Interpreting NK Cell Transcripts Versus T Cell Transcripts in Renal Transplant Biopsies
Bibliographic record
Abstract
NK cell transcripts are increased in biopsies with antibody-mediated rejection, whereas T cell transcripts are increased in T cell-mediated rejection. However, NK and T cells share many features, creating potential ambiguity. Therefore to estimate the NK- versus T cell transcript burdens separately, we defined nonoverlapping transcripts selective for NK cells (N = 4) or T cells (N = 5). We compared NK- versus T cell transcript burdens in microarrays from 403 kidney transplant biopsies (182 early, 221 late). In late biopsies, high NK-cell transcript expression was associated with antibody-mediated rejection, correlating with microvascular inflammation and donor specific HLA antibody. However, some early biopsies with T cell-mediated rejection had high NK-cell transcript expression, as well as T cell transcripts, without evidence of antibody-mediated rejection or DSA, correlating with interstitial inflammation and tubulitis. Both NK-cell and T cell transcripts were moderately increased in many kidneys with inflammation secondary to injury or atrophy scarring. These results support the distinct role of NK cells in late antibody-mediated rejection, but indicate a role for NK-transcript expressing cells (NK cells or T cells with NK features) both in T cell-mediated rejection and in inflammation associated with injury and atrophy scarring.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".