Three-Dimensional Tunable Fibronectin-Collagen Platforms for Control of Cell Adhesion and Matrix Deposition
Why this work is in the frame
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Bibliographic record
Abstract
The extracellular matrix (ECM) is a complex fibrillar network that couples a cell with its environment and directly regulates cells’ functions via structural, mechanical, and biochemical signals. The goal of this study was to engineer and characterize ECM-mimicking protein platforms with material properties covering both physiological and pathological (tumorous) tissues. We designed and fabricated three-dimensional (3D) fibrillar scaffolds comprising the two major components of the ECM, namely collagen (Col) and fibronectin (Fn), using a previously developed freeze-drying method. While scaffolds porous architecture and mechanics were controlled by varying Col I concentration, Fn deposition and conformation were tuned using varied immersion temperature and assessed via intramolecular Förster Resonance Energy Transfer (FRET). Our data indicate that all scaffolds were able to support various crucial cellular functions such as adhesion, proliferation and matrix deposition. Additionally, we show that, keeping the stiffness constant and tuning the conformation of the Fn layer used to coat the Col scaffolds, we were able to control not only the invasion of cells but also the conformation of the matrix they would deposit, from a compact to an unfolded structure (as observed in the breast tumor microenvironment). Therefore, these tunable scaffolds could be used as 3D cell culture models, in which ECM microarchitecture, mechanics and protein conformation are controlled over large volumes to investigate long-term mechanisms such as wound healing phases and/or vascularization mechanisms in both physiological and pathological (tumorous) microenvironments. These findings have implications for tissue engineering and regenerative medicine.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it