Investigating the Use of Human Monocytes and Vascular Smooth Muscle-like Cells Differentiated from Adipose Derived Stromal Cells for Vascular Tissue Regeneration
Bibliographic record
Abstract
In vascular tissue engineering, it is desirable to promote the proliferation and uniform distribution of the tissue-specific cells within a biomaterial scaffold, and to stimulate the cells to synthesize and deposit appropriate extracellular matrix (ECM). The ability to generate and accumulate tissue-specific ECM is especially important since the ECM not only provides structural support for the engineered tissue, but also directs signalling towards surrounding cells in order to initiate remodelling processes. However, currently there is limited ability to promote ECM production during in vitro vascular tissue engineering. Aside from the limited ECM-promoting strategies, another challenge that has been recognized is the lack of a vascular smooth muscle cell (VSMC) source that is effective, robust and safe. The thesis project investigated in vitro VSMC-monocyte co-culture systems. It was found that achieving a desired release profile of growth factors and hydrolytic proteases to direct ECM-promoting and ECM-degrading activities in the multi-cellular microenvironment is effective at promoting ECM accumulation during vascular tissue engineering. Additionally, adipose derived stromal cells (ASCs) were differentiated into VSMC-like cells. ASC-VSMC-monocyte co-culture systems were constructed to track the potential phenotypic changes of ASC-VSMCs (in order to further assess the feasibility of applying ASC-VSMCs as a replacement cell source for mature tissue VSMCs). The study demonstrated that the degradable polar hydrophobic ionic polyurethane (D-PHI) biomaterial scaffold based ASC-VSMC-monocyte co-culture system showed more release of proinflammatory cytokines at week 1 and 2. However, it showed more anti-inflammatory/wound-healing cytokine release at week 4. This study revealed important signalling factors and some mechanisms underlying phenotypic switches of ASC-VSMCs exposed to monocytes during in vitro tissue engineering processes. This can provide insights into the feasibility of ASC-VSMCs for vascular tissue development. Additionally, this work improved understanding of the balance between inflammation and wound-healing processes during tissue regeneration and remodeling using biomaterial scaffolds.
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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".