δV1-1 Reduces Pulmonary Ischemia Reperfusion-Induced Lung Injury by Inhibiting Necrosis and Mitochondrial Localization of PKCδ and p53
Bibliographic record
Abstract
Ischemia-reperfusion (IR)-induced lung injury is one of the major contributing factors of morbidity and mortality after lung transplantation. To determine the IR-induced molecular changes in lung epithelial cells, we developed a cell-culture model that simulates lung preservation and transplantation. Six hours of cold ischemic time (CIT) and reperfusion elicited production of multiple inflammatory cytokines and chemokines and increased expression of endoplasmic reticulum (ER) proteins. Prolonged hypothermic condition (18 h CIT) reduced ER stress protein levels, and induced apoptosis and necrosis (via mechanisms related to mitochondrial permeability transition pore opening). Protein kinase C (PKCδ) was activated during CIT, and its downregulation via small interference (si) (in siRNA) RNA reduced IR-induced cytokine production and apoptotic cell death. δV1-1, a PKCδ peptide inhibitor, reduced translocation of PKCδ and p53 to the mitochondria after 18 h CIT, rescued ER stress protein expression, and converted the major mode of cell death from necrosis to apoptosis. Administration of δV1-1 effectively reduced lung transplantation and IR-induced pulmonary injury in rats. Therefore, inhibition of PKCδ by δV1-1 could be an effective strategy to ameliorate IR-induced lung injury by inhibiting the signaling pathways leading to necrosis.
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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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".