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Record W4322727037 · doi:10.34119/bjhrv6n1-299

Antifungal activity of yacon (Smallanthus sonchifolius) extract against candida spp.

2023· article· en· W4322727037 on OpenAlexaff
Micaela Federizzi, Patrícia Martinez Oliveira, Cheila Denise Ottonelli Stopiglia, Vanusa Manfredini, Ana Luisa Reetz Poletto

Bibliographic record

VenueBrazilian Journal of Health Review · 2023
Typearticle
Languageen
FieldNursing
TopicMicrobial Metabolites in Food Biotechnology
Canadian institutionsPROTO Manufacturing (Canada)
FundersUniversidade Federal do PampaFundação de Amparo à Pesquisa do Estado do Rio Grande do Sul
KeywordsYacónAntifungalCandida albicansMinimum inhibitory concentrationFood scienceBiologyTraditional medicineChemistryBotanyMicrobiologyMedicineAntimicrobial

Abstract

fetched live from OpenAlex

Introduction: Yacon (Smallanthus sonchifolius) is a functional food, rich in fructooligosaccharides, and has been widely used in scientific research, showing effects in reducing lipid and glycemic levels, in addition to having antibacterial, antioxidant and neuroprotective properties. The objective was to evaluate the hydroalcoholic extract of yacon leaf in Candida species. Methods: Thirty-three samples of different species of the genus Candida treated with yacon leaf extract at concentrations of 25 to 400ug/ml were analyzed using the Minimum Inhibitory Concentration method described by the M27-A3 protocol of the Clinical and Laboratory Standards Institute. Results: Among the species of C. albicans, 16 samples showed sensitivity to the extract at the concentrations used. Conclusion: Yeasts of the Candida genus are able to adapt to different environments due to their high degree of resistance to antifungal agents, making it increasingly necessary to use natural substances as alternatives for the treatment of diseases caused by fungi. The antifungal activity of yacon leaf extract against fungi of the genus Candida is being reported for the first time in this study, making yacon a new therapeutic alternative.

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)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.902
Threshold uncertainty score0.957

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.035
GPT teacher head0.346
Teacher spread0.311 · 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 designNot applicable
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

Citations0
Published2023
Admission routes1
Has abstractyes

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Same venueBrazilian Journal of Health ReviewSame topicMicrobial Metabolites in Food BiotechnologyFrench-language works237,207