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Record W3042256866 · doi:10.1126/sciadv.aba9589

Cell invasion in digital microfluidic microgel systems

2020· article· en· W3042256866 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueScience Advances · 2020
Typearticle
Languageen
FieldEngineering
TopicElectrowetting and Microfluidic Technologies
Canadian institutionsCanada Research ChairsUniversity Health NetworkUniversity of Toronto
Fundersnot available
KeywordsMicrofluidicsDigital microfluidicsCellTranscriptomeNanotechnologyCell biologyMaterials scienceBiologyOptoelectronicsBiochemistryGene expressionGene

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.092
Threshold uncertainty score0.407

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.008
GPT teacher head0.202
Teacher spread0.194 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it