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Record W4399238828 · doi:10.1016/j.bone.2024.117139

An improved linear systems model of hydrothermal isometric tension testing to aid in assessing bone collagen quality: Effects of ribation and type-2 diabetes.

2024· article· en· W4399238828 on OpenAlexafffund
Faezeh Iranmanesh, Daniel Dapaah, Jeffry S. Nyman, Thomas L. Willett

Bibliographic record

VenueBone · 2024
Typearticle
Languageen
FieldMedicine
TopicBone health and osteoporosis research
Canadian institutionsUniversity of Waterloo
FundersNational Institute of Arthritis and Musculoskeletal and Skin DiseasesBiomedical Laboratory Research and Development, VA Office of Research and DevelopmentCanadian Institutes of Health ResearchU.S. Department of Veterans Affairs
KeywordsIsometric exerciseDenaturation (fissile materials)Differential scanning calorimetryTension (geology)Biomedical engineeringDynamic TensionMaterials scienceUltimate tensile strengthBiological systemChemistryMathematicsBiophysicsComposite materialThermodynamicsPhysicsMedicineBiologyPhysical therapy

Abstract

fetched live from OpenAlex

This study sought to further develop and validate a previously proposed physics-based model that maps denaturation kinetics from differential scanning calorimetry (DSC) to the isometric tension generated during hydrothermal isometric tension (HIT) testing of collagenous tissues. The primary objectives of this study were to verify and validate two physics-based model parameters: α, which indicates the amount of instantaneous isometric tension developed per unit of collagen denaturation, and β, which captures the proportionality between temperature and the generated isometric tension post denaturation initiation. These parameters were used as measures of bone collagen quality, employing data from HIT and DSC testing of human bone collagen from two previous studies. Additionally, given the physical basis of the model, the study aimed to further validate Max.Slope, the rate of change in isometric tensile stress with change in temperature, as an independent measure of collagen network connectivity. Max.Slope has previously been positively correlated with measures of cortical bone fracture resistance. Towards this verification and validation, the hypotheses were a) that α would correlate strongly with HIT denaturation temperature, Td, and the enthalpy of melting (ΔH) from DSC, and b) that β would correlate positively and strongly with Max.Slope. The model was employed in the analysis of HIT-DSC data from the testing of demineralized bone collagen isolated from cadaveric human femurs in two prior studies. In one study, data were collected from HIT-DSC testing of cortical bone collagen from 74 donors. Among them, 38 had a history of type 2 diabetes +/− chronic kidney disease, while the remaining 36 had no history of T2D again with or without CKD. Cortical bone specimens were extracted from the lateral mid-shaft. The second study involved 15 donor femora, with four cortical bone specimens extracted from each. Of these four, two specimens underwent a 4-week incubation in 0.1 M ribose at 37 °C to induce non-enzymatic ribation and advanced glycation endproducts, while the other two served as non-ribated controls. The examination involved investigating correlations between the model parameters α and β and various measures, such as Max.Slope, Td, ΔH, age, and duration of type 2 diabetes. The results revealed positive correlations between the model parameter β and Max.Slope (r = 0.55–0.58). The parameter α was found to be associated with Td, but also sensitive to the shape of the HIT curve around Td resulting in difficulties with variability and interpretation. As a result, while both hypotheses are confirmed, Max.Slope and β are better indicators of bone collagen quality because they are measures of the connectivity or, more generally, the integrity of the bone collagen network.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.914
Threshold uncertainty score0.359

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.053
GPT teacher head0.372
Teacher spread0.318 · 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

Citations2
Published2024
Admission routes2
Has abstractyes

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