Evidence map›Paper›PMID 42573872›Full record

ReviewActa parasitologica2026

Decoding Leukocyte Dynamics: Functional Biomarkers and Precision Diagnostics in Parasitic Infections.

Jingrong Li, Hamed Soleimani Samarkhazan

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In one paragraph

Review in Acta parasitologica, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Jingrong LiWest China Hospital, Sichuan University, Sichuan, China.ORCID http://orcid.org/0000-0003-1506-6574
Hamed Soleimani SamarkhazanStudent Research Committee, Department of Hematology and Blood Banking, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran. hamed.soleimani.s@gmail.com.ORCID http://orcid.org/0000-0003-1045-7613

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundParasitic infections affect over 1.5 billion people globally, with leukocytes serving as critical mediators of both protective immunity and immunopathology. Traditional diagnostic approaches relying on static cell counts lack specificity and fail to capture the functional complexity of host-parasite interactions.

objectiveThis review synthesizes cutting-edge evidence on how leukocyte functional dynamics, encompassing transcriptional, metabolic, and spatial profiles, revolutionize diagnostic precision, prognostic stratification, and therapeutic development in parasitic diseases.

methodsWe systematically evaluated recent advances in single-cell multi-omics, spatial transcriptomics, AI-driven flow cytometry, and CRISPR-based functional genomics applied to parasitic infections including malaria, leishmaniasis, schistosomiasis, and Chagas disease. KEY

findingsGranulocyte NETosis exacerbates endothelial damage in malaria; monocyte transcriptional signatures predict leishmaniasis relapse; eosinophil activation markers distinguish active helminthiasis from allergic inflammation. Single-cell technologies reveal infection-specific immune subsets (TREM2

conclusionsThe integration of functional leukocyte profiling with pathogen detection enables a "precision parasitology" framework, moving beyond pathogen-centric approaches to tailor interventions based on individual immune trajectories, overcoming drug resistance, and advancing elimination goals for neglected tropical diseases.

Indexed as

BiomarkersLeukocytesParasitic DiseasesAnimalsHost-Parasite InteractionsHumansBiomarkersLeukocyte functional plasticityParasite immune evasionPoint-of-care diagnosticsPrecision parasitologySingle-cell multi-omics

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.