RNA expression profiling of nonhuman primate renal allograft rejection identifies tolerance
Bibliographic record
Abstract
Tolerance induction to prevent allograft rejection is a long-standing clinical goal. However, convincing and dependable tolerance identification remains elusive. Hypothesizing that intragraft RNA expression is informative in both rejection and tolerance, we profile intrarenal allograft RNA expression in a mixed chimerism renal allograft model of cynomolgus monkeys and identify biologically significant tolerance. Analysis of 67 genes identified 3 dominant factors, each with a different pattern of gene expressions, relating to T cell-mediated rejection (TCMR), chronic antibody-mediated rejection (CAMR), or Tolerance. Clustering these 3 factors created 9 groups. One of the 9 clustered groups, the Tolerance cluster, showed the lowest probability of terminal rejection, the longest duration of allograft survival, and the lowest relative risk of terminal rejection. The Tolerance factor consists of a novel set of gene expressions including cytokine and immunoregulatory genes adding mechanistic insights into tolerance. The Tolerance factor could not be identified within current pathologic diagnostic categories. The TCMR and CAMR factors are dominant to the Tolerance factor, causing rejection even if the Tolerance factor is present. These 3 factors determine the probability of terminal rejection or tolerance. This novel a posteriori approach permits identification of pathways of rejection, including 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 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 | 0.000 |
| 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) | 0.001 | 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".