Modeling organ-specific vasculature with organ-on-a-chip devices
Bibliographic record
Abstract
Organ-on-a-chip devices, also known as microphysiological systems, have gained significant attention in recent years. Recent advances in tissue engineering and microfabrication have enabled these devices to provide more precise control over cellular microenvironments to mimic the tissue-level or organ-level function of the human body. These more complex tissue models can provide either an improvement in the functional expression and maturation of cells or an avenue to probe biological events and function that would otherwise be difficult to visualize and mechanistically study. This high-value information, when complimented with the existing gold-standards of cell-based assays and animal models, could potentially lead to more informed decision-making in drug development. A prevalent biological component in many organ-on-a-chip devices is an engineered vascular interface that is present in almost all organs of the human body. The vasculature and the vascular interface are particularly susceptible to biomechanical forces, they function as the conduits for inter-cellular and inter-organ interactions, and regulate drug transport. In this review, we examine the various approaches taken to model the human vasculature with an emphasis on the engineering of organ-specific vasculatures, and discuss various challenges and opportunities ahead as the field advances.
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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 | 0.001 | 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) | 0.001 | 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".