Effect of sonication parameters on the efficacy of focused ultrasound and microbubble-mediated blood-spinal cord barrier opening using short-burst, phase keying exposures
Bibliographic record
Abstract
Focused ultrasound (FUS) and microbubbles can open the blood-spinal cord barrier (BSCB) and enhance therapeutic delivery. Short-burst phase keying (SBPK) exposures (pulse-train of closely timed short bursts) have been developed to address clinical-scale, spine specific targeting challenges in a dual-aperture configuration, and have led to successful BSCB opening (BSCBO) in animal models. Here we study the effect of varying sonication parameters in vivo. The effect of varying acoustic pressure (P, n = 4), burst length (BL, n = 3), burst repetition frequency (BRF, n = 4), pulse-train length(PL, n = 4), and total treatment duration (τ, n = 4), compared with a control sonication (P = 0.28 MPa, BL = 2 cycles, BRF = 20 kHz, PL = 10 ms, τ = 120 s, frequency = 514 kHz, PRF = 1 Hz), was investigated in Sprague Dawley rats (3-4 locations/spinal cord). BSCBO was assessed using T1-weighted contrast-enhanced MRI. Statistical significance was assessed using a paired t-test (p < 0.05). Increased P led to increased MRI enhancement (0.23 MPa: 17.8 ± 4.9%, 0.28 MPa: 25.9 ± 8.0%, 0.33 MPa: 33.9 ± 8.8%). τ = 300 s showed increased enhancement compared with the control (29.5 ± 8.7 vs 22.2 ± 2.8%, p = 0.03), while BL = 5 showed a trend towards increasing enhancement, although without significance (36.6 ± 7.2% vs. 31.2 ± 3.0%, p = 0.17). Varying PL or BRF did not impact mean enhancement. Preliminary results show that increasing treatment duration improves the efficacy of SBPK FUS-induced BSCBO and increasing burst length may have some benefit. Future work will include histological assessment of tissue damage.
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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 | 0.001 |
| 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) | 0.001 | 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".