Integrated Microphysiological Systems: Transferable Organ Models and Recirculating Flow
Bibliographic record
Abstract
Studying and understanding of tissue and disease mechanisms largely depend on the availability of suitable and representative biological model systems. These model systems should be carefully engineered and faithfully reproduce the biological system of interest to understand physiological effects, pharmacokinetics, and toxicity to better identify new drug compounds. By relying on microfluidics, microphysiological systems (MPSs) enable the precise control of culturing conditions and connections of advanced in vitro 3D organ models that better reproduce in vivo environments. This review focuses on transferable in vitro organ models and integrated MPSs that host these transferable biological units and enable interactions between different tissue types. Interchangeable and transferrable in vitro organ models allow for independent quality control of the biological model before system assembly and building MPS assays on demand. Due to the complexity and different maturation times of individual in vitro tissues, off-chip production and quality control entail improved stability and reproducibility of the systems and results, which is important for large-scale adoption of the technology. Lastly, the technical and biological challenges and open issues for realizing and implementing integrated MPSs with transferable in vitro organ models 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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| 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 | 0.001 |
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".