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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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 teacher head, 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".