The Effect of Zingiber officinale Roscoe (Ginger) on Dentin Microhardness: An in vitro Study
Bibliographic record
Abstract
It has been established that conventional root canal irrigants cause detrimental effect on the physico-chemical properties of root dentin. This study aims to determine the effect of a novel root irrigant, ginger or Zingiber officinale Roscoe essential oil on root dentin microhardness. Eighty root halves of extracted human teeth were used and prepared by embedding them individually in autopolymerizing acrylic resin. These samples were then divided into four groups (n = 20 each), i.e., i) 0.5% Z. officinale Roscoe oil, ii) 2.5% sodium hypochlorite (NaOCl, positive control), iii) 17% ethylenediaminetetraacetic acid (EDTA, positive control) and iv) normal saline (negative control). Dentin microhardness was measured using Vickers Microhardness Tester with a 50 g load and a 10-second dwell time, before and after immersion in respective solutions for 5 minutes. Measurements were taken in Vickers hardness number (VHN). All samples showed reduction in the microhardness reading; with Z. officinale Roscoe: 34.24 to 25.89, NaOCl: 31.47 to 26.34, EDTA: 33.08 to 26.04 and saline: 29.82 to 27.61 VHN. Although there was significant reduction within the Z. officinale Roscoe group (p < 0.001), the change was indifferent when compared among groups (post hoc Tukey’s HSD test p > 0.05). Within the limitations of this study, Zingiber officinale Roscoe essential oil does alter dentin microhardness but to a similar extent as NaOCl and EDTA.
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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".