Design and Development of Nanomaterial-Based Drug Carriers to Overcome the Blood–Brain Barrier by Using Different Transport Mechanisms
Bibliographic record
Abstract
Central nervous system (CNS) diseases are the leading causes of death and disabilities in the world. It is quite challenging to treat CNS diseases efficiently because of the blood-brain barrier (BBB). It is a physical barrier with tight junction proteins and high selectivity to limit the substance transportation between the blood and neural tissues. Thus, it is important to understand BBB transport mechanisms for developing novel drug carriers to overcome the BBB. This paper introduces the structure of the BBB and its physiological transport mechanisms. Meanwhile, different strategies for crossing the BBB by using nanomaterial-based drug carriers are reviewed, including carrier-mediated, adsorptive-mediated, and receptor-mediated transcytosis. Since the viral-induced CNS diseases are associated with BBB breakdown, various neurotropic viruses and their mechanisms on BBB disruption are reviewed and discussed, which are considered as an alternative solution to overcome the BBB. Therefore, most recent studies on virus-mimicking nanocarriers for drug delivery to cross the BBB are also reviewed and discussed. On the other hand, the routes of administration of drug-loaded nanocarriers to the CNS have been reviewed. In sum, this paper reviews and discusses various strategies and routes of nano-formulated drug delivery systems across the BBB to the brain, which will contribute to the advanced diagnosis and treatment of CNS diseases.
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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 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".