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<i>Solanum macrocarpon</i> Leaf Extracts Reduced Blood Pressure and Heart Rate After Oral Administration to Spontaneously Hypertensive Rats

2019· article· en· W3003254615 on OpenAlexaff
Olayinka A. Oluwagunwa, Adeola M. Alashi, Rotimi E. Aluko

Bibliographic record

VenueCurrent Topics in Nutraceutical Research · 2019
Typearticle
Languageen
FieldMedicine
TopicAntioxidant Activity and Oxidative Stress
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsGallic acidBlood pressureRutinChemistryPolyphenolNutraceuticalCaffeic acidOral administrationTraditional medicinePharmacologyAntioxidantMedicineFood scienceBiochemistryInternal medicine

Abstract

fetched live from OpenAlex

The aim of this work was to determine the ability of polyphenol-enriched aqueous vegetable leaf extracts to ameliorate blood pressure and heart rate dysfunctions in spontaneously hypertensive rats (SHRs). Solanum macrocarpon (SM), a common edible vegetable leaf in West Africa was cultivated using varying levels (0-80 kg/ha) of urea that was applied at planting (T1) or 2 weeks after planting (T2). In vitro analysis showed that total polyphenolic compounds varied from 489.87 ± 5.66 to 513.56 ± 1.42 mg gallic acid equivalent/g dry wt of extract. Rutin, caffeic acid and myricetin were the major polyphenols quantified in the extracts. All the extracts inhibited in vitro activities of renin and angiotensin converting enzyme to varying extents. Two extracts (SM40T1 and SM80T2) were orally administered to SHRs at 100 or 500 mg/kg body wt. Results showed that systolic blood pressure was reduced to a similar level by SM40T1 (10.8 mmHg) and SM80T2 (10.5 mmHg) for the 100 mg/kg body wt dose; the 500 mg/kg body wt dose produced similar values. Diastolic blood pressure and heart rate were also depressed by the two extracts with similar effects observed for both doses. We conclude that the leaf extracts could serve as potential ingredients to formulate antihypertensive functional foods and nutraceuticals.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.628
Threshold uncertainty score0.761

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.085
GPT teacher head0.410
Teacher spread0.325 · 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 designObservational
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

Citations9
Published2019
Admission routes1
Has abstractyes

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