Coupling Microfluidic Platforms, Microfabrication, and Tissue Engineered Scaffolds to Investigate Tumor Cells Mechanobiology
Bibliographic record
Abstract
The tumor microenvironment (TME) is composed of dynamic and complex networks composed of matrix substrates, extracellular matrix (ECM), non-malignant cells, and tumor cells. The TME is in constant evolution during the disease progression, most notably through gradual stiffening of the stroma. Within the tumor, increased ECM stiffness drives tumor growth and metastatic events. However, classic in vitro strategies to study the TME in cancer lack the complexity to fully replicate the TME. The quest to understand how the mechanical, geometrical, and biochemical environment of cells impacts their behavior and fate has been a major force driving the recent development of new technologies in cell biology research. Despite rapid advances in this field, many challenges remain in order to bridge the gap between the classical culture dish and the biological reality of actual tissue. Microfabrication coupled with microfluidic approaches aim to engineer the actual complexity of the TME. Moreover, TME bioengineering allows artificial modulations with single or multiple cues to study different phenomena occurring in vivo. Some innovative cutting-edge tools and new microfluidic approaches could have an important impact on the fields of biology and medicine by bringing deeper understanding of the TME, cell behavior, and drug effects.
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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 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".