Association between Carbonic Anhydrase VI Gene Copy Number Variations and Dental Caries Experience
Bibliographic record
Abstract
The current study examined the association between the carbonic anhydrase VI (CA VI) copy number variations (CNVs) and dental caries experience in adults. In total, 202 of 35-72 years old subjects participating in the Lithuanian National Oral Health Survey (LNOHS) agreed to provide saliva samples; thus, their data were included in the current study. Information about sociodemographic, environmental, and behavioural determinants was acquired via the self-administered World Health Organization (WHO) questionnaire. Fluoride levels in the drinking water were recorded based on information provided by water suppliers. Dental caries experience was recorded by one calibrated examiner using the WHO criteria for recording caries on smooth (including proximal, buccal, and oral) or occlusal surfaces. Caries experience was measured as the total number of decayed (D3), missing (M), filled (F) surfaces. DNA was extracted from saliva samples to examine CA VI CNVs using the QX200 Droplet Digital PCR system. Negative binomial regression and Poisson regression analyses were employed for data analyses. Based on multivariable regression analyses, higher copy number of CA VI were associated with higher caries experience on smooth surfaces (IRR 1.04, 95% CI: 1.005-1.08) and occlusal surfaces (IRR 1.02, 95% CI: 1.003-1.04). Positive associations between higher copy number of CA VI and higher caries experience on smooth and occlusal surfaces were found, suggesting that the CA VI coding gene may be associated with caries development. Future studies are needed to validate our results and to examine the underlying mechanisms of such associations.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".