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Record W4294168465 · doi:10.1002/bies.202200080

The buried gems of disease tolerance in animals: Evolutionary and interspecies comparative approaches

2022· article· en· W4294168465 on OpenAlexafffund
Mohamed B. F. Hawash, Mohamed A. Eldeeb, Rahma Gaber, Kareem Morsy

Bibliographic record

VenueBioEssays · 2022
Typearticle
Languageen
FieldImmunology and Microbiology
TopicVector-borne infectious diseases
Canadian institutionsMcGill UniversityMontreal Neurological Institute and HospitalUniversité de Montréal
FundersCanadian Institutes of Health Research
KeywordsBiologyEvolutionary biologyComparative biologyZoology

Abstract

fetched live from OpenAlex

Host defense mechanisms are categorized into different strategies, namely, avoidance, resistance and tolerance. Resistance encompasses mechanisms that directly kill the pathogen while tolerance is mainly concerned with alleviating the harsh consequences of the infection regardless of the pathogen burden. Resistance is well-known strategy in immunology while tolerance is relatively new. Studies addressed tolerance mainly using mouse models revealing a wide range of interesting tolerance mechanisms. Herein, we aim to emphasize on the interspecies comparative approaches to explore potential new mechanisms of disease tolerance. We will discuss mechanisms of tolerance with focus on those that were revealed using comparative study designs of mammals followed by summarizing the reasons for adopting comparative approaches on disease tolerance studies. Disease tolerance is a relatively new concept in immunology, we believe combining comparative studies with model organism study designs will enhance our understanding to tolerance and unveil new mechanisms of tolerance.

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)0.000
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 score0.245
Threshold uncertainty score0.306

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.042
GPT teacher head0.252
Teacher spread0.210 · 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

Citations3
Published2022
Admission routes2
Has abstractyes

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