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Introduction : Physicians in India are frequently the first point of contact for patients with chronic kidney disease. Hence, awareness of clinical practice guidelines for Chronic Kidney Disease (CKD) among the Postgraduates/ residents of MD Medicine is of paramount importance. The aim of this study is to assess MD Medicine Postgraduates’ and Residents’ awareness and knowledge of clinical practice guidelines for Chronic Kidney Disease (CKD). Materials and methods : The present study is a cross-sectional observation study. A questionnaire of clinical vignettes on CKD with multiple choices was prepared. A one-time survey of MD Medicine postgraduates and residents from various medical colleges in three states of southern India has been collected. Results : A total of 228 Postgraduates or Residents of MD Medicine participated in the study. The awareness of clinical practice guidelines for CKD was low across all postgraduate years (PGYs) of MD Medicine. We measured the CKD awareness in postgraduates and residents of MD Medicine based on the Postgraduate year, Medical college and institute, type of institution (either Government funded or capitation fee), the presence of a nephrology fellowship in the institution, and the consultation given to the Nephrology patients in the Medicine OPD. Conclusions : The modest awareness of clinical practice guidelines for CKD across all PGYs has suggested that incorporation of these guidelines into the medicine postgraduate training curriculum is not robust at present. We also discussed the means to improve the understanding of nephrology by the postgraduates/residents of MD Medicine
RESUMEN
Genomic islands (GIs) are regions in the genome which are believed to have been acquired via horizontal gene transfer events and are thus likely to be compositionally distinct from the rest of the genome. Majority of the genes located in a GI encode a particular function. Depending on the genes they encode, GIs can be classifi ed into various categories, such as ‘metabolic islands’, ‘symbiotic islands’, ‘resistance islands’, ‘pathogenicity islands’, etc. The computational process for GI detection is known and many algorithms for the same are available. We present a new method termed as Improved N-mer based Detection of Genomic Islands Using Sequence-clustering (INDeGenIUS) for the identifi cation of GIs. This method was applied to 400 completely sequenced species belonging to proteobacteria. Based on the genes encoded in the identifi ed GIs, the GIs were grouped into 6 categories: metabolic islands, symbiotic islands, resistance islands, secretion islands, pathogenicity islands and motility islands. Several new islands of interest which had previously been missed out by earlier algorithms were picked up as GIs by INDeGenIUS. The present algorithm has potential application in the identifi cation of functionally relevant GIs in the large number of genomes that are being sequenced. Investigation of the predicted GIs in pathogens may lead to identifi cation of potential drug/vaccine candidates.