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Record W2606293602 · doi:10.1021/acssensors.7b00136

Steric Hindrance Assay for Secreted Factors in Stem Cell Culture

2017· article· en· W2606293602 on OpenAlexafffund
Wendi Zhou, Sahar Sadat Mahshid, Weijia Wang, Alexis Vallée‐Bélisle, Peter W. Zandstra, Edward H. Sargent, Shana O. Kelley

Bibliographic record

VenueACS Sensors · 2017
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAdvanced biosensing and bioanalysis techniques
Canadian institutionsUniversité de MontréalUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health ResearchCanada First Research Excellence Fund
KeywordsStem cellHaematopoiesisCell biologyCellCell cultureChemistryBiologyMolecular biologyBiochemistryComputational biologyGenetics

Abstract

fetched live from OpenAlex

The ex vivo expansion of hematopoietic stem cells is significantly inhibited by secreted proteins that induce negative feedback loops. The ability to effectively monitor these factors is critical for their real-time regulation and control and, by extension, enhancing stem cell expansion. Here, we describe a novel monitoring strategy for the detection of soluble signaling factors in stem cell cultures using a DNA-based sensing mechanism on a chip-based nanostructured microelectrode platform. We combine DNA hybridization engineering with antibody-capturing chemistry in an amplified steric hindrance hybridization assay. This method enables the quantification of important secreted proteins, showcased by the detection of 10 pg·mL –1 level concentrations of three proteins in stem cell culture samples. This approach is the first universal nonsandwich technique that permits pg·mL –1 level quantification of small proteins in stem cell culture media without signal loss.

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 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: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.561

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)0.0000.000

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 head0.016
GPT teacher head0.280
Teacher spread0.264 · 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.

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

Citations17
Published2017
Admission routes2
Has abstractyes

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