Modulating the (sympathetic) nervous system with anesthetic-loaded nanodroplets
Bibliographic record
Abstract
Neurotransmitters are used by the sympathetic nervous system to signal danger and regulate the body. Exploiting these neural and hormonal responses may hold promise for new treatments for hypertension, anxiety, and chronic stress. We have developed a platform using transcranial focused ultrasound, bubble activity from vaporizing nanodroplets, and locally released neuroactive substances to perturb the sympathetic nervous system. By sonicating the periaqueductal grey region (540 kHz, 10 ms pulse length, 1 Hz PRF, 0.275–1.65 MPa), with and without systemically injected nanodroplets, local brain activity is altered, neurotransmitters and hormones are detected in the bloodstream, and systolic and diastolic blood pressure is reduced. By mapping the suppression and activation of neurons, and identifying the specific neuron subtype, we compare midbrain and frontal cortex stimulation. We compare healthy and hypertensive rodents and assess differences in cavitation activity across sex, brain region, and disease state. We investigate the influence of inhaled isoflurane level, which was found to mask the hypotensive effect of FUS during sedation compared to the conscious state. Understanding how transcranial focused ultrasound and cavitation activity, in combination with locally delivered anesthetics, affects the sympathetic nervous system, could have a significant impact in treating hypertension, anxiety, and chronic stress.
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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".