Structure, degradation, drug release and mechanical properties relationships of iron-based drug eluting scaffolds: The effects of PLGA
Bibliographic record
Abstract
The effects of poly(lactic‑ co ‑glycolic acid) (PLGA) on structure, degradation, drug release and mechanical properties relationships of iron-based drug eluting scaffolds have been studied comprehensively. The porous structure of the iron has been incorporated with the curcumin-loaded PLGA (CP) particles through dipping method to produce CP-coated porous Fe (CP-Fe). The CP-Fe degradation has been escalated with the increase of PLGA composition due to the hydrolysis of PLGA. The degradation of iron substrate triggered the kinetics of curcumin release as there was a direct correlation between the curcumin release rate and the degradation rate of the CP-Fe scaffold. The stiffness of the CP particles and the interfacial interactions developed between the CP coating and iron surface have enhanced scaffolds' mechanical strengths. The curcumin released from the scaffold significantly arrested osteosarcoma cells growth. It is demonstrated that the PLGA played an important role to control the scaffold degradation and curcumin release as well as enhancing the mechanical properties of the drug device as an integrated system for favorable scaffold-based drug design.
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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".