Building an experimental model of the human body with non-physiological parameters
Bibliographic record
Abstract
New advances in engineering and biomedical technology have enabled recent efforts to capture essential aspects of human physiology in microscale, in-vitro systems. The application of these advances to experimentally model complex processes in an integrated platform — commonly called a ‘human-on-a-chip (HOC)’ — requires that relevant compartments and parameters be sized correctly relative to each other and to the system as a whole. Empirical observation, theoretical treatments of resource distribution systems and natural experiments can all be used to inform rational design of such a system, but technical and fundamental challenges (e.g. small system blood volumes and context-dependent cell metabolism, respectively) pose substantial, unaddressed obstacles. Here, we put forth two fundamental principles for HOC design: inducing in-vivo-like cellular metabolic rates is necessary and may be accomplished in-vitro by limiting O 2 availability and that the effects of increased blood volumes on drug concentration can be mitigated through pharmacokinetics-based treatments of solute distribution. Combining these principles with natural observation and engineering workarounds, we derive a complete set of design criteria for a practically realizable, physiologically faithful, five-organ millionth-scale ([Formula: see text]) microfluidic model of the human body.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".