Effects of Early-Life Adversity on Tooth Enamel Formation
Bibliographic record
Abstract
In a systemic effort to survive environmental stress, organ systems fluctuate and adapt to overcome external pressures. The evolutionary drive back toward homeostasis makes it difficult to determine if an organism experienced a toxic exposure to stress, especially in early prenatal and neonatal periods of development. Previous studies indicate that primary human teeth may provide historical records of experiences related to stressors during that early time window. To assess the molecular effects of early-life adversity on enamel formation, we used a limited bedding and nesting (LBN) mouse model of early-life adversity (ELA) to assess changes in the enamel organ gene expression and enamel matrix mineralization. On average, postnatal day 12 (P12) ELA mice weighed significantly less than the controls. When adjusted for animal weight, ELA molar enamel volume was reduced as compared with the controls, and the relative mineral density of molar enamel was significantly increased. There were no obvious changes in enamel matrix crystal morphology or structure in ELA as compared with the control mouse enamel. RNAseq showed extracellular matrix organization to be the most significantly affected GO and reactome pathways, whereas butanote metabolism was the most significantly altered KEGG pathway. Transcripts expressing the enamel matrix proteins amelogenin (Amelx) and enamelin (Enam) were among the top 4 most differentially expressed genes. When evaluating molecular mechanisms for the changes in gene expression in ELA enamel organs, we found significantly increased expression of Dlx3, while transcripts for clock genes Per1 and Nrd1 were downregulated. These findings support the possibility that the developing enamel organ is sensitive to the pressures of early-life adversity and produces molecular and structural biomarkers reflecting these challenges.
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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 | 0.001 |
| 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".