Synthesis of fatty acid ethyl esters in mammalian tissues after ethanol exposure: a systematic review of the literature
Bibliographic record
Abstract
The ability to undergo non-oxidative metabolism from ethanol to fatty acid ethyl esters (FAEEs) varies greatly among tissues and organs. To gain a greater understanding of non-oxidative ethanol metabolism to FAEE, we aimed to collect all published data on FAEE synthesis in mammalian organs and tissues to identify all tissues, organs, and enzymes that are known to, or likely possess FAEE-synthetic activity. A systematic search for relevant papers was performed and two independent reviewers examined potentially relevant abstracts (articles on FAEEs that pertain to ethanol exposure) to determine whether they met the inclusion criteria. Information on FAEE synthesis was retrieved from papers meeting the inclusion/exclusion criteria and summarized by organ/tissue/matrix examined. The systematic search through four databases yielded 78 articles that investigated FAEE synthesis by tissues, tissue fractions and cell lines, and 29 articles that attempted to purify and/or characterize the enzymes involved in FAEE synthesis. Two enzyme activities have been studied: FAEE synthase (FAEES, which conjugates ethanol and free fatty acid) and acyl-CoA: ethanol O-acyltransferase (AEAT, which conjugates ethanol and fatty acyl-CoA). Both activities are expressed by a variety of different enzymes. FAEES activity is the most widely studied and has been purified from several tissues and shown to be associated with several well-known enzymes, while the identity of enzymes possessing AEAT activity remains unknown. The organs and tissues that have been shown to synthesize FAEEs are discussed, with special emphasis on the studies that attempted to elucidate the enzymology of FAEE synthesis in those tissues.
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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.014 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".