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Record W4384693985 · doi:10.1139/cjas-2023-0021

Efficacy of aqueous extract of <i>Vernonia amygdalina</i> leaf against strongyle and coccidia infections in sheep

2023· article· en· W4384693985 on OpenAlexvenueno aff
Issah Bagulo, Abdul-Rahman Ibn Iddriss, Mohammed Abubakari, Victor Asiedu Nsor, Joshua Katusime

Bibliographic record

VenueCanadian Journal of Animal Science · 2023
Typearticle
Languageen
FieldVeterinary
TopicHelminth infection and control
Canadian institutionsnot available
Fundersnot available
KeywordsVernonia amygdalinaCoccidiaDistilled waterBiologyVeterinary medicineAnthelminticAlbendazoleTraditional medicineAqueous extractMedicineParasite hostingChemistryZoology

Abstract

fetched live from OpenAlex

Gastrointestinal parasites are developing resistance to various commercial anthelmintics. Hence, the need to explore the efficacies of herbal plants against gastrointestinal parasites. The study was therefore conducted to determine the efficacy of aqueous extract of Vernonia amygdalina leaf against strongyles and coccidia spp. in sheep. A total of 60 sheep were used for the study. The animals were randomly assigned to one of the four groups. Those in group A, B, C, and D were given albendazole (ABZ), 10% aqueous V. amygdalina extract (BL10) , 20% aqueous V. amygdalina extract (BL20), and 10 mL distilled water, respectively. All four treatments were given orally, depending on the body weight of the animals, with the exception of the distilled water, which was constant (10 mL) for each animal. Faecal samples were collected from each sheep and examined using McMaster technique. The data were analyzed using R version 4.2. The study brought to light that the prevalence of strongyles in sheep was 70% in the study area at pre-treatment. BL10 had efficacies of 52.58% and 65.08% against strongyles and coccidia spp., respectively. BL20 produced similar anthelmintic effect against strongyles as ABZ. Strongyle spp. showed resistance against ABZ in the study area.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.815
Threshold uncertainty score0.764

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.035
GPT teacher head0.307
Teacher spread0.272 · 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

Citations2
Published2023
Admission routes1
Has abstractyes

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