Proteomic-Based Identification of Cleaved Urinary β2-microglobulin as a Potential Marker for Acute Tubular Injury in Renal Allografts
Bibliographic record
Abstract
Our aim is to develop noninvasive tests to monitor the renal allograft posttransplant. Previously, we have reported that an unbiased proteomic-based approach can detect urine protein peaks associated with acute tubulointerstitial renal allograft rejection. Identification of these proteins peaks by mass spectrometry demonstrated that they all derive from nontryptic cleaved forms of beta2-microglobulin. In vitro experiments showed that cleavage of intact beta2-microglobulin requires a urine pH < 6 and the presence of aspartic proteases. Patients with acute tubulointerstitial rejection had lower urine pH than stable transplants and healthy individuals. In addition, they had higher amounts of aspartic proteases and intact beta2-microglobulin in urine. These factors ultimately lead to increased amounts of cleaved urinary beta2-microglobulin. Cleaved beta2-microglobulin as an indicator of acute tubular injury may become a useful tool for noninvasive monitoring of renal allografts.
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.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".