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Record W2029153188 · doi:10.3390/metabo4030572

Breath Ethane Concentrations in Healthy Volunteers Correlate with a Systemic Marker of Lipid Peroxidation but Not with Omega-3 Fatty Acid Availability

2014· article· en· W2029153188 on OpenAlexaff
Brian C. Ross, Iain Glen

Bibliographic record

VenueMetabolites · 2014
Typearticle
Languageen
FieldEngineering
TopicAdvanced Chemical Sensor Technologies
Canadian institutionsNOSM UniversityLakehead University
Fundersnot available
KeywordsLipid peroxidationOxidative stressChemistryFatty acidOmega 3 fatty acidReactive oxygen speciesVitamin EInternal medicineBiochemistryPolyunsaturated fatty acidEndocrinologyAntioxidantMedicineDocosahexaenoic acid

Abstract

fetched live from OpenAlex

Ethane in human breath derives from lipid peroxidation, specifically the reaction between omega-3 fatty acids and reactive oxygen species. It has been proposed to be a non-invasive marker of oxidative stress, a deleterious process which may play an important role in the pathophysiology of several common diseases. It is unclear, however, whether ethane concentration actually correlates with systemic oxidative stress or whether it is primarily a marker of airway biochemistry. To investigate this possibility the breath ethane concentrations in 24 healthy volunteers were compared to that of a systemic measure of oxidative stress, plasma hydroperoxides, as well as to blood concentrations of the lipophilic anti-oxidant vitamin E, and the abundance of omega-3 fatty acids. Breath ethane concentrations were significantly (p < 0.05) positively correlated with blood hydroperoxide concentrations (rp = 0.60) and negatively with that of vitamin E (rp = -0.65), but were not correlated with either the total omega-3 fatty acid concentration (rp = -0.22) or that of any individual species of this fatty acid class. This data supports the hypothesis that breath ethane is a marker of systemic lipid peroxidation, as opposed to that of omega-3 fatty acid abundance.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.029
Threshold uncertainty score0.683

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 head0.007
GPT teacher head0.206
Teacher spread0.198 · 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 designBench or experimental
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

Citations13
Published2014
Admission routes1
Has abstractyes

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