Organ-on-a-Chip Platforms for Drug Delivery and Cell Characterization: A Review
Bibliographic record
Abstract
Developments in micro-and nanofluidic technologies have led to new kinds of cell culture and screening systems that are collectively termed organ-on-a-chip systems.Organ-on-a-chip systems are in vitro microfabricated devices that mimic dynamic interactions of in vivo microenvironments.In addition to existing two-dimensional and three-dimensional tissues, organ-on-a-chip systems can mimic the biomechanical and biochemical microenvironments of in vivo tissues as well as the interactional effects of the microenvironments on cell and tissue functions.Owing to those features, organ-ona-chip systems have become excellent platforms for drug screening and delivery tests.In this review, specific examples of organ-on-a-chip devices and their applications in tissue engineering and drug delivery tests are presented.The utility and performance of stateof-the-art organ-on-a-chip systems, including lung-on-a-chip, heart-on-a-chip, vessel-ona-chip, liver-on-a-chip, and tumor-on-a-chip, are also covered in this review.Limitations of conventional systems, basic fabrication processes for organ-on-a-chip devices, and future prospects of organ-on-a-chip systems are discussed.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".