Multiscale brain research on a microfluidic chip
Bibliographic record
Abstract
One major challenge in current brain research is generating an integrative understanding of the brain's functions and disorders from its multiscale neuronal architectures and connectivity. Thus, innovative neurotechnology tools are urgently required for deciphering the multiscale functional and structural organizations of the brain at hierarchical scales from the molecular to the organismal level by multiple brain research initiatives launched by the European Union, United States, Australia, Canada, China, Korea, and Japan. To meet this demand, microfluidic chips (μFCs) have rapidly evolved as a trans-scale neurotechnological toolset to enable multiscale studies of the brain due to their unique advantages in flexible microstructure design, multifunctional integration, accurate microenvironment control, and capacity for automatic sample processing. Here, we review the recent progress in applying innovative μFC-based neuro-technologies to promote multiscale brain research and uniquely focus on representative applications of μFCs to address challenges in brain research at each hierarchical level. We discuss the current trend of combinational applications of μFCs with other neuro- and biotechnologies, including optogenetics, brain organoids, and 3D bioprinting, for better multiscale brain research. In addition, we offer our insights into the existing outstanding questions at each hierarchical level of brain research that could potentially be addressed by advancing microfluidic techniques. This review will serve as a timely guide for bioengineers and neuroscientists to develop and apply μFC-based neuro-technologies for promoting basic and translational brain research.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.000 | 0.012 |
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 teacher head, 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".