Quantitative and carbon isotope ratio analysis of fatty acids isolated from human brain hemispheres
Bibliographic record
Abstract
Abstract Our knowledge surrounding the overall fatty acid profile of the adult human brain has been largely limited to extrapolations from brain regions in which the distribution of fatty acids varies. This is especially problematic when modeling brain fatty acid metabolism, therefore, an updated estimate of whole‐brain fatty acid concentration is necessitated. Here, we sought to conduct a comprehensive quantitative analysis of fatty acids from entire well‐characterized human brain hemispheres (n = 6) provided by the Douglas‐Bell Canada Brain Bank. Additionally, exploratory natural abundance carbon isotope ratio (CIR; δ13C, 13C/12C) analysis was performed to assess the origin of brain fatty acids. Brain fatty acid methyl esters (FAMEs) were quantified by gas chromatography (GC)‐flame ionization detection and minor n‐6 and n‐3 polyunsaturated fatty acid pentafluorobenzyl esters by GC‐mass spectrometry. Carbon isotope ratio values of identifiable FAMEs were measured by GC‐combustion‐isotope ratio mass spectrometry. Overall, the most abundant fatty acid in the human brain was oleic acid, followed by stearic acid (STA), palmitic acid (PAM), docosahexaenoic acid (DHA), and arachidonic acid (ARA). Interestingly, cholesterol as well as saturates including PAM and STA were most enriched in 13C, while PUFAs including DHA and ARA were most depleted in 13C. These findings suggest a contribution of endogenous synthesis utilizing dietary sugar substrates rich in 13C, and a combination of marine, animal, and terrestrial PUFA sources more depleted in 13C, respectively. These results provide novel insights on cerebral fatty acid origin and concentration, the latter serving as a valuable resource for future modeling of fatty acid metabolism in the human brain. image
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.002 | 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".