Host biomarkers and parasite biomass are associated with severe malaria in Mozambican children: a case–control study
Bibliographic record
Abstract
Severe pediatric malaria remains a pressing global health issue. Laboratory parameters may provide early risk and severity stratification for better disease management, beyond current clinical severity scores. This study aimed to identify host biomarkers of immune and endothelial activation and parasite biomass in children with severe malaria (SM) compared to uncomplicated malaria (UM). We conducted a case-control study in a rural hospital in southern Mozambique from 2014 to 2016, recruiting patients under 10 years old with Plasmodium falciparum SM as cases, and patients with UM matched by age, sex, and parasitemia as controls. We compared plasma levels of biomarkers associated with total parasite mass (HRP-2), biomarkers of host response to infection (Angpt-1, Angpt-2, sTie-2, BDNF, CysC, sFlt-1, IL-6, IL-8, IP-10, sTNFR-1 and sTREM-1). All biomarker levels except Angpt-1, BDNF and CysC were significantly higher in children with SM. HRP-2 levels significantly differed between cases and controls, strongly correlating with Angpt-2, sTie-2, sFlt-1, TNRF, and sTREM-1, both in SM and UM. In conclusion, host biomarkers indicative of immune and endothelial activation were associated with malaria severity and HRP-2, even after controlling for matching variables, potentially offering targets for risk-stratification and adjuvant therapy.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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".