GSM Based Health Monitoring System for Paralysis Patients

Healthcare systems are a critical component of each country's economy and public health. In today's fast-paced world, it's difficult for people to be continually available for their loved ones who may require assistance while they are going through a difficult time. Physiological parameters are measured constantly or at regular intervals by patient monitoring systems. According to a recent World Health Organization survey, over 5.6 million people are paralysed, accounting for 1.9 percent of the population, or roughly 1 in every 50 people. Paraplegic health surveillance in hospitals indicates that a variety of exercises, stimulation, and medications are available to safeguard the paralysed. However, there is no specialised monitoring system in place to follow the health of paralysed persons. To deal with these problems, a monitoring system is put in place, which is used to keep track on the patients' health. Bio sensors, such as pulse rate, blood pressure, and airflow sensor, are used in this monitoring system to sense the vital framework of patients, and these parameters are continually monitored and relayed to the caretaker through GSM. This is something that a microcontroller can help with (MSP430).</jats:p

Structural variation across 138,134 samples in the TOPMed consortium

AbstractEver larger Structural Variant (SV) catalogs highlighting the diversity within and between populations help researchers better understand the links between SVs and disease. The identification of SVs from DNA sequence data is non-trivial and requires a balance between comprehensiveness and precision. Here we present a catalog of 355,667 SVs (59.34% novel) across autosomes and the X chromosome (50bp+) from 138,134 individuals in the diverse TOPMed consortium. We describe our methodologies for SV inference resulting in high variant quality and &gt;90% allele concordance compared to long-read de-novo assemblies of well-characterized control samples. We demonstrate utility through significant associations between SVs and important various cardio-metabolic and hemotologic traits. We have identified 690 SV hotspots and deserts and those that potentially impact the regulation of medically relevant genes. This catalog characterizes SVs across multiple populations and will serve as a valuable tool to understand the impact of SV on disease development and progression.</jats:p

Genome sequencing unveils a regulatory landscape of platelet reactivity

AbstractPlatelet aggregation at the site of atherosclerotic vascular injury is the underlying pathophysiology of myocardial infarction and stroke. To build upon prior GWAS, here we report on 16 loci identified through a whole genome sequencing (WGS) approach in 3,855 NHLBI Trans-Omics for Precision Medicine (TOPMed) participants deeply phenotyped for platelet aggregation. We identify the RGS18 locus, which encodes a myeloerythroid lineage-specific regulator of G-protein signaling that co-localizes with expression quantitative trait loci (eQTL) signatures for RGS18 expression in platelets. Gene-based approaches implicate the SVEP1 gene, a known contributor of coronary artery disease risk. Sentinel variants at RGS18 and PEAR1 are associated with thrombosis risk and increased gastrointestinal bleeding risk, respectively. Our WGS findings add to previously identified GWAS loci, provide insights regarding the mechanism(s) by which genetics may influence cardiovascular disease risk, and underscore the importance of rare variant and regulatory approaches to identifying loci contributing to complex phenotypes.</jats:p

Variant-specific inflation factors for assessing population stratification at the phenotypic variance level

AbstractIn modern Whole Genome Sequencing (WGS) epidemiological studies, participant-level data from multiple studies are often pooled and results are obtained from a single analysis. We consider the impact of differential phenotype variances by study, which we term ‘variance stratification’. Unaccounted for, variance stratification can lead to both decreased statistical power, and increased false positives rates, depending on how allele frequencies, sample sizes, and phenotypic variances vary across the studies that are pooled. We develop a procedure to compute variant-specific inflation factors, and show how it can be used for diagnosis of genetic association analyses on pooled individual level data from multiple studies. We describe a WGS-appropriate analysis approach, implemented in freely-available software, which allows study-specific variances and thereby improves performance in practice. We illustrate the variance stratification problem, its solutions, and the proposed diagnostic procedure, in simulations and in data from the Trans-Omics for Precision Medicine Whole Genome Sequencing Program (TOPMed), used in association tests for hemoglobin concentrations and BMI.</jats:p