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Preliminary Comparative Phytochemical, Elemental and Acute Toxicity Analyses of Selected Conventional and Herbal Medicines

2020· article· en· W3016301612 on OpenAlexaboutno aff
Jonah Omonofa Oluwaiye, Helen O. Kwanashie, Sherifat Bola Anafi

Bibliographic record

VenueThe FASEB Journal · 2020
Typearticle
Languageen
FieldMedicine
TopicMedicinal Plant Extracts Effects
Canadian institutionsnot available
Fundersnot available
KeywordsTraditional medicinePhytochemicalMedicineAcute toxicityPharmacologyToxicityInternal medicine

Abstract

fetched live from OpenAlex

Use of herbal medicines for treatment, management and prevention of different diseases is as old as man. In the last few decades, there has been an upsurge in the use of complementary and alternative medicines, which includes herbal medicines, either alone, or combined with conventional medicines. Furthermore, some 75–80% of persons living in the less developed countries depend on traditional medicine (mostly herbal medicines), for their healthcare needs. Despite the general belief worldwide that herbal medicines are safe, with no toxic effects because they are of natural origin, numerous reports such as cases of liver and neuro toxicities from using Cimicifuga racemosa and Valeriana officinalis solely, confirm that this belief is not necessarily valid. The aim of this study was to determine some toxicological parameters of selected conventional and herbal medicines with special reference to antimalarials. The conventional antimalarials studied were artesunate (AS), artemeter/lumefantrine (AL) and pyrimethamine/sulphadoxine (SP) while the herbal medicines were the antimalarial Herb 25 (R) (25), herbal blood purifier (BP) and herbal orange rind (OR). In addition to qualitative phytochemical analyses, acute toxicity was determined in 7‐day old cockerels, following oral (p.o.) and intraperitoneal (i.p.) administration, using Lorke’s method. For metal analyses, samples were wet digested with nitric and perchloric acids, and the concentrations of Cadmium (Cd), Calcium (Ca), Chromium (Cr), Cobalt (Co), Copper (Cu), Nickel (Ni), Iron (Fe), Magnesium (Mg), Manganese (Mn) and Zinc (Zn) were determined using flame atomic absorption spectrophotometric (AAS) method. Conventional medicines studied contained no phytochemicals (as expected), while the herbal medicines were found to contain bioactive constituents such as anthracenes, cardiac glycosides, flavonoids, steroids, tannins and triterpenes ‐ which were possibly responsible for the latter’s therapeutic effects and toxicity. The p.o./i.p. LD 50 values in mg/kg were: AT‐(470/3,807), AL‐(288/3,807), SP‐(1,264/>5,000), 25‐ (141/2,154), BP‐(141/1,264) and OR‐(774/>5,000). Thus, both conventional and herbal medicines had similar LD 50 values although those of herbal medicines tended to be lower, implying higher toxicity. Conventional and herbal medicines contained medicinal elements, namely: Ca, Fe and Zn. Nutritional elements present were Cu, Mg and Mn, although Cu could also be toxic if levels are sufficiently high. Levels of Cd, one of the toxic metals found to be present in nearly all the samples were <0.009 ppm which was below the standard limits set by regulatory bodies like FDA (<0.08 ppm), Health Canada (<3 ppm) and WHO (<0.3 ppm). Levels of another toxic metal, Cr, present in concentration range of 0.56–1.35 ppm, were also below WHO limit of 15 ppm. Co and Ni were below detection levels in all samples ‐ conventional and herbal. Based on the toxicological parameters examined, the herbal medicines were likely to be slightly more toxic compared to the conventional medicines; debunking the widely‐held view that, herbal medicines are always safer than conventional medicines.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.056
GPT teacher head0.349
Teacher spread0.293 · 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 source (direct Gemma or distilled Codex), 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".

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Citations2
Published2020
Admission routes1
Has abstractyes

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