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Record W3114983740

The effects of molecular crowding on the kinetics and small molecule inhibition of alkaline phosphatase

2018· article· en· W3114983740 on OpenAlex
Michael Cordara, Kyle Poffenroth, John K. Chik

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueURSCA Proceedings · 2018
Typearticle
Languageen
FieldMedicine
TopicAlkaline Phosphatase Research Studies
Canadian institutionsMount Royal University
Fundersnot available
KeywordsPolyethylene glycolChemistryKineticsDextranNon-competitive inhibitionAlkaline phosphataseEnzymeHydrolysisMacromolecular crowdingUncompetitive inhibitorEnzyme kineticsPhosphataseBiophysicsBiochemistryChromatographyActive siteBiologyMacromolecule
DOInot available

Abstract

fetched live from OpenAlex

Enzymes have adapted to function in complex environments crowded with many other solutes. To get a better understanding of in vivo crowding, we used polyethylene glycol (MW 8000) and dextran (MW 6000) as in vitro crowding agents and observed their effects both the kinetics of alkaline phosphatase-catalyzed para-nitrophenyl phosphate hydrolysis and the inhibition of this reaction by competitive and uncompetitive inhibitors. Reaction kinetics were followed using UV-visible spectrometry and the initial rate was analyzed using Michaelis-Menten kinetics to arrive at an apparent Vmax and Km for each reaction condition. We observed that polyethylene glycol increased Vmax while a similar amount of dextran strongly reduced Vmax. Crowding by these agents also significantly altered the effectiveness of small-molecule inhibitors and suggests that the action of drugs can be different going from “bench” research to “bedside” application. *Indicates presenters

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.

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)0.002
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 score0.033
Threshold uncertainty score0.362

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.014
GPT teacher head0.272
Teacher spread0.258 · 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