Beneficial effects of natural compounds on experimental liver ischemia-reperfusion injury
Bibliographic record
Abstract
Liver ischemia-reperfusion injury (IRI) is a phenomenon inherent to hepatic surgery that severely compromises the organ functionality, whose underlying mechanisms involve cellular and molecular interrelated processes leading to the development of an excessive inflammatory response. Liver resident cells and those recruited in response to injury generate pro-inflammatory signals such as reactive oxygen species, cytokines, chemokines, proteases and lipid mediators that contribute to hepatocellular necrosis and apoptosis. Besides, dying hepatocytes release damage-associated molecular patterns that actívate inflammasomes to further stimulate inflammatory responses leading to massive cell death. Since liver IRI is a complication of hepatic surgery in man, extensive preclinical studies have assessed potential protective strategies, including the supplementation with natural compounds, with the objective to downregulate nuclear factor-κB functioning, the main effector of inflammatory responses. This can be accomplished by either the activation of peroxisome proliferator-activated receptor-α, G protein-coupled receptor 120 or antioxidant signaling pathways, the synthesis of specific pro-resolving mediators, downregulation of Toll-like receptor 4 activity or additional contributory mechanisms that are beginning to be understood. The latter aspect is a crucial issue to be accomplished in preclinical studies, in order to establish adequate conditions for the supplementation with natural products before major liver surgeries in man involving warm IR, such as hepatic trauma or resection of large intrahepatic tumors.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".