Ketamine ameliorates ischemia–reperfusion injury after liver autotransplantation by suppressing activation of Kupffer cells in rats
Bibliographic record
Abstract
This study aimed to investigate the protective effects of ketamine against hepatic ischemia-reperfusion (I/R) injury by suppressing activation of Kupffer cells (KCs) in rat liver autotransplantation. Male Sprague-Dawley rats were randomized into 3 groups (n = 10 each). Group I, the sham group, received saline. Group II received saline and underwent orthotopic liver autotransplantation (OLAT). Group III received 10 mg/kg ketamine and underwent OLAT. Blood samples were obtained at 3, 6, 12, and 24 h after I/R, and following ALT, AST, LDH, IL-6, TNF-α, IL-1β, and IL-10 in serum were detected. Model rats were sacrificed at the indicated time points and the graft liver tissues were evaluated histologically. KCs were isolated from rat liver tissues, and inflammatory products and proteins of NF-κB signaling pathway were detected using quantitative RT-PCR and Western blotting. Our results showed that ketamine significantly decreased ALT, AST, LDH, IL-6, TNF-α, and IL-1β levels and increased IL-10 level. Furthermore, ketamine alleviated the histopathology changes, by less KC infiltration and lower hepatocyte apoptosis. Moreover, activity of NF-κB signaling pathway in KCs was suppressed. In addition, production of pro- and anti-inflammatory factors is consistent with the results in tissues. Ketamine ameliorated I/R injury after liver transplantation by suppressing activation of KCs in rats.
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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 | 0.000 | 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".