State of the field: cellular and exosomal therapeutic approaches in vascular regeneration
Bibliographic record
Abstract
Pathologies of the vasculature including the microvasculature are often complex in nature, leading to loss of physiological homeostatic regulation of patency and adequate perfusion to match tissue metabolic demands. Microvascular dysfunction is a key underlying element in the majority of pathologies of failing organs and tissues. Contributing pathological factors to this dysfunction include oxidative stress, mitochondrial dysfunction, endoplasmic reticular (ER) stress, endothelial dysfunction, loss of angiogenic potential and vascular density, and greater senescence and apoptosis. In many clinical settings, current pharmacologic strategies use a single or narrow targeted approach to address symptoms of pathology rather than a comprehensive and multifaceted approach to address their root cause. To address this, efforts have been heavily focused on cellular therapies and cell-free therapies (e.g., exosomes) that can tackle the multifaceted etiology of vascular and microvascular dysfunction. In this review, we discuss 1) the state of the field in terms of common therapeutic cell population isolation techniques, their unique characteristics, and their advantages and disadvantages, 2) common molecular mechanisms of cell therapies to restore vascularization and/or vascular function, 3) arguments for and against allogeneic versus autologous applications of cell therapies, 4) emerging strategies to optimize and enhance cell therapies through priming and preconditioning, and, finally, 5) emerging strategies to bolster therapeutic effect. Relevant and recent clinical and animal studies using cellular therapies to restore vascular function or pathologic tissue health by way of improved vascularization are highlighted throughout these sections.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".