New concepts in chronic antibody-mediated kidney allograft rejection: prevention and treatment
Bibliographic record
Abstract
PURPOSE OF REVIEW: Chronic antibody-mediated rejection (AMR) is a cardinal cause of transplant failure, with currently no proven effective prevention or treatment. The present review will focus on new therapeutic concepts currently under clinical evaluation. RECENT FINDINGS: One interesting treatment approach may be interference with interleukin-6 (IL-6) signaling to modulate B-cell immunity and donor-specific antibody (DSA) production. Currently, a large phase III randomized controlled trial is underway to clarify the safety and efficacy of clazakizumab, a high-affinity anti-IL-6 antibody, in chronic AMR. A prevention/treatment strategy may be costimulation blockade using belatacept to interfere with germinal center responses and DSA formation. In a recent uncontrolled study, belatacept conversion was shown to stabilize renal function and dampen AMR activity. Moreover, preliminary clinical results suggest efficacy of CD38 antibodies to deplete plasma and natural killer cells to treat AMR, with anecdotal reports demonstrating at least transient resolution of active rejection. SUMMARY: There are promising concepts on the horizon for the prevention and treatment of chronic AMR. The design of adequately powered placebo-controlled trials to clarify the safety and efficacy of such new therapies, however, remains a big challenge, and will rely on the definition of precise surrogate endpoints predicting long-term allograft survival. Mapping the natural history of AMR would greatly help the understanding of who would derive benefits from treatment.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".