Recent advances in cancer-on-a-chip tissue models to dissect the tumour microenvironment
Bibliographic record
Abstract
Abstract Three-dimensional cancer-on-a-chip tissue models aim to replicate the key hallmarks of the tumour microenvironment and allow for the study of dynamic interactions that occur during tumour progression. Recently, complex cancer-on-a-chip models incorporating multiple cell types and biomimetic extracellular matrices have been developed. These models have generated new research directions in engineering and medicine by allowing for the real-time observation of cancer-host cell interactions in a physiologically relevant microenvironment. However, these cancer-on-a-chip models have yet to overcome limitations including the complexity of device manufacturing, the selection of optimal materials for preclinical drug screening studies, long-term microfluidic cell culture as well as associated challenges, and the technical robustness or difficulty in the use of these microfluidic platforms. In this review, an overview of the tumour microenvironment, its unique characteristics, and the recent advances of cancer-on-a-chip models that recapitulate native features of the tumour microenvironment are presented. The current challenges that cancer-on-a-chip models face and the future directions of research that are expected to be seen are also discussed. Graphical Abstract
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".