Amplicon Deep Sequencing Reveals Multiple Genetic Events Lead to Treatment Failure with Atovaquone-Proguanil in Plasmodium falciparum
Bibliographic record
Abstract
ABSTRACT Atovaquone-proguanil (AP) is used as treatment for uncomplicated malaria, and as a chemoprophylactic agent against Plasmodium falciparum . Imported malaria remains one of the top causes of fever in Canadian returning travelers. Twelve sequential whole-blood samples before and after AP treatment failure were obtained from a patient diagnosed with P. falciparum malaria upon their return from Uganda and Sudan. Ultradeep sequencing was performed on the cytb, dhfr, and dhps markers of treatment resistance before and during the episode of recrudescence. Haplotyping profiles were generated using three different approaches: msp2-3D7 agarose and capillary electrophoresis, and cpmp using amplicon deep sequencing (ADS). A complexity of infection (COI) analysis was conducted. De novo cytb Y268C mutants strains were observed during an episode of recrudescence 17 days and 16 h after the initial malaria diagnosis and AP treatment initiation. No Y268C mutant reads were observed in any of the samples prior to the recrudescence. SNPs in the dhfr and dhps genes were observed upon initial presentation. The haplotyping profiles suggest multiple clones mutating under AP selection pressure (COI > 3). Significant differences in COI were observed by capillary electrophoresis and ADS compared to the agarose gel results. ADS using cpmp revealed the lowest haplotype variation across the longitudinal analysis. Our findings highlight the value of ultra-deep sequencing methods in the understanding of P. falciparum haplotype infection dynamics. Longitudinal samples should be analyzed in genotyping studies to increase the analytical sensitivity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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".