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Record W4323350197 · doi:10.1155/2023/2899271

TIRAP, TRAM, and Toll-Like Receptors: The Untold Story

2023· review· en· W4323350197 on OpenAlexafffund
Valérie Lannoy, Anthony Côté-Biron, Claude Asselin, Nathalie Rivard

Bibliographic record

VenueMediators of Inflammation · 2023
Typereview
Languageen
FieldImmunology and Microbiology
TopicImmune Response and Inflammation
Canadian institutionsUniversité de Sherbrooke
FundersCanadian Institutes of Health Research
KeywordsTLR3Proinflammatory cytokineReceptorInnate immune systemTRIFSignal transducing adaptor proteinPattern recognition receptorSignal transductionCell biologyToll-like receptorBiologyTollMyeloid cellsImmunologyInflammationGenetics

Abstract

fetched live from OpenAlex

Toll-like receptors (TLRs) are the most studied receptors among the pattern recognition receptors (PRRs). They act as microbial sensors, playing major roles in the regulation of the innate immune system. TLRs mediate their cellular functions through the activation of MyD88-dependent or MyD88-independent signaling pathways. Myd88, or myeloid differentiation primary response 88, is a cytosolic adaptor protein essential for the induction of proinflammatory cytokines by all TLRs except TLR3. While the crucial role of Myd88 is well described, the contribution of other adaptors in mediating TLR signaling and function has been underestimated. In this review, we highlight important results demonstrating that TIRAP and TRAM adaptors are also required for full signaling activity and responses induced by most TLRs.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0000.001
Scholarly communication0.0020.004
Open science0.0010.001
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0040.004

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 head0.024
GPT teacher head0.280
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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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

Citations45
Published2023
Admission routes2
Has abstractyes

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