The Effect of Hair Pigment on the Incorporation of Fatty Acid Ethyl Esters (FAEE)
Bibliographic record
Abstract
AIMS: The objective of the current study was to determine whether FAEE incorporation is affected by hair pigmentation. METHODS: Black hooded LE rats were injected intraperitoneally daily with ethanol. Prior to dosing, black and white patches of fur were shaved and analyzed for baseline levels of FAEE using an adapted extraction procedure and GCMS method. Once the shaved 'patches' had grown back they were re-sampled along with hair outside the 'patches', referred to as 'no patch' hair, and tested for post-treatment FAEE levels in the same manner. Blood was also sampled for pharmacokinetic analysis of ethanol. RESULTS: Total FAEE levels were significantly higher in post-treatment hair (black and white) compared to baseline (pre-treatment) levels. Total FAEE levels were also significantly higher in post-treatment 'patch' hair (black and white) compared to 'no patch' hair. No significant differences were found between post-treatment black and white hair. The FAEE profiles were similar between black and white hair, with FAEE levels being highest for ethyl myristate, followed by ethyl stearate, palmitate, and then oleate. CONCLUSION: FAEE incorporation into hair does not appear to be affected by hair pigment, which is in congruence with what is known about the chemistry of drug-melanin interactions. This is important in avoiding potential bias and discrimination in the interpretation of alcohol abuse based on hair color.
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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 | 0.000 |
| 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".