Selective targeting of nanomedicine to inflamed cerebral vasculature to enhance the blood–brain barrier
Bibliographic record
Abstract
Drug targeting to inflammatory brain pathologies such as stroke and traumatic brain injury remains an elusive goal. Using a mouse model of acute brain inflammation induced by local tumor necrosis factor alpha (TNFα), we found that uptake of intravenously injected antibody to vascular cell adhesion molecule 1 (anti-VCAM) in the inflamed brain is >10-fold greater than antibodies to transferrin receptor-1 and intercellular adhesion molecule 1 (TfR-1 and ICAM-1). Furthermore, uptake of anti-VCAM/liposomes exceeded that of anti-TfR and anti-ICAM counterparts by ∼27- and ∼8-fold, respectively, achieving brain/blood ratio >300-fold higher than that of immunoglobulin G/liposomes. Single-photon emission computed tomography imaging affirmed specific anti-VCAM/liposome targeting to inflamed brain in mice. Intravital microscopy via cranial window and flow cytometry showed that in the inflamed brain anti-VCAM/liposomes bind to endothelium, not to leukocytes. Anti-VCAM/LNP selectively accumulated in the inflamed brain, providing de novo expression of proteins encoded by cargo messenger RNA (mRNA). Anti-VCAM/LNP-mRNA mediated expression of thrombomodulin (a natural endothelial inhibitor of thrombosis, inflammation, and vascular leakage) and alleviated TNFα-induced brain edema. Thus VCAM-directed nanocarriers provide a platform for cerebrovascular targeting to inflamed brain, with the goal of normalizing the integrity of the blood-brain barrier, thus benefiting numerous brain pathologies.
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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) | 0.000 | 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".