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Record W2041356700 · doi:10.1039/c4lc00531g

Hepatic organoids for microfluidic drug screening

2014· article· en· W2041356700 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueLab on a Chip · 2014
Typearticle
Languageen
FieldEngineering
Topic3D Printing in Biomedical Research
Canadian institutionsUniversity of Toronto
FundersCanadian Institutes of Health Research
KeywordsOrganoidMicrofluidicsDrugDrug discoveryNanotechnologyChemistryCell biologyComputational biologyBiologyBioinformaticsPharmacologyMaterials science

Abstract

fetched live from OpenAlex

We introduce the microfluidic organoids for drug screening (MODS) platform, a digital microfluidic system that is capable of generating arrays of individually addressable, free-floating, three-dimensional hydrogel-based microtissues (or 'organoids'). Here, we focused on liver organoids, driven by the need for early-stage screening methods for hepatotoxicity that enable a "fail early, fail cheaply" strategy in drug discovery. We demonstrate that arrays of hepatic organoids can be formed from co-cultures of HepG2 and NIH-3T3 cells embedded in hydrogel matrices. The organoids exhibit fibroblast-dependent contractile behaviour, and their albumin secretion profiles and cytochrome P450 3A4 activities are better mimics of in vivo liver tissue than comparable two-dimensional cell culture systems. As proof of principle for screening, MODS was used to generate and analyze the effects of a dilution series of acetaminophen on apoptosis and necrosis. With further development, we propose that the MODS platform may be a cost-effective tool in a "fail early, fail cheaply" paradigm of drug development.

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)0.001
metaresearch head score (Gemma)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.417
Threshold uncertainty score0.421

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.014
GPT teacher head0.257
Teacher spread0.242 · 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