Organ chips with integrated multifunctional sensors enable continuous metabolic monitoring at controlled oxygen levels
Bibliographic record
Abstract
Despite remarkable advances in Organ-on-a-chip (Organ Chip) microfluidic culture technology, recreating tissue-relevant physiological conditions, such as the region-specific oxygen concentrations, remains a formidable technical challenge, and analysis of tissue functions is commonly carried out using one analytical technique at a time. Here, we describe two-channel Organ Chip microfluidic devices fabricated from polydimethylsiloxane and gas impermeable polycarbonate materials that are integrated with multiple sensors, mounted on a printed circuit board and operated using a commercially available Organ Chip culture instrument. The novelty of this system is that it enables the recreation of physiologically relevant tissue-tissue interfaces and oxygen tension as well as non-invasive continuous measurement of transepithelial electrical resistance, oxygen concentration and pH, combined with simultaneous analysis of cellular metabolic activity (ATP/ADP ratio), cell morphology, and tissue phenotype. We demonstrate the reliable and reproducible functionality of this system in living human Gut and Liver Chip cultures. Changes in tissue barrier function and oxygen tension along with their functional and metabolic responses to chemical stimuli (e.g., calcium chelation, oligomycin) were continuously and noninvasively monitored on-chip for up to 23 days. A physiologically relevant microaerobic microenvironment that supports co-culture of human intestinal cells with living Lactococcus lactis bacteria also was demonstrated in the Gut Chip. The integration of multi-functional sensors into Organ Chips provides a robust and scalable platform for the simultaneous, continuous, and non-invasive monitoring of multiple physiological functions that can significantly enhance the comprehensive and reliable evaluation of engineered tissues in Organ Chip models in basic research, preclinical modeling, and drug development.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".