NK cells mediate chronic kidney allograft injury (169.26)
Bibliographic record
Abstract
Abstract Chronic allograft injury remains the leading cause of kidney graft loss after transplantation and no-specific therapy currently exist. We have recently demonstrated that NK cells can kill syngeneic tubular epithelial cells (TEC) and participate in kidney ischemia-reperfusion injury. In this study, we investigated the capacity of NK cells to mediate kidney injury in transplantion. C57BL/6 (B6, H-2b) kidneys were transplanted into nephrectomized F1 (B6 x BALB/c, CB6F1) mice. Serum creatinine levels increased from baseline (22+5 uM, n=8) to 38±6 uM (n=6, P<0.001) at 60 days post transplant, demonstrating a clear loss of kidney function. Infiltrates and low grade renal tubular cell injury were consistently present in B6-to-CB6F1transplants suggesting a non T cell pathway. This was supported by results using a B6-Rag-/--to- CB6F1Rag-/- (B6Rag-/-xBALB/cRag-/-) F1 kidney transplant that eliminates T and B cell but not NK cell participation. Similar levels of kidney dysfunction (creatinine: 34+8 uM, n=6, p<0.01) and histological injury were observed 65 days post transplant. Finally depletion of NK cells prevented kidney injury (25±6 vs 34+8 uM in no antibody injection, n=6, p<0.05) as well as improved histology. In conclusion, these data demonstrate for the first time a critical role for NK cells in mediating chronic kidney injury, which is independent of T and B cells. NK cells are newly appreciated and formidable effectors to chronic kidney injury and graft loss.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".