Automated fabrication of a scalable heart-on-a-chip device by 3D printing of thermoplastic elastomer nanocomposite and hot embossing
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The successful translation of organ-on-a-chip devices requires the development of an automated workflow for device fabrication, which is challenged by the need for precise deposition of multiple classes of materials in micro-meter scaled configurations. Many current heart-on-a-chip devices are produced manually, requiring the expertise and dexterity of skilled operators. Here, we devised an automated and scalable fabrication method to engineer a Biowire II multiwell platform to generate human iPSC-derived cardiac tissues. This high-throughput heart-on-a-chip platform incorporated fluorescent nanocomposite microwires as force sensors, produced from quantum dots and thermoplastic elastomer, and 3D printed on top of a polystyrene tissue culture base patterned by hot embossing. An array of built-in carbon electrodes was embedded in a single step into the base, flanking the microwells on both sides. The facile and rapid 3D printing approach efficiently and seamlessly scaled up the Biowire II system from an 8-well chip to a 24-well and a 96-well format, resulting in an increase of platform fabrication efficiency by 17,5000–69,000% per well. The device's compatibility with long-term electrical stimulation in each well facilitated the targeted generation of mature human iPSC-derived cardiac tissues, evident through a positive force-frequency relationship, post-rest potentiation, and well-aligned sarcomeric apparatus. This system's ease of use and its capacity to gauge drug responses in matured cardiac tissue make it a powerful and reliable platform for rapid preclinical drug screening and development.
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.
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 | 0.001 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it