Augmented Parkin-dependent mitophagy underlies the hepatoprotective effect of remote ischemic conditioning used prior to hemorrhagic shock
Bibliographic record
Abstract
Hemorrhagic shock-resuscitation (HSR) following trauma contributes to organ dysfunction by causing ischemia–reperfusion injury (IRI). We previously showed that ‘remote ischemic preconditioning’ (RIPC) exerted multi-organ protection from IRI. Maintenance of mitochondrial quality by clearance of dysfunctional mitochondria via mitophagy is vital in restoring organ integrity. We hypothesized that parkin-dependent mitophagy played a role in RIPC-induced hepatoprotection following HSR. The hepatoprotective effect of RIPC in a murine model of HSR-IRI was investigated in wild type and parkin-/- animals. Mice were subjected to HSR ± RIPC and blood and organs were collected, followed by cytokine ELISAs, histology, qPCR, Western blots, and transmission electron microscopy. HSR increased hepatocellular injury, as measured by plasma ALT and liver necrosis, while antecedent RIPC prevented this injury; in parkin-/- mice, RIPC failed to exert hepatoprotection. The ability of RIPC to lessen HSR-induced rises in plasma IL-6 and TNFα, was lost in parkin-/- mice. While RIPC alone did not induce mitophagy, the application of RIPC prior to HSR caused a synergistic increase in mitophagy, this increase was not observed in parkin-/- mice. RIPC induced shifts in mitochondrial morphology favoring mitophagy in WT but not in parkin-/- animals. RIPC was hepatoprotective in WT mice following HSR but not in parkin-/- mice. Loss of protection in parkin–/– mice corresponded with the failure of RIPC plus HSR to upregulate the mitophagic process. Improving mitochondrial quality by modulating mitophagy, may prove to be an attractive therapeutic target in disease processes caused by IRI.
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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) | 0.001 | 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".