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Record W2328673449 · doi:10.1021/acs.jafc.5b00188

Development of a Rapid Immunochromatographic Lateral Flow Device Capable of Differentiating Phytase Expressed from Recombinant <i>Aspergillus niger</i> <i> phy</i>A2 and Genetically Modified Corn

2015· article· en· W2328673449 on OpenAlexaff
Xiaojin Zhou, Elizabeth Hui, Xiaolin Yu, Zhen Lin, Ling-Kui Pu, TU Zhi-guan, Jun Zhang, Qi Liu, Jian Zheng, Juan Zhang

Bibliographic record

VenueJournal of Agricultural and Food Chemistry · 2015
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPhytase and its Applications
Canadian institutionsArtron Bioresearch (Canada)
Fundersnot available
KeywordsPhytaseAspergillus nigerRecombinant DNAGenetically modified organismBiotechnologyChemistryBiologyFood scienceBiochemistryEnzymeGene

Abstract

fetched live from OpenAlex

Phytase is a phosphohydrolase considered highly specific for the degradation of phytate to release bound phosphorus for animal consumption and aid in the reduction of environmental nutrient loading. New sources of phytase have been sought that are economically and efficiently productive including the construction of genetically modified (GM) phytase products designed to bypass the costs associated with feed processing. Four monoclonal antibodies (EH10a, FA7, AF9a, and CC1) raised against recombinant Aspergillus niger phyA2 were used to develop a highly specific and sensitive immunochromatographic lateral flow device for rapid detection of transgenic phytase, such as in GM corn. Antibodies sequentially paired and tested along lateral flow strips showed that the EH10a-FA7 antibody pair was able to detect the recombinant yeast-phytase at 5 ng/mL, whereas the AF9a-CC1 antibody pair to GM phytase corn was able to detect at 2 ng/mL. Concurrent to this development, evidence was revealed which suggests that antibody binding sites may be glycosylated.

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 score0.120
Threshold uncertainty score0.256

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 head0.021
GPT teacher head0.194
Teacher spread0.173 · 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

Citations15
Published2015
Admission routes1
Has abstractyes

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