Cell invasion in digital microfluidic microgel systems
Bibliographic record
Abstract
Microfluidic methods for studying cell invasion can be subdivided into those in which cells invade into free space and those in which cells invade into hydrogels. The former techniques allow straightforward extraction of subpopulations of cells for RNA sequencing, while the latter preserve key aspects of cell interactions with the extracellular matrix (ECM). Here, we introduce "cell invasion in digital microfluidic microgel systems" (CIMMS), which bridges the gap between them, allowing the stratification of cells on the basis of their invasiveness into hydrogels for RNA sequencing. In initial studies with a breast cancer model, 244 genes were found to be differentially expressed between invading and noninvading cells, including genes correlating with ECM-remodeling, chemokine/cytokine receptors, and G protein transducers. These results suggest that CIMMS will be a valuable tool for probing metastasis as well as the many physiological processes that rely on invasion, such as tissue development, repair, and protection.
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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) | 0.000 | 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 | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".