The use of two-photon microscopy to study the biological effects of focused ultrasound on the brain
Bibliographic record
Abstract
Focused ultrasound (FUS) has been used to successfully disrupt the blood-brain barrier (BBB), aiding in the delivery of therapeutic agents to the brain and leading to improvements in disease pathology. Although significant progress has been made in the development of FUS technology, there is still a lack of understanding of the biophysical mechanisms of the BBB disruption and the microscopic effects of this disruption on brain cells. In this study, we combine a custom built ultrasound transducer with two-photon microscopy to conduct real time monitoring of BBB disruption in vivo. We have manufactured and tested a single element piezoelectric transducer with frequencies ranging from 1.15 to 1.30 MHz. Sonications were performed using 0.07-0.25 MPa estimated in situ pressure, 10 ms pulses, 1 Hz pulse repetition frequency for a total duration of 120 s in the presence of microbubbles. BBB disruption was observed through a cranial window created in the rat skull after intravenous injection of dextran conjugated- Texas Red (MW: 10,000 - 70,000 Da). Using this experimental setup, we have observed and characterized 3 different leakage patterns following BBB disruption. Our results indicate that varying the acoustic power leading to in situ pressure changes, may allow us to control the mechanism of BBB disruption. Furthermore, we have labelled astrocytes in vivo in order to visualize the effects of FUS on this cell population. Combination of our custom transducers with two-photon microscopy will allow significant advancement in allow significant advancement in the understanding of the mechanisms and cellular effects of FUS-induced BBB disruption.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".