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Differential Effects of Trans and Polyunsaturated Fatty Acids on Ischemia/ Reperfusion Injury and its Associated Cardiovascular Disease States

2013· article· en· W2171330929 on OpenAlexafffund
Riya Ganguly, Matthew Lytwyn, Grant N. Pierce

Bibliographic record

VenueCurrent Pharmaceutical Design · 2013
Typearticle
Languageen
FieldNursing
TopicFatty Acid Research and Health
Canadian institutionsSt. Boniface Hospital
FundersCanadian Institutes of Health Research
KeywordsPolyunsaturated fatty acidFatty acidDiseaseMyocardial infarctionBiochemistryIschemiaPathologicalChemistryMedicineBiologyInternal medicine

Abstract

fetched live from OpenAlex

Fatty acids have an important role in providing energy for sustained contractile activity and viability of the heart. However, considerable evidence now supports a role for fatty acids in the modulation of cardiovascular pathology as well. This may be beneficial or detrimental due to the structural differences in the various fatty acids. Polyunsaturated fatty acids appear to provide important protection to the heart during ischemic reperfusion challenge. Conversely, trans fatty acids are thought to mediate detrimental cardiac effects. Potentially pathological features of ischemic cardiomyocytes may be manifested as qualitative findings in studies of myocardial infarction and atherosclerosis. These general conclusions, however, are complicated by opposing effects that different fatty acids have even within the same group (i.e. n-6 versus n-3 fatty acids within the polyunsaturated fatty acids group, and industrial versus ruminant trans fats). Understanding more about how these fatty acid species alter ischemic myocardial injury is an increasingly attractive area of research. The identification of further targets of fatty acid interactions has great potential to provide valuable information for the modulation of cardiovascular 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 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 score0.168
Threshold uncertainty score0.926

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 integrity0.0000.001
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 head0.048
GPT teacher head0.342
Teacher spread0.294 · 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

Citations12
Published2013
Admission routes2
Has abstractyes

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