Renal PKC-ε deficiency attenuates acute kidney injury and ischemic allograft injury via TNF-α-dependent inhibition of apoptosis and inflammation
Bibliographic record
Abstract
Acute kidney injury (AKI) increases the risk of morbidity and mortality after major surgery and transplantation. We investigated the effect of PKC-ε deficiency on AKI and ischemic allograft damage after kidney transplantation. PKC-ε-deficient and wild type (WT) control mice were subjected to 35 min of renal pedicle clamping to induce AKI. PKC-ε deficiency was associated with a marked improvement in survival and an attenuated loss of kidney function. Furthermore, functional MRI experiments revealed better renal perfusion in PKC-ε-deficient mice than in WT mice one day after IRI. Acute tubular necrosis and neutrophil infiltration were markedly reduced in PKC-ε-deficient mice. To determine whether this resistance to ischemia-reperfusion injury resulted from changes in local renal cells or infiltrating leukocytes, we studied a life-supporting renal transplant model of ischemic graft injury. We transplanted kidneys from H(2b) PKC-ε-deficient mice (129/SV) and their corresponding WT littermates into major histocompatibility complex-incompatible H(2d) recipients (BALB/c) and induced ischemic graft injury by prolonged cold ischemia time. Recipients of WT allografts developed severe renal failure and died within 10 days of transplantation. Recipients of PKC-ε-deficient allografts had better renal function and survival; they had less generation of ROS and upregulation of proinflammatory proteins (i.e., ICAM-1, inducible nitric oxide synthase, and TNF-α) and showed less tubular epithelial cell apoptosis and inflammation in their allografts. These data suggest that local renal PKC-ε expression mediates proapoptotic and proinflammatory signaling and that an inhibitor of PKC-ε signaling could be used to prevent hypoxia-induced AKI.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.001 | 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".