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.

The study focuses on assessing the status of respiratory morbidity in Delhi over a four years period from 2000-2003. An attempt was made to investigate the role of important pollutants (SO(2), NO(2), SPM and RSPM) and various meteorological factors (temperature minimum & maximum, relative humidity at 0830 and 1730 hrs. and wind speed) in being responsible for respiratory admissions on account of COPD, asthma and emphysema. The study showed that winter months had greater exposure risk as pollutants often get trapped in the lower layers of atmosphere resulting in high concentrations. Statistical analysis revealed that two pollutants have significant positive correlation with the number of COPD cases viz., SPM (r = 0.474; p < 0.01) and RSPM (r = 0.353; p < 0.05), while a meteorological factor temperature (minimum) has a significant negative correlation (r = -0.318; p < 0.05) with COPD. Stepwise multiple regression analysis was performed for COPD as dependent variable and R(2) value of 0.33 was obtained indicating that SPM and RH(1730) were able to explain 33 percent variability in COPD. The partial correlation of SPM and RH(1730) on COPD was higher than any other combination and therefore they can be regarded as important contributing variables on COPD.

Alzheimer's disease (AD) is a progressive neurodegenerative disorder characterized by loss of memory and other cognitive functions. AD can be classified into familial AD (FAD) and sporadic AD (SAD) based on heritability and into early onset AD (EOAD) and late onset AD (LOAD) based on age of onset. LOAD cases are more prevalent with genetically complex architecture. In spite of significant research focused on understanding the etiological mechanisms, search for diagnostic biomarker(s) and disease-modifying therapy is still on. In this article, we aim to comprehensively review AD literature on established etiological mechanisms including role of beta-amyloid and apolipoprotein E (APOE) along with promising newer etiological factors such as epigenetic modifications that have been associated with AD suggesting its multifactorial nature. As genomic studies have recently played a significant role in elucidating AD pathophysiology, a systematic review of findings from genome-wide linkage (GWL), genome-wide association (GWA), genome-wide expression (GWE), and epigenome-wide association studies (EWAS) was conducted. The availability of multi-dimensional genomic data has further coincided with the advent of computational and network biology approaches in recent years. Our review highlights the importance of integrative approaches involving genomics and systems biology perspective in elucidating AD pathophysiology. The promising newer approaches may provide reliable means of early and more specific diagnosis and help identify therapeutic interventions for LOAD.

These results indicate that all genotypes of ApoE ε4 allele, that is, ε2/4, ε3/4, and ε4/4, are associated with an increased risk of AD, whereas ApoE ε2/2, ε2/3, and ε3/3 are protective for AD.

Background: Alzheimer disease (AD) is a progressive neurodegenerative disease with a complex multifactorial etiology. Here, we aim to identify a biomarker pool comprised of genetic variants and blood biomarkers as predictor of AD risk. Methods: We performed a case-control study involving 108 cases and 159 non-demented healthy controls to examine the association of multiple biomarkers with AD risk. Results: The APOE genotyping revealed that ε4 allele frequency was significantly high (p value = 0.0001, OR = 2.66, 95% CI 1.58-4.46) in AD as compared to controls, whereas ε2 (p = 0.0430, OR = 0.29, CI 0.07-1.10) was overrepresented in controls. In biochemical assays, significant differences in levels of total copper, free copper, zinc, copper/zinc ratio, iron, epidermal growth factor receptor (EGFR), leptin, and albumin were also observed. The AD risk score (ADRS) as a linear combination of 6 candidate markers involving age, education status, APOE ε4 allele, levels of iron, Cu/Zn ratio, and EGFR was created using stepwise linear discriminant analysis. The area under the ROC curve of the ADRS panel for predicting AD risk was significantly high (AUC = 0.84, p < 0.0001, 95% CI 0.78-0.89, sensitivity = 70.0%, specificity = 83.8%) compared to individual parameters. Conclusion: These findings support the multifactorial etiology of AD and demonstrate the ability of a panel involving 6 biomarkers to discriminate AD cases from non-demented healthy controls.