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Record W2070550276 · doi:10.1139/o02-168

Effects of lactic acid and NaCl on creatine kinase from rabbit muscle

2003· article· en· W2070550276 on OpenAlexvenueno aff
Hongmin Tang, Wen‐Bin Ou, Hai‐Meng Zhou

Bibliographic record

VenueBiochemistry and Cell Biology · 2003
Typearticle
Languageen
FieldChemistry
TopicAnalytical Chemistry and Chromatography
Canadian institutionsnot available
Fundersnot available
KeywordsLactic acidGuanidineMolten globuleChemistryCircular dichroismDenaturation (fissile materials)BiochemistryPolyacrylamide gel electrophoresisGel electrophoresisNative stateFolding (DSP implementation)EnzymeBiophysicsNuclear chemistryBacteriaBiology

Abstract

fetched live from OpenAlex

The lactic acid induced unfolding and the salt-induced folding of creatine kinase (CK) were studied by enzyme activity, fluorescence emission spectra, circular dichroism spectra, and native polyacrylamide gel electrophoresis. The results showed that the kinetics of CK inactivation was a monophase process. Lactic acid caused inactivation and unfolding of CK with no aggregation during CK denaturation. The unfolding of the whole molecule and the inactivation of CK in solutions of different concentration of lactic acid were compared. Much lower lactic acid concentration values were required to bring about inactivation than were required to produce significant conformational changes of the enzyme molecule. At higher concentrations of lactic acid (more than 0.2 mM) the CK dimers were partially dissociated, as proved by native polyacrylamide gel electrophoresis. NaCl induced the molten globule state with a compact structure after CK was denatured with 0.8 mM lactic acid, and the increasing of anions led to a tight side-chain. The above results suggest that the effect of lactic acid differed from that of other denaturants such as guanidine hydrochloride, HCI, or urea during CK folding, and the molten globule state indicates that intermediates exist during CK folding.

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 score0.043
Threshold uncertainty score0.807

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)0.0010.000

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 head0.004
GPT teacher head0.199
Teacher spread0.195 · 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

Citations17
Published2003
Admission routes1
Has abstractyes

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