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Malaria

Malaria

Malaria pathogenesis in humans reflects a delicate interplay between parasite biology and host response. Clinical illness reflects a host’s immune response to the parasite’s asexual replication cycle, which is often asymptomatic in individuals with partial immunity. From the parasite's perspective, transmission between mosquito and human with minimal host pathology is evolutionarily advantageous. Among the six Plasmodium species infecting humans, P. falciparum and P. vivax dominate in global...

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Related Experiment Video

Updated: Jun 3, 2026

Detection and Quantification of Plasmodium falciparum in Aqueous Red Blood Cells by Attenuated Total Reflection Infrared Spectroscopy and Multivariate Data Analysis

Detection and Quantification of Plasmodium falciparum in Aqueous Red Blood Cells by Attenuated Total Reflection Infrared Spectroscopy and Multivariate Data Analysis

Published on: November 2, 2018

Population-Level Raman Biochemical Staging of Malaria in Human Red Blood Cells Using Interpretable Machine Learning.

Lintong Wu¹, Anoushka Gupta¹, Abhai K Tripathi^2,3

¹Department of Mechanical Engineering, Johns Hopkins University, Baltimore, Maryland 21218, United States.

|June 1, 2026

Summary

This summary is machine-generated.

Accurate malaria staging is vital for treatment decisions. Raman spectroscopy and machine learning identify parasite biochemical signatures, enabling noninvasive, stage-aware diagnostics for infection and transmission potential.

Keywords:

Biochemical Signatures Label-free Diagnostics Machine Learning Malaria Staging Raman Spectroscopy Spatially Offset Raman Spectroscopy

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Last Updated: Jun 3, 2026

Detection and Quantification of Plasmodium falciparum in Aqueous Red Blood Cells by Attenuated Total Reflection Infrared Spectroscopy and Multivariate Data Analysis

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Superior Auto-Identification of Trypanosome Parasites by Using a Hybrid Deep-Learning Model

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Published on: October 27, 2023

Area of Science:

Biomedical Optics
Spectroscopy
Machine Learning

Background:

Accurate malaria staging is crucial for effective treatment and transmission monitoring.
Mature Plasmodium falciparum parasites sequester, necessitating noninvasive diagnostics targeting circulating ring and gametocyte stages.
Label-free optical detection offers reagent-free analysis of intrinsic biochemical signatures.

Purpose of the Study:

To develop a population-level Raman spectroscopy framework for defining biochemical signatures of malaria-infected red blood cells.
To utilize interpretable machine learning for stage-specific parasite classification.
To assess the feasibility of noninvasive, subsurface detection of malaria infection signatures.

Main Methods:

Confocal and spatially offset Raman spectroscopy (SORS) were applied to synchronized Plasmodium falciparum cultures.
Interpretable machine learning, specifically SHapley Additive exPlanations (SHAP), was used to identify discriminative spectral features.
A skin-mimetic phantom was used to evaluate subsurface detection capabilities.

Main Results:

Aggregate biochemical alterations associated with parasite development were captured.
SHAP analysis identified key spectral regions, including hemoglobin-associated vibrational modes, as discriminative features.
Subsurface detection of infection signatures was demonstrated through a tissue-mimetic phantom, confirming detectability under scattering conditions.

Conclusions:

A rigorous biochemical basis for stage-specific Raman classification of malaria parasites was established.
The framework provides a foundation for developing noninvasive, stage-aware diagnostics.
This approach has the potential to identify malaria infection and assess transmission potential.