Functional role of periostin in development and wound repair: implications for connective tissue disease
Bibliographic record
Abstract
Integrity of the extracellular matrix (ECM) is essential for maintaining the normal structure and function of connective tissues. ECM is secreted locally by cells and organized into a complex meshwork providing physical support to cells, tissues, and organs. Initially thought to act only as a scaffold, the ECM is now known to provide a myriad of signals to cells regulating all aspects of their phenotype from morphology to differentiation. Matricellular proteins are a class of ECM related molecules defined through their ability to modulate cell-matrix interactions. Matricellular proteins are expressed at high levels during development, but typically only appear in postnatal tissue in wound repair or disease, where their levels increase substantially. Members of the CCN family, tenascin-C, osteopontin, secreted protein acidic rich in cysteine (SPARC), bone sialoprotein, thrombospondins, and galectins have all been classed as matricellular proteins. Periostin, a 90 kDa secreted homophilic cell adhesion protein, was recently added to matricellular class of proteins based on its expression pattern and function during development as well as in wound repair. Periostin is expressed in connective tissues including the periodontal ligament, tendons, skin and bone, and is also prominent in neoplastic tissues, cardiovascular disease, as well as in connective tissue wound repair. This review will focus on the functional role of periostin in tissue physiology. Fundamentally, it appears that periostin influences cell behaviour as well as collagen fibrillogenesis, and therefore exerts control over the structural and functional properties of connective tissues in both health and disease. Periostin is a novel matricellular protein with close homology to Drosophila fasciclin 1. In this review, the functional role of periostin is discussed in the context of connective tissue physiology, in development, disease, and wound repair.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".