Regulatory T‐cell therapy for inflammatory bowel disease: more questions than answers
Bibliographic record
Abstract
T regulatory (Treg) cells are critical for maintaining immune homeostasis and establishing tolerance to foreign, non-pathogenic antigens including those found in commensal bacteria and food. Because of their multiple suppressive mechanisms, Tregs represent a promising strategy for engineering tolerance to self and non-self antigens in chronic inflammatory diseases. Already in clinical trials in the transplantation setting, the question remains whether this therapy would be effective for the treatment of mucosal inflammatory diseases that do not pose an immediate threat to life. In this review we will discuss evidence from both animal models and patients suggesting that Treg therapy would be beneficial in the context of inflammatory bowel disease (IBD). We will examine the role of T-cell versus Treg dysfunction in IBD and discuss the putative antigens that could be potential targets of antigen-directed Treg therapy. Finally, the challenges of using Treg therapy in IBD will be discussed, with a specific emphasis on the role that the microbiota may play in the outcome of this treatment. As Treg therapy becomes a bedside reality in the field of transplantation, there is great hope that it will soon also be deployed in the setting of IBD and ultimately prove more effective than the current non-specific immunosuppressive therapies.
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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".