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Record W3083243333 · doi:10.9734/jpri/2020/v32i2130755

Blood Sugar Lowering Potentials of Aqueous and Ethanol Extracts of the Mixture of Rinds of Citrullus vulgaris Schrad (Watermelon) and Chrysophyllum albidum G. (Udara) Fruits on Alloxan-Induced Diabetic Wistar Rats

2020· article· en· W3083243333 on OpenAlexaff
Gogo James Owo, Simon Beresford

Bibliographic record

VenueJournal of Pharmaceutical Research International · 2020
Typearticle
Languageen
FieldMedicine
TopicDiet, Metabolism, and Disease
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsCitrullusAlloxanTraditional medicineBlood sugarDiabetes mellitusEthanolChemistrySugarPharmacologyBiochemistryFood scienceBiologyBotanyMedicineEndocrinology

Abstract

fetched live from OpenAlex

Citrullus vulgaris Schrad, commonly known as “watermelon” and Chrysophyllum albidum G., commonly called ‘‘Udara’’ are two important plants known to possess high antioxidant and therapeutic properties especially antidiabetic properties. The present study is aimed at investigating the blood glucose lowering potentials of both the aqueous and ethanolic extracts of the mixture of rinds of C. vulgaris Schrad and C. albidum in normal and alloxan-induced-diabetic rats. Aqueous and ethanol rinds extracts of the mixture were administered in wistar albino rats of weight range of 150-200g to determine their blood glucose lowering activity. The oral administration of aqueous mixed rinds extracts at dose of 1500 mg/kg body weight (Group 4) for 9 days led to a highly significant blood glucose reduction at P<0.05 when compared to the diabetic control (Group 2) and the ethanol mixed rinds extracts at the same dose (Group 6). There was a significant reduction in blood glucose (P<0.05) by other group dose extracts (Groups 3,5 and 6) compared to diabetic control. Hence, aqueous mixed rinds extracts of Citrullus vulgaris Schrad and C. albidum might be recommended as a potential hypoglycemic drug in the treatment of diabetes mellitus.

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)0.003
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 score0.031
Threshold uncertainty score0.461

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
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 head0.091
GPT teacher head0.401
Teacher spread0.310 · 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

Citations5
Published2020
Admission routes1
Has abstractyes

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