Enantioselectivity in some physiological and pathophysiological roles of hydroxyeicosatetraenoic acids
Bibliographic record
Abstract
The phenomenon of chirality has been shown to greatly impact drug activities and effects. Different enantiomers may exhibit different effects in a certain biological condition or disease state. Cytochrome P450 (CYP) enzymes metabolize arachidonic acid (AA) into a large variety of metabolites with a wide range of activities. Hydroxylation of AA by CYP hydroxylases produces hydroxyeicosatetraenoic acids (HETEs), which are classified into mid-chain (5, 8, 9, 11, 12, and 15-HETE), subterminal (16-, 17-, 18- and 19-HETE) and terminal (20-HETE) HETEs. Except for 20-HETE, these metabolites exist as a racemic mixture of R and S enantiomers in the physiological system. The two enantiomers could have different degrees of activity or sometimes opposing effects. In this review article, we aimed to discuss the role of mid-chain and subterminal HETEs in different organs, importantly the heart and the kidneys. Moreover, we summarized their effects in some conditions such as neutrophil migration, inflammation, angiogenesis, and tumorigenesis, with a focus on the reported enantiospecific effects. We also reported some studies using genetically modified models to investigate the roles of HETEs in different conditions.
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 | 0.001 | 0.000 |
| 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".