3D printed PCL/nHAp scaffolds: Influence of scaffold structural parameters on osteoblast performance in vitro
Bibliographic record
Abstract
Abstract Scaffolds play a key role in bone tissue engineering (BTE) as they provide a mechanically and biologically supportive template to treat bone defects. Yet, the ideal scaffold structures are far from certain, leaving a lot to be discovered in terms of the scaffold structure–performance relationships. In this study, we investigated the influence of pore size and internal structure on osteoblast performance in vitro. Three‐dimensional (3D) scaffolds were printed from polycaprolactone (PCL) reinforced with 30% (wt.) nano‐hydroxyapatite (nHAp), with two different internal structures (lattice and staggered) and four pore sizes (0.280, 0.380, 0.420, and 0.550 mm). Scaffolds were seeded with pre‐osteoblast cells (MC3T3‐E1). Metabolic activity of cells, osteoblast differentiation, and capability of osteoblasts to deposit mineralized matrix were examined in vitro. Staggered scaffolds better supported cellular performance. The pore size of 0.280 mm was more favorable to support cell proliferation while the pore size ≥0.420 mm was more effective to promote osteoblast differentiation and mineralization. Findings revealed that osteoblast activities were affected differently by the pore size. Our study further suggests that the structure with a gradient pore size would be better than a single size in terms of supporting cell proliferation, differentiation, and secretion of a mineralized matrix.
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 | 0.000 | 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".