Targeting lipid nanoparticles to the blood-brain barrier to ameliorate acute ischemic stroke
Bibliographic record
Abstract
Effective delivery of mRNA or small molecule drugs to the brain is a significant challenge in developing treatment for acute ischemic stroke (AIS). To address the problem, we have developed targeted nanomedicine to increase drug concentrations in endothelial cells of the blood-brain barrier (BBB) of the injured brain. Inflammation during ischemic stroke causes continuous neuronal death and an increase in the infarct volume. To enable targeted delivery to the inflamed BBB, we conjugated lipid nanocarriers (NCs) with antibodies that bind cell adhesion molecules expressed at the BBB. In the transient middle cerebral artery occlusion mouse model, NCs targeted to vascular cellular adhesion molecule-1 (VCAM) achieved the highest level of brain delivery, nearly two orders of magnitude higher than untargeted ones. VCAM-targeted lipid nanoparticles with luciferase-encoding mRNA and Cre-recombinase showed selective expression in the ischemic brain. Anti-inflammatory drugs administered intravenously after ischemic stroke reduced cerebral infarct volume by 62% (interleukin-10 mRNA) or 35% (dexamethasone) only when they were encapsulated in VCAM-targeted NCs. Thus, VCAM-targeted lipid NCs represent a new platform for strongly concentrating drugs within the compromised BBB of penumbra, thereby ameliorating AIS.
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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 | 0.001 |
| 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".