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Record W4402080181 · doi:10.1039/d4lc00413b

Single cell glucose-stimulated insulin secretion assay using nanowell-in-microwell plates

2024· article· en· W4402080181 on OpenAlexafffund
Deasung Jang, Kerryn Matthews, Pan Deng, Samuel G. Berryman, Cuilan Nian, Simon P. Duffy, Francis C. Lynn, Hongshen Ma

Bibliographic record

VenueLab on a Chip · 2024
Typearticle
Languageen
FieldMedicine
TopicPancreatic function and diabetes
Canadian institutionsVancouver General HospitalBritish Columbia Institute of TechnologyBC Children's HospitalUniversity of British Columbia
FundersBreakthrough T1D CanadaNatural Sciences and Engineering Research Council of CanadaUniversity of British ColumbiaCanadian Institutes of Health ResearchChina Scholarship CouncilBC Children's HospitalMichael Smith Health Research BCSociety for Laboratory Automation and ScreeningDiabetes CanadaJuvenile Diabetes Research Foundation Canada
KeywordsIsletSecretionInsulinCellCell biologyBiologyFunction (biology)Cell cultureChemistryBiochemistryEndocrinologyGenetics

Abstract

fetched live from OpenAlex

) fabricated on glass-bottom imaging microwell plates. Insulin secreted from single cells is captured on microbeads and then stained using a detection antibody. The nanowell microstructure limits diffusion of secreted insulin. The glass substrate provides an optically flat surface for quantitative microscopy to measure the concentration of secreted insulin. We used this approach to measure GSIS from SCβ cells and donor-derived islet cells after 15 minutes exposure to 3.3 mM and 16.7 mM glucose. Both cell types exhibited significant GSIS heterogeneity, where elite cells (<20%) produced the majority of the secreted insulin (55-78%). This assay provides an immediate readout of single cell glucose-stimulated insulin secretion in a flexible well plate-based format.

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)0.002
metaresearch head score (Gemma)0.001
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: Empirical · Consensus signal: none
Teacher disagreement score0.005
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0050.003

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 head0.257
Teacher spread0.235 · 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
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

Citations6
Published2024
Admission routes2
Has abstractyes

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