Association between Downstream Taste Signaling Genes, Oral Microbiome, and Severe Early Childhood Caries
Bibliographic record
Abstract
Polymorphisms in taste receptor genes have been shown to play a role in early childhood caries (ECC), a multifactorial, biofilm-mediated disease. This study aimed to evaluate associations between severe-ECC (S-ECC), the oral microbiome, and variants in genes that encode components of the G protein-coupled receptor (GPCR) signaling cascade involved in taste sensation. A total of 176 children (88 caries-free; 88 with S-ECC) were recruited. Analyses of 16S and ITS1 rRNA microbial genes and seven (GNAQ, GNAS, GNAT3, GNAI2, RAC1, RALB, and PLCB2) human genes were pursued using next-generation sequencing. Regression analyses were performed to evaluate associations between genetic variants, S-ECC, and the supragingival plaque microbiome. Results suggest that PLCB2 rs2305645 (T), rs1869901 (G), and rs2305649 (G) alleles had a protective effect on S-ECC (rs2305645, odds ratio (OR) = 0.27 (95% confidence interval (CI): 0.14–0.51); rs1869901, OR = 0.34 (95% CI: 0.20–0.58); and rs2305649, OR = 0.43 (95% CI: 0.26–0.71)). Variants in GNAQ, GNAS, GNAT3, PLCB2, RALB, and RAC1 were associated with oral fungal and bacterial community composition. This study revealed that three loci at PLCB2 are significantly associated with S-ECC. Variants in multiple genes were associated with the composition of dental biofilm. These findings contribute to the current knowledge about the role of genetics in S-ECC.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.002 | 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".