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Record W2894840762 · doi:10.1039/c8lc00708j

Self-filling microwell arrays (SFMAs) for tumor spheroid formation

2018· article· en· W2894840762 on OpenAlexafffund
Amir Seyfoori, Ehsan Samiei, Neda Jalili, Brent Godau, Mehdi Rahmanian, Leila Farahmand, Keivan Majidzadeh‐A, Mohsen Akbari

Bibliographic record

VenueLab on a Chip · 2018
Typearticle
Languageen
FieldEngineering
Topic3D Printing in Biomedical Research
Canadian institutionsUniversity of Victoria
FundersNatural Sciences and Engineering Research Council of CanadaCanada Foundation for Innovation
KeywordsSpheroidNanotechnologyChemistryMaterials scienceBiochemistryIn vitro

Abstract

fetched live from OpenAlex

Tumor spheroid formation in microwell arrays is a promising approach for high-throughput screening of chemotherapeutic agents. This method offers the advantage of better mimicking the complexities of tumors as compared to conventional monolayer culture systems. However, using these technologies to their full potential is hindered by the inability to seed the cells within the wells uniformly and with high yield and reproducibility. Moreover, standard manufacturing approaches for fabrication of microwell arrays rely on lithography and etching techniques, which are costly, labor-intensive, and time-consuming. Herein, we report on the development of self-filling microwell arrays (SFMAs) in which cells are directed from a loading chamber to microwells using inclined guiding channels. The SFMAs are fabricated by replica molding of three-dimensionally (3D) printed molds in agarose. We characterize the fabrication process, demonstrate the ability to culture breast adenocarcinoma MCF-7 and glioma U87 in SFMAs and perform drug toxicity studies. We envision that the proposed innovative approach opens avenues of opportunities for high-throughput three-dimensional cell culture for drug screening and disease modeling.

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 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 categoriesInsufficient payload (model declined to judge)
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 score0.281
Threshold uncertainty score1.000

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)0.0000.001

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 head0.018
GPT teacher head0.261
Teacher spread0.244 · 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 teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

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

Citations59
Published2018
Admission routes2
Has abstractyes

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