The Transcriptome of Human Cytotoxic T Cells: Measuring the Burden of CTL-Associated Transcripts in Human Kidney Transplants
Bibliographic record
Abstract
Having defined CTL-associated transcripts (CATs) in CTL in vitro, we used microarrays to quantify the burden of CAT sets compared with individual transcripts in human renal transplant biopsies with T-cell mediated rejection (TCMR). CAT sets in TCMR resembled diluted CTL RNA, maintaining overall hierarchy of expression relative to CTL in vitro. NK selective sets were not detected in TCMR, indicating the CATs mainly reflect T cells. We selected 25 highly expressed CATs that diluted quantitatively in kidney RNA (QCATs) and remained detectable after 32-fold dilution. QCAT burden in 14 kidneys with TCMR was 3 to 15% of CTL RNA, correlating with infiltration. One biopsy diagnosed as TCMR only by endothelialitis had little interstitial infiltrate and lowest CAT burden. CAT sets were more consistent than individual CATs such as perforin or granzyme B, which showed heterogeneity. In luster and principal component analysis, QCATs grouped biopsies with TCMR together, in close relationship to in vitro CTL. Thus QCAT sets robustly measure the burden of CTL and effector memory T cells in biopsies as %CTL RNA, in a manner not achieved by measurement of individual transcripts.
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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 | 0.000 | 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".