3D Printed In Vitro Dentin Model to Investigate Occlusive Agents against Tooth Sensitivity
Bibliographic record
Abstract
Tooth sensitivity is a painful and very common problem. Often stimulated by consuming hot, cold, sweet, or acidic foods, it is associated with exposed dentin microtubules that are open to dental pulp. One common treatment for tooth hypersensitivity is the application of occlusive particles to block dentin microtubules. The primary methodology currently used to test the penetration and occlusion of particles into dentin pores relies upon dentin discs cut from extracted bovine/human teeth. However, this method is limited due to low accessibility to the raw material. Thus, there is a need for an in vitro dentin model to characterize the effectiveness of occlusive agents. Three-dimensional printing technologies have emerged that make the printing of dentin-like structures possible. This study sought to develop and print a biomaterial ink that mimicked the natural composition and structure of dentin tubules. A formulation of type I collagen (Col), nanocrystalline hydroxyapatite (HAp), and alginate (Alg) was found to be suitable for the 3D printing of scaffolds. The performance of the 3D printed dentin model was compared to the natural dentin disk by image analysis via scanning electron microscopy (SEM), both pre- and post-treatment with occlusive microparticles, to evaluate the degree of dentinal tubule occlusion. The cytocompatibility of printed scaffolds was also confirmed in vitro. This is a promising biomaterial system for the 3D printing of dentin mimics.
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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) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".