New concepts in chronic antibody-mediated kidney allograft rejection: prevention and treatment
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it