Data evaluating triamcinolone acetonide and triamcinolone hexacetonide loaded poly(δ-valerolactone-co-allyl-δ-valerolactone) microparticles
Bibliographic record
Abstract
Advanced drug delivery strategies can be used to enhance the therapeutic effectiveness of locally delivered corticosteroids. Poly(δ-valerolactone-co-allyl-δ-valerolactone) microparticles (PVL-co-PAVL MPs) were evaluated for delivery of two corticosteroids, triamcinolone acetonide and triamcinolone hexacetonide. PVL-co-PAVL MPs were prepared using a modified oil-in-water emulsification method, followed by a UV-initiated cross-linking process. The resulting PVL-co-PAVL MPs were purified with an excess amount of water and then acetone to remove residual surfactant, cross-linker, and catalyst before lyophilization. Triamcinolone acetonide and triamcinolone hexacetonide were independently loaded into the resulting PVL-co-PAVL MPs via a post-loading swelling-equilibrium method. The drug-loaded MPs were characterized in terms of drug loading (determined by high-performance liquid chromatography, HPLC), thermal properties (determined by differential scanning calorimetry, DSC), and in vitro drug release kinetics (with quantification of drug using HPLC) to better understand the suitability of PVL-co-PAVL MPs for delivery of corticosteroids. These data demonstrate the potential of PVL-co-PAVL MPs as a promising drug delivery platform for the sustained release of corticosteroids. Raw data have been made available on Mendeley Data. Additional details on PVL-co-PAVL MPs were previously reported [1].
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".