Browsing by Author "Yazdanparast, Ehsan"
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- INsPECT, an open-source and versatile software for automated quantification of (Leishmania) intracellular parasitesPublication . Yazdanparast, Ehsan; Dos Anjos, António; Garcia, Deborah; Loeuillet, Corinne; Shahbazkia, Hamid R.; Vergnes, BaptisteIntracellular protozoan parasites are causative agents of infectious diseases that constitute major health problems for developing countries. Leishmania sp., Trypanosoma cruzi or Toxoplasma gondii are all obligate intracellular protozoan parasites that reside and multiply within the host cells of mammals, including humans. Following up intracellular parasite proliferation is therefore an essential and a quotidian task for many laboratories working on primary screening of new natural and synthetic drugs, analyzing drug susceptibility or comparing virulence properties of natural and genetically modified strains. Nevertheless, laborious manual microscopic counting of intracellular parasites is still the most commonly used approach. Here, we present INsPECT (Intracellular ParasitE CounTer), an open-source and platform independent software dedicated to automate infection level measurement based on fluorescent DNA staining. It offers the possibility to choose between different types of analyses (fluorescent DNA acquisitions only or in combination with phase contrast image set to further separate intra-from extracellular parasites), and software running modes (automatic or custom). A proof-of-concept study with intracellular Leishmania infantum parasites stained with DAPI (49,6-diamidino-2-phenylindole) confirms a good correspondence between digital results and the "gold standard" microscopic counting method with Giemsa. Interestingly, this software is versatile enough to accurately detect intracellular T. gondii parasites on images acquired with High Content Screening (HCS) systems. In conclusion, INsPECT software is proposed as a new fast and simple alternative to the classical intracellular Leishmania quantification methods and can be adapted for mid to large-scale drug screening against different intracellular parasites.
- Recognition of leishmania parasite and macrophage infection rate using image processing techniquesPublication . Yazdanparast, Ehsan; Shahbazkia, HamidrezaLeishmaniasis is an epidemic dangerous disease in tropical and sub tropical regions of the world and if it is not treated conveniently, it may cause several health infections and could even lead to the death of patients. Currently, there is no efficient vaccine for the disease and available treatments cause serious side effects such as toxicity and parasite resistance. Therefore, ongoing research in Drug Discovery and other related biological areas concentrate on finding adequate drug candidates for the disease. Drug discovery pipeline for Leishmaniasis disease facilitates so many biological techniques till now to understand level of effectiveness of different drug candidates for treating the disease. The accuracy and reliability of such techniques, however, highly depends on manual process of detecting, extracting, counting and analyzing components of interest such as cells, parasites and cytoplasm regions by expert biologists. Such kind of activities is subjected to so many human-made errors and is often considered to be too time and energy consuming. The existing computational based solutions, which are reduced, also suffer from limited level of analysis and in some cases inaccuracy of the results are evident. For instance, there is no package dedicated to Intracellular Parasites Counting, which is a central operation when researching drug candidates’ effects. The current research aims at addressing the urgent need to find a solution to the problem of investigating Infection Ratio of cells more accurately, which is in fact still lacking. A computational framework which fulfills all those mentioned tasks automatically, in an acceptable time range with maximum possible accuracy of the results is suggested. The proposed solution mainly uses Image Processing approaches to process clinical images and analyze the results. The visual and statistical results then could be used independently or as a complementary tool for laboratories to investigate infection ratio of drug candidates and even at a higher level introduce vaccines for the disease.