Critical Challenges of Intravenous Nanomaterials Crossing the Blood‐Brain Barrier: from Blood to Brain
Bibliographic record
Abstract
Abstract Despite recent advancements in the development of blood‐brain barrier (BBB)‐crossing nanomaterials for intravenous administration, there have been very few successful cases in clinical trials. Ongoing challenges within the body impede the precise therapeutic effects of these nanomaterials from reaching their intended target area. Therefore, a comprehensive analysis of the entire pathway that BBB‐crossing nanomaterials must traverse‐from the bloodstream to the brain‐along with an understanding of the obstacles encountered along the way, is essential for advancing these materials to clinical trials. This review begins with a brief overview of the structure and function of the BBB, as well as the pathways and strategies for crossing it. Next, it is discussed and analyzed the common challenges that BBB‐crossing nanomaterials in reaching their target sites in the brain from the bloodstream. To address these challenges, an “eight‐step” guideline strategy is proposed. By leveraging the principles of precision medicine, the design and customization of cascade‐targeted BBB‐crossing nanomaterials that can overcome multiple obstacles show promise for future clinical trials and practical applications. Finally, a perspective on the future direction of this field is offered.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".