Generation of Potent and Stable Human CD4+ T Regulatory Cells by Activation-independent Expression of FOXP3
Bibliographic record
Abstract
Therapies based on enhancing the numbers and/or function of T regulatory cells (Tregs) represent one of the most promising approaches to restoring tolerance in many immune-mediated diseases. Several groups have investigated whether human Tregs suitable for cellular therapy can be obtained by in vitro expansion, in vitro conversion of conventional T cells into Tregs, or gene transfer of the FOXP3 transcription factor. To date, however, none of these approaches has resulted in a homogeneous and stable population of cells that is as potently suppressive as ex vivo Tregs. We developed a lentivirus-based strategy to ectopically express high levels of FOXP3 that do not fluctuate with the state of T-cell activation. This method consistently results in the development of suppressive cells that are as potent as Tregs and can be propagated as a homogeneous population. Moreover, using this system, both naïve and memory CD4(+) T cells can be efficiently converted into Tregs. To date, this is the most efficient and reliable protocol for generating large numbers of suppressive CD4(+) Tregs, which can be used for further biological study and developed for antigen-specific cellular therapy applications.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".