The roles of resident, central and effector memory <scp>CD</scp>4 T‐cells in protective immunity following infection or vaccination
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".