Chimeric antigen receptors on Regulatory T cells as a treatment strategy in autoimmune diseases
Bibliographic record
Abstract
Abstract Research question Can CAR-MOG Tregs help curb the neuroinflammation in a MS mouse model? Objectives Methods To test this out, we sorted Tregs from human peripheral blood or murine spleens using T cell enrichment followed by flow-cytometric sorting and amplified using Dynabeads (human CD3/CD28 activator) for Tregs proliferation and stained for Treg markers to ascertain the purity of isolated Tregs. We then constructed a second-generation CAR against MOG monoclonal antibody (8.18 C5) and transduce Tregs and Tconvs to test the transduction efficacy and suppressive function of CAR-Tregs. To test the killing function of CAR-MOG-Tconv and compared this to that of CAR MOG Tregs, we set up a cytotoxicity assay against a cell line modified to express MOG. Additionally we performed suppression assays with control Tregs and CAR transduced Tregs to determine if CAR transduction affects the immunosuppressive capacity of Tregs. Results CAR Tconv and CAR Tregs show specificity against K562 cell line modified to express MOG, CAR Tregs have a lower killing capacity compared to CAR Tconv. CAR Tregs do not lose their immunosuppressive function and are comparable to the untransduced Tregs. CAR Tregs upon co-culture with Target cell line (K562 MOG) express activation markers and increased expression of cytokines such as IL-10, and TGF beta. Supported by Canada Graduate Scholarships Doctoral Award (CGS D)
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".