Bibliographic record
Abstract
A method was developed to prepare silk fibroin microspheres using lipid vesicles as templates to efficiently load protein drugs in active form for controlled release. The lipid was subsequently removed by methanol or sodium chloride treatments, resulting in silk microspheres consisting of beta-sheet structure and about 2 mum in diameter. NaCl treated microspheres had smoother surfaces compared to the methanol treatments based on SEM analysis, and both types of microspheres had a mixture of multilamellar and unilamellar structures. A model protein drug, horseradish peroxidase, was encapsulated in the microspheres. Freeze-thaw cycles during preparation led to higher loading of the peroxidase due to improved mixing between the silk and drug, while without this process the drug and silk remained in separate layers or domains in microspheres. This partitioning was determined with fluorescein-labeled silk and rhodamine-labeled dextran. Small molecules such as the enzyme substrate 3,3',5,5'-tetramethylbenzidine, Mw=240 Da, and its oxidized product freely diffused through the MeOH- and NaCl-processed silk microspheres so that enzyme loading and activity could be determined. Enzyme activity was retained during processing and in the final microspheres. The enzyme release profile depended on the NaCl-process used in microsphere preparation. The physically cross-linked beta-sheet structure of silk fibroin and the residual lipids in the microspheres played important roles in controlling enzyme release profiles. The silk microspheres have the potential for diverse applications where controlled protein release from biocompatible, mechanically tough, and slowly biodegradable carriers is desirable.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.117 | 0.011 |
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; both teacher heads agree on what is shown here.
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".