MD Anderson Automates Laboratory Workflow with Stone Bond Technologies' EE-LIMS™
News Jul 03, 2008
The newly established Small Interfering Ribonucleic Acid (siRNA) Facility at M. D. Anderson Cancer Center has been charged with providing high quality siRNA screening services for both internal and external customers.
M. D. Anderson has chosen Stone Bond Technologies’ EE-LIMS™ to document activities within the laboratory workflow at its siRNA facility, increasing throughput, while reducing the risk of errors, and providing automation for the management of operations in its siRNA Laboratory.
As one of the newest fields in the study of nucleic acids, small interfering RNA has shown considerable potential in laboratory studies to change how diseases are treated. Considering how difficult it is to work with these molecules and the specialized equipment devoted to their isolation and study, labs specializing in this area have a strong need to document the work that goes into producing genetic data that corresponds to the RNA in a given sample.
According to Stone Bond, its LIMS is an award winning Web-based laboratory information management system, recipient of the Bio-IT World magazine’s Grand Prize winner for technical innovation in its annual Best Practice awards, in the category of IT Infrastructure and Informatics.
Built on Stone Bond’s versatile Enterprise Enabler integration platform, EE-LIMS automates laboratory workflows, invoicing, data and sample tracking, collections and other business processes to help laboratories reduce overhead and administrative costs.
Scientists at McGill have found the answer to a question that perplexed Charles Darwin; if natural selection works at the level of the individual, fighting for survival and reproduction, how can a single colony produce worker ants that are so dramatically different in size – from “minor” workers to large-headed soldiers with huge mandibles – especially if they are sterile?
Scientists have developed a successful method to make truly personalized predictions of future disease outcomes for patients with certain types of chronic blood cancers. The study combined extensive genetic and clinical information to predict the prognosis for patients with myeloproliferative neoplasms.
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