Tolerogenic dendritic cells and negative vaccination in transplantation: from rodents to clinical trials
Bibliographic record
Abstract
The use of immunosuppressive (IS) drugs to treat transplant recipients has markedly reduced the incidence of acute rejection and early graft loss. However, such treatments have numerous adverse side effects and fail to prevent chronic allograft dysfunction. In this context, therapies based on the adoptive transfer of regulatory cells are promising strategies to induce indefinite transplant survival. The use of tolerogenic dendritic cells (DC) has shown great potential, as preliminary experiments in rodents have demonstrated that administration of tolerogenic DC prolongs graft survival. Recipient DC, Donor DC, or Donor Ag-pulsed recipient DC have been used in preclinical studies and administration of these cells with suboptimal immunosuppression increases their tolerogenic potential. We have demonstrated that autologous unpulsed tolerogenic DC injected in the presence of suboptimal immunosuppression are able to induce Ag-specific allograft tolerance. We derived similar tolerogenic DC in different animal models (mice and non-human primates) and confirmed their protective abilities in vitro and in vivo. The mechanisms involved in the tolerance induced by autologous tolerogenic DC were also investigated. With the aim of using autologous DC in kidney transplant patients, we have developed and characterized tolerogenic monocyte-derived DC in humans. In this review, we will discuss the preclinical studies and describe our recent results from the generation and characterization of tolerogenic monocyte-derived DC in humans for a clinical application. We will also discuss the limits and difficulties in translating preclinical experiments to theclinic.
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 | 0.005 | 0.002 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".