Targeting and Crossing the Blood-Brain Barrier with Extracellular Vesicles
Bibliographic record
Abstract
The blood-brain barrier (BBB) is one of the most complex and selective barriers in the human organism. Its role is to protect the brain and preserve the homeostasis of the central nervous system (CNS). The central elements of this physical and physiological barrier are the endothelial cells that form a monolayer of tightly joined cells covering the brain capillaries. However, as endothelial cells regulate nutrient delivery and waste product elimination, they are very sensitive to signals sent by surrounding cells and their environment. Indeed, the neuro-vascular unit (NVU) that corresponds to the assembly of extracellular matrix, pericytes, astrocytes, oligodendrocytes, microglia and neurons have the ability to influence BBB physiology. Extracellular vesicles (EVs) play a central role in terms of communication between cells. The NVU is no exception, as each cell can produce EVs that could help in the communication between cells in short or long distances. Studies have shown that EVs are able to cross the BBB from the brain to the bloodstream as well as from the blood to the CNS. Furthermore, peripheral EVs can interact with the BBB leading to changes in the barrier's properties. This review focuses on current knowledge and potential applications regarding EVs associated with the BBB.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".