Ischaemic Preconditioning and Intermittent Clamping Does not Influence Mediators of Liver Regeneration in a Human Liver Sinusoidal Endothelial Cell Model of Ischaemia-Reperfusion Injury
Bibliographic record
Abstract
BACKGROUND: The role of surgical technique on liver regeneration following surgery remains inconclusive. The aim of the study was to assess the effect of ischaemic preconditioning (IPC) and intermittent clamping (IC) on mediators of regeneration produced by human liver sinusoidal endothelial cells (SECs), using an in vitro hypoxia-reoxygenation model to mimic ischaemia-reperfusion injury (IRI). METHODS: Following extraction from samples obtained from liver resection (n = 5), confluent culture flasks of SECs were subjected to IRI (1 hour hypoxia + 1 hour reoxygenation), IPC prior to IRI (10 minutes hypoxia + 10 minutes reoxygenation + 1 hour hypoxia + 1 hour reoxygenation), IC (15 minutes hypoxia + 5 minutes reoxygenation x 3 + 1 hour reoxygenation) and compared to controls. The production of various mediators was determined over 48 hours. RESULTS: Interleukin (IL)-6, IL-8, granulocyte-colony stimulating factor (G-CSF) and hepatocyte growth factor (HGF) were produced by SECs. Both IPC and IC did not significantly influence the profile of IL-6, IL-8, G-CSF and HGF by SECs compared to IRI over the study period. CONCLUSION: IPC and IC did not influence the production of pro-regenerative mediators in a SECs model of IRI. The role of surgical technique on liver regeneration remains to be determined.
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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 | 0.000 |
| 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".