Investigating the hypotensive effect of focused ultrasound neuromodulation and barbiturate-loaded nanodroplets in healthy and hypertensive rats
Bibliographic record
Abstract
BACKGROUND: Current strategies for reducing blood pressure (BP) are ineffective and unsafe for many patient populations, including drug-resistant hypertension and during pregnancy. Stimulating the periaqueductal grey (PAG) region has shown promise in treating drug-resistant hypertension in patients using deep brain stimulation. OBJECTIVE: To develop a minimally invasive neuromodulation technique for the sustained treatment of hypertension. METHODS: We have investigated BP reduction using focused ultrasound (FUS) (540 kHz) and anesthetic-loaded ultrasound-responsive nanodroplets to deliver pentobarbital to the PAG in normotensive (N = 27) and hypertensive (N = 20) male and female rats. BP, heart rate and plasma hormone content were collected before and after FUS exposure, and neuronal activity was mapped in the PAG using C-Fos and neuron subtype staining. Cavitation activity was monitored by detecting acoustic emissions from vaporizing nanodroplets, and neuromodulation was verified with immunohistochemistry. RESULTS: Systolic and diastolic BP were reduced for 6 h following a single sonication of the PAG (-37/-28 mmHg systolic/diastolic), and the offline effect was extended to 4 days with consecutive sonications combined with systemically injected pentobarbital-loaded nanodroplets. In contrast, FUS applied to the frontal cortex had no effect on BP. Immunohistochemistry revealed stimulation of inhibitory neurons in the PAG region, indicating that the hypotensive effect was associated with a GABAergic pathway. The acoustic emissions from vaporizing droplets were found to correlate with neuron activity and change in BP, offering the potential for real-time treatment monitoring using ultrasound. CONCLUSIONS: This work has implications for developing a new treatment for hypertension that has greater safety and broader applicability for vulnerable patient populations.
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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 | 0.001 |
| 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".