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Record W3118234731

Effects of Crowding and Confinement on Enzyme Kinetics

2017· article· en· W3118234731 on OpenAlexaff
Brian M. Lozinski, John K. Chik

Bibliographic record

VenueURSCA Proceedings · 2017
Typearticle
Languageen
FieldChemistry
TopicVarious Chemistry Research Topics
Canadian institutionsMount Royal University
Fundersnot available
KeywordsMicelleKineticsPulmonary surfactantChemistrySubstrate (aquarium)Chemical engineeringEnzyme kineticsSolventBilayerPolarChromatographyEnzymeAqueous solutionOrganic chemistryMembraneBiochemistryActive site
DOInot available

Abstract

fetched live from OpenAlex

Reverse micelles are formed by the aggregation of water inside of a bilayer formed by a surfactant in a non-polar solvent. The nanometer scale pool of water within the reverse micelles provides an environment to study the effects of confinement on enzymes as measured by changes in kinetics. The size of these reverse micelles is directly related to the ratio of water and surfactant. We and others have been studying the effects of crowding, the presence of large concentrations of solutes, on enzyme kinetics. What effect would it have on enzyme kinetics if these solutes were added to the confined environment of the reverse micelle? We have studied the effects of confinement on alpha-chymotrypsin in a system using dioctyl sulfosuccinate (AOT) as a surfactant, isooctane as the non-polar solvent, and N-succinyl-L-phenylalanine p-nitroanilide (SPN) as the substrate. Results will be analyzed within the Michaelis-Menten framework. * Indicates faculty mentor.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.014
GPT teacher head0.266
Teacher spread0.252 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2017
Admission routes1
Has abstractyes

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