Multi‐Sensor Origami Platform: A Customizable System for Obtaining Spatiotemporally Precise Functional Readouts in 3D Models
Bibliographic record
Abstract
Bioprinting technology offers unprecedented opportunities to construct in vitro tissue models that recapitulate the 3D morphology and functionality of native tissue. Yet, it remains difficult to obtain adequate functional readouts from such models. In particular, it is challenging to position sensors in desired locations within pre-fabricated 3D bioprinted structures. At the same time, bioprinting tissue directly onto a sensing device is not feasible due to interference with the printer head. As such, a multi-sensing platform inspired by origami that overcomes these challenges by "folding" around a separately fabricated 3D tissue structure is proposed, allowing for the insertion of electrodes into precise locations, which are custom-defined using computer-aided-design software. The multi-sensing origami platform (MSOP) can be connected to a commercial multi-electrode array (MEA) system for data-acquisition and processing. To demonstrate the platform, how integrated 3D MEA electrodes can record neuronal electrical activity in a 3D model of a neurovascular unit is shown. The MSOP also enables a microvascular endothelial network to be cultured separately and integrated with the 3D tissue structure. Accordingly, how impedance-based sensors in the platform can measure endothelial barrier function is shown. It is further demonstrated the device's versatility by using it to measure neuronal activity in brain organoids.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".