Unlocking the potential of Tregs: innovations in CAR technology
Bibliographic record
Abstract
Regulatory T cells (Tregs) adoptive immunotherapy is emerging as a viable treatment option for both autoimmune and alloimmune diseases. However, numerous challenges remain, including limitations related to cell number, availability of target-specific cells, stability, purity, homing ability, and safety concerns. To address these challenges, cell engineering strategies have emerged as promising solutions. Indeed, it has become feasible to increase Treg numbers or enhance their stability through Foxp3 overexpression, post-translational modifications, or demethylation of the Treg-specific demethylated region (TSDR). Specificity can be engineered by the addition of chimeric antigen receptors (CARs), with new techniques designed to fine-tune specificity (tandem chimeric antigen receptors, universal chimeric antigen receptors, synNotch chimeric antigen receptors). The introduction of B-cell targeting antibody receptor (BAR) Tregs has paved the way for effective regulation of B cells and plasma cells. In addition, other constructs have emerged to enhance Tregs activation and function, such as optimized chimeric antigen receptors constructs and the use of armour proteins. Chimeric antigen receptor expression can also be better regulated to limit tonic signaling. Furthermore, various opportunities exist for enhancing the homing capabilities of CAR-Tregs to improve therapy outcomes. Many of these genetic modifications have already been explored for conventional CAR-T therapy but need to be further considered for CAR-Tregs therapies. This review highlights innovative CAR-engineering strategies that have the potential to precisely and efficiently manage immune responses in autoimmune diseases and improve transplant outcomes. As these strategies are further explored and optimized, CAR-Treg therapies may emerge as powerful tools for immune intervention.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".