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Record W2157494890 · doi:10.1186/1476-9255-7-57

The role of toll-like receptors in acute and chronic lung inflammation

2010· article· en· W2157494890 on OpenAlexafffund
Erin I. Lafferty, Salman T. Qureshi, Markus Schnare

Bibliographic record

VenueJournal of Inflammation · 2010
Typearticle
Languageen
FieldImmunology and Microbiology
TopicImmune Response and Inflammation
Canadian institutionsMcGill University
FundersCanadian Institutes of Health ResearchDeutsche ForschungsgemeinschaftMcGill University Health CentreMcGill University
KeywordsMedicineInflammationTollReceptorHuman physiologyMolecular medicineImmunologyLungToll-like receptorIntensive care medicineInternal medicineInnate immune system

Abstract

fetched live from OpenAlex

By virtue of its direct contact with the environment, the lung is constantly challenged by infectious and non-infectious stimuli that necessitate a robust yet highly controlled host response coordinated by the innate and adaptive arms of the immune system. Mammalian Toll-like receptors (TLRs) function as crucial sentinels of microbial and non-infectious antigens throughout the respiratory tract and mediate host innate immunity. Selective induction of inflammatory responses to harmful environmental exposures and tolerance to innocuous antigens are required to maintain tissue homeostasis and integrity. Conversely, dysregulated innate immune responses manifest as sustained and self-perpetuating tissue damage rather than controlled tissue repair. In this article we review aspects of Toll-like receptor function that are relevant to the development of acute lung injury and chronic obstructive lung diseases as well as resistance to frequently associated microbial infections.

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)0.000
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 score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.002
GPT teacher head0.215
Teacher spread0.212 · 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

Citations122
Published2010
Admission routes2
Has abstractyes

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