In vitro evaluation of bone cell response to novel <scp>3D</scp>‐printable nanocomposite biomaterials for bone reconstruction
Bibliographic record
Abstract
Critically-sized segmental bone defects represent significant challenges requiring grafts for reconstruction. 3D-printed synthetic bone grafts are viable alternatives to structural allografts if engineered to provide appropriate mechanical performance and osteoblast/osteoclast cell responses. Novel 3D-printable nanocomposites containing acrylated epoxidized soybean oil (AESO) or methacrylated AESO (mAESO), polyethylene glycol diacrylate, and nanohydroxyapatite (nHA) were produced using masked stereolithography. The effects of volume fraction of nHA and methacrylation of AESO on interactions of differentiated MC3T3-E1 osteoblast (dMC3T3-OB) and differentiated RAW264.7 osteoclast cells with 3D-printed nanocomposites were evaluated in vitro and compared with a control biomaterial, hydroxyapatite (HA). Higher nHA content and methacrylation significantly improved the mechanical properties. All nanocomposites supported dMC3T3-OB cells' adhesion and proliferation. Higher amounts of nHA enhanced cell adhesion and proliferation. mAESO in the nanocomposites resulted in greater adhesion, proliferation, and activity at day 7 compared with AESO nanocomposites. Excellent osteoclast-like cells survival, defined actin rings, and large multinucleated cells were only observed on the high nHA fraction (30%) mAESO nanocomposite and the HA control. Thus, mAESO-based nanocomposites containing higher amounts of nHA have better interactions with osteoblast-like and osteoclast-like cells, comparable with HA controls, making them a potential future alternative graft material for bone defect repair.
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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".