Novel combination of non-invasive morphological and solid-state characterisation of drug-loaded core-shell electrospun fibres
Bibliographic record
Abstract
In recent years, core-shell nanofibrous drug delivery systems have received increasing attention due to their ability to incorporate two or more active pharmaceutical ingredients (APIs) individually into the desired layer (either core or sheath) and thereby finely tune the release profiles of even incompatible drugs in one system. This study aims to perform formulation and solid-state characterisation of levofloxacin-loaded polylactic acid (PLA) - naproxen-sodium-loaded polyvinyl pyrrolidone (PVP) bicomponent core-shell fibrous sheets and examine the electro spinnability of the precursor combinations. The selected drugs have potential therapeutic relevance in similar systems intended for wound healing; however, in this study, they are used as model drugs to understand the physicochemical properties of a drug loaded system. In order to determine the best core- and shell-solution combination, a full factorial experimental design is used. A combination of various morphological (scanning electron microscopy and transmission electron microscopy) and microstructural characterisation techniques (X-ray photoelectron spectroscopy and Raman spectroscopy) was applied to non-invasively obtain information about the structure of the fibres and the embedded drugs. The results indicate that core-shell fibres of different compositions could be successfully prepared with various structural homogeneities. The best core-shell structure was obtained using a combination of 15% (w/w) shell concentration and 8% (w/w) PLA solution concentration. In addition to the conventional core-shell structural verification methods, the Raman spectroscopy method was implemented to reveal not only the core-shell structure of the PLA/PVP nanofibers but also the form of the embedded drugs. The Raman mapping of the fibres confirm the above results, and it is shown that an amorphous solid dispersion is formed as a result of the coaxial electrospinning process.
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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".