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Record W647599110

The iFat-1 Transgene Permits Conditional Endogenous n-3 Polyunsaturated Fatty Acid Enrichment both in vitro and in vivo

2013· dissertation· en· W647599110 on OpenAlexfundno aff
Shannon Clarke

Bibliographic record

VenueThe Atrium (University of Guelph) · 2013
Typedissertation
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMetabolism and Genetic Disorders
Canadian institutionsnot available
FundersCanadian Institutes of Health Research
KeywordsEndogenyIn vivoTransgeneIn vitroPolyunsaturated fatty acidChemistryBiologyBiochemistryBiotechnologyComputational biologyFatty acidGene
DOInot available

Abstract

fetched live from OpenAlex

Based on their highly bioactive properties in membrane phospholipids, there is growing recognition that dietary n-3 polyunsaturated fatty acids (PUFA) may be of significant benefit in the prevention and treatment of many lifestyle related pathologies, however direct evidence is lacking. The fat-1 transgenic mouse, a genetic model of n-3 PUFA enrichment, is a useful tool in nutritional research which has provided enhanced insight into the health effects of lifelong n-3 PUFA exposure. However, the influence of timing of n-3 PUFA exposure on health related outcomes remains unclear. This thesis describes the functional characterization of the novel Cre recombinase dependent inducible fat-1 (iFat-1) transgene. In the presence of Cre, the iFat-1 transgene was found to reduce phospholipid n-6/n-3 PUFA ratios both in vitro (100%) and in vivo (upwards of 70%), suggesting that the iFat-1 transgene has potential application to address temporal effects of n-3 PUFA in health and disease.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.002

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 head0.009
GPT teacher head0.207
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 source (direct Gemma or distilled Codex), 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

Citations0
Published2013
Admission routes1
Has abstractyes

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