An <i>ex vivo</i> experimental demonstration of passive acoustic mapping through the human spinal column
Bibliographic record
Abstract
The delivery of drugs to the central nervous system is grossly limited by the presence of the blood-brain and blood-spinal cord barriers. These barriers can be transiently permeabilized using low-intensity focused ultrasound combined with circulating microbubbles. The spectral content of acoustic emissions from cavitating microbubbles can be analyzed to control for and mitigate potential bioeffects. However, there is a large degree of uncertainty as to where these cavitation signals are originating. Passive beamforming of microbubble emissions recorded using multi-element arrays can enable spatial mapping of cavitation activity. Passive acoustic mapping (PAM) is challenging to implement in the presence of an intervening bone layer but has been successfully demonstrated through the skull. Here we present the first experimental demonstration of PAM through human vertebral bone. A tube containing flowing microbubbles was placed in the canal of ex vivo human thoracic vertebrae (stack of 3 vertebrae) and excited (250 kHz) through the right laminae. A 64-element large aperture 2D array was used to receive the harmonic emissions through the left laminae. Reconstructed maps successfully localized the cavitation activity to the spinal canal. Future work will examine effects of phase/amplitude correction, receiver number, and location of the cavitation relative to the vertebral anatomy.
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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 | 0.001 |
| 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".