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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.003 | 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".