Exploring Multi‐Organ Crosstalk via the TissUse HUMIMIC Chip System: Lessons Learnt So Far
Bibliographic record
Abstract
Three-dimensional (3D) in vitro cell culture models have revolutionized biomedical research by mimicking the complex 3D in vivo environment in the human body. Different types of 3D models have been established, including heterotypic systems such as, organ-on-a-chips which have been further developed into multiorgan-on-chip systems that simulate or mimic the mutual and multiplex physiological communication between (distant) organs that may not be physically connected with each other known as multiorgan crosstalk/interactions. These multiorgan interactions have been shown to be mediated by various factors including cells, soluble mediators (growth factors, cytokines etc.,) and cellular vesicles and are responsible for regulating metabolic, inflammatory, and tissue repair processes in the body. Different multiorgan-on-chip systems have been developed to mimic and study these interactions and their role in various molecular and toxicological processes. Of these, the TissUse HUMIMIC Starter and Chip microphysiological system is a commercially available multiorgan model that has been used to study inter-organ crosstalk between organs such as the gut and liver, liver and brain, liver and kidney, among others and applied in cellular, molecular and toxicology studies to among other things aid in the reduction of animals in drug and toxicological research. In this review, we provide a brief overview of multiorgan systems and summarize studies that have specifically used the TissUse system to investigate multiorgan crosstalk in the human body to deliver an update in the field of multiorgan microphysiological systems.
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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 | 0.002 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| 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".