High-performance silk/polylactic acid composite scaffold material with immunomodulation and osteogenesis function
Bibliographic record
Abstract
The choice of suitable materials and effective structural design are crucial in influencing the therapeutic outcomes of bone tissue engineering scaffolds. This study introduces a controllable biodegradable composite scaffold composed of flat silkworm cocoon (FSC) and polylactic acid (PLA) as an innovative strategy for promoting bone healing in complex injuries. We focused on optimizing the scaffold's structural design, mechanical properties, and underlying mechanisms of osteogenesis. Initial experiments established the parameters for hot pressing the FSC, followed by mechanical performance tests to identify the optimal preparation conditions. Composite scaffolds incorporating PLA films were subsequently fabricated using these optimized parameters. The results indicate that the FSC/PLA composite scaffold exhibits outstanding biocompatibility, mechanical strength, and in vitro mineralization capabilities, alongside an appropriate degradation rate. Furthermore, the composite scaffolds demonstrated significant potential in promoting osteogenic differentiation and facilitating macrophage polarization toward an anti-inflammatory M2 phenotype. In vivo implantation of the scaffold in defective regions enhanced osteogenesis and mitigated inflammatory responses associated with degradation. This investigation presents an optimal composite scaffold that closely mimics the complex structure of bone, offering a novel approach to enhance bone regeneration and effectively address substantial bone defects.
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".