Oral tissue spheroid, organoid, and organ‐on chip microphysiological modeling strategies towards enhanced emulation of health and disease
Bibliographic record
Abstract
Diseases and disorders of dental, oral, and craniofacial (DOC) tissues represent a significant global health burden and have been found to have the greatest age-standardized prevalence and incidence of all reported diseases worldwide. While the application of novel therapies has been suggested to address the different types of oral health diseases, only a limited number of interventional regenerative therapies have been reported to improve clinical therapeutic outcomes. The lack of novel therapies in DOC tissue regeneration may be in part attributed to the highly resource-intensive translational path from preclinical models to clinical trials. Recently, stakeholders and regulatory agencies have begun to encourage the use of alternative preclinical models using human tissues for testing therapeutic interventions in place of animal models. This advocacy may provide an opportunity to reduce or eliminate animal testing, ultimately limiting resource expenditure and providing a more efficient regulatory pathway for the approval of novel DOC therapies. While the complexity of DOC physiology, defects, and diseases is not effectively recapitulated in traditional 2D or 3D in vitro culture models, the emergence of more sophisticated in vitro models (or so-called microphysiological systems that include spheroid, organoid and organ on-chip (OoC) systems) has enabled effective modeling of clinically simulated disease states in several DOC tissue and organ systems. Here, we aim to provide an overview and collective comparison of these microphysiological systems, outline their current uses in DOC research, and identify important gaps in both their utilization and abilities to recapitulate essential features of native oral-craniofacial physiology, towards enabling the therapeutic performance of de novo interventions targeted at regeneration outcomes in vivo.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".