Role of skeletal muscle in mandible development.
Bibliographic record
Abstract
As a continuation of the previous study on palate development (Rot and Kablar, 2013), here we explore the relationship between the secondary cartilage mandibular condyles (parts of the temporomandibular joint) and the contributions (mechanical and secretory) from the adjacent skeletal musculature. Previous analysis of Myf5-/-:MyoD-/- mouse fetuses lacking skeletal muscle demonstrated the importance of muscle contraction and static loading in mouse skeletogenesis. Among abnormal skeletal features, micrognathia (mandibular hypoplasia) was detected: small, bent and posteriorly displaced mandible. As an example of Waddingtonian epigenetics, we suggest that muscle, in addition to acting via mechanochemical signal transduction pathways, networks and promoters, also exerts secretory stimuli on skeleton. Our goal is to identify candidate molecules at that muscle-mandible interface. By employing Systematic Subtractive Microarray Analysis approach, we compared gene expression between mandibles of amyogenic and wild type mouse fetuses and we identified up- and down-regulated genes. This step was followed by a bioinformatics approach and consultation of web-accessible mouse databases. We searched for individual tissue-specific gene expression and distribution, and for the functional effects of mutations in a particular gene. The database search tools allowed us to generate a set of candidate genes with involvement in mandibular development: Cacna1s, Ckm, Des, Mir300, Myog and Tnnc1. We also performed mouse-to-human translational experiments and found analogies. In the light of our findings we discuss various players in mandibular morphogenesis and make an argument for the need to consider mandibular development as a consequence of reciprocal epigenetic interactions of both skeletal and non-skeletal compartments.
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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) | 0.001 | 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".