Taking regulatory T-cell therapy one step further
Bibliographic record
Abstract
PURPOSE OF REVIEW: Adoptive cell therapy using CD4FOXP3 regulatory T cells (Treg) has emerged as a promising therapeutic strategy to treat autoimmunity and alloimmunity. Preclinical studies suggest that the efficacy of Treg therapy can be improved by modifying the antigen specificity, stability and function of therapeutic Tregs. We review recent innovations that considerably enhance the possibilities of controlling these parameters. RECENT FINDINGS: Antigen-specific Tregs can be generated by genetically modifying polyclonal Tregs to express designated T-cell receptors or single-chain chimeric antigen receptors. The benefits of this approach can be further extended by using novel strategies to fine-tune the antigen-specificity and affinity of Treg in vivo. CRISPR/Cas 9 technology now enables the modification of therapeutic Tregs so they are safer, more stable and long lived. The differentiation and homing properties of Tregs can also be modulated by gene editing or modifying ex-vivo stimulation conditions. SUMMARY: A new wave of innovation has considerably increased the number of strategies that could be used to increase the therapeutic potential of Treg therapy. However, the increased complexity of these approaches may limit their wide accessibility. Third-party therapy with off-the-shelf Treg products could be a solution.
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 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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".