Revealing Layer‐Specific Ultrastructure and Nanomechanics of Fibrillar Collagen in Human Aorta via Atomic Force Microscopy Testing: Implications on Tissue Mechanics at Macroscopic Scale
Bibliographic record
Abstract
Soft biological tissues are natural biomaterials with structures that have evolved to perform physiological functions, for example, conferring elasticity while preserving the mechanical integrity of arteries. Furthermore, the mechanical properties of the tissue extracellular matrix (ECM) significantly affect cell behavior and organ function. ECM mechanical properties are strongly affected by collagen ultrastructure, and perturbations in collagen networks can cause tissue mechanical failure. It is thus crucial to understand the ultrastructural mechanical properties of soft tissues. Herein, the ultrastructural and nanomechanical properties of arterial tissues are reported. Specifically, maps of aorta tissue stiffness in its three constitutive layers, namely tunica intima, media, and adventitia, are reported. Atomic force microscopy (AFM) with large and ultrasharp tips is used to explore tissue stiffness at two scales. Quasistatic tensile tests are further conducted to understand a potential correspondence between small‐scale mechanical properties obtained via AFM indentation and macroscopic behavior of the tissue at low and large strains. Furthermore, gradients in stiffness across the various layers as well as deformation rate effects are investigated. It is envisioned that the established methodology serves as a tool to investigate the effect of ECM remodeling associated with vascular diseases such as aneurysms and arterial stiffening linked to hypertension.
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 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".