Modulating the T Lymphocyte Immune Response via Secretome Produced miRNA: From Tolerance Induction to the Enhancement of the Anticancer Response
Bibliographic record
Abstract
T cells are key mediators of graft tolerance/rejection, development of autoimmunity, and the anticancer response. Consequently, differentially modifying the T cell response is a major therapeutic target. Most immunomodulatory approaches have focused on cytotoxic agents, cytokine modulation, monoclonal antibodies, mitogen activation, adoptive cell therapies (including CAR-T cells). However, these approaches do not persistently reorient the systemic immune response thus necessitating continual therapy. Previous murine studies from our laboratory demonstrated that the adoptive transfer of polymer-grafted (PEGylated) allogeneic leukocytes resulted in the induction of a persistent and systemic tolerogenic state. Further analyses demonstrated that miRNA isolated from the secretome of polymer-modified or control allogeneic responses effectively induced either a tolerogenic (TA1 miRNA) or proinflammatory (IA1 miRNA) response both in vitro and in vivo that was both systemic and persistent. In a murine Type 1 diabetes autoimmune model, the tolerogenic TA1 therapeutic effectively attenuated the disease process via the systemic upregulation of regulatory T cells while simultaneously downregulating T effector cells. In contrast, the proinflammatory IA1 therapeutic enhanced the anticancer efficacy of naïve PBMC by increasing inflammatory T cells and decreasing regulatory T cells. The successful development of this secretome miRNA approach may prove useful treating both autoimmune diseases and cancer.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.008 |
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".