A linear systems model of the hydrothermal isometric tension test for assessing collagenous tissue quality
Bibliographic record
Abstract
Collagen is the most abundant structural protein in the animal kingdom. Its thermal and thermomechanical properties are often measured using differential scanning calorimetry (DSC) and hydrothermal isometric tension (HIT) tests, respectively. In living tissues, not all collagenous structures (molecules, fibrils, etc.) have the same "quality," and the heterogeneity among these structures in specific tissues increases with remodeling, aging, and/or disease states. In this paper, first, a peak-fitting analysis is carried out to separate and distinguish the sequential denaturation events in a DSC endotherm, which presumably stem from heterogeneity in the collagen fibrils. The fitting analysis uses one of two functions: a Gaussian function or a function proposed by Miles. The individual endotherms were then convolved with a physics-based parametric function, J(T), proposed by the authors, to model the development of the isometric tension in two stages: 1) tension development due to a sudden increase in conformational entropy as each collagen packet denatures, and 2) additional isometric tension development due to increasing temperature, consistent with rubber thermo-elasticity. The proposed function parameters were then found by fitting to actual HIT curves using a global optimization technique. This model provides a decoupling of the effects of denaturation kinetics and collagen network connectivity and therefore an improved interpretation of HIT test results during the temperature ramp from ambient temperature to 90 °C. The simple model outputs are two parameters, α and β, that have physical meaning and aid in assessing collagenous tissue quality in terms of connectivity and integrity.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".