AGEing of collagen: The effects of glycation on collagen’s stability, mechanics and assembly
Bibliographic record
Abstract
• Ribose + collagen induces dose-dependent Advanced Glycation End-products (AGEs). • The stability of collagen’s triple helix structure is reduced by AGEs. • The bending stiffness of collagen increases with AGEs. • Glycated collagen loses the ability to self-assemble into fibrillar networks. Advanced Glycation End Products (AGEs) are the end result of the irreversible, non-enzymatic glycation of proteins by reducing sugars. These chemical modifications accumulate with age and have been associated with various age-related and diabetic complications. AGEs predominantly accumulate on proteins with slow turnover rates, of which collagen is a prime example. Glycation has been associated with tissue stiffening and reduced collagen fibril remodelling. In this study, we investigate the effects of glycation on the stability of type I collagen, its molecular-level mechanics and its ability to perform its physiological role of self-assembly. Collagen AGEing is induced in vitro by incubation with ribose. We confirm and assess glycation using fluorescence measurements and changes in collagen’s electrophoretic mobility. Susceptibility to trypsin digestion and circular dichroism (CD) spectroscopy are used to probe changes in collagen’s triple helical stability, revealing decreased stability due to glycation. Atomic Force Microscopy (AFM) imaging is used to quantify how AGEing affects collagen flexibility, where we find molecular-scale stiffening. Finally we use microscopy to show that glycated collagen molecules are unable to self-assemble into fibrils. These findings shed light on the molecular mechanisms underlying AGE-induced tissue changes, offering insight into how glycation modifies protein structure and stability.
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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 | 0.000 |
| 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) | 0.001 | 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".