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Record W1781814734 · doi:10.1007/s11745-015-4063-3

Brain and Liver Headspace Aldehyde Concentration Following Dietary Supplementation with n‐3 Polyunsaturated Fatty Acids

2015· article· en· W1781814734 on OpenAlexafffund
Brian Ross, Slim Babay, Imran Malik

Bibliographic record

VenueLipids · 2015
Typearticle
Languageen
FieldNursing
TopicFatty Acid Research and Health
Canadian institutionsNOSM UniversityLakehead University
FundersLakehead University
KeywordsDocosahexaenoic acidPolyunsaturated fatty acidOxidative stressEicosapentaenoic acidFatty acidChemistryHexanalBiochemistryThiobarbituric acidFood scienceLipid peroxidation

Abstract

fetched live from OpenAlex

Reactive oxygen species react with unsaturated fatty acids to form a variety of metabolites including aldehydes. Many aldehydes are volatile enough to be detected in headspace gases of blood or cultured cells and in exhaled breath, in particular propanal and hexanal which are derived from omega-3 and omega-6 polyunsaturated fatty acids, respectively. Aldehydes are therefore potential non-invasive biomarkers of oxidative stress and of various diseases in which oxidative stress is thought to play a role including cancer, cardiovascular disease and diabetes. It is unclear, however, how changes in the abundance of the fatty acid precursors, for example by altered dietary intake, affect aldehyde concentrations. We therefore fed male Wistar rats diets supplemented with either palm oil or a combination of palm oil plus an n-3 fatty acid (alpha-linolenic, eicosapentaenoic, or docosahexaenoic acids) for 4 weeks. Fatty acid analysis revealed large changes in the abundance of both n-3 and n-6 fatty acids in the liver with smaller changes observed in the brain. Despite the altered fatty acid abundance, headspace concentrations of C1-C8 aldehydes, and tissue concentrations of thiobarbituric acid reactive substances, did not differ between the 4 dietary groups. Our data suggest that tissue aldehyde concentrations are independent of fatty acid abundance, and further support their use as volatile biomarkers of oxidative stress.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.127
Threshold uncertainty score0.504

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.036
GPT teacher head0.322
Teacher spread0.286 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations5
Published2015
Admission routes2
Has abstractyes

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