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Record W2156803129 · doi:10.1098/rspb.2007.0809

Evolution of parasite virulence when host responses cause disease

2007· article· en· W2156803129 on OpenAlexaff
Troy Day, Andrea L. Graham, Andrew F. Read

Bibliographic record

VenueProceedings of the Royal Society B Biological Sciences · 2007
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicEvolution and Genetic Dynamics
Canadian institutionsQueen's University
FundersBiotechnology and Biological Sciences Research CouncilWellcome Trust
KeywordsImmunopathologyVirulenceBiologyDiseaseImmunologyPathogenImmune systemHost (biology)Evolutionary biologyEcologyGeneticsMedicineGenePathology

Abstract

fetched live from OpenAlex

The trade-off hypothesis of virulence evolution rests on the assumption that infection-induced mortality is a consequence of host exploitation by parasites. This hypothesis lies at the heart of many empirical and theoretical studies of virulence evolution, despite growing evidence that infection-induced mortality is very often a by-product of host immune responses. We extend the theoretical framework of the trade-off hypothesis to incorporate such immunopathology and explore how this detrimental aspect of host defence mechanisms affects the evolution of pathogen exploitation and hence infection-induced mortality. We argue that there are qualitatively different ways in which immunopathology can arise and suggest ways in which empirical studies can tease apart these effects. We show that immunopathology can cause infection-induced mortality to increase or decrease as a result of pathogen evolution, depending on how it covaries with pathogen exploitation strategies and with parasite killing by hosts. Immunopathology is thus an important determinant of whether public and animal health programmes will drive evolution in a clinically beneficial or detrimental direction. Immunopathology complicates our understanding of disease evolution, but can nevertheless be readily accounted for within the framework of the trade-off hypothesis.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.289
Threshold uncertainty score0.471

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.018
GPT teacher head0.273
Teacher spread0.256 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations128
Published2007
Admission routes1
Has abstractyes

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