins (EGFR, ACTB, CDC2, IRAK1, APOE, ABCA1 and AMPH) forming the central hub nodes. Functional annotation of 108 genes revealed their role in several biological activities such as neurogenesis, regulation of MAP kinase activity, response to calcium ion, endocytosis paralleling the AD specific attributes. Finally, 3 potential biochemical biomarkers were found from the overlap of 108 AD proteins with proteins from CSF and plasma proteome. EGFR and ACTB were found to be the two most significant AD risk genes.ConclusionsWith the assumption that common genetic signals obtained from different methodological platforms might serve as robust AD risk markers than candidates identified using single dimension approach, here we demonstrated an integrated genomic convergence approach for disease candidate gene prioritization from heterogeneous data sources linked to AD.Electronic supplementary materialThe online version of this article (doi:10.1186/1471-2164-15-199) contains supplementary material, which is available to authorized users.

Quality in laboratory has huge impact on diagnosis and patient management as 80-90% of all diagnosis is made on the basis of laboratory tests. Monitoring of quality indicators covering the critical areas of pre-analytical, analytical and post-analytical phases like sample misidentification, sample rejection, random and systemic errors, critical value reporting and TATs have a significant impact on performance of laboratory. This study was conducted in diagnostic laboratories receiving approximately 42,562 samples for clinical chemistry, hematology and serology. The list of quality indicators was developed for the steps of total testing process for which errors are frequent and improvements are possible. The trend was observed for all the QI before and after sensitisation of the staff over the period of 12 months. Incomplete test requisition form received in the lab was the most poor quality indicator observed (7.89%), followed by sample rejection rate (4.91%). Most significant improvement was found in pre-and post-analytical phase after sensitisation of staff but did not have much impact on analytical phase. Use of quality indicators to assess and monitor the quality system of the clinical laboratory services is extremely valuable tool in keeping the total testing process under control in a systematic and transparent way.

Advances in instrument technology and automation have simplified tasks in laboratory diagnostics reducing errors during analysis thereby improving the quality of test results. However studies show that most laboratory errors occur in the pre-analytical phase. In view of the paucity of studies examining pre-analytical errors, we examined a total of 1513 request forms received at our laboratory during a 3 month period. The forms were scrutinized for the presence of specific parameters to assess the pre-analytical errors affecting the laboratory results. No diagnosis was provided on 61.20% of forms. Type of specimen was not mentioned in 61.60% of the forms and 89.25% of all forms were illegible. Critical results were encountered in 17.30% of patients, and of these 76.60% were not communicated due to incomplete forms. Thus, by following standard operating procedures vigorously from patient preparation to sample processing the laboratory results can be significantly improved without any extra cost.