The role of excessive versus acute administration of erythropoietin in attenuating hepatic ischemia–reperfusion injury
Bibliographic record
Abstract
Ischemia-reperfusion injury (I/R) is the main cause of primary graft nonfunction. Our aim was to evaluate the effect of excessive versus acute administration of erythropoietin (EPO) in attenuating the hepatic injury induced by I/R in mice. The effect of segmental (70%) hepatic ischemia was evaluated in a transgenic mouse line with constitutive overexpression of human EPO cDNA and in wild-type (WT) mice. Mice were randomly allocated to 5 main experimental groups: (i) WT-sham, (ii) WT ischemia, (iii) WT ischemia + recombinant human erythropoietin (rhEPO), (iv) transgenic-sham, and (v) transgenic ischemia. The EPO-pretreated mice showed a significant reduction in liver enzyme levels and intrahepatic caspase-3 activity and fewer apoptotic hepatocytes (p < 0.05 for all) compared with the WT untreated I/R group. EPO decreased c-Jun N-terminal kinase (JNK) phosphorylation and nuclear factor-κB (NF-κB) expression during I/R. In transgenic I/R livers, baseline histology showed diffused hepatic injury, and no significant beneficial effect was noted between the WT untreated and the transgenic I/R mice. In conclusion, acute pretreatment with EPO in WT mice attenuated in vivo I/R liver injury. However, in excessive EPO overexpression, the initial liver injury abolished the beneficial effect of EPO. These findings have important implications for the potential use of acute EPO in I/R injury during liver transplantation.
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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) | 0.001 | 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) | 0.000 | 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 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".