A Programmable, 3D Neuron‐On‐Chip Platform Integrating Near Real‐Time Biosensing and Multiaxial Loading for Mechanobiological Injury Profiling
Bibliographic record
Abstract
Mechanical forces imparted to the central nervous system (CNS) generate complex, load-dependent injury patterns, yet the molecular mechanisms linking physical insult to biomarker response remain poorly defined. Here, the Neuron-Injury-on-a-Chip (NIOC) platform is presented as a programmable 3D microfluidic system integrating multiaxial loading with real-time biosensing. The system features a polydimethylsiloxane (PDMS) tube internally coated with polydopamine to support Cath. a-differentiated (CAD) neuron adhesion and viability. Controlled extension, torsion, and combined loads simulate physiologically relevant CNS trauma. Finite element modeling confirms uniform strain transmission, while embedded electrochemical biosensors enable near real-time detection of total tau (T-Tau) and neurofilament light chain (NFL) at picogram levels. qPCR and immunostaining validate gene-level responses (Mapt, Gap-43) and apoptosis (Caspase-3). Load-specific biomarker trajectories and apoptotic thresholds are uncovered, with synergistic injury responses under combined loading. NIOC represents a first-in-class platform for decoding mechanobiological injury, offering new opportunities for biomarker discovery, injury stratification, and neuroprotective screening.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| 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".