Hydration-Dependent Structuring in the Cornea: A Model for Bound Water in Collagenous Tissues
Bibliographic record
Abstract
Most mechanically competent connective tissues share similarities in their constitution, form and function. Most notably, they are comprised of a dense, hydrated extracellular matrix containing collagens and proteoglycans. Both of these constitutive elements contribute to the structuring of connective tissues through macromolecular interactions mediated by interfacial forces in the aqueous medium that encompasses them. More specifically, these elements are arranged such that the swelling potential of proteoglycans is spatially confined by a relatively inextensible meshwork of collagen fibrils – leading to a system that exists in an equilibrium-like state of prestress. This prestress allows for the physiologic functioning and versatile viscoelastic properties that connective tissues exhibit. A consequence of this construction is that these elements bind large amounts of water at their interfaces, through the presentation of hydrophilic moieties, and are susceptible to physical or chemical conditions that may affect their water binding affinity. Although well-understood as the driving phenomenon in the interactions of soluble globular proteins, this principle of bound interfacial water and its effect on the physical and morphological properties of hydrated connective tissues has not been extensively investigated in extracellular matrix biology. The studies contained within this thesis employ the cornea as an exemplary connective tissue model to study the role that bound water plays in influencing the form and function of such collagenous tissues. To do so, a coarse-grained multiscale model of corneal hydration was first developed to elucidate various confinement niches containing bound water to characterise both the interactions and quantity of the bound water therein. Simultaneously, manipulation of factors influencing osmotic potential and osmolarity in corneas were employed to elucidate the relationship between bound water, hydration, and physicochemical properties of the bulk tissue. Finally, a multiscale microscopical analysis was performed to obtain a 3-dimensional contextualized overview of the corneal structure in high resolution. Ultimately, these studies have begun to elucidate some of the important morphological and physicochemical dependent properties that hydrated connective tissues exhibit and paves the way for the development of new analytical methods to quantify and characterise this bound interfacial water
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".