Biomechanical testing of ex vivo porcine tendons following high intensity focused ultrasound thermal ablation
Bibliographic record
Abstract
INTRODUCTION: Magnetic resonance-guided focused ultrasound (MRgFUS) has been demonstrated to be able to thermally ablate tendons with the aim to non-invasively disrupt tendon contractures in the clinical setting. However, the biomechanical changes of tendons permitting this disrupting is poorly understood. We aim to obtain a dose-dependent biomechanical response of tendons following magnetic resonance-guided focused ultrasound (MRgFUS) thermal ablation. METHODS: Ex vivo porcine tendons (n = 72) were embedded in an agar phantom and randomly assigned to 12 groups based on MRgFUS treatment. The treatment time was 10, 20, or 30s, and the applied acoustic power was 25, 50, 75, or 100W. Following each MRgFUS treatment, tendons underwent biomechanical tensile testing on an Instron machine, which calculated stress-strain curves during tendon elongation. Rupture rate, maximum treatment temperature, Young's modulus and ultimate strength were analyzed for each treatment energy. RESULTS: The study revealed a dose-dependent response, with tendons rupturing in over 50% of cases when energy delivery exceeded 1000J and 100% disruption at energy levels beyond 2000J. The achieved temperatures during MRgFUS were directly proportional to energy delivery. The highest recorded temperature was 56.8°C ± 9.34 (3000J), while the lowest recorded temperate was 18.6°C ± 0.6 (control). The Young's modulus was highest in the control group (47.3 MPa ± 6.5) and lowest in the 3000J group (13.2 MPa ± 5.9). There was no statistically significant difference in ultimate strength between treatment groups. CONCLUSION: This study establishes crucial thresholds for reliable and repeatable disruption of tendons, laying the groundwork for future in vivo optimization. The findings prompt further exploration of MRgFUS as a non-invasive modality for tendon disruption, offering hope for improved outcomes in patients with musculotendinous contractures.
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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) | 0.002 | 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".