Retrospective study on socio-demographic factors responsible for acceptance of IUCD among postpartum women

Background: Despite the availability of modern and scientific measures, unacceptably high numbers of maternal deaths still occur in developing countries. Spacing methods of family planning may avoid maternal and infant deaths. The Government of India launched postpartum IUCD (PPIUCD) services in the year 2000; although acceptance of Postpartum IUCD is a real concern.Methods: The retrospective study was conducted in rural government hospital in Maharashtra during 2016 - 2017. We analyzed sociodemographic variables and acceptance of Postpartum IUCD among postpartum women. The sample size was 595 (N=595). The sociodemographic factors studied included age, type of delivery, sex of newborn, socioeconomic status, educational status, etc.Results: The total postpartum women included in the study was 595, out of which, 202 (34%) accepted for postpartum IUCD whereas 393 (66%) rejected for the same. The most common age group was 20-25 years (65%), followed by age group 25-30 years (30%). Primipara was the comment group (45%) and normal vaginal delivery was common (95%). The educational status of both, the postpartum women and their husband, showed statistically significant association with acceptance of postpartum IUCD (p<0.05).Conclusions: The acceptance Postpartum IUCD was low (34%). The women’s and their husband’s educational status is an important factor in acceptance of Postpartum IUCD (p<0.05). Due attention should be given to enhancing educational level of women, also effective counselling both for pregnant woman and her husband during ANC is required

Features’ compendium for machine learning in NGS data Analysis

BackgroundCurrent studies on the cancer genome, majorly involve use of next generation sequencing (NGS) technologies followed by data analysis pipelines. Many of these pipelines comprise of tools using machine learning algorithms especially for downstream analysis. Features are important components of machine learning systems and inclusion of informative features improves the accuracy of the machine learning algorithms. The algorithms used inNGS analysis leads to the generation of huge feature space.Sometimes, this high dimensionality leads to slower analysis time and lesser accuracy due to inherentbias of the model and/or redundancy of fewfeatures. With growth and interest in NGS studies, there has been a rapid development of new NGS analysis tools and improvement in the performance of the previous ones byincluding new features and excludingthe redundant ones. To enable these development, there is a dire need for standardizing this plethora of features available from literature.ResultsCurrent work presents a compendium of features that have been used in the literature for machine learning in NGS data pipeline and analysis. The features have beenfurther classified, assuming each stage of NGS data processing as individual category. The simple classification is a) Pre-processing features (b) Sequencing technology specific features (c) Downstream featuresor features for biological interpretation and analysis. This categorization will facilitate the use of correct features in a simplified manner.Conclusions The work will facilitate a uniform model for NGS tools development that utilize machine learning approaches for study of cancer data.A model for feature database and management based on this standardization is also proposed

Determination of system level alterations in host transcriptome due to Zika virus (ZIKV) Infection in retinal pigment epithelium

Previously, we reported that Zika virus (ZIKV) causes ocular complications such as chorioretinal atrophy, by infecting cells lining the blood-retinal barrier, including the retinal pigment epithelium (RPE). To understand the molecular basis of ZIKV-induced retinal pathology, we performed a meta-analysis of transcriptome profiles of ZIKV-infected human primary RPE and other cell types infected with either ZIKV or other related flaviviruses (Japanese encephalitis, West Nile, and Dengue). This led to identification of a unique ZIKV infection signature comprising 43 genes (35 upregulated and 8 downregulated). The major biological processes perturbed include SH3/SH2 adaptor activity, lipid and ceramide metabolism, and embryonic organ development. Further, a comparative analysis of some differentially regulated genes (ABCG1, SH2B3, SIX4, and TNFSF13B) revealed that ZIKV induced their expression relatively more than dengue virus did in RPE. Importantly, the pharmacological inhibition of ABCG1, a membrane transporter of cholesterol, resulted in reduced ZIKV infectivity. Interestingly, the ZIKV infection signature revealed the downregulation of ALDH5A1 and CHML, genes implicated in neurological (cognitive impairment, expressive language deficit, and mild ataxia) and ophthalmic (choroideremia) disorders, respectively. Collectively, our study revealed that ZIKV induces differential gene expression in RPE cells, and the identified genes/pathways (e.g., ABCG1) could potentially contribute to ZIKV-associated ocular pathologies