Mimicking Human Pathophysiology in Organ‐on‐Chip Devices
Bibliographic record
Abstract
Abstract Convergence of life sciences, engineering, and basic sciences has opened new horizons for biologically inspired innovations, and a considerable number of organ‐on‐a‐chip platforms have been developed for mimicking physiological systems of biological organs such as the brain, heart, lung, kidney, liver, and gut. Various biophysicochemical factors can also be introduced into such organ‐on‐a‐chip platforms to study metabolic and systemic effects spanning from drug toxicity to different pathologic manifestations. There is also a pressing need to develop better disease models for common pathologies using variations of these platforms. This can be achieved by recapitulating the unique microenvironment of a disease to investigate the cause and development of abnormal conditions as well as the structural and functional changes resulting from such a pathology. In this review, the organ‐on‐a‐chip platforms that have been developed to model different pathologies of neurodegenerative, cardiovascular, respiratory, hepatic, and digestive systems, along with cancer are summarized. Although the field is still in its infancy, it is anticipated that developing disease model‐on‐a‐chip platforms will likely be a valuable addition to the field of disease modeling, pathology studies, and improved drug discovery.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".