T regulatory cell therapy in transplantation
Bibliographic record
Abstract
PURPOSE OF REVIEW: There is great hope that cellular therapy with regulatory T cells (Tregs) will be an effective way to induce alloantigen specific tolerance, ultimately allowing for reduction or elimination of nonspecific immunosuppression. In the past, considerable effort was focused on defining the optimal ways to isolate and expand Tregs from peripheral or cord blood. Now that expansion of therapeutically relevant numbers of Tregs is feasible, we need to consider what is going to happen to the cells when they are transferred in vivo. RECENT FINDINGS: For optimal function, Tregs must be able to traffic to the correct location(s) and, despite the presence of immunosuppressive therapy, live long enough to transfer their regulatory function to recipient T cells. Within the Treg pool, there are also functionally specialized subsets, identified by chemokine receptor expression and/or cytokine production, which control their trafficking and relative ability to suppress different types of T helper cells, respectively. Recent findings imply that the plasticity of appropriately obtained populations of Tregs may not be of as great concern as previously suggested. Experimental data have also provided evidence as to how one might design adjunctive treatment that best supports the viability and function of Tregs after transfer. SUMMARY: Knowledge of how Tregs work in transplantation comes from studies that do not recapitulate how these cells will be used in humans. There is a need to develop better preclinical models to study how the in-vivo function of human Tregs can be optimized to ensure they can meet the challenge of inducing transplantation tolerance.
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 | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.005 |
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".