From “truly naïve” to “exhausted senescent” T cells: When markers predict functionality
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.
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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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.024 | 0.018 |
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 it