RNA expression profiling of renal allografts in a nonhuman primate identifies variation in NK and endothelial gene expression
Bibliographic record
Abstract
RNA transcript expression estimates are a promising method to study the mechanisms and classification of renal allograft rejections. Here we use the Nanostring platform to profile RNA expression in renal allografts in a nonhuman primate (NHP), the Cynomolgus monkey. We analyzed protocol and indication 278 archival renal allograft samples, both protocol and indication from 76 animals with diagnoses of chronic antibody-mediated rejection (CAMR), acute cellular rejection (TCMR), and MIXED (both CAMR and TCMR), plus normals and samples with no pathological rejection using a Cynomolgus-specific probe set of 67 genes. Analysis identified RNA expression heterogeneity of endothelial and NK genes within CAMR and TCMR, including the stages of CAMR. Three factors were partitioned into additional groups. One group with the longest allograft survival time is pure CAMR without NK or CD3. Three mixed groups show variation in NK and CD3. TCMR was split into 2 groups with variation in NK genes. Additional validation of the complete gene-set correlated many of the genes with diagnoses of CAMR, MIXED, and TCMR rejections and with Banff histologic criteria defined in human subjects. These NHP data demonstrate the utility of RNA expression profiling to identify additional heterogeneity of endothelial and NK RNA gene expressions.
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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".