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Dose‐dependent anticonvulsant effects of linoleic and α‐linolenic polyunsaturated fatty acids on pentylenetetrazol induced seizures in rats

2008· article· en· W2002131557 on OpenAlexaff
Ameer Y. Taha, Elvis Filo, David W.L., W. McIntyre Burnham

Bibliographic record

VenueEpilepsia · 2008
Typearticle
Languageen
FieldNursing
TopicFatty Acid Research and Health
Canadian institutionsUniversity of GuelphUniversity of Toronto
Fundersnot available
KeywordsPolyunsaturated fatty acidPentylenetetrazolLinoleic acidAnticonvulsantChemistryLinolenic acidalpha-Linolenic acidEpilepsyEndocrinologyFatty acidInternal medicinePharmacologyDocosahexaenoic acidBiochemistryBiologyMedicine

Abstract

fetched live from OpenAlex

PURPOSE: Linoleic and alpha-linolenic polyunsaturated fatty acids, derived from plant oils, have been reported to reduce neuronal excitability ex vivo and in cell culture. The evidence derived from animal seizure models, however, has been contradictory. The goal of the present study was to assess the dose-dependent anticonvulsant effects of a fatty acid mixture containing linoleic and alpha-linolenic acids in a 4 to 1 ratio (the "SR-3" compound). METHODS: The maximal pentylenetetrazol seizure model and Long-Evans hooded rats were used. RESULTS: Daily intraperitoneal injection of SR-3 for 21 consecutive days raised omega-3 polyunsaturated fatty acid (n-3 PUFA) composition in the unesterified fatty acid fraction of brain lipids (p < 0.05), and increased latency to seizure onset when administered at 200 mg/kg (p < 0.05), but not at 40 mg/kg (p > 0.05). There were no significant effects of SR-3 on seizure occurrence or on seizure severity (p > 0.05). A toxic effect of the SR-3 compound on peristalsis was observed at a dose of 400 mg/kg and above. CONCLUSION: Linoleic and alpha-linolenic polyunsaturated fatty acids in a 4 to 1 ratio raises n-3 PUFA composition of unesterified fatty acids in the brain and increases resistance to pentylenetetrazol-induced seizures.

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 categoriesMeta-epidemiology (narrow)
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 score0.411
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.028
GPT teacher head0.298
Teacher spread0.271 · 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.

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

Citations54
Published2008
Admission routes1
Has abstractyes

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