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Record W2110328190 · doi:10.1080/00032710500536137

Effect of Mercury(II) Traces on Catalytic Activity of Peanut and Horseradish Peroxidases

2006· article· en· W2110328190 on OpenAlexaff
Nailya A. Bagirova, Svetlana V. Muginova, Tatyana N. Shekhovtsova, Irina G. Gazaryan, Robert B. van Huystee

Bibliographic record

VenueAnalytical Letters · 2006
Typearticle
Languageen
FieldEngineering
TopicElectrochemical sensors and biosensors
Canadian institutionsWestern University
Fundersnot available
KeywordsChemistryHorseradish peroxidaseMercury (programming language)PeroxidaseThioureaHydrogen peroxideCatalysisEnzymeNuclear chemistryStereochemistryBiochemistryOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract Mercury(II) in the range of 0.1–1 µg L−1 concentrations was found to be a much more efficient inhibitor of native peanut peroxidase (PNP) than of horseradish peroxidase (HRP) in the reaction of o‐dianisidine oxidation with hydrogen peroxide. The possible reason for the different degree of mercury(II) effects on the catalytic activity of both enzymes was studied. It was shown that the different number of glycans in PNP and HRP molecules (three and eight, respectively), or their absence in the molecule of wild‐type recombinant horseradish peroxidase refolded from E. coli inclusion bodies (recHRP), does not play a significant role in the effects caused by mercury(II). The efficient inhibition of PNP by mercury(II) in the absence of any other additives (for example, thiourea) originates from a greater mobility of the distal calcium ion in the enzyme molecule. A model scheme for the interaction of the studied plant peroxidases with mercury(II) was proposed. The PNP‐based enzymatic method for mercury(II) determination with c min =0.04 µg L−1 (0.2 nmol L−1) was developed and the possibility of PNP application for analysis of different samples was demonstrated.

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 score0.008
Threshold uncertainty score0.487

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 head0.003
GPT teacher head0.194
Teacher spread0.191 · 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

Citations7
Published2006
Admission routes1
Has abstractyes

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