Age-dependent capillary thinning dynamics of physically-associated salivary mucin networks
Bibliographic record
Abstract
Human whole saliva consists of a physically-associated network of mucin molecules. The interchain physical associations control the rheological characteristics of the solution and in particular, the lifetime of filaments and threads. Measurements show that the shear rheology of salivary mucin solutions (as measured by steady shear viscosity and small amplitude oscillatory shear) is quite insensitive to sample age over a 24 h period following sample collection. By contrast, the filament thinning dynamics vary dramatically, with the characteristic relaxation time of the saliva and the breakup time of a fluid thread decreasing significantly with sample age. We interpret our results within the framework of a sticky finitely extensible network model which respects the known physical dimensions and properties of the mucin molecules in saliva and models them as a network of physically associating and finitely extensible polymer chains. The model predicts an initially strain-hardening response in the transient extensional rheology, followed by a sudden extensional thinning and filament rupture as the chains approach their maximum extensibility and the tension between junction points increases rapidly. We show that the model can accurately capture the changes observed in the filament thinning dynamics with sample age by incorporating a steady decrease in the molecular weight of the supramolecular aggregates of mucin. These experimental observations highlight the importance of considering sample age and enzymatic degradation when reporting extensional rheological measurements of saliva.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".