Imaging blood–brain barrier dysfunction in animal disease models
Bibliographic record
Abstract
The blood-brain barrier (BBB) is a highly complex structure, which separates the extracellular fluid of the central nervous system (CNS) from the blood of CNS vessels. A wide range of neurologic conditions, including stroke, epilepsy, Alzheimer's disease, and brain tumors, are associated with perturbations of the BBB that contribute to their pathology. The common consequence of a BBB dysfunction is increased permeability, leading to extravasation of plasma constituents and vasogenic brain edema. The BBB impairment can persist for long periods, being involved in secondary inflammation and neuronal dysfunction, thus contributing to disease pathogenesis. Therefore, reliable imaging of the BBB impairment is of major importance in both clinical management of brain diseases and in experimental research. From landmark studies by Ehrlich and Goldman, the use of dyes (probes) has played a critical role in understanding BBB functions. In recent years methodologic advances in morphologic and functional brain imaging have provided insight into cellular and molecular interactions underlying BBB dysfunction in animal disease models. These imaging techniques, which range from in situ staining to noninvasive in vivo imaging, have different spatial resolution, sensitivity, and capacity for quantitative and kinetic measures of the BBB impairment. Despite significant advances, the translation of these techniques into clinical applications remains slow. This review outlines key recent advances in imaging techniques that have contributed to the understanding of BBB dysfunction in disease and discusses major obstacles and opportunities to advance these techniques into the clinical realm.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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".