Dynamic analyses of a soft tissue–implant interface: Biological responses to immediate versus delayed dental implants
Bibliographic record
Abstract
AIM: To qualitatively and quantitatively evaluate the formation and maturation of peri-implant soft tissues around 'immediate' and 'delayed' implants. MATERIALS AND METHODS: Miniaturized titanium implants were placed in either maxillary first molar (mxM1) fresh extraction sockets or healed mxM1 sites in mice. Peri-implant soft tissues were evaluated at multiple timepoints to assess the molecular mechanisms of attachment and the efficacy of the soft tissue as a barrier. A healthy junctional epithelium (JE) served as positive control. RESULTS: No differences were observed in the rate of soft-tissue integration of immediate versus delayed implants; however, overall, mucosal integration took at least twice as long as osseointegration in this model. Qualitative assessment of Vimentin expression over the time course of soft-tissue integration indicated an initially disorganized peri-implant connective tissue envelope that gradually matured with time. Quantitative analyses showed significantly less total collagen in peri-implant connective tissues compared to connective tissue around teeth around implants. Quantitative analyses also showed a gradual increase in expression of hemidesmosomal attachment proteins in the peri-implant epithelium (PIE), which was accompanied by a significant inflammatory marker reduction. CONCLUSIONS: Within the timeframe examined, quantitative analyses showed that connective tissue maturation never reached that observed around teeth. Hemidesmosomal attachment protein expression levels were also significantly reduced compared to those in an intact JE, although quantitative analyses indicated that macrophage density in the peri-implant environment was reduced over time, suggesting an improvement in PIE barrier functions. Perhaps most unexpectedly, maturation of the peri-implant soft tissues was a significantly slower process than osseointegration.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.002 | 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".