Pharma and Clinical Research Conferences

A team of scientists has developed a new genomic method to detect the cause of rare genetic diseases . The method, called Analysis of expression Variation-Dosage Outlier Test, and abbreviated as ANEVA-DOT, makes use of gene transcription data to detect differences in the activity levels of maternal and paternal alleles of genes, which people acquire from their parents. It compares activity levels of maternal and paternal alleles across the genome and detects when the activity of an allele goes beyond the normal range to become a viable cause of a certain disease. Usually, many rare genetic diseases develop from DNA mutations that affect only a single allele of a gene. The method uses the calculation of a healthy range of differences in maternal allele and paternal allele activity for every gene from gene transcription data. Thus, it can be used to identify genes with abnormal expression levels in alleles. Typically, standard methods of sequencing genes and their transcripts are...

In a first, researchers at the University of Illinois have identified the presence of a specific type of antibody, called APCA (anti-citrullinated protein autoantibodies) in human tear fluid. They demonstrated that eye drop treatment made from pooled human antibodies reduced the severity of the disease condition in patients with dry eye disease. Dry eye disease is developed from the abnormalities in the tear fluid and causes dryness surrounding the areas cover the cornea, the transparent outer layer of the eye which can lead to cause sensitivity to light and compromise the vision of the patient. In this study, researchers identified ACPAs as a contributor to the development of webs on the surface of eyes affected by severe dry eye disease, which are developed from the strands of DNA extrude of neutrophils and ultimately cause inflammation. The researchers called these webs as a “vicious cycle of inflammation”. The eye drops treat dry eye disease by partially knocking the immune...

In a recent study, researchers have designed an algorithm using artificial intelligence approach that may be able to alert patients and medical professionals about the possible side effects of Drug-Drug interactions that might occur due to the combination of multiple drugs.     To create this alert system, researchers used an autoencoder model, which is basically a type of artificial neural network that is designed on how the human brain processes information. This model is capable of processing both unlabeled and labeled data. Traditionally, programmers need to label data from millions of different combinations of possible interactions to produce the result.   Analysis of Drug-Drug interactions followed by adverse reactions are significant in case of clinical perspective as general patients are prescribed multiple drugs for different disease conditions.   The more medication a patient takes, the greater is the possibility of drug-drug interactions and ulti...