Bibliographic record
Abstract
PURPOSE OF REVIEW: We discuss the use of tolerogenic dendritic cells (TolDCs) as a therapeutic tool in solid organ transplantation, with particular emphasis on recent experimental and preclinical data supporting the clinical translation of TolDC therapy. RECENT FINDINGS: TolDC have been successfully used in rodents to promote long-term allograft survival. Although most studies have focused on donor dendritic cells or donor antigen-pulsed dendritic cells, our group investigated a strategy based on the administration of autologous dendritic cells (not pulsed with donor antigens). We discuss the therapeutic efficacy, mechanisms, and potential risks and advantages of each strategy. We also highlight recent findings on the generation of clinical grade human TolDC from blood monocytes. Finally, we discuss preliminary experience with dendritic cells in humans and critical issues regarding the implementation of TolDC therapy to clinical organ transplantation. SUMMARY: TolDC hold therapeutic promise for the treatment of transplanted patients. Cell therapy with unpulsed, autologous dendritic cells appears as a well tolerated, clinically relevant approach that might help in improving long-term allograft survival and limit the harmful effects of immunosuppressive treatments.
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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.004 |
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".