Developing Renal Allograft Surveillance Strategies – Urinary Biomarkers of Cellular Rejection
Bibliographic record
Abstract
PURPOSE OF REVIEW: Developing tailored immunosuppression regimens requires sensitive, non-invasive tools for serial post-transplant surveillance as the current clinical standards with serum creatinine and proteinuria are ineffective at detecting subclinical rejection. The purpose of this review is: (i) to illustrate the rationale for allograft immune monitoring, (ii) to discuss key steps to bring a biomarker from bench-to-bedside, and (iii) to present an overview of promising biomarkers for cellular rejection. SOURCES OF INFORMATION: PubMed. FINDINGS: Recent multicentre prospective observational cohort studies have significantly advanced biomarker development by allowing for the adequately powered evaluation of multiple biomarkers capable of detecting allograft rejection. These studies demonstrate that urinary CXCR3 chemokines (i.e. CXCL9 and CXCL10) are amongst the most promising for detecting subclinical inflammation; increasing up to 30 days prior to biopsy-proven acute rejection; decreasing in response to anti-rejection therapy; and having prognostic significance for the subsequent development of allograft dysfunction. Urinary CXCR3 chemokines are measured by simple and cost-effective ELISA methodology, which can readily be implemented in clinical labs. LIMITATIONS: Many biomarker studies are performed in highly selected patient groups and lack surveillance biopsies to accurately classify healthy transplants. Few validation studies have been done in unselected, consecutive patient populations to characterize population-based diagnostic performance. IMPLICATIONS: Based on these data, prospective interventional trials should be undertaken to determine if chemokine-based post-transplant monitoring strategies can improve long-term renal allograft outcomes. This last step will be necessary to move novel biomarkers from the bench-to-bedside.
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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 | 0.002 | 0.005 |
| 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 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".