L-PRF Enrichment with Nanohydroxiapatite: An In Vitro Proof of Concept Study
Bibliographic record
Abstract
Nowadays platelet concentrates (PCs) show interesting potential in oral surgery for soft tissue healing promotion. However, inductive properties for bone formation remains still controversial. Recently, numerous studies explored the merging of two different tissue healing inducing agents: PCs with osteoinducing agents or scaffolds or antiresorptive drugs. Research trend looks towards the amelioration of the inductive properties of PCs, mainly towards bone regeneration. This paper aims to evaluate i) the durability of the pristine L-PRF membranes in dissolution assay; ii) the opportunity of coupling the obtained membranes with an osteoconductive molecule, such as nanohydroxyapatites (nHAp). Thus, the durability of pristine membrane in SBF was tested. At each time point, one sample was analyzed with SEM; image processing revealed an average fiber diameter of 0.103 ± 0.05μm without any statistically significant differences during time. Degradation assay showed a two-folds increase of the weight related to the SBF absorption in the first 2 days. From the third day a constant degradation was observed. In the time frame of this experiment, the dimensional stability of the fibrin structure up to day 7 suggested that PRF membranes may also be used uncovered in the oral cavity. Subsequently, the effects of the nHAp addition during the forming process of PRF (thus during centrifugation) were investigated.
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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 | 0.000 |
| 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 | 0.001 | 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".