Integrating mechanical sensor readouts into organ-on-a-chip platforms
Bibliographic record
Abstract
Organs-on-a-chip have emerged as next-generation tissue engineered models to accurately capture realistic human tissue behaviour, thereby addressing many of the challenges associated with using animal models in research. Mechanical features of the culture environment have emerged as being critically important in designing organs-on-a-chip, as they play important roles in both stimulating realistic tissue formation and function, as well as capturing integrative elements of homeostasis, tissue function, and tissue degeneration in response to external insult and injury. Despite the demonstrated impact of incorporating mechanical cues in these models, strategies to measure these mechanical tissue features in microfluidically-compatible formats directly on-chip are relatively limited. In this review, we first describe general microfluidically-compatible Organs-on-a-chip sensing strategies, and categorize these advances based on the specific advantages of incorporating them on-chip. We then consider foundational and recent advances in mechanical analysis techniques spanning cellular to tissue length scales; and discuss their integration into Organs-on-a-chips for more effective drug screening, disease modeling, and characterization of biological dynamics.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".