From “truly naïve” to “exhausted senescent” T cells: When markers predict functionality
Bibliographic record
Abstract
The study of T cell biology has been accelerated by substantial progress at the technological level, particularly through the continuing advancement of flow cytometry. The conventional approach of observing T cells as either T helper or T cytotoxic is overly simplistic and does not allow investigators to clearly identify immune mechanisms or alterations in physiological processes that impact on clinical outcomes. The complexity of T cell sub-populations, as we understand them today, combined with the immunological and functional diversity of these subsets represent significant complications for the study of T cell biology. In this article, we review the use of classical markers in delineating T cell sub-populations, from "truly naïve" T cells (recent thymic emigrants with no proliferative history) to "exhausted senescent" T cells (poorly proliferative cells that display severe functional abnormalities) wherein the different phenotypes of these populations reflect their disparate functionalities. In addition, since persistent infections and chronological aging have been shown to be associated with significant alterations in human T cell distribution and function, we also discuss age-associated and cytomegalovirus-driven alterations in the expression of key subset markers.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".