AIMC Topic: Leishmaniasis, Visceral

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GlyTrait: A Versatile Bioinformatics Tool for Glycomics Analysis.

Journal of proteome research
We developed GlyTrait, a Python-based framework designed to enhance Glycomics analysis through the innovative calculation and interpretation of derived traits from -glycome data. Glycomics research often grapples with the interpretability and biologi...

Climate change and its impact on spatial and temporal distribution of visceral leishmaniasis transmission risk in Nepal.

BMC infectious diseases
Visceral leishmaniasis (VL), also known as kala-azar, has posed significant challenges to elimination efforts due to increased reporting of new cases from high mountain and previously considered non-endemic areas in Nepal. Understanding the potential...

Interpretating SPR-Derived Reaction Kinetics via Self-Organizing Maps for Diagnostic Applications.

ACS sensors
Biosensors emerge as promising, cost-effective infectious disease diagnostics in resource-limited settings, requiring neither laboratory infrastructure nor specialized personnel. Surface plasmon resonance (SPR)-based biosensors remain preeminent for ...

AI-driven analysis by identifying risk factors of VL relapse in HIV co-infected patients.

Scientific reports
Visceral Leishmaniasis (VL), also known as Kala-Azar, poses a significant global public health challenge and is a neglected disease, with relapses and treatment failures leading to increased morbidity and mortality. This study introduces an explainab...

Unsupervised machine learning identifies biomarkers of disease progression in post-kala-azar dermal leishmaniasis in Sudan.

PLoS neglected tropical diseases
BACKGROUND: Post-kala-azar dermal leishmaniasis (PKDL) appears as a rash in some individuals who have recovered from visceral leishmaniasis caused by Leishmania donovani. Today, basic knowledge of this neglected disease and how to predict its progres...