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Record W3095694989 · doi:10.1155/2020/9835167

Cytotoxic, Antioxidant, and Metabolic Enzyme Inhibitory Activities of Euphorbia cyparissias Extracts

2020· article· en· W3095694989 on OpenAlexaff
Mona Alonazi, Habib Horchani, Mona S. Alwhibi, Abir Ben Bacha

Bibliographic record

VenueOxidative Medicine and Cellular Longevity · 2020
Typearticle
Languageen
FieldMedicine
TopicNatural Antidiabetic Agents Studies
Canadian institutionsCégep de Rivière-du-Loup
FundersDeanship of Scientific Research, King Saud UniversityKing Saud University
KeywordsEuphorbiaAntioxidantEnzymeXanthine oxidaseBiochemistryProteaseCatechinBiologyChemistryTraditional medicinePharmacologyPolyphenolBotanyMedicine

Abstract

fetched live from OpenAlex

Plants of the Euphorbia genus present a wide range of therapeutic applications. This study is aimed at investigating new antidigestive enzyme agents from Euphorbia cyparissias through inhibition of lipid and carbohydrate absorption, to evaluate their potential applications for the treatment of metabolic syndrome. Lipase, phospholipase, protease, α-amylase, β-glucosidase, and xanthine oxidase activities under treatment with aqueous and ethanolic extracts of Euphorbia cyparissias were observed to evaluate the inhibitory effect of these extracts, as well as their antioxidant and cytotoxic effects. Results showed that ethanolic and aqueous extracts exhibited important inhibitory activity in a concentration-related manner on digestive enzymes, which is more effective than the commercial drugs used as controls. Results also showed that, out of the two extracts tested, the ethanolic extract presented the most promising results in inhibiting the activities of all digestive enzymes used. Moreover, the two extracts displayed a higher reducing power than that of the positive control used. The obtained results, together with previous reports in the literature, strongly suggest that Euphorbia cyparissias extracts may be natural inhibitors of the digestive enzymes and thus a potential new drug for metabolic syndrome treatment.

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 score0.100
Threshold uncertainty score0.917

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 studies0.0000.001
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 head0.036
GPT teacher head0.271
Teacher spread0.235 · 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

Citations2
Published2020
Admission routes1
Has abstractyes

Explore more

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