MétaCan
Menu
Back to cohort
Record W4389882427 · doi:10.32920/24625194

Oil-free Microfluidic Spheroid Generation for Cancer Drug Testing

2023· preprint· en· W4389882427 on OpenAlexaff
Jennifer Kieda

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicInnovative Microfluidic and Catalytic Techniques Innovation
Canadian institutionsToronto Metropolitan UniversityCARE Canada
Fundersnot available
KeywordsDoxorubicinMicrofluidicsPolyethylene glycolDrug deliveryDextranDrugSpheroidChemistryBiomedical engineeringMaterials scienceNanotechnologyBiophysicsChromatographyPharmacologyChemotherapyMedicineIn vitroBiochemistrySurgeryBiology

Abstract

fetched live from OpenAlex

In this thesis, a droplet microfluidics platform for on-chip polymerization of all aqueous hydrogel multicellular spheroids (MCSs) is developed. First, dextran-alginate droplets containing MCF-7 breast cancer cells, surrounded by polyethylene glycol, are generated at a flow-focusing junction. Droplets travel to a second flow-focusing junction where they are introduced to calcium chloride and polymerize on-chip to form hydrogel MCSs. In drug-free experiments, hydrogels are incubated for six days, and cellular viability is evaluated. In drug experiments, to test the effects of a chemotherapy drug, doxorubicin, three drug concentrations and two controls are applied to the spheroids for 48 hours. Image analysis is conducted using confocal microscopy z-stack images and MATLAB. It is shown that in drug-free experiments and conditions, MCSs show strong viability, and in drug experiments, the viability of MCSs decrease with increasing doxorubicin concentration.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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: Methods · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

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)0.0030.001

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.

Opus teacher head0.064
GPT teacher head0.286
Teacher spread0.222 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreMethods

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".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

Explore more

Same topicInnovative Microfluidic and Catalytic Techniques InnovationFrench-language works237,207