Imaging the microstructure of the arterial wall – ex vivo to in vivo potential
Bibliographic record
Abstract
Microstructural imaging enables researchers to visualise changes in the arterial wall, allowing for (i) a deeper understanding of the role of specific components in arterial mechanics, (ii) the observation of cellular responses, (iii) insights into pathological alterations in tissue microstructure, and/or (iv) advancements in tissue engineering aimed at replicating healthy native tissue. In this prospective review, we present various imaging modalities spanning from ex vivo to in vivo applications within arterial tissue. The pros, cons, and sensitivities of these modalities are highlighted. By consolidating the latest advancements in microstructural imaging of arterial tissue, the authors aim for this paper to serve as a guide for researchers designing experiments at various stages. Furthermore, the integration of non-invasive, non-destructive imaging techniques into studies provides an additional layer of microstructural information, enhancing scientific findings, improving our understanding of disease, and potentially enabling earlier or more effective diagnostic capabilities. STATEMENT OF SIGNIFICANCE: Imaging the specific microstructural components of the arterial wall provides critical insights into vascular biology, mechanics, and pathology. It enables the visualisation of key structural components and their roles in arterial function, supports the analysis of cell-matrix interactions, and reveals microarchitectural changes associated with disease progression. This level of specificity also informs the design of biomimetic materials and scaffolds in tissue engineering, facilitating the replication of native arterial properties. By synthesising recent developments in microstructural imaging techniques, this paper serves as a reference for investigators designing experiments across a range of vascular research applications. Moreover, the incorporation of non-invasive, non-destructive imaging methods offers a means to acquire detailed microstructural data without compromising tissue integrity. This enhances the interpretability and translational potential of findings, deepens our understanding of vascular disease mechanisms, and may ultimately contribute to the development of earlier and more precise diagnostic approaches.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".