Acute effects of focused ultrasound-induced increases in blood-brain barrier permeability on rat microvascular transcriptome
Bibliographic record
Abstract
Therapeutic treatment options for central nervous system diseases are greatly limited by the blood-brain barrier (BBB). Focused ultrasound (FUS), in conjunction with circulating microbubbles, can be used to induce a targeted and transient increase in BBB permeability, providing a unique approach for the delivery of drugs from the systemic circulation into the brain. While preclinical research has demonstrated the utility of FUS, there remains a large gap in our knowledge regarding the impact of sonication on BBB gene expression. This work is focused on investigating the transcriptional changes in dorsal hippocampal rat microvessels in the acute stages following sonication. Microarray analysis of microvessels was performed at 6 and 24 hrs post-FUS. Expression changes in individual genes and bioinformatic analysis suggests that FUS may induce a transient inflammatory response in microvessels. Increased transcription of proinflammatory cytokine genes appears to be short-lived, largely returning to baseline by 24 hrs. This observation may help to explain some previously observed bioeffects of FUS and may also be a driving force for the angiogenic processes and reduced drug efflux suggested by this work. While further studies are necessary, these results open up intriguing possibilities for novel FUS applications and suggest possible routes for pharmacologically modifying the technique.
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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) | 0.002 | 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".