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Record W2791749859 · doi:10.1111/imm.12929

The roles of resident, central and effector memory <scp>CD</scp>4 T‐cells in protective immunity following infection or vaccination

2018· review· en· W2791749859 on OpenAlex

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.

Bibliographic record

VenueImmunology · 2018
Typereview
Languageen
FieldImmunology and Microbiology
TopicImmune Cell Function and Interaction
Canadian institutionsInstitute of Infection and Immunity
FundersMedical Research CouncilAstraZeneca
KeywordsEffectorVaccinationImmunityImmunologyBiologyImmune system

Abstract

fetched live from OpenAlex

Immunological memory provides rapid protection to pathogens previously encountered through infection or vaccination. CD4 T-cells play a central role in all adaptive immune responses. Vaccines must, therefore, activate CD4 T-cells if they are to generate protective immunity. For many diseases, we do not have effective vaccines. These include human immunodeficiency virus (HIV), tuberculosis and malaria, which are responsible for many millions of deaths each year across the globe. CD4 T-cells play many different roles during the immune response coordinating the actions of many other cells. In order to harness the diverse protective effects of memory CD4 T-cells, we need to understand how memory CD4 T-cells are generated and how they protect the host. Here we review recent findings on the location of different subsets of memory CD4 T-cells that are found in peripheral tissues (tissue resident memory T-cells) and in the circulation (central and effector memory T-cells). We discuss the generation of these cells, and the evidence that demonstrates how they provide immune protection in animal and human challenge models.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.973
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.017
GPT teacher head0.283
Teacher spread0.266 · 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