Bibliographic record
Abstract
Achieving transplant tolerance with regulatory T cell (Treg) adoptive immunotherapy is currently under investigation as a therapy to reduce graft rejection, improve long-term outcomes, and patient quality of life. Initial approaches involve expansion of naturally-occurring Tregs, either polyclonal or antigen-specific, however both of these approaches have several technical limitations that restrict implementation at a large-scale. To circumvent these limitations, this work describes an alternate approach to generate antigen-specific Tregs by expressing a chimeric antigen receptor specific for HLA-A*02:01 (A2-CAR), which activates Tregs in the presence of HLA-A*02:01, a tissue antigen allele that is commonly mismatched between transplant donor and recipient. In the first CAR Treg studies, the antigen-binding region (scFv) of the A2-CAR was derived from the mouse BB7.2 hybridoma, which could cause immunogenic responses and limit its efficacy in humans. Additionally, most CAR Treg studies to date employ CD28 and CD3 signaling domains to activate the cell, but alternative co-receptor signaling moieties have not been adequately tested. Two major improvements to CAR Treg technology are explored: (1) the scFv is humanized to reduce the immunogenicity of the CAR construct itself, rendering it less likely to cause immune responses in humans and (2) a collection of a variety of co-receptor intracellular domains are tested in place of CD28 to determine whether alternative signals can bestow Tregs with more beneficial functional properties. In the final chapter, a method for staining FOXP3, the Treg master transcription factor, using mass cytometry is described to enable thorough tracking of FOXP3⁺ Tregs and the rest of the immune compartment in patient samples from various tissues. Collectively, this body of work furthers our understanding of Treg immunotherapies and provides further support for their use in transplant settings.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".