Immunodetection of noncollagenous matrix proteins during periodontal tissue regeneration
Bibliographic record
Abstract
The interface between denuded dentin and regenerative periodontal tissue was investigated in a rat alveolar bone defect model using morphological and immunocytochemical approaches. The dentin surface was surgically exposed along the palatal roots of maxillary first molars. At 3 weeks post treatment, animals were perfused and treated regions from decalcified mandibles were embedded in Epon for ultrastructural studies or LR White for post-embedding immunogold labeling. Thin tissue sections were incubated with antibodies against noncollagenous matrix (osteopontin, bone sialoprotein, osteocalcin and fibronectin) and plasma (alpha2HS-glycoprotein and albumin) proteins. While in some cases, regenerative events took place directly on the denuded dentin surface, the interface between the denuded dentin and regenerating periodontal tissue was frequently characterized by the presence of an interfacial zone. This zone sometimes showed an electron-dense, cement line-like, planar accumulation of organic material immunoreactive for osteopontin and bone sialoprotein. Immunolabeling for osteocalcin and alpha2HS-glycoprotein was moderate and diffuse throughout the interfacial zone, whereas labeling with antibodies to albumin and fibronectin resulted in a weak reaction. It is concluded that accumulation of bone sialoprotein and osteopontin is a primary event during the formation of regenerative cementum onto denuded root surfaces.
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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 | 0.000 |
| 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".