Ficolin-1 in pediatric <i>Plasmodium falciparum</i> malaria and its possible role in parasite clearance and anemia
Bibliographic record
Abstract
ABSTRACT Plasmodium falciparum malaria causes significant disease, especially in young children. A successful immune response to P. falciparum is a major determinant of clinical outcome. The ficolins are a family of lectins that act as pattern recognition molecules and can activate the lectin complement pathway and may promote inflammation and facilitate opsonization and lysis of pathogens. Here, we have investigated the potential roles of ficolin-1 and ficolin-2 in the context of P. falciparum infection. We measured ficolin-1 and ficolin-2 concentrations in plasma from Malawian children presenting with uncomplicated or severe malaria or healthy controls (HCs) by ELISA. Using flow cytometry, we assessed whether ficolin-1 could bind to infected red blood cells (iRBCs) and whether it binds sialic acid on the iRBCs. Ficolin-1 and ficolin-2 plasma levels were measured in children from all clinical groups. Compared to HCs (reference), Ficolin-1 concentrations in plasma were higher in children with uncomplicated (geometric mean ratio: 1.88; 95% confidence interval [CI]: 1.25–2.82) and severe malaria (1.65; 95% CI: 1.10–2.46). Ficolin-1 levels were positively associated with peripheral blood monocyte (1.30; 1.02–1.67) and neutrophil counts (1.06; 1.00–1.13). Ficolin-2 was not associated with malaria. Hemoglobin levels were negatively associated with ficolin-1 (−0.38; −0.68 to –0.09) and ficolin-2 (−0.36; −0.68 to –0.04). Ficolin-1 bound more to iRBCs compared to uninfected RBCs, and binding was reduced in a ficolin-1 mutant that did not bind to sialic acid. These results highlight a largely overlooked role for ficolin-1 in the immune response to P. falciparum infection and point to a potential role for lectins contributing to parasite clearance and anaemia.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".