Controlled Release of Alendronate from Polymeric Films
Bibliographic record
Abstract
Bisphosphonate drugs alter the balance of bone resorption and formation, leading to a net increase in bone density. Therefore, these drugs are commonly used to treat osteoporosis or as an adjunct to cancer chemotherapy. Local delivery of bisphosphonates, such as alendronate, from polymeric films has the potential to improve efficacy and decrease side-effects common to oral bisphosphonate therapy. Alendronate was effectively encapsulated in film formulations composed of poly(lactic-co-glycolic acid) (PLGA) blended with poly(DL-lactic acid)-block-methoxy poly(ethylene glycol) (diblock co-polymer) and the films were characterized for elasticity, swelling, thermal and drug-release properties. Increasing the proportion of diblock co-polymer in the formulation decreased the glass transition temperature of PLGA, allowing for improved handling via increases in film elasticity. Immersion in aqueous media caused a rapid stiffening and swelling of the films. The inclusion of diblock co-polymer increased the rate of drug release from the films over a 3-week period. Drug-loaded polymeric films containing 0.25% alendronate increased osteoblast viability after 4 days compared to polymer alone. After 5 weeks, there was a significant increase in alkaline phosphatase activity and calcium nodule formation in osteoblasts grown on films containing 1.25% alendronate in 5% diblock co-polymer in PLGA.
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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".