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Phytochemical composition, Antinociceptive and anti-inflammatory activities of ethanolic and aqueous stem bark extracts of Pavetta owariensis P. Beauv

2022· article· en· W4225353229 on OpenAlexaff
Tahiri Sylla, ASSI Mambo Serge, Dongui Bini Kouamé

Bibliographic record

VenueJournal of Pharmacognosy and Phytochemistry · 2022
Typearticle
Languageen
FieldMedicine
TopicNatural Antidiabetic Agents Studies
Canadian institutionsUniversité de MontréalUniversité du Québec à Montréal
Fundersnot available
KeywordsPhytochemicalTraditional medicineBark (sound)AnthraquinonesTerpenoidPolyphenolChemistryGallic acidNutraceuticalBotanyBiologyMedicineAntioxidantFood scienceBiochemistry

Abstract

fetched live from OpenAlex

In Africa, Pavetta owariensis P. Beauv. (Rubiaceae) is found in Ivory Coast, Guinea, Sierra Leone, Nigeria, Cameroon and Ghana. The roots, leaves, stem bark and twigs are used by traditional healers to treat various diseases. For example, the stem bark is used in children's baths to protect them from skin and scalp infections. First, it was a question of determining chemical constituents present in stem bark of the medicinal plant. The phytochemical study revealed the presence of sterols and polyterpenes, polyphenols, flavonoids, catechic tannins, gallic tannins, alkaloids, free quinones, saponins, anthraquinones terpenoids and anthocyanins in aqueous and ethanolic stem bark extracts of Pavetta owariensis. On the one hand, studies carried out on the stem bark of Pavetta owariensis have shown that the aqueous and ethanolic extracts are analgesics which suppress sensitivity to pain in the same way as paracetamol (Doliprane®). On the other hand, the extracts have also shown anti-inflammatory potential like meloxicam (Mobic®) but in high doses.

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 score0.009
Threshold uncertainty score0.681

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 head0.011
GPT teacher head0.263
Teacher spread0.252 · 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
Published2022
Admission routes1
Has abstractyes

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