Integrating Bioprinting and Increased Throughput: Next-Generation Models for Cardiac Research
Bibliographic record
Abstract
Over the past two decades, three-dimensional cell culture (3DCC) and bioprinting (3DBP) technology have been at the forefront of developing engineered human cardiac tissue. Compared to 2D culture systems, 3DCC-based models more accurately replicate key characteristics of living tissues such as diffusion dynamics, interactions between cells and the extracellular matrix, as well as the presence of supporting stromal components. The rise of new 3DBP techniques serves to facilitate the robust and rapid generation of 3D tissue alongside real-time assessment of their characteristics. In order to capitalize on their translational potential, high-throughput screening (HTS) is required as research draws nearer to replicating clinical trials with cardiovascular-bioprinted tissues, and testing for the safety and efficacy of novel therapeutics. In this review, we summarize trending cardiac tissue models, as well as the state of their related or integrated HTS process and development. With an emphasis on the need for scale-up, compatibility, and standardization in HTS, the coalescence of 3DBP models and screening can provide improved disease modeling, drug efficacy, and toxicity testing.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".