Quantification of T cell receptor rearrangement excision circles to estimate thymic function: an important new tool for endocrine-immune physiology
Bibliographic record
Abstract
Although the thymus constitutes a target organ for most protein and steroid hormones, it has been quite difficult to determine the precise control exerted in vivo by the endocrine system upon thymic function. The biological role of the thymus is to ensure the generation of a diversified population of peripheral T cells able to respond to non-self-antigens but nevertheless tolerant to self-antigens. For a long time, thymic function could not be monitored, as a consequence of the absence of adequate technology to differentiate recent thymic emigrants from naive T cells. The generation of T cell receptor (TCR) diversity occurs in the thymus through recombination of gene segments encoding the variable parts of the TCR alpha and beta chains. During these processes, by-products of the rearrangements are generated in the form of TCR excision circles (TRECs). As these molecules are lost upon further cell division, their quantification is actually considered as a very valuable tool to estimate thymic function. The most appropriate TREC is deltaRec-Psi(J)alpha TREC or signal joint TREC resulting from deltaRec-Psi(J)alpha rearrangement (TCRD deletion) that occurs late during thymopoiesis, before V(alpha)-J(alpha) rearrangement. Here we describe how TREC quantification is a powerful and reliable method to evaluate the impact of hormones and endocrine disorders upon thymic function.
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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 | 0.001 |
| 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 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.004 |
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".