The potential of autologous regulatory T cell (Treg) therapy to prevent Cardiac Allograft Vasculopathy (CAV) in paediatric heart transplant recipients
Bibliographic record
Abstract
Paediatric heart transplant is an established treatment for end stage heart failure in children, however patients have to commit to lifelong medical surveillance and adhere to daily immunosuppressants to minimise the risk of rejection. Compliance with immunosuppressants can be burdensome with their toxic side effects and need for frequent blood monitoring especially in children. Though the incidence of early rejection episodes has significantly improved overtime, the long-term allograft health and survival is determined by Cardiac Allograft Vasculopathy (CAV) which affects a vast number of post-transplant patients. Once CAV has set in, there is no medical or surgical treatment to reverse it and graft survival is significantly compromised across all age groups. Current treatment strategies include novel immunosuppressant agents and drugs to lower blood lipid levels to address the underlying immunological pathophysiology and to manage traditional cardiac risk factors. Translational researchers are seeking novel immunological approaches that can lead to permanent acceptance of the allograft such as using regulatory T cell (Tregs) immunotherapy. Clinical trials in the setting of graft versus host disease, autoimmunity and kidney and liver transplantation using Tregs have shown the feasibility and safety of this strategy. This review will summarise current knowledge of the latest clinical therapies for CAV and pre-clinical evidence in support of Treg therapy for CAV. We will also discuss the different Treg sources and the considerations of translating this into a feasible immunotherapy in clinical practice in the paediatric population.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".