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A DIETARY PHASE 2 ENZYME INDUCER IN ANIMAL MODEL OF ESSENTIAL HYPERTENSION: PP.29.158

2010· article· en· W2013782396 on OpenAlexaff
Ali Banigesh, V Senanyake, Ligang Wu, P Lee, David N. Juurlink

Bibliographic record

VenueJournal of Hypertension · 2010
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicGarlic and Onion Studies
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsSulforaphaneMedicineBlood pressureCruciferous vegetablesIsothiocyanateInternal medicineGlucoraphaninEndocrinologyPharmacologyBrassicaBiochemistryCancerBotanyGlucosinolate

Abstract

fetched live from OpenAlex

Numerous studies have demonstrated the health benefits of fruits, vegetables, spices and herbs. For instance, consuming a clove of garlic (or equivalent) decreases cholesterol levels and attenuates hypertension. Previous studies in our laboratory have shown that broccoli sprouts rich in glucoraphanin, a precursor of a potent phase 2 protein inducing isothiocyanate sulforaphane, decreases oxidative stress and ameliorates hypertension. The question this study addressed was the importance of a food matrix in causing these beneficial effects, i.e., can one see the same effects by administration of sulforaphane alone? Sulforaphane (5, 10 and 20 μMoles/Kg) was administered by daily gavage to 5-wk old Spontaneously Hypertensive Stroke Prone rats (SHRsp) for 4 months. Blood pressure was measured weekly by tail cuff and at the end of the experiment by an external catheter inserted into the carotid artery in anesthetized animals. For comparison, age-matched normotensive Sprague Dawley (SD) rats were treated in the same manner. SHRsp control rats had significantly higher Systolic Blood Pressure (SBP) (179.9 ± 4.3 mm Hg) than control SD rats (83.98 ± 1.69). Sulforaphane treatment significantly lowered SHRsp blood pressure to 157.7 ± .21. There was no effect of sulforaphane treatment on SD rat SBP (93.9 ± 4.26). We conclude that the health benefit previously demonstrated in our laboratory is due to sulforaphane.

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 score0.679
Threshold uncertainty score0.204

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 integrity0.0000.000
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 head0.069
GPT teacher head0.261
Teacher spread0.191 · 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

Citations0
Published2010
Admission routes1
Has abstractyes

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