A recently developed bifacial platelet-rich fibrin matrix
Bibliographic record
Abstract
Platelet-rich plasma (PRP) is used clinically in liquid or gel form to promote tissue repair. Because of the poor mechanical properties, conventional PRP is often difficult to handle when used in clinical settings and requires secure implantation in a specific site, otherwise when released growth factors could be washed out during an operation. In this study, we analyzed the end product of a recently developed commercially available system (FIBRINET), which is a dense pliable, platelet-rich fibrin matrix (PRFM). We characterized the mechanical properties of PRFM and tested whether PRFM releases growth factors and whether released factors induce the proliferation of mesenchymal stem cells (MSC). Mechanical properties as well as platelet distribution were evaluated in PRFM. PRFM demonstrated robust mechanical properties, with a tear elastic modulus of 937.3 +/- 314.6 kPa, stress at a break of 1476.0 +/- 526.3 kPa, and an elongation at break of 146.3 +/- 33.8 %. PRFM maintained its mechanical properties throughout the testing process. Microscopic observations showed that the platelets were localized on one side of the matrix. Elevated levels of PDGF-AA, PDGF-AB, EGF, VEGF, bFGF and TGF-beta1 were measured in the day 1-conditioned media (CM) of PRFM and growth factor levels decreased thereafter. BMP2 and BMP7 were not detectable. MSC culture media supplemented with 20% PRFM-CM stimulated MSC cell proliferation; at 24 and 48 hours the induction of the proliferation was significantly greater than the induction obtained with media supplemented with 20% foetal bovine serum. The present study shows that the production of a dense, physically robust PRFM made through high-speed centrifugation of intact platelets and fibrin in the absence of exogenous thrombin yields a potential tool for accelerating tissue repair.
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 | 0.001 | 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".