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Leishmaniasis

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Prediction of lymph node parasite load from clinical data in dogs with leishmaniasis: An application of radial basis artificial neural networks.

Veterinary parasitology
Quantification of Leishmania infantum load via real-time quantitative polymerase chain reaction (qPCR) in lymph node aspirates is an accurate tool for diagnostics, surveillance and therapeutics follow-up in dogs with leishmaniasis. However, qPCR requ...

Approved drugs successfully repurposed against based on machine learning predictions.

Frontiers in cellular and infection microbiology
Drug repurposing is a promising approach towards the discovery of novel treatments against Neglected Tropical Diseases, such as Leishmaniases, presenting the advantage of reducing both costs and duration of the drug discovery process. In previous wor...

Scoping Review of Deep Learning Techniques for Diagnosis, Drug Discovery, and Vaccine Development in Leishmaniasis.

Transboundary and emerging diseases
, a single-cell parasite prevalent in tropical and subtropical regions worldwide, can cause varying degrees of leishmaniasis, ranging from self-limiting skin lesions to potentially fatal visceral complications. As such, the parasite has been the subj...

DeepLeish: a deep learning based support system for the detection of Leishmaniasis parasite from Giemsa-stained microscope images.

BMC medical imaging
BACKGROUND: Leishmaniasis is a vector-born neglected parasitic disease belonging to the genus Leishmania. Out of the 30 Leishmania species, 21 species cause human infection that affect the skin and the internal organs. Around, 700,000 to 1,000,000 of...

A deep learning-based model for detecting Leishmania amastigotes in microscopic slides: a new approach to telemedicine.

BMC infectious diseases
BACKGROUND: Leishmaniasis, an illness caused by protozoa, accounts for a substantial number of human fatalities globally, thereby emerging as one of the most fatal parasitic diseases. The conventional methods employed for detecting the Leishmania par...

Using machine learning to dissect host kinases required for Leishmania internalization and development.

Molecular and biochemical parasitology
The Leishmania life cycle alternates between promastigotes, found in the sandfly, and amastigotes, found in mammals. When an infected sandfly bites a host, promastigotes are engulfed by phagocytes (i.e., neutrophils, dendritic cells, and macrophages)...

Application of Machine Learning (ML) approach in discovery of novel drug targets against Leishmania: A computational based approach.

Computational biology and chemistry
Molecules with potent anti-leishmanial activity play a crucial role in identifying treatments for leishmaniasis and aiding in the design of novel drugs to combat the disease, ultimately protecting individuals and populations. Various methods have bee...