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Dichrostachys cinerea (L.) Wight and Arn. (Fabaceae), a plant used in the traditional treatment of lymphatic filariasis in Côte d'Ivoire: Ethnopharmacological characteristics

2023· article· en· W4376140405 on OpenAlexfundno aff
Kouadio Béné, Yomeh Cynthia Viviane Yapo, Yao Kanga

Bibliographic record

VenueGSC Biological and Pharmaceutical Sciences · 2023
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAfrican Botany and Ecology Studies
Canadian institutionsnot available
FundersInternational Development Research Centre
KeywordsBotanyBiologyXylemTraditional medicineMedicinal plantsCocos nuciferaMedicine

Abstract

fetched live from OpenAlex

Dichrostachys cinerea (L.) Wight & Arn. (Fabaceae) is a plant used in the traditional treatment of lymphatic filariasis. It was revealed following a recent ethnomedicinal survey. The present study aims to highlight the distinctive ethnopharmacological characteristics of Dichrostachys cinerea. The aim was to identify some groups of chemical compounds by thin layer chromatography, to assay some minerals and finally to characterise the specific anatomical and micrographic features of the plant. Terpenes and sterols, saponosides, flavonoids and tannins are the main phytocompounds revealed. Magnesium with 110.4 mg/100 g dry matter is the most abundant mineral. Anatomical sections revealed from the periphery to the interior of the organs, epidermis, collenchyma, cortical parenchyma, sclerenchyma, liber or phloem, the wood or xylem and the medullary parenchyma. In the plant powder, it was observed starch grains and calcium oxalate crystals. These results add to the data on Dichrostachys cinerea, a taxon much used in traditional Ivorian medicine for the treatment of lymphatic filariasis.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.051
Threshold uncertainty score0.547

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.201
GPT teacher head0.316
Teacher spread0.115 · 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

Citations1
Published2023
Admission routes1
Has abstractyes

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