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Record W4390048839 · doi:10.3390/md22010011

Shotgun-Based Mass Spectrometry Analysis of Phospholipid and Triacylglycerol Molecular Species and Eicosanoids in Salmon Muscle Tissue on Feeding Microbial Oil

2023· article· en· W4390048839 on OpenAlexafffund
JuDong Yeo, Stefanie M. Colombo, Nigel I. Guerra, Christopher C. Parrish

Bibliographic record

VenueMarine Drugs · 2023
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAquaculture Nutrition and Growth
Canadian institutionsDalhousie UniversityMemorial University of Newfoundland
FundersCanada First Research Excellence FundOcean Frontier Institute
KeywordsPhospholipidFood scienceFish oilAquacultureMuscle tissueBiologyBiochemistryMicroorganismFish <Actinopterygii>ChemistryFisheryBacteriaEndocrinology

Abstract

fetched live from OpenAlex

The continuous growth of aquaculture places a growing demand on alternative sources of fish oil (FO). Certain microorganisms provide a sustainable replacement for FO due to their content of EPA and DHA, which are essential for fish health. Appreciable evidence shows that changes in feeding sources may alter the nutritional components of salmon; however, the influence of diets on lipid species remains unclear. In this study, the identification and semi-quantification of lipid molecular species in salmon muscle during feeding with a microbial oil (MO) were carried out by focusing on triacylglycerol (TAG) and diacyl-phospholipid using shotgun-based mass spectrometry analysis. DHA in the MO diet was efficiently incorporated into phospholipid structures on feeding, followed by accumulation in salmon muscle. The MO diet elevated the level of certain EPA-containing TAGs, such as TAG C52:5 (16:0_16:0_20:5) and TAG C54:6 (16:0_18:1_20:5), indicating that the MO diet may be an excellent source for enhancement of the abundance of ω3 lipids. Further, prostaglandins (PGs) PGE2 and PGF3α were identified and quantified for the first time in salmonid tissue.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.802
Threshold uncertainty score0.562

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.010
GPT teacher head0.217
Teacher spread0.207 · 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

Citations3
Published2023
Admission routes2
Has abstractyes

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