Three-dimensional cranial suture morphometric changes in young rats during normal growth
Bibliographic record
Abstract
The age-based morphometric changes of cranial sutures are not well established, particularly in a quantitative manner. Most prior work utilized planar reslicing approaches to analyze sutures and the quantitative measurements of suture morphometry were limited to a short segment not following the true skull shape. The present study aimed to investigate the age-based morphometric changes of the coronal suture during normal growth of young rats using a novel curved-reslicing approach. Longitudinal in vivo micro-computed tomography (μCT) scans were completed at five time points (7, 9, 11, 16 and 21 weeks of age) during normal growth for 12 Sprague-Dawley rats (six female, six male). Curved-reslicing was performed on μCT slices to generate 11 equidistant cross-sectional images that covered the middle 90 % of skull thickness and the entire length of the coronal suture. The suture linear interdigitation index (LII) and width were measured using a marching algorithm. The average coronal suture LII increased by 15.3 % while the width decreased by 53.5 % at 21 weeks compared to 7 weeks of age, and repeated measures one-way analysis of variance with post-hoc multiple comparisons with Bonferroni adjustment revealed that these differences are statistically significant (p < 0.01). Linear mixed-effect models (LMM) were created for the prediction of rat coronal suture LII and width based on age, relative location through the skull thickness and initial morphometric measurements at the inner surface of the skull. When random effects are considered, the LMM was able to explain up to 97 % and 78 % of variations of suture LII and width, respectively. The presented study has established a novel curved-reslicing method to obtain quantitative 3D information surrounding cranial sutures and demonstrated strong predictive capabilities for suture morphometric changes with age. Future studies considering craniofacial sutures abnormalities will benefit from the presented work through novel methods of studying 3D quantitative morphometry.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".