Organ‐On‐A‐Chip Platforms: A Convergence of Advanced Materials, Cells, and Microscale Technologies
Bibliographic record
Abstract
Significant advances in biomaterials, stem cell biology, and microscale technologies have enabled the fabrication of biologically relevant tissues and organs. Such tissues and organs, referred to as organ-on-a-chip (OOC) platforms, have emerged as a powerful tool in tissue analysis and disease modeling for biological and pharmacological applications. A variety of biomaterials are used in tissue fabrication providing multiple biological, structural, and mechanical cues in the regulation of cell behavior and tissue morphogenesis. Cells derived from humans enable the fabrication of personalized OOC platforms. Microscale technologies are specifically helpful in providing physiological microenvironments for tissues and organs. In this review, biomaterials, cells, and microscale technologies are described as essential components to construct OOC platforms. The latest developments in OOC platforms (e.g., liver, skeletal muscle, cardiac, cancer, lung, skin, bone, and brain) are then discussed as functional tools in simulating human physiology and metabolism. Future perspectives and major challenges in the development of OOC platforms toward accelerating clinical studies of drug discovery are finally highlighted.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| 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".