Novel method for delivery of stem cell-derived growth factors for heart therapy applications: «In vitro» and «In vivo» analysis
Bibliographic record
Abstract
Cardiovascular diseases still remain the leading cause of morbidity in Canada with heart failure and stroke being two of the three main causes of death. Although, medical and surgical options for treatment and care after the onset of the disease do exist, more than half the patients suffering from heart disease die within 5years of diagnosis. Currently, heart transplant still remains the last resort for patients with end-stage heart failure, with the demand for organ donors alarmingly exceeding the supply. Numerous groups have now turned towards investigating the possibility of stimulating the regenerative ability of the heart through various strategies, with cellular therapy and specifically stem cell regenerative therapy increasingly gaining popularity. Several clinical studies demonstrate the possibility of injecting adult stem cells to induce therapeutic angiogenesis and revascularization and in turn promote damaged tissue regeneration. Although these trials show that stem cells possess the potential to facilitate tissue repair, there has been uncertainty over the mechanisms responsible for this regenerative effect produced by the transplanted stem cells. Furthermore, recent evidence has emerged suggesting that a paracrine effect, created by growth factors secreted by the injected stem cells, is actually the key mediator in assisting therapeutic regeneration, which has now been demonstrated both in vitro and in small/large animal models through direct injection of the stem cell harnessed factors. However, other groups have also observed that bolus injection of individual or dual proteins, although showing initial success, does not have a prolonged effect enough to promote complete tissue regeneration. Thus, a novel method of delivering the stem cell harnessed factor cocktail would be needed to augment the effect of the stem cell derived growth factors delivered at the target site, which forms the main goal of this thesis. Specifically, we investigate the strategy of using controlled delivery particle systems which not only provide a continuous release but also protect the encapsulated factors from the harsh in vivo conditions. The use of our stem cell harnessed factor cocktail loaded polymeric system, mimicking the paracrine effect created by injected cells, could completely replace the concept of stem cell administration and pave the way for a new strategy in cellular therapy.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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".