Differential drivers of intraspecific and interspecific competition during malaria–helminth co-infection
Bibliographic record
Abstract
Various host and parasite factors interact to determine the outcome of infection. We investigated the effects of two factors on the within-host dynamics of malaria in mice: initial infectious dose and co-infection with a helminth that limits the availability of red blood cells (RBCs). Using a statistical, time-series approach to model the within-host ‘epidemiology’ of malaria, we found that increasing initial dose reduced the time to peak cell-to-cell parasite propagation, but also reduced its magnitude, while helminth co-infection delayed peak cell-to-cell propagation, except at the highest malaria doses. Using a mechanistic model of within-host infection dynamics, we identified dose-dependence in parameters describing host responses to malaria infection and uncovered a plausible explanation of the observed differences in single vs co-infections. Specifically, in co-infections, our model predicted a higher background death rate of RBCs. However, at the highest dose, when intraspecific competition between malaria parasites would be highest, these effects of co-infection were not observed. Such interactions between initial dose and co-infection, although difficult to predict a priori, are key to understanding variation in the severity of disease experienced by hosts and could inform studies of malaria transmission dynamics in nature, where co-infection and low doses are the norm.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".