Effect of Er:YAG laser irradiation on deciduous enamel roughness and bacterial adhesion: An in vitro study
Bibliographic record
Abstract
Laser irradiation has been proposed as a preventive method against dental caries since it is capable to inhibit enamel demineralization by reducing carbonate and modifying organic matter, yet it can produce significant morphological changes. The purpose of this study was to evaluate the influence of Er:YAG laser irradiation on superficial roughness of deciduous dental enamel and bacterial adhesion. Fifty-four samples of deciduous enamel were divided into three groups (n = 18 each). G1_control (nonirradiated); G2_100 (7.5 J/cm2) and G3_100 (12.7 J/cm2) were irradiated with Er:YAG laser at 7.5 and 12.7 J/cm2, respectively, under water irrigation. Surface roughness was measured before and after irradiation using a profilometer. Afterwards, six samples per group were used to measure bacterial growth by XTT cell viability assay. Adhered bacteria were observed using confocal laser scanning microscopy (CLSM) and a scanning electron microscopy (SEM). Paired t-, one-way analysis of variance (ANOVA), Kruskal-Wallis and pairwise Mann–Whitney U tests were performed to analyze statistical differences (p < .05). Before treatment, samples showed homogenous surface roughness, and after Er:YAG laser irradiation, the surfaces showed a significant increase in roughness values (p < .05). G3_100 (12.7 J/cm2) showed the highest amount of Streptococcus mutans adhered (p < .05). The increase in the roughness of the tooth enamel surfaces was proportional to the energy density used; the increase in surface roughness caused by laser irradiation did not augment the adhesion of Streptococcus sanguinis; only the use of the energy density of 12.7 J/cm2 favored significantly the adhesion of S. mutans.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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".