Collagen Fibril Orientation Instructs Fibroblast Differentiation Via Cell Contractility
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Collagen alignment is one of the key microarchitectural signatures of many pathological conditions, including scarring and fibrosis. Investigating how collagen alignment modulates cellular functions will pave the way for understanding tissue scarring and regeneration and new therapeutic strategies. However, current approaches for the fabrication of three-dimensional (3D) aligned collagen matrices are low-throughput and require special devices. To overcome these limitations, a simple approach to reconstitute homogeneous 3D collagen matrices with adjustable degree of fibril alignment using 3D printed inclined surfaces is developed. By characterizing the mechanical properties of reconstituted matrices, it is found that the elastic modulus of collagen matrices is enhanced with an increase in the alignment degree. The reconstituted matrices are used to study fibroblast behavior to reveal the progression of scar formation where a gradual enhancement of collagen alignment can be observed. It is found that matrices with aligned fibrils trigger fibroblast differentiation into myofibroblasts via cell contractility, while collagen stiffening through a crosslinker does not. The results suggest the impact of collagen fibril organization on the regulation of fibroblast differentiation. Overall, this approach to reconstitute 3D collagen matrices with fibril alignment opens opportunities for biomimetic pathological-relevant tissue in vitro, which can be applied for other biomedical research.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| 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