Atomic Force and Confocal Microscopic Studies of Collagen-Cell-Based Scaffolds for Vascular Tissue Engineering
Bibliographic record
Abstract
Collagen is the most used naturally occurring scaffold material. It’s a structural protein ubiquitous among mammalian. The ability of collagen type I to host different cell phenotype in vitro and its low antigenecity in vivo are well known. However, the principal drawback of collagenbased materials consists in their low mechanical properties. For vascular tissue engineering this represents a major limit, as the aim is to mimic the structure of a native vessel, which is known to be resistant and viscoelastic. Moreover, vascular cells are known to be susceptible in vivo to reorganize the matrix in which they proliferate. Therefore, the aim of this project is to study the micro structural organization of collagen-based scaffolds, and to assess the interactions between collagen and smooth muscle cells during regeneration. This knowledge will then allow the development of appropriate strategies to tailor the microstructure of the scaffold and its properties. Smooth muscle cells (SMCs) were selected to study the interactions between cells and matrix during the proliferation. Atomic Force Microscopy (AFM) in dry state in tapping mode and Confocal Laser Scanning Microscopy (CLSM) in reflection mode were used to investigate the microstructure of the scaffold. For the former technique cells were seeded on top of the collagen gel after jellification, while for the latter, cells were embedded into the collagen gel and stained with Rhodamine. The contact points between matrix and cells were investigated, as well as the capacity of vascular cells to induce a structural reorganization of collagen fibrils in the scaffold.
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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".