Lab-On-A-Chip for the Development of Pro-/Anti-Angiogenic Nanomedicines to Treat Brain Diseases
Bibliographic record
Abstract
There is a huge demand for pro-/anti-angiogenic nanomedicines to treat conditions such as ischemic strokes, brain tumors, and neurodegenerative diseases such as Alzheimer's and Parkinson's. Nanomedicines are therapeutic particles in the size range of 10-1000 nm, where the drug is encapsulated into nano-capsules or adsorbed onto nano-scaffolds. They have good blood-brain barrier permeability, stability and shelf life, and able to rapidly target different sites in the brain. However, the relationship between the nanomedicines' physical and chemical properties and its ability to travel across the brain remains incompletely understood. The main challenge is the lack of a reliable drug testing model for brain angiogenesis. Recently, microfluidic platforms (known as "lab-on-a-chip" or LOCs) have been developed to mimic the brain micro-vasculature related events, such as vasculogenesis, angiogenesis, inflammation, etc. The LOCs are able to closely replicate the dynamic conditions of the human brain and could be reliable platforms for drug screening applications. There are still many technical difficulties in establishing uniform and reproducible conditions, mainly due to the extreme complexity of the human brain. In this paper, we review the prospective of LOCs in the development of nanomedicines for brain angiogenesis-related conditions.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".