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Record W2783382833 · doi:10.1002/jcp.26476

A new gold standard approach to characterize the transport of Si across cell membranes in animals

2018· article· en· W2783382833 on OpenAlexafffund
Alexandre P. Garneau, Andrée‐Anne Marcoux, Rachelle Frenette‐Cotton, Richard R. Bélanger, Paul Isenring

Bibliographic record

VenueJournal of Cellular Physiology · 2018
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicSilicon Effects in Agriculture
Canadian institutionsUniversité de MontréalUniversité Laval
FundersCanadian Institutes of Health ResearchKidney Foundation of Canada
KeywordsUnavailabilityMembraneSiliconChemistryBiological systemNanotechnologyBiochemical engineeringAnalytical Chemistry (journal)Materials scienceBiologyEnvironmental chemistryOptoelectronicsBiochemistryMathematicsEngineering

Abstract

fetched live from OpenAlex

Silicon (Si) is increasingly recognized as an essential trace element in animals, especially since the identification of mammalian Si transport systems and Si responsive genes not long ago. During many years, however, efforts to gain substantial insight into the biological role of this element in animals have achieved partial success due in part to the unavailability of validated protocols to study Si movement across biological membranes. To circumvent such limitations, we have developed a general transport assay in which cellular Si content was determined by automated electrothermal atomic absorption spectrophotometry. We have found this assay to provide great analytic sensitivity with Si detection thresholds of less than 1 µM, that is, below or very close to the concentration range of animal cells. We have also found this assay to provide valid and cost-effective determinations in Si transport studies while requiring workable quantities of samples. The protocol described here should thus become gold standard toward accelerated progress in the field of Si transport.

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)0.001
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 score0.415
Threshold uncertainty score0.190

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0010.000
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 head0.015
GPT teacher head0.225
Teacher spread0.210 · 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

Citations4
Published2018
Admission routes2
Has abstractyes

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