Efektivitas minyak kayu manis dalam menghambat pertumbuhan koloni candida albicans pada resin akrilik(Effectivity of cinnamon oil to inhibit colony growth of candida albicans on acrylic resin)
Bibliographic record
Abstract
Background: \nAcrylic is usually used as material of dentures, espe \ncially base of dentures which can become entrapment of plaque and microorganism, includes Candida albicans. This is due to micro porosity of acrylic surface which is difficult to clean with only brushing. Thus, prevalence of candidal infection is higher at patient with poor oral hygiene. Denture immersion in a chemical solution is capable of rapid inactivation of pathogenic microorganisms. \n But the cost of chemical solution is relatively expensive. Thus, herbal medicines as chemical cleansing agent can be alternative to solve this problem. Cinnamon cassia is among the earliest known spices used by humankind. \nEssential oil of Cinnamon cassia has antifungal, antibacterial, anti cancer, anti spasmodic effects and decrease blood pressure. \nPurpose: The aim of this research was to evaluate the effect of essential oil of Cinnamon cassia on colony growth of Candida albicans on heat cured acrylic plate. \nMethod: Samples of acrylic plate were contaminated by Candida albicans and were immersed in essential oil of Cinnamon cassia with different concentration: 0,01%, 0,03%, 0,05% and water as control. Then samples of acrylic were vibrated to fall off Candida albicans and the colonies of candida were counted by Quebec Colony Counter. \nResults: There were significant difference between of each \ngroups except group of essential oils 0,03% and 0,05%. \nConclusion: Essential oil of Cinnamon cassia with concentration 0,03% can inhibit colony growth of Candida albicans on heat cured acrylic
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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) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".