Exogenous glucocorticoids amplify the costs of infection by reducing resistance and tolerance, but effects are mitigated by co-infection
Bibliographic record
Abstract
Individual variation in parasite defences, such as resistance and tolerance, can underlie heterogeneity in fitness and could influence disease transmission dynamics. Glucocorticoid hormone concentrations often change in response to fluctuating environmental conditions and mediate changes in immune function, resource allocation and tissue repair. Thus, changes in glucocorticoid hormone concentrations might mediate individual variation in investment in resistance versus tolerance. In this study, we experimentally increased glucocorticoid concentrations in red-winged blackbirds ( Agelaius phoeniceus) that were naturally infected with haemosporidian parasites, and assessed changes in resistance and tolerance of infection. Glucocorticoid treatment increased burdens of Plasmodium, the parasite causing avian malaria, but only in the absence of co-infection with another Haemosporidian, Haemoproteus. Thus, glucocorticoids might reduce resistance to infection, but co-infection can mitigate the negative consequences of increased hormone concentrations. Glucocorticoid treatment also decreased tolerance of infection. We found no evidence that the inflammatory immune response or rate of red blood cell production underlie the effects of glucocorticoids on resistance and tolerance. Our findings suggest that exogenous glucocorticoids can increase the costs of haemosporidian infections by both increasing parasite numbers and reducing an individual's ability to cope with infection. These effects could scale up to impact populations of both host and parasite.
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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 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".