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Record W2969219532 · doi:10.3390/biomedicines7030063

Herbal Tea for the Management of Pharyngitis: Inhibition of Streptococcus pyogenes Growth and Biofilm Formation by Herbal Infusions

2019· article· en· W2969219532 on OpenAlexafffund
Niluni M. Wijesundara, H.P. Vasantha Rupasinghe

Bibliographic record

VenueBiomedicines · 2019
Typearticle
Languageen
FieldPharmacology, Toxicology and Pharmaceutics
TopicPharmacological Effects of Natural Compounds
Canadian institutionsDalhousie University
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Food Inspection AgencyDalhousie UniversityDartmouth College
KeywordsStreptococcus pyogenesMinimum inhibitory concentrationMinimum bactericidal concentrationPharyngitisTraditional medicineShootBiofilmMicrobiologyChemistryMedicineBiologyAntimicrobialBacteriaStaphylococcus aureusHorticultureInternal medicine

Abstract

fetched live from OpenAlex

Herbal teas are becoming popular as functional beverages due to their various health promotional properties. This study aimed at assessing 13 hot water infusions (HWIs) from different herbs against streptococcal pharyngitis (strep throat). Licorice root exhibited the lowest minimum inhibitory concentrations (MIC) of 1.56 mg/mL, followed by barberry root, thyme, and oregano flowering shoots, with a MIC of 3.13 mg/mL. At their respective minimum bactericidal concentrations (MBC), licorice showed the bactericidal effect on S. pyogenes within 12 h after exposure while others need 24 h for a similar outcome. The HWIs exhibited inhibitory activity on biofilm formation, ranging from 1.56 to 6.25 mg/mL, which confirmed by ruptured cells or clusters of dead cell debris observed in scanning electron microscope (SEM). Overall, non-toxic concentrations of efficacious HWIs from licorice root, barberry root, thyme, and oregano flowering shoots may provide potential sources for developing herbal teas or biomedicine for the management of S. pyogenes infections.

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 score0.054
Threshold uncertainty score0.437

Codex and Gemma teacher scores by category

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

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 head0.048
GPT teacher head0.382
Teacher spread0.335 · 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

Citations14
Published2019
Admission routes2
Has abstractyes

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