New developments in transplant proteomics
Bibliographic record
Abstract
PURPOSE OF REVIEW: Despite modern immunosuppression, renal allograft rejection remains a major contributor to graft loss. Novel biomarkers may help improve posttransplant outcomes through the early detection and treatment of rejection. Our objective is to provide an overview of proteomics, review recent discovery-based rejection studies, and explore innovative approaches in biomarker development. RECENT FINDINGS: Urine MMP7 was identified as a biomarker of subclinical and clinical rejection using two-dimensional liquid chromatography tandem-mass spectrometry (LC-MS/MS) and improved the overall diagnostic discrimination of urine CXCL10 : Cr alone for renal allograft inflammation. A novel peptide signature to classify stable allografts from acute rejection, chronic allograft injury, and polyoma virus (BKV) nephropathy was identified using isobaric tag for relative and absolute quantitation (TRAQ) and label-free MS, with independent validation by selected reaction monitoring mass spectrometry (SRM-MS). Finally, an in-depth exploration of peripheral blood mononuclear cells identified differential proteoform expression in healthy transplants versus rejection. SUMMARY: There is still much in the human proteome that remains to be explored, and further integration of renal, urinary, and exosomal data may offer deeper insight into the pathophysiology of rejection. Functional proteomics may be more biologically relevant than protein/peptide quantity alone, such as assessment of proteoforms or activity-based protein profiling. Discovery-based studies have identified potential biomarker candidates, but external validation studies are required.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.005 |
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".