Understanding and Predicting Strain‐Specific Patterns of Pathogenesis in the Rodent Malaria<i>Plasmodium chabaudi</i>
Bibliographic record
Abstract
Despite considerable success elucidating important immunological and resource-based mechanisms that control the dynamics of infection in some diseases, little is known about how differences in these mechanisms result in strain differences in patterns of pathogenesis. Using a combination of data and theory, we disentangle the role of ecological factors (e.g., resource abundance) in the dynamics of pathogenesis for the malaria species Plasmodium chabaudi in CD4+ T cell-depleted mice. We build a series of nested models to systematically test a number of potential regulatory mechanisms and determine the "best" model using statistical techniques. The best-fit model is further tested using an independent data set from mixed-clone competition experiments. We find that parasites preferentially invade older red blood cells even when they are more fecund in younger reticulocytes and that inoculum size has a strong effect on burst size in reticulocytes. Importantly, the results suggest that strain-specific differences in virulence arise from differences in red blood cell age-specific invasion rates and burst sizes, since these are lower for the less virulent strain, as well as from differences in levels of erythropoesis induced by each strain. Our analyses highlight the importance of model selection and validation for revealing new biological insights.
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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 | 0.002 | 0.005 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".