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Record W3007879750 · doi:10.1002/fsn3.1426

Safety assessment of glutaminase from <i>Aspergillus niger</i>

2020· article· en· W3007879750 on OpenAlexaff
Trung D. Vo, Christina Sulaiman, Shahrzad Tafazoli, Barry Lynch, Ashley Roberts, Go Chikamatsu

Bibliographic record

VenueFood Science & Nutrition · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCarcinogens and Genotoxicity Assessment
Canadian institutionsOakville-Trafalgar Memorial HospitalCanadian Association of GastroenterologyIntertek (Canada)
Fundersnot available
KeywordsAspergillus nigerGlutaminaseFood scienceChemistryToxicityGlutamineBiotechnologyBiologyBiochemistry

Abstract

fetched live from OpenAlex

Abstract Glutaminase (glutamine aminohydrolase EC 3.5.1.2) is used in the production of food ingredients rich in l ‐glutamic acid that are added to finished foods for the purpose of enhancing or improving the savory flavor profile of food. The glutaminase enzyme preparation evaluated in these studies, designated as Sumizyme GT hereafter, is obtained by fermentation of Aspergillus niger strain GT147. The safety of Sumizyme GT was evaluated in a series of standard toxicological studies, including a 90‐day oral toxicity study in rats, an in vitro bacterial reverse mutation assay, an in vitro mammalian chromosome aberration test, and an in vivo alkaline Comet assay. Sumizyme GT was not mutagenic or genotoxic, and administration of the enzyme by gavage at doses up to 2,570 mg total organic solids (TOS)/kg body weight (bw) per day for 90 days was without any systemic toxicity. The no‐observed‐adverse‐effect level was concluded to be 2,570 mg TOS/kg bw per day, the highest dose tested. Considering that A. niger has an established history of safe use in the food industry and its safety in the production of food ingredients and food enzymes is well documented, the results of these studies provide further support of the safety of glutaminase from A. niger when used in food production.

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 score0.026
Threshold uncertainty score0.427

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 head0.015
GPT teacher head0.267
Teacher spread0.253 · 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

Citations19
Published2020
Admission routes1
Has abstractyes

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