Bisphosphonate-Derivatized Liposomes to Control Drug Release from Collagen/Hydroxyapatite Scaffolds
Bibliographic record
Abstract
A drug delivery system was developed by combining composite scaffolds made up of collagen and hydroxyapatite (Col/HA) with bisphosphonate (BP)-derivatized liposomes. The Col/HA scaffold was prepared by a freeze-drying method to yield a porous scaffold. The liposomes were composed of distearoylphosphocholine, cholesterol, distearoylphosphoethanolamine-poly(ethylene glycol) (DSPE-PEG), and a bone-binding bisphosphonate (BP) attached to the DSPE-PEG (DSPE-PEG-BP). By taking advantage of the specific interaction between the liposomal BP and the HA incorporated into the scaffold, the BP-decorated liposomes (BP-liposomes) were shown to display a strong affinity to Col/HA scaffolds. Three different model drugs, carboxyfluorescein (CF), doxorubicin (DOX), and lysozyme (LYZ) were entrapped in liposomes; there were no differences in drug release from the liposomes whether the liposomes were BP decorated or not. Whereas unencapsulated drugs and drugs encapsulated in PEG-liposomes displayed rapid release from the scaffolds, the drugs entrapped in BP-liposomes showed a slower release from the Col/HA scaffolds. We conclude that the proposed system can prolong the in situ residence of model drugs and has the potential to provide a sustained drug release platform in bone regeneration and 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".