Clinical Implications of 20-Hydroxyeicosatetraenoic Acid in the Kidney, Liver, Lung and Brain: An Emerging Therapeutic Target
Bibliographic record
Abstract
Cytochrome P450-mediated metabolism of arachidonic acid (AA) is an important pathway for the formation of eicosanoids. The ω-hydroxylation of AA generates significant levels of 20-hydroxyeicosatetraenoic acid (20-HETE) in various tissues. In the current review, we discussed the role of 20-HETE in the kidney, liver, lung, and brain during physiological and pathophysiological states. Moreover, we discussed the role of 20-HETE in tumor formation, metabolic syndrome and diabetes. In the kidney, 20-HETE is involved in modulation of preglomerular vascular tone and tubular ion transport. Furthermore, 20-HETE is involved in renal ischemia/reperfusion (I/R) injury and polycystic kidney diseases. The role of 20-HETE in the liver is not clearly understood although it represents 50%-75% of liver CYP-dependent AA metabolism, and it is associated with liver cirrhotic ascites. In the respiratory system, 20-HETE plays a role in pulmonary cell survival, pulmonary vascular tone and tone of the airways. As for the brain, 20-HETE is involved in cerebral I/R injury. Moreover, 20-HETE has angiogenic and mitogenic properties and thus helps in tumor promotion. Several inhibitors and inducers of the synthesis of 20-HETE as well as 20-HETE analogues and antagonists are recently available and could be promising therapeutic options for the treatment of many disease states in the future.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".