Trends in hydrogel-based encapsulation technologies for advanced cell therapies applied to limb ischemia
Bibliographic record
Abstract
Ischemia occurs when blood flow is reduced or restricted, leading to a lack of oxygen and nutrient supply and removal of metabolites in a body part. Critical limb ischemia (CLI) is a severe clinical manifestation of peripheral arterial disease. Atherosclerosis serves as the main cause of CLI, which arises from the deposition of lipids in the artery wall, forming atheroma and causing inflammation. Although several therapies exist for the treatment of CLI, pharmacotherapy still has low efficacy, and vascular surgery often cannot be performed due to the pathophysiological heterogeneity of each patient. Gene and cell therapies have emerged as alternative treatments for the treatment of CLI by promoting angiogenesis. However, the delivery of autologous, heterologous or genetically modified cells into the ischemic tissue remains challenging, as these cells can die at the injection site and/or leak into other tissues. The encapsulation of these cells within hydrogels for local delivery is probably one of the promising options today. Hydrogels, three-dimensional (3D) cross-linked polymer networks, enable manipulation of physical and chemical properties to mimic the extracellular matrix. Thus, specific biostructures can be developed by adjusting prepolymer properties and encapsulation process variables, such as viscosity and flow rate of fluids, depending on the final biomedical application. Electrostatic droplet extrusion, micromolding, microfluidics, and 3D printing have been the most commonly used technologies for cell encapsulation due to their versatility in producing different hydrogel-based systems (e.g., microgels, fibers, vascularized architectures and perfusable single vessels) with great potential to treat ischemic diseases. This review discusses the cell encapsulation technologies associated with hydrogels which are currently used for advanced therapies applied to limb ischemia, describing their principles, advantages, disadvantages, potentials, and innovative therapeutic ideas.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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".