Spatiotemporal Localization of Periostin and Its Potential Role in Epithelial-Mesenchymal Transition during Palatal Fusion
Bibliographic record
Abstract
The medial epithelial seam (MES) between the palatal shelves degrades during palatal fusion to achieve the confluence of palatal mesenchyme. Cellular mechanisms underlying the degradation of MES have been proposed, such as apoptosis, epithelial-mesenchymal transition (EMT) and migration of medial edge epithelia (MEE). Extracellular matrix components have been shown to play an important role in EMT in many model systems. Periostin (also known as osteoblast-specific factor-2) is a secreted mesenchymal extracellular matrix component that affects the ability of cells to migrate and/or facilitates EMT during both embryonic development and pathologic conditions. In this study, we evaluated the spatiotemporal expression patterns of periostin during mouse palatal fusion by in situ hybridization and immunofluorescence. Periostin mRNA and protein were present in the palatal mesenchyme, the protein being distributed in a fine fibrillar network and in the basement membrane, but absent from the epithelium. During MES degradation, the protein was strongly expressed in the basement membrane underlying the MES and in some select MEE. Confocal microscopic analysis using an EMT marker, twist1, and an epithelial marker, cytokeratin 14, provided evidence that select MEE were undergoing EMT in association with periostin. Moreover, the major extracellular matrix molecules in basement membrane, laminin and collagen type IV were degraded earlier than periostin. The result is that select MEE establish interactions with periostin in the mesenchymal extracellular matrix, and these new cell-matrix interactions may regulate MEE transdifferentiation during palatal fusion.
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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) | 0.000 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".