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Record W4410513389 · doi:10.1126/scisignal.adw1245

Technical recommendations for analyzing oxylipins by liquid chromatography–mass spectrometry

2025· review· en· W4410513389 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueScience Signaling · 2025
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicEicosanoids and Hypertension Pharmacology
Canadian institutionsInstitut universitaire de cardiologie et de pneumologie de QuébecUniversity of WaterlooConcordia University
FundersAgency for Toxic Substances and Disease RegistryNational Institute of Environmental Health SciencesNational Heart, Lung, and Blood InstituteInstituto de Salud Carlos IIIMedical Research CouncilNational Institutes of HealthNational Institute of Neurological Disorders and StrokeNational Institute of General Medical SciencesMinisterio de Ciencia e InnovaciónBundesministerium für Bildung und ForschungAgencia Estatal de InvestigaciónFundação de Amparo à Pesquisa do Estado de São PauloEuropean CommissionAlzheimer's AssociationCentro de Estudos Ambientais e Marinhos, Universidade de AveiroWellcome TrustDeutsche ForschungsgemeinschaftCenters for Disease Control and PreventionNatural Sciences and Engineering Research Council of CanadaU.S. Department of Agriculture
KeywordsLipidomicsMass spectrometryChromatographyChemistryLiquid chromatography–mass spectrometryOxylipinBiochemistry

Abstract

fetched live from OpenAlex

Several oxylipins are potent lipid mediators that regulate diverse aspects of health and disease and whose quantitative analysis by liquid chromatography-mass spectrometry (LC-MS) presents substantial technical challenges. As members of the lipidomics community, we developed technical recommendations to ensure best practices when quantifying oxylipins by LC-MS.

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.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.947
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.031
GPT teacher head0.367
Teacher spread0.337 · 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