Osteogenic potential of apical papilla stem cells mediated by platelet-rich fibrin and low-level laser
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
To evaluate the osteogenic potential of platelet-rich fibrin (PRF) and low-level laser therapy (LLLT) on human stem cells from the apical papilla (SCAP) we isolated, characterized, and then cultured in an osteogenic medium cells with PRF and/or LLLT (660 nm, 6 J/m2-irradiation). Osteogenic differentiation was assessed by bone nodule formation and expression of bone morphogenetic proteins (BMP-2 and BMP-4), whereas the molecular mechanisms were achieved by qRT-PCR and RNA-seq analysis. Statistical analysis was performed by ANOVA and Tukey's post hoc tests (p < 0.05* and p < 0.01**). Although PRF and LLLT increased bone nodule formation after 7 days and peaked at 21 days, the combination of PRF + LLLT led to the uppermost nodule formation. This was supported by increased levels of BMP-2 and -4 osteogenic proteins (p < 0.005). Furthermore, the PRF + LLLT relative expression of specific genes involved in osteogenesis, such as osteocalcin, was 2.4- (p = 0.03) and 28.3- (p = 0.001) fold higher compared to the PRF and LLLT groups, and osteopontin was 22.9- and 1.23-fold higher, respectively (p < 0.05), after 7 days of interaction. The transcriptomic profile revealed that the combination of PRF + LLLT induces MSX1, TGFB1, and SMAD1 expression, after 21 days of osteogenic differentiation conditions exposition. More studies are required to understand the complete cellular and molecular mechanisms of PRF plus LLLT on stem cells. Overall, we demonstrated for the first time that the combination of PRF and LLLT would be an excellent therapeutic tool that can be employed for dental, oral, and craniofacial repair and other tissue engineering applications.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it