Quantification of blood-brain barrier permeability by dynamic contrast-enhanced NIRS
Bibliographic record
Abstract
Abstract The blood-brain barrier (BBB) is integral to maintaining a suitable microenvironment for neurons to function properly. Despite its importance, there are no bedside methods of assessing BBB disruption to help guide management of critical-care patients. The aim of this study was to demonstrate that dynamic contrast-enhanced (DCE) near-infrared spectroscopy (NIRS) can quantify the permeability surface-area product (PS) of the BBB. Experiments were conducted in rats in which the BBB was opened by image-guided focused ultrasound. DCE-NIRS data were acquired with two dyes of different molecular weight, indocyanine green (ICG, 67 kDa) and 800CW carboxylate (IRDye, 1166 Da), and PS maps were generated by DCE computer tomography (CT) for comparison. Both dyes showed a strong correlation between measured PS values and sonication power (R2 = 0.95 and 0.92 for ICG and IRDye respectively), and the PS values for IRDye were in good agreement with CT values obtained with a contrast agent of similar molecular weight. These proof-of-principle experiments demonstrate that DCE NIRS can quantify BBB permeability. The next step in translating this method to critical care practice will be to adapt depth sensitive methods to minimize the effects of scalp contamination on NIRS PS values.
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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 | 0.001 | 0.001 |
| 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 | 0.000 | 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".