Bibliographic record
Abstract
PURPOSE: Controlled drug release, especially tumor-targeted drug release, remains a great challenge. Here, we prepare a novel fluoride anion-modified gelatin nanogel system and investigate its characteristics of ultrasound-triggered drug release. METHODS: Adriamycin gelatin nanogel modified with fluoride anion (ADM-GNMF) was prepared by a modified co-precipitation method with fluoride anion and sodium sulfate. The loading and encapsulation efficiency of the anti-neoplastic agent adriamycin (ADM) were measured by high performance liquid chromatography (HPLC). The size and shape of ADM-GNMF were determined by electron microscopy and photo-correlation spectroscopy. The size distribution and drug release efficiency of ADM-GNMF, before and after sonication, were measured by two designed measuring devices that consisted of either a submicron particle size analyzer and an ultrasound generator as well as an ultrasound generator, automatic sampler, and HPLC. RESULTS: The ADM-GNMF was stable in solution with an average diameter of 46+/-12 nm; the encapsulation and loading efficiency of adriamycin were 87.2% and 6.38%, respectively. The ultrasound-triggered drug release and size change were most efficient at a frequency of 20 kHz, power density of 0.4w/cm2, and a 1~2 min duration. Under this ultrasound-triggered condition, 51.5% of drug in ADM-GNMF was released within 1~2 min, while the size of ADM-GNMF changed from 46 +/- 12 nm to 1212 +/- 35 nm within 1~2 min of sonication and restored to its previous size in 2~3 min after the ultrasound stopped. In contrast, 8.2% of drug in ADM-GNMF was released within 2~3 min without sonication, and only negligible size changes were found. CONCLUSIONS: The ADM-GNMF system efficiently released the encompassed drug in response to ultrasound, offering a novel and promising controlled drug release system for targeted therapy for cancer or other diseases.
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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".