Biocompatibility Testing of Single-Walled Carbon Nanotubes on Murine Preosteoblasts: Higher Osteoblastic Differentiation with BMP-9 Than with BMP-2
Bibliographic record
Abstract
Carbon nanotubes (CNTs) have been used in orthopaedic applications because of their exceptional mechanical properties. However, the influence of CNTs on the behaviour of bone-forming cells and on the ability of these cells to respond to growth factors, such as bone morphogenetic proteins (BMPs), remains poorly known. Therefore, in the present study, single-walled CNTs (SWCNTs) were synthesised using an induction thermal plasma process and purified using a multistep procedure. The impact of these purified SWCNTs on the Smad activation, cell proliferation and differentiation, with or without BMP-2 and BMP-9 (1.92 nM), was also studied using western blot, mitochondrial enzymatic activity, TUNEL, RT-PCR and alkaline phosphatase activity analyses. Pre-treatment of MC3T3-E1 preosteoblasts with SWCNTs accelerated the Smad1/5/8 activation, induced by both BMP-2 and BMP-9, within 15 min. It also slightly affected their proliferation at 48 h without apoptosis. Interestingly, at 72 h, BMP-9 favoured the differentiation of MC3T3-E1 preosteoblasts pretreated with SWCNTs to a larger extent than BMP-2 did. Therefore, the combination of BMP-9 with SWCNTs appears to be a promising avenue for bone applications.
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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 | 0.000 | 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) | 0.001 | 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".