NK Cells Induce Apoptosis in Tubular Epithelial Cells and Contribute to Renal Ischemia-Reperfusion Injury
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Renal ischemia-reperfusion injury (IRI) can result in acute renal failure with mortality rates of 50% in severe cases. NK cells are important participants in early-stage innate immune responses. However, their role in renal tubular epithelial cell (TEC) injury in IRI is currently unknown. Our data indicate that NK cells can kill syngeneic TEC in vitro. Apoptotic death of TEC in vitro is associated with TEC expression of the NK cell ligand Rae-1, as well as NKG2D on NK cells. In vivo following IRI, there was increased expression of Rae-1 on TEC. FACS analyses of kidney cell preparations indicated a quantitative increase in NKG2D-bearing NK cells within the kidney following IRI. NK cell depletion in wild-type C57BL/6 mice was protective, while adoptive transfer of NK cells worsened injury in NK, T, and B cell-null Rag2(-/-)gamma(c)(-/-) mice with IRI. NK cell-mediated kidney injury was perforin (PFN)-dependent as PFN(-/-) NK cells had minimal capacity to kill TEC in vitro compared with NK cells from wild-type, FasL-deficient (gld), or IFN-gamma(-/-) mice. Taken together, these results demonstrate for the first time that NK cells can directly kill TEC and that NK cells contribute substantially to kidney IRI. NK cell killing may represent an important underrecognized mechanism of kidney injury in diverse forms of inflammation, including transplantation.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.000 | 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 it