Detection of novel Plasmodium falciparum haplotypes under treatment pressure in paediatric severe malaria
Bibliographic record
Abstract
Background. In Africa, the clearance time for Plasmodium falciparum severe malaria varies significantly, likely due to the complexity of P. falciparum infections and the sequestration phenomenon exhibited by this parasite. This study aims to evaluate different methods to study the intra-host dynamics of polyclonal infections during parasite clearance under antimalarial treatment. Additionally, it seeks to determine the association between parasite clearance rate following artesunate or quinine treatment and the genetic complexity of P. falciparum in Beninese children with severe malaria. Methods. Sixty-five P. falciparum severe malaria individuals diagnosed by microscopy and treated with artesunate or quinine were sampled every 8 h for 24 h. Using whole-genome sequencing (WGS) data, we estimated the multiplicity of infection (MOI) with three algorithms ( Fws , THE REAL McCOIL and RoH). We then characterized the P. falciparum genetic complexity in WGS-identified polyclonal infections using amplicon sequencing (AmpSeq) on DNA extracted from plasma and the red blood cell pellet. Results. AmpSeq demonstrated greater sensitivity in detecting multiple genomes within isolates compared to WGS methods. The MOI from AmpSeq was significantly higher in red blood cell pellets compared to plasma (2.4 vs. 1.8 distinct microhaplotypes per isolate). However, at parasitaemia over 1,000 parasites per microlitre, the same MOI was detected in both plasma and pellet samples in 85.4% of the isolates. We observed a high variability in parasite clearance rate among participants, but it was not associated with parasite MOI at diagnosis. Interestingly, in 60.9% of participants, previously undetected microhaplotypes appeared in circulation 16 h after treatment initiation. Conclusion. These findings demonstrate that combining different haplotyping techniques effectively determines parasite genetic complexity. Additionally, plasma can be effectively used for parasite genotyping at sufficient parasitaemia levels. The parasite clearance rate of severe malaria is independent of parasite MOI. However, genotyping a single blood sample upon hospital admission does not capture the full spectrum of parasite genotypes present in the infection.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".