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Additional file 8 of Microfluidic chip systems for characterizing glucose-responsive insulin-secreting cells equipped with FailSafe kill-switch

2024· article· en· W6902081639 on OpenAlexaff

Bibliographic record

VenueFigshare · 2024
Typearticle
Languageen
FieldEngineering
Topic3D Printing in Biomedical Research
Canadian institutionsUniversity Health NetworkUniversity of TorontoLunenfeld-Tanenbaum Research Institute
Fundersnot available
KeywordsMicrofluidicsMicrochannelPerfusionComputational fluid dynamicsBoundary (topology)InsulinPhase (matter)Process (computing)

Abstract

fetched live from OpenAlex

Additional file 8. Supplementary Figure: The perfusion glucose stimulated insulin secretion (GSIS) assay-on-a-chip. (a) The design and specifications of the microfluidic-based perfusion GSIS on-a-chip consisting of a 1.5 m long and 800 µm wide microchannel with switchable inlets corresponding to LG, HG and KCl+HG solutions. Using phase contrast imaging, 8 different locations of cell-seeded microchannels were imaged and analyzed in ImageJ to estimate the cell density of adhered cells (cells per unit area of microchannel) to be used for normalizing the kinetic data of insulin secretion. (b) The initial and boundary conditions of the perfusion GSIS-on-a-chip was applied for CFD modeling to predict insulin secretion distribution across the microchannels over time. The predictability of the model was assessed by evaluating the coefficient of determination between predicted values from the CFD model and measured values of insulin secretion obtained from the perfusion GSIS assay on-chip corresponding to (c) βiPLCs, (d) FSβiPLCs and (e) GCV treated FSβiPLCs.

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)0.003
metaresearch head score (Gemma)0.013
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.797
Threshold uncertainty score0.290

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.013
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0020.002
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0040.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.7970.196

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 head0.022
GPT teacher head0.245
Teacher spread0.223 · 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.

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

Citations0
Published2024
Admission routes1
Has abstractyes

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