Identification of a novel biomarker gene set with sensitivity and specificity for distinguishing between allograft rejection and tolerance#*
Bibliographic record
Abstract
Here we examined whether the expression of a novel immunoregulatory gene set could be used to predict outcomes in murine models of rapamycin-induced cardiac tolerance, spontaneous hepatic tolerance, and cardiac rejection. The expression of the immunoregulatory gene set was assessed with the GeXP multiplex reverse-transcription polymerase chain reaction (RT-PCR) analysis system, and it was correlated to the pathological and biochemical parameters of the allografts. In rejecting cardiac grafts, the increased expression of an inflammatory set of genes, which included CD45, CD4, CD25, suppressor of cytokine signaling 2, cytotoxic T lymphocyte-associated protein 4 (CTLA4), selectin lymphocyte, interferon-γ (IFN-γ), programmed cell death 1 (Pdcd1), and granzyme B (Gzmb), was seen 8 days after transplantation along with histological evidence of severe allograft rejection. In tolerant cardiac allografts, the expression of fibrinogen-like protein 2 (Fgl2), Pdcd1, killer cell lectin-like receptor G1 (Klrg1), CTLA4, and lymphocyte-activation gene 3 was associated with tolerance. In a model of liver allograft tolerance, the increased expression of lectin galactose-binding soluble 1, Fgl2, CD39, phosphodiesterase 3B, Klrg1, forkhead box P3 (Foxp3), and transforming growth factor β as well as the inflammatory set of genes was observed 8 to 14 days after transplantation (ie, when there was severe inflammatory injury). At a later time when the liver allografts had been fully accepted and were histologically normal, the expression of the inflammatory set of genes returned to the baseline, but the expression of the tolerogenic set of genes was still increased. Genes that were expressed in tolerant cardiac and liver allografts included Fgl2, Klrg1, and Foxp3, whereas genes associated with rejection included CD25, Gzmb, and IFN-γ. Our data indicate that monitoring the graft expression of a novel biomarker gene set with the GeXP multiplex RT-PCR analysis system may allow differentiation between rejection and tolerance.
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 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".