In vitro elastogenesis (87.1)
Bibliographic record
Abstract
Background: Elastic fibers are one of the main components of the extracellular matrix (ECM), providing dynamic tissues such as lungs, skin, blood vessels, heart valve leaflets and ligaments with flexibility and elasticity. However, the complex process of elastic fiber assembly is poorly understood und the generation of elastic fibers on a human base is a missing link in tissue engineering. Methods: A constant shear stress providing, fluid flow bioreactor system was designed and evaluated. For in vitro studies, human vascular smooth muscle cells (VSMCs) were seeded onto three‐dimensional (3D) electropsun scaffolds and cultured in the bioreactor system. After 6 days dynamic culture, the scaffolds were harvested and analyzed regarding elastin gene and protein expression. The produced ECM was investigated applying immunostaining of elastogenesis‐associated proteins and transmission electron microscopy (TEM). Results: After a 6 day dynamic culture of VSMCs on 3D scaffolds, a significant increase of elastin gene and protein expression was detected when comparing to the 2D cultures and static controls. Moreover, TEM confirmed a proper developed ECM similar to native fetal tissue, including long bundels of microfibrils, collagen and elastin. This is the first report showing developing elastic fibers as well as the availability of elastogenesis‐associated proteins on a human‐based tissue engineered construct.
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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) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.007 |
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".