Prospecção de extratos vegetais como coadjuvantes de higiene bucal em cães raça Labrador Retriever
Bibliographic record
Abstract
The present study aimed to prospect plant extracts as a therapeutic resource, compared to the oral microbiota of dogs. For this purpose, ethanol extracts from different parts of Anacardium Ocidentalis (cashew tree), Stryphnodendron adstringens (barbatimão), Punica granatum (pomegranate), Uncaria tomentosa (cat's claw), Psidium guajava L. (guava), Momordica charantia L. (melon from São Caetano) and Harpagophytum procumbens (devil's claw), prepared in different concentrations. Microorganisms collected from the oral mucosa of 12 Labrador dogs, maintained under the same management and feeding, were seeded in BHI Agar in a petri dish in which paper disks impregnated with 20µL of each extract were circulated. 0.12% chlorhexidine digluconate solution was used as a positive control. The plates were incubated for 12 hours at 36 ºC. Three swabs were collected for each animal and each experiment was carried out in triplicate. The results of the inhibition halo measurements were subjected to the Kruskall-Wallis analysis of variance, followed by the Dunn test, with a significance level of 5%. Based on the results, pomegranate, guava, and barbatimão extracts were selected, as they were statistically significant at concentrations of 250; 125; 62,6mg/mL which suggests that the three are potential candidates for use in the development of pharmaceutical products for use in oral hygiene of dogs.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".