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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.009 | 0.009 |
| Meta-epidemiology (broad) | 0.010 | 0.006 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.008 | 0.012 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.009 | 0.005 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.027 | 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 itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
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".