Current status and prospects for the application of cannabinoids in organ transplantation
Bibliographic record
Abstract
Graft versus host disease and allograft rejection are frequent complications of allogeneic hematopoietic stem cell transplantation and solid organ transplantation, respectively. The probability of developing either condition is dependent upon the magnitude of genetic disparity between donor and recipient. In both contexts, alloimmune-mediated processes are responsible for disease pathogenesis and the subsequent complications that are associated with significant morbidity and mortality. Existing prophylactic regimens consisting of intensive immunosuppression are limited by high incidences of graft failure, infection, and toxicity. Cannabinoids are a diverse family of natural and synthetic molecules that, through interaction with the endocannabinoid system, have potent immunoregulatory properties. However, the applicability of cannabinoids to the prevention of graft-versus-host disease and allograft rejection has not been established. This article offers insight into our current understanding of the immunopathophysiology of graft-versus-host disease and allograft rejection, relevant cannabinoid- mediated immune modulation, and emerging evidence on the role of cannabinoids in transplant immunology. With the need for more effective prophylactic strategies and the concordant interest in cannabinoid-based therapeutics, it is pertinent to determine whether the endocannabinoid system can be therapeutically targeted in the post-transplant setting.
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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 | 0.006 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".