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Record W4392611000 · doi:10.1016/j.coviro.2024.101397

Tissue-resident memory T cells in protective immunity to influenza virus

2024· article· en· W4392611000 on OpenAlex
Seung‐Woo Lee, Karen Yeung, Tania H. Watts

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueCurrent Opinion in Virology · 2024
Typearticle
Languageen
FieldMedicine
TopicInfluenza Virus Research Studies
Canadian institutionsUniversity of Toronto
FundersCanadian Institutes of Health ResearchUniversity of TorontoCanada Research Chairs
KeywordsBiologyImmunologyImmune systemImmunityVirusInfluenza A virusVirologyPathogenImmunological memoryPandemicDiseaseCoronavirus disease 2019 (COVID-19)MedicineInfectious disease (medical specialty)Pathology

Abstract

fetched live from OpenAlex

Influenza virus is an important human pathogen with significant pandemic potential. Tissue-resident memory T cells (Trm) in the lung provide critical protection against influenza, but unlike Trm at other mucosal sites, Trm in the respiratory tract (RT) are subject to rapid attrition in mice, mirroring the decline in protective immunity to influenza virus over time. Conversely, dysfunctional Trm can drive fibrosis in aged mice. The requirement for local antigen to induce and maintain RT Trm must be considered in vaccine strategies designed to induce this protective immune subset. Here, we discuss recent studies that inform our understanding of influenza-specific respiratory Trm, and the factors that influence their development and persistence. We also discuss how these biological insights are being used to develop vaccines that induce Trm in the RT, despite the limitations to monitoring Trm in humans.

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.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.600
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.001

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 head0.140
GPT teacher head0.464
Teacher spread0.324 · 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