The possible protective effect of colchicine against liver damage induced by renal ischemia–reperfusion injury: role of Nrf2 and NLRP3 inflammasome
Bibliographic record
Abstract
Ischemia–reperfusion injury (IRI) induces an inflammatory response and production of reactive oxygen species, which affects the organs remote to the sites of renal IR. However, remote effects of renal IRI on the liver need further investigations. Renal injury associated with liver disease is a common clinical problem. Colchicine is an established drug for microtubule stabilization that may reduce tissue injury and has antioxidant and antiinflammatory effects. The aim of the present study was (i) to assess the hepatic changes after induction of renal IRI, (ii) to explore the possible protective effect of colchicine on liver injury following renal IRI, and (iii) to investigate the possible mechanisms underlying the potential effect. Forty rats were randomly divided into four groups: sham operation group, colchicine-treated group, IR group, and colchicine-treated IR group. Colchicine treatment improved liver function (ALT/AST) after renal IRI, decreased hepatic oxidative stress and cell apoptosis by reducing hepatic MDA, upregulating hepatic total antioxidant capacity, Nrf2, and HO-1. Furthermore, colchicine inhibited inflammatory responses by downregulating hepatic NLRP3 inflammasome, IL-1β, and caspase-1. Colchicine attenuates renal IRI-induced liver injury in rats. This effect may be due to reducing inflammation and oxidative stress markers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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".