Transcranial photoacoustic imaging with ICG J-aggregates for imaging blood-brain barrier leak following LPS-induced neuroinflammation
Bibliographic record
Abstract
Transcranial photoacoustic imaging shows promise for non-invasive evaluation of brain injury and blood-brain barrier disruption (BBB-D). Our study used neonatal rats with immature cerebral blood vessels, making them more susceptible to brain injury. Neuroinflammation was induced using lipopolysaccharide (LPS), leading to BBB-D. We employed a small-animal photoacoustic imaging system that integrated a wavelength-tunable laser (680-970 nm) and a high-frequency ultrasound transducer to obtain transcranial ultrasound and photoacoustic (PA) images. BBB-D was visualized by the migration and accumulation of indocyanine green (ICG) J-aggregate nanoprobes in the brain, resulting in enhanced PA signal. Following LPS injection, a two-fold increase in PA signal intensity was observed at 2 hours, peaking at a four-fold increase at 4 hours. The enhanced PA signal persisted up to 24 hours and remained within 30% of the baseline at 48 hours. These findings have significant implications for early detection of BBB-D using transcranial photoacoustic imaging, made possible by the use of neonatal rats with thin skulls and photoacoustic contrast agents with distinct spectral signatures in vivo.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".