Emerging Technologies for Exploring the Cellular Mechanisms in Vascular Diseases
Bibliographic record
Abstract
Vascular diseases (VDs) and cardiovascular diseases (CVDs) are the leading causes of morbidity and mortality worldwide. Current diagnostic and therapeutic approaches are limited by insufficient resolution and a lack of mechanistic understanding at the cellular level. Traditional imaging and clinical assays do not fully capture the dynamic molecular and structural complexities underlying vascular pathology. Recent technological innovations, including single-cell and spatial transcriptomics, super-resolution and photoacoustic imaging, microfluidic organ-on-chip platforms, Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR)/CRISPR-associated protein 9 (Cas9)-based gene editing, and artificial intelligence (AI), have created new opportunities for investigating the cellular and molecular basis of VDs. These techniques enable high-resolution mapping of cellular heterogeneity and functional alterations, facilitating the integration of large-scale data for biomarker discovery, disease modeling, and therapeutic development. This review focuses on evaluating the translational readiness, limitations, and potential clinical applications of these emerging technologies. Understanding the cellular and molecular mechanisms of VDs is essential for developing targeted therapies and precise diagnostics. Integrating single-cell and multiomics approaches highlights disease-driving cell types and gene programs. Optogenetics and organ-on-chip platforms allow for controlled manipulation and physiologically relevant modeling, while AI enhances data integration, risk prediction, and clinical interpretability. Future efforts should prioritize multi-center, large-scale validation studies, harmonization of assay protocols, and integration with clinical datasets and human samples. Multi-omics approaches and computational modeling hold promise for unraveling disease complexity, while advances in regulatory science and digital simulation (such as digital twins) may further accelerate personalized medicine in vascular disease research and treatment.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".