Ekstrak Cranberry (Vaccinium macrocarpon) dalam Menghambat Pertumbuhan Bakteri Plak
Bibliographic record
Abstract
Periodontitis is an inflammation on periodontal structure that caused by microorganisms. The plaque starts from supragingival expands towards subgingival thus causing inflammation in periodontal. Nowadays, herbs acting as antimicrobial agent are often used in the therapy. Cranberry is usually used to treat UTI with prevent bacterial adhesion on mucosa of the urinary tract. Cranberry contains tannins and also NDM which is rich in Proanthocyanidins. AIM : To find the minimum inhibitory concentration of cranberry extract on the growth of dental plaque. METHOD: This experiment was done by using dilution method. The extract was diluted into 8 different concentrations (100%;50%;25%;12,5%;6,25%, 3,12%;1,56%;0,78%). Then crosscheck was done to see the growth of dental plaque in Mueller Hinton Agar. The counting was done by using Quebec Colony Counter. The result was recorded and analyzed with One-Way ANOVA. RESULT: The result showed that antimicrobial activity was active at concentration 25% as Minimum Inhibitory Concentration with average total colony is about 34 CFU/ml. Statistical test showed that there were significant differences of total colony from each concentration. CONCLUSION: Cranberry extract (Vaccinium macrocarpon) could inhibit the growth of dental plaque at concentration 25%.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.027 | 0.008 |
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".