Localized delivery of compounds into articular cartilage by using high-intensity focused ultrasound
Bibliographic record
Abstract
Localized delivery of drugs into an osteoarthritic cartilaginous lesion does not yet exist, which limits pharmaceutical management of osteoarthritis (OA). High-intensity focused ultrasound (HIFU) provides a means to actuate matter from a distance in a non-destructive way. In this study, we aimed to deliver methylene blue locally into bovine articular cartilage in vitro. HIFU-treated samples (n = 10) were immersed in a methylene blue (MB) solution during sonication (f = 2.16 MHz, peak-positive-pressure = 3.5 MPa, mechanical index = 1.8, pulse repetition frequency = 3.0 kHz, cycles per burst: 50, duty cycle: 7%). Adjacent control 1 tissue (n = 10) was first pre-treated with HIFU followed by immersion into MB; adjacent control 2 tissue (n = 10) was immersed in MB without ultrasound exposure. The MB content was higher (p < 0.05) in HIFU-treated samples all the way to a depth of 600 µm from AC surface when compared to controls. Chondrocyte viability and RNA expression levels associated with cartilage degeneration were not different in HIFU-treated samples when compared to controls (p > 0.05). To conclude, HIFU delivers molecules into articular cartilage without major short-term concerns about safety. The method is a candidate for a future approach for managing OA.
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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 | 0.001 | 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".