Mitoquinone Alleviates Donation after Cardiac Death Kidney Injury during Hypothermic Machine Perfusion in Rat Model
Bibliographic record
Abstract
Transplanted organs are subjected to harmful conditions through stopping blood flow, hypothermic storage of the graft, and subsequent reperfusion. In particular, kidneys donated from patients after cardiac arrest (DCD) are classified as more vulnerable to ischemia-reperfusion injury (IRI). Hypothermic machine perfusion is proposed as a solution for better kidney storage before transplantation, and it is a good platform for additional graft treatment. Antioxidants have gained interest in regenerative medicine due to their ability to scavenge reactive oxygen species (ROS), which play a key role in IRI. We evaluated the effect of Mitoquinone (MitoQ), a strong mitochondria-targeted antioxidant, administered directly to the perfusing buffer. Rat kidneys were isolated, randomly classified into one of the following groups, donation after brainstem death (DBD), DCD, and DCD with MitoQ, and perfused for 22 hours with a hypothermic machine perfusion system. Subsequently, we detected levels of kidney injury (KIM-1) and oxidative stress (ROS/RNS, cytochrome C oxidase, and mitochondrial integrity) markers. We compared the activation of the apoptosis pathway (caspase 3 and 9), the concentration of phosphorylated Akt (pAkt), and the pAkt/total Akt ratio. MitoQ reduces KIM-1 concentration, total ROS/RNS, and the level of caspases. We observed a decrease in pAkt and the pAkt/total Akt ratio after drug administration. The length of warm ischemia time negatively impacts the graft condition. However, MitoQ added to the perfusing system as an 'on pump' therapy mitigates injury to the kidney before transplantation by inhibiting apoptosis and reducing ROS/RNS levels. We propose MitoQ as a potential drug for DCD graft preconditioning.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".