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Record W2126750664 · doi:10.1039/c004590j

Droplet-based microfluidic system for multicellular tumor spheroid formation and anticancer drug testing

2010· article· en· W2126750664 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

VenueLab on a Chip · 2010
Typearticle
Languageen
FieldEngineering
TopicInnovative Microfluidic and Catalytic Techniques Innovation
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsSpheroidMulticellular organismCell cultureCellMicrofluidicsChemistryBiophysicsFlow cytometryBiomedical engineeringCell biologyIn vitroNanotechnologyBiologyMaterials scienceImmunologyBiochemistryMedicine

Abstract

fetched live from OpenAlex

Creating multicellular tumor spheroids is critical for characterizing anticancer treatments since it may provide a better model than monolayer culture of tumor cells. Moreover, continuous dynamic perfusion allows the establishment of long term cell culture and subsequent multicellular spheroid formation. A droplet-based microfluidic system was used to form alginate beads with entrapped breast tumor cells. After gelation, the alginate beads were trapped in microsieve structures for cell culture in a continuous perfusion system. The alginate environment permitted cell proliferation and the formation of multicellular spheroids was observed. The dose-dependent response of the tumor spheroids to doxorubicin, and anticancer drug, showed multicellular resistance compared to conventional monolayer culture. The microsieve structures maintain constant location of each bead in the same position throughout the device seeding process, cell proliferation and spheroid formation, treatment with drug, and imaging, permitting temporal and spatial tracking.

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 score0.230
Threshold uncertainty score0.690

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.011
GPT teacher head0.219
Teacher spread0.208 · 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