A reliable micro-CT-based method reveals dynamic changes to alveolar bone and tooth root following ligature-induced periodontal injury in the mouse
Bibliographic record
Abstract
This study presents a method development and evaluation framework for assessing longitudinally the dynamic alveolar bone changes in a murine periodontal injury model using 3D Slicer software. Accurate and reproducible measurement of bone loss is crucial for periodontal research, yet traditional two-dimensional (2D) histological approaches lack the ability to capture three-dimensional (3D) alterations, while inconsistencies in image alignment, region of interest (ROI) selection, and segmentation have limited the widespread adoption of 3D micro-CT analysis in small animal models. Here, we present a standardized workflow, incorporating defined criteria for ROI selection, scan alignment, and segmentation suitable for live micro-CT scanning. We validated this method using the ligature-induced periodontal injury model in mice. Multiple micro-CT scans were performed over 35 days to evaluate changes to alveolar bone and tooth roots. Quantitative analysis highlighted significant bone loss and early-stage remodeling within the first two weeks. Following ligature removal at 3 weeks, bone loss largely resolved by the end of week 5. However, we find that although the total bone volume mostly recovers, permanent changes at the alveolar crest persist, and additional cementum was formed at the apical tooth root. By enhancing methodological consistency, this standardized protocol improves the accuracy and comparability of longitudinal studies and minimizes variability in small animal studies, providing a reliable framework for functional investigations. Through its application, we show for the first time that, beyond alveolar bone regeneration, cementum apposition at the root apex is also observed. This opens up studies investigating how root loss at the apex could be restored.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".