CD5 levels define functionally heterogeneous populations of naïve human CD4 <sup>+</sup> T cells
Bibliographic record
Abstract
Abstract Studies in murine models show that subthreshold TCR interactions with self‐peptide are required for thymic development and peripheral survival of naïve T cells. Recently, differences in the strength of tonic TCR interactions with self‐peptide, as read‐out by cell surface levels of CD5, were associated with distinct effector potentials among sorted populations of T cells in mice. However, whether CD5 can also be used to parse functional heterogeneity among human T cells is less clear. Our study demonstrates that CD5 levels correlate with TCR signal strength in human naïve CD4 + T cells. Further, we describe a relationship between CD5 levels on naïve human CD4 + T cells and binding affinity to foreign peptide, in addition to a predominance of CD5 hi T cells in the memory compartment. Differences in gene expression and biases in cytokine production potential between CD5 lo and CD5 hi naïve human CD4 + T cells are consistent with observations in mice. Together, these data validate the use of CD5 surface levels as a marker of heterogeneity among human naïve CD4 + T cells with important implications for the identification of functionally biased T‐ cell populations that can be exploited to improve the efficacy of adoptive cell therapies.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".