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Record W2314705950 · doi:10.1021/jp409543p

Investigating Water Interactions with Collagen Using<sup>2</sup>H Multiple Quantum Filtered NMR Spectroscopy To Provide Insights into the Source of Double Quantum Filtered Signal in Tissue

2014· article· en· W2314705950 on OpenAlexafffund
Cassidy R. VanderSchee, Kristopher J. Ooms

Bibliographic record

VenueThe Journal of Physical Chemistry B · 2014
Typearticle
Languageen
FieldMaterials Science
TopicCollagen: Extraction and Characterization
Canadian institutionsThe King's University
FundersNatural Sciences and Engineering Research Council of CanadaVedecká Grantová Agentúra MŠVVaŠ SR a SAV
KeywordsNuclear magnetic resonance spectroscopySpectroscopySIGNAL (programming language)QuantumNuclear magnetic resonanceChemistryMaterials sciencePhysicsComputer scienceQuantum mechanics

Abstract

fetched live from OpenAlex

In an effort to provide insight into the molecular origins of the (2)H double quantum filtered (DQF) NMR signal observed in connective tissue, specifically spinal disc tissue, (2)H multiple quantum filtered (MQF) NMR spectroscopy is used to study the structure and dynamics of D2O in collagen as a function of hydration. Residual quadrupolar coupling constants are measured and decrease from 3500 to 20 Hz while T2 relaxation times increase from 0.65 to 20 ms as hydration increases. Analysis of the data indicates that the quadrupolar coupling and T2 relaxation arises when water molecules spend time in restricted environments. The residual quadrupolar coupling is influenced almost exclusively by the most restricted water sites, the clefts of the triple helices not exposed on the surface of the fibrils, while the T2 relaxation has secondary contributions from less restricted water environments. The magnitudes of the measured values are consistent with results from DQF NMR studies of spinal disc tissue, supporting the assertion that water binding to collagen is a major contributor to the DQF NMR signal observed in spinal disc tissue.

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 score0.056
Threshold uncertainty score0.433

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 head0.018
GPT teacher head0.264
Teacher spread0.246 · 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

Citations10
Published2014
Admission routes2
Has abstractyes

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