Microencapsulated Stem Cells for Tissue Repairing: Implications in Cell-Based Myocardial Therapy
Bibliographic record
Abstract
Stem cells have the unique properties of self-renewal, pluripotency and a high proliferative capability, which contributes to a large biomass potential. Hence, these cells act as a useful source for acquiring renewable adult cell lines. This, in turn, acts as a potent therapeutic tool to treat various diseases related to the heart, liver and kidney, as well as neurodegenerative diseases such as Parkinson's and Alzheimer's disease. However, a major problem that must be overcome before it can be effectively implemented into the clinical setting is a suitable delivery system that can retain an optimal quantity of the cells at the targeted site for a maximal clinical benefit; a system that will give a mechanical as well as an immune protection to the foreign cells, while at the same time enhancing the yields of differentiated cells, maintaining cell microenvironments and sustaining the differentiated cell functions. To address this issue we opted for a novel delivery system, termed the 'artificial cells', which are semipermeable microcapsules with strong and thin multilayer membrane components with specific mass transport properties. Here, we briefly introduce the concept of artificial cells for encapsulation of stem cells and investigate the application of microencapsulation technology as an ideal tool for all stem transplantations and relate their role to the emerging field of cellular cardiomyoplasty.
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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 | 0.000 | 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".