Blood brain barrier-on-a-chip permeation to model neurological diseases using microfluidic biosensors
Bibliographic record
Abstract
The need to understand human body functions, monitor disease progression, and advance in drug development have consistently been major driving forces for medical innovations and advancements. Organ-on-a-Chip technology, particularly Blood-brain-barrier (BBB)-on-chip technology, creates an avenue to closely replicate the brain environment and provides real-time monitoring of cells. Located at the interface between the blood and the brain parenchyma, the blood-brain barrier is crucial for protecting the brain due to its semi-permeable nature, and is responsible for regulating the movement of molecules between the blood and the brain. Therefore its integrity and perfect functionality are essential for the unperturbed functioning of the central nervous system. The lack of effective in-vitro models to investigate these diseases due to various technical and economic limitations poses a significant challenge. Moreover, the use of in-vivo models involving other primates and rodents for experimentation further poses a challenge due to physiological variations between humans and other species and has ethical constraints. Our study explores how the BBB-on-chip overcomes a lot of these limitations posed by the other in-vitro and in-vivo models, making it a more efficient and accurate model to investigate the blood-brain barrier. The study further explains the evolution, applications, and future prospects of the BBB on-chip technologies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".