Optimization of nanoparticles for cardiovascular tissue engineering
Bibliographic record
Abstract
Nano-particulate delivery systems have increasingly been playing important roles in cardiovascular tissue engineering. Properties of nanoparticles (e.g. size, polydispersity, loading capacity, zeta potential, morphology) are essential to system functions. Notably, these characteristics are regulated by fabrication variables, but in a complicated manner. This raises a great need to optimize fabrication process variables to ensure the desired nanoparticle characteristics. This paper presents a comprehensive experimental study on this matter, along with a novel method, the so-called Geno-Neural approach, to analyze, predict and optimize fabrication variables for desired nanoparticle characteristics. Specifically, ovalbumin was used as a protein model of growth factors used in cardiovascular tissue regeneration, and six fabrication variables were examined with regard to their influence on the characteristics of nanoparticles made from high molecular weight poly(lactide-co-glycolide). The six-factor five-level central composite rotatable design was applied to the conduction of experiments, and based on the experimental results, a geno-neural model was developed to determine the optimum fabrication conditions. For desired particle sizes of 150, 200, 250 and 300 nm, respectively, the optimum conditions to achieve the low polydispersity index, higher negative zeta potential and higher loading capacity were identified based on the developed geno-neural model and then evaluated experimentally. The experimental results revealed that the polymer and the external aqueous phase concentrations and their interactions with other fabrication variables were the most significant variables to affect the size, polydispersity index, zeta potential, loading capacity and initial burst release of the nanoparticles, while the electron microscopy images of the nanoparticles showed their spherical geometries with no sign of large pores or cracks on their surfaces. The release study revealed that the onset of the third phase of release can be affected by the polymer concentration. Circular dichroism spectroscopy indicated that ovalbumin structural integrity is preserved during the encapsulation process. Findings from this study would greatly contribute to the design of high molecular weight poly(lactide-co-glycolide) nanoparticles for prolonged release patterns in cardiovascular engineering.
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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 | 0.001 | 0.001 |
| 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".