Microfluidic Synthesis and Angiogenic Activity of Ginsenoside Rg<sub>1</sub>-Loaded PPF Microspheres
Bibliographic record
Abstract
Next generation drug-loaded polymer scaffolds for hard tissue engineering require unique structures to enhance release kinetics while enabling bone cell growth (osteogenesis). This study examined the encapsulation of the pro-angiogenic mediator, ginsenoside Rg 1, into biodegradable poly(propylene fumarate) (PPF) microspheres to facilitate osteogenesis, while examining the release mechanism using advanced X-ray absorption near edge structure spectroscopy (XANES). Ginsenoside Rg 1 -loaded PPF microspheres were prepared using both an emulsion method and a microfluidic device, with the microfluidic technique providing tunable unimodal PPF spheres ranging in size from 3 to 52 μm by varying the flow rates. The morphology and composition of the Rg 1 -loaded PPF microspheres were characterized using FTIR, XRD, and XANES to examine the distribution of ginsenoside Rg 1 throughout the polymer matrix. Encapsulation efficiency and release profiles were studied and quantified by UV–Vis spectrophotometry, showing high encapsulation efficiencies of 95.4 ± 0.8% from the microfluidic approach. Kinetic analysis showed that Rg 1 release from the more monodisperse PPF microspheres was slower with a significantly smaller burst effect than from the polydisperse spheres, with the release following Fickian diffusion. The released Rg 1 maintained its angiogenic effect in vitro, showing that the PPF microspheres are promising to serve as vehicles for long-term controlled drug delivery leading to therapeutic angiogenesis in bone tissue engineering strategies.
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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) | 0.000 | 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".