Human CD4<sup>+</sup>CD25<sup>+</sup>CD127<sup>low</sup>FOXP3<sup>+</sup> regulatory T lymphocytes and CD4<sup>+</sup>CD25<sup>-</sup>FOXP3<sup>-</sup> conventional T lymphocytes: a differential transcriptome profile
Bibliographic record
Abstract
CD4+CD25+ FOXP3+ regulatory T cells (Tregs) represent a subpopulation of CD4+ T cells central for the suppression of physiological and pathological immune reactions. Although distinct cell surface antigens are expressed in regulatory T cells, those components are also present on the surface of activated CD4+CD25- FOXP3-T cells, thus making the discrimination between Tregs and conventional CD4+ T difficult and isolation of Tregs complex. Yet, the molecular components driving Tregs’ function are still not fully characterized. Aiming at unraveling molecular components specifically marking Tregs, and upon using quantitative real-time PCR (qRT-PCR) followed by bioinformatics analysis, we identified, in this study, differential transcriptional profiles, in peripheral blood CD4 + CD25 + CD127low FOXP3+ Tregs versus CD4 + CD25-FOXP3- conventional T cells, for set of genes with distinct immunological roles. In conclusion, this study identifies some novel genes that appeared to be differentially transcribed in CD4+ Tregs versus conventional T cells. The identified genes could serve as novel molecular targets relevant to Tregs’ function and isolation.
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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 | 0.001 |
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".