A CLINICAL STUDY ON EFFICACY OF JEEVANTYAADI GHRITA IN SUSHKAKSHIPAAKA (DRY EYE)

Dry eye also known as keratoconjuctivitis sicca or xerophthalmia, is a multi-factorial disease that results in discomfort, visual disturbance and tear film instability with potential damage to the ocular surface on progress of the disease, if proper steps are not adopted for prevention or management from the beginning of the disease. Dry eye is accompanied by increased osmolarity of the tear film and inflammation of the ocular surface. It is a commonest ocular disorder especially among the elderly because of reduction in tear film secretion. As far as the prevailing practices available for dry eye are concerned, no specific treatment is effective addressing the symptoms visualized in dry eye. The condition dry eye can be well compared to Sushkakshipaaka (Sushka=Dry, Akshi=Eye, Paaka=inflammation) explained in Ayurveda owing to its simulating clinical features and pathogenesis. Various regulated principles of management are in place addressing the above clinical complications by reaching the target tissues and receptors for enriching the ocular surface, and thus extending milestone in recovering from dry eye symptoms. Thus patients (n=10) registered with dry eye in outpatients department of Shalakya tantra of Shri Kalabyraveshwara Ayurveda Medical College Hospital and Research Centre Bangalore, were taken up for the study and treated with Jeevantyadi ghrita tarpana five days of two sittings with an interval of ten days. There was no drops outs and treatment event was successful

Ontology-Based Meta-Analysis of Global Collections of High-Throughput Public Data

The investigation of the interconnections between the molecular and genetic events that govern biological systems is essential if we are to understand the development of disease and design effective novel treatments. Microarray and next-generation sequencing technologies have the potential to provide this information. However, taking full advantage of these approaches requires that biological connections be made across large quantities of highly heterogeneous genomic datasets. Leveraging the increasingly huge quantities of genomic data in the public domain is fast becoming one of the key challenges in the research community today.We have developed a novel data mining framework that enables researchers to use this growing collection of public high-throughput data to investigate any set of genes or proteins. The connectivity between molecular states across thousands of heterogeneous datasets from microarrays and other genomic platforms is determined through a combination of rank-based enrichment statistics, meta-analyses, and biomedical ontologies. We address data quality concerns through dataset replication and meta-analysis and ensure that the majority of the findings are derived using multiple lines of evidence. As an example of our strategy and the utility of this framework, we apply our data mining approach to explore the biology of brown fat within the context of the thousands of publicly available gene expression datasets.Our work presents a practical strategy for organizing, mining, and correlating global collections of large-scale genomic data to explore normal and disease biology. Using a hypothesis-free approach, we demonstrate how a data-driven analysis across very large collections of genomic data can reveal novel discoveries and evidence to support existing hypothesis

Somatic cancer variant curation and harmonization through consensus minimum variant level data

Abstract Background To truly achieve personalized medicine in oncology, it is critical to catalog and curate cancer sequence variants for their clinical relevance. The Somatic Working Group (WG) of the Clinical Genome Resource (ClinGen), in cooperation with ClinVar and multiple cancer variant curation stakeholders, has developed a consensus set of minimal variant level data (MVLD). MVLD is a framework of standardized data elements to curate cancer variants for clinical utility. With implementation of MVLD standards, and in a working partnership with ClinVar, we aim to streamline the somatic variant curation efforts in the community and reduce redundancy and time burden for the interpretation of cancer variants in clinical practice. Methods We developed MVLD through a consensus approach by i) reviewing clinical actionability interpretations from institutions participating in the WG, ii) conducting extensive literature search of clinical somatic interpretation schemas, and iii) survey of cancer variant web portals. A forthcoming guideline on cancer variant interpretation, from the Association of Molecular Pathology (AMP), can be incorporated into MVLD. Results Along with harmonizing standardized terminology for allele interpretive and descriptive fields that are collected by many databases, the MVLD includes unique fields for cancer variants such as Biomarker Class, Therapeutic Context and Effect. In addition, MVLD includes recommendations for controlled semantics and ontologies. The Somatic WG is collaborating with ClinVar to evaluate MVLD use for somatic variant submissions. ClinVar is an open and centralized repository where sequencing laboratories can report summary-level variant data with clinical significance, and ClinVar accepts cancer variant data. Conclusions We expect the use of the MVLD to streamline clinical interpretation of cancer variants, enhance interoperability among multiple redundant curation efforts, and increase submission of somatic variants to ClinVar, all of which will enhance translation to clinical oncology practice

Additional file 1: of Somatic cancer variant curation and harmonization through consensus minimum variant level data

Example of the MVLD data format. (XLSX 32 kb