3D bioprinting for organ and organoid models and disease modeling
Bibliographic record
Abstract
INTRODUCTION: 3D printing, a versatile additive manufacturing technique, has diverse applications ranging from transportation, rapid prototyping, clean energy, and medical devices. AREAS COVERED: The authors focus on how 3D printing technology can enhance the drug discovery process through automating tissue production that enables high-throughput screening of potential drug candidates. They also discuss how the 3D bioprinting process works and what considerations to address when using this technology to generate cell laden constructs for drug screening as well as the outputs from such assays necessary for determining the efficacy of potential drug candidates. They focus on how bioprinting how has been used to generate cardiac, neural, and testis tissue models, focusing on bio-printed 3D organoids. EXPERT OPINION: The next generation of 3D bioprinted organ model holds great promises for the field of medicine. In terms of drug discovery, the incorporation of smart cell culture systems and biosensors into 3D bioprinted models could provide highly detailed and functional organ models for drug screening. By addressing current challenges of vascularization, electrophysiological control, and scalability, researchers can obtain more reliable and accurate data for drug development, reducing the risk of drug failures during clinical trials.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 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".