Enhanced Detection of Bubble Emissions Through the Intact Spine for Monitoring Ultrasound-Mediated Blood-Spinal Cord Barrier Opening
Bibliographic record
Abstract
Objective: We previously developed short burst, phase keying (SBPK) focused ultrasound (FUS) to mitigate standing waves in the human vertebral canal. Here, we show microbubble emissions from these pulses can be detected through the human vertebral arch and that these pulses are effective for blood-spinal cord barrier (BSCB) opening. Methods: At f0= 514 kHz, circulating microbubbles were sonicated through ex vivo human vertebrae (60 kPa-1 MPa) using a dual-aperture approach and SBPK exposures engineered to incorporate pulse inversion (PI). Signals from a 250 kHz receiver were analyzed using PI, short-time Fourier analysis and the maximum projection over the pulse train. In rats (n = 14), SBPK FUS+microbubbles was applied to 3 locations/spinal cord at fixed pressures (~0.20-0.47 MPa). MRI and histology were used to assess opening and tissue damage. Results: In human vertebrae between 0.2-0.4 MPa, PI amplified the microbubble/baseline ratio at f0/2 and 2f0by 202 ± 40% (132-291%). This was maximal at 0.4 MPa, coinciding with the onset of broadband emissions. In vivo, opening was achieved at 40/42 locations, with mean MRI enhancement of 46 ± 32%(16%-178%). Using PI, f0/2 was detected at 14/40 opening locations. At the highest pressures (f0/2 present) histology showed widespread bleeding throughout the focal region. At the lowest pressures, opening was achieved without bleeding. Conclusion: This study confirmed that PI can increase sensitivity to transvertebral detection of microbubble signals. Preliminary in vivo investigations show that SBPK FUS can increase BSCB permeability without tissue damage. Significance: SBPK is a clinically relevant pulse scheme and, in combination with PI, provides a means of mediating and monitoring BSCB opening noninvasively.
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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".