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Record W1941214492 · doi:10.1002/nbm.3102

Quantification of rate constants for successive enzymatic reactions with DNP hyperpolarized MR

2014· article· en· W1941214492 on OpenAlexaff
Hyla Allouche‐Arnon, Yonatan Hovav, Lanette J. Friesen‐Waldner, Jacob Sosna, John M. Gomori, Shimon Vega, Rachel Katz‐Brull

Bibliographic record

VenueNMR in Biomedicine · 2014
Typearticle
Languageen
FieldChemistry
TopicAdvanced NMR Techniques and Applications
Canadian institutionsWestern University
FundersIsrael Science Foundation
KeywordsBetaineChemistryCholine oxidaseCholineCholine chlorideReaction rate constantReaction rateKineticsAnalytical Chemistry (journal)Nuclear magnetic resonanceEnzymeChromatographyOrganic chemistryBiochemistry

Abstract

fetched live from OpenAlex

A kinetic model is provided to obtain reaction rate constants in successive enzymatic reactions that are monitored using NMR spectroscopy and hyperpolarized substrates. The model was applied for simulation and analysis of the successive oxidation of choline to betaine aldehyde, and further to betaine, by the enzyme choline oxidase. This enzymatic reaction was investigated under two different sets of conditions: two different choline molecular probes were used, [1,1,2,2-D4 , 1-(13) C]choline chloride and [1,1,2,2-D4 , 2-(13) C]choline chloride, in different MR systems (clinical scanner and high-resolution spectrometer), as well as in different reactors and reaction volumes (4.8 and 0.7 mL). The kinetic analysis according to the model yielded similar results in both set-ups, supporting the robustness of the model. This was achieved despite the complex and negating influences of reaction kinetics and polarization decay, and in the presence of uncontrolled mixing characteristics, which may introduce uncertainties in both effective timing and effective pulses. The ability to quantify rate constants using hyperpolarized MR in the first seconds of consecutive enzyme activity is important for further development of the utilization of dynamic nuclear polarization-MR for biological determinations.

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: none
Teacher disagreement score0.682
Threshold uncertainty score0.326

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 head0.020
GPT teacher head0.309
Teacher spread0.289 · 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

Citations25
Published2014
Admission routes1
Has abstractyes

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