Effects of silver diamine fluoride on oral bacteriome and mycobiome
Bibliographic record
Abstract
Introduction: Silver diamine fluoride (SDF) is a simple and non-invasive agent to arrest caries in children. The purpose of this study is to investigate changes to the oral bacteriome and mycobiome in young children treated with SDF Methods: Plaque samples were collected from 45 children with early childhood caries (ECC). Frequency regimens of SDF were two applications 4 months apart (4M), two applications 6 months apart (6M), and two applications 1 month apart (1M). DNA was extracted and sent to Genome Quebec Innovation Center for library preparation and paired-end Illumina MiSeq PE300 sequencing of the V4 region of bacterial 16S rRNA and fungal ITS1 rRNA genes. Sequencing data was analyzed using QIIME2 2018.11. Results: A total of 195 carious lesions in 44 children were treated at baseline and followed over two subsequent study visits. The overall arrest rates were 77.69% at Visit 2 and 93.61% at Visit 3 respectively. Arrest rates were higher for all lesions after two applications of SDF. The alpha diversity and beta diversity analysis showed no significant differences in the supragingival bacteriome and mycobiome for all three regimens. Taxonomic assignment showed that Streptococcus, Corynebacterium and Actinomyces were the most abundant genera for Bacteria and Candida, Blumeria, and Malassezia were the most abundant genera for Fungi. Streptococcus mutans and Veillonella dispar both showed a decrease in relative abundance, but these changes were a trend and not found to be statistically significant. Candida albicans was highly abundant in all groups regardless of number of visits or regimens. The differential abundance analysis showed significant changes in both bacterial and fungal species, particularly Lactobacillus spp., Bifidobacterium spp. and Candida spp. Our study found a vast diversity of fungal species and differential abundance analysis suggested that SDF treatment may have an effect on the abundance of specific fungi. Conclusion: SDF was an effective modality for arresting dental caries with higher arrest rates for all lesions after two applications of SDF. Further studies with a larger sample size are needed to confirm whether the presence or absence of various bacterial and fungal species are the result of SDF application at various frequencies.
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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 | 0.001 |
| 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) | 0.002 | 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".