Sinus floor elevation using particulate PLGA‐coated biphasic calcium phosphate bone graft substitutes: A prospective histological and radiological study
Bibliographic record
Abstract
BACKGROUND: Poly (lactic-co-glycolic acid) (PLGA) is widely used for the development of delivery systems for drugs and therapeutic biomolecules in tissue engineering applications. Particles of biphasic calcium phosphate can be covered by PLGA to change their manipulating characteristics. PURPOSE: Aim of this study was to investigate the radiological and histomorphometric results of the use of PLGA-coated biphasic calcium phosphate granules in sinus floor elevation and to analyze the underlying molecular processes by immunohistochemical staining. MATERIALS AND METHODS: A randomized clinical study was designed to include patients in need of sinus floor elevation. Patients were assigned to receive either PLGA-coated biphasic calcium phosphate particles (group I) or the equivalent but noncoated particles (group II). Cone beam computed tomography (CBCT) scans were performed before and 6 months after the procedure to assess the bone height gain. At the time of implant placement, bone core biopsies were obtained at the site of implant placement. Histological sections were subjected to histomorphometric and immunohistochemical evaluation of differentiation markers (Musashi-1 [MSI1]). RESULTS: No statistically significant differences were observed between groups for the radiologic parameters. No differences were observed histologically or histomorphometrically. However, PLGA-coated particles (group I) were more colonized by MSI1-positive osteoblast precursors (P = 0.0001, chi-squared test) and were penetrated by more CD34-positive vascular structures (P = 0.001, chi-squared test) than noncoated particles (group II). CONCLUSIONS: PLGA-coated particles are associated with more MSI11-positive cells and more extensive microvascularization than noncoated particles.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".