Collagen Fibril Orientation Instructs Fibroblast Differentiation Via Cell Contractility
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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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".