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Record W2493296989 · doi:10.1021/acs.jmedchem.6b00410

Allosteric Sensing of Fatty Acid Binding by NMR: Application to Human Serum Albumin

2016· article· en· W2493296989 on OpenAlex

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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueJournal of Medicinal Chemistry · 2016
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicProtein Interaction Studies and Fluorescence Analysis
Canadian institutionsMcMaster University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsChemistryHuman serum albuminAlbuminOleic acidAllosteric regulationBiochemistryGlycationFatty acidPlasma protein bindingSerum albuminChromatographyEnzymeReceptor

Abstract

fetched live from OpenAlex

Human serum albumin (HSA) serves not only as a physiological oncotic pressure regulator and a ligand carrier but also as a biomarker for pathologies ranging from ischemia to diabetes. Moreover, HSA is a biopharmaceutical with a growing repertoire of putative clinical applications from hypovolemia to Alzheimer's disease. A key determinant of the physiological, diagnostic, and therapeutic functions of HSA is the amount of long chain fatty acids (LCFAs) bound to HSA. Here, we propose to utilize (13)C-oleic acid for the NMR-based assessment of albumin-bound LCFA concentration (CONFA). (13)C-Oleic acid primes HSA for a LCFA-dependent allosteric transition that modulates the frequency separation between the two main (13)C NMR peaks of HSA-bound oleic acid (ΔνAB). On the basis of ΔνAB, the overall [(12)C-LCFA]Tot/[HSA]Tot ratio is reproducibly estimated in a manner that is only minimally sensitive to glycation, albumin concentration, or redox potential, unlike other methods to quantify HSA-bound LCFAs such as the albumin-cobalt binding assay.

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)0.000
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 score0.030
Threshold uncertainty score0.297

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 head0.008
GPT teacher head0.273
Teacher spread0.265 · 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