Self‐Assembled Hydrogel Fiber Bundles from Oppositely Charged Polyelectrolytes Mimic Micro‐/Nanoscale Hierarchy of Collagen
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
Fiber bundles are present in many tissues throughout the body. In most cases, collagen subunits spontaneously self-assemble into a fibrilar structure that provides ductility to bone and constitutes the basis of muscle contraction. Translating these natural architectural features into a biomimetic scaffold still remains a great challenge. Here, we propose a simple strategy to engineer biomimetic fiber bundles that replicate the self-assembly and hierarchy of natural collagen fibers. The electrostatic interaction of methacrylated gellan gum (MeGG) with a countercharged chitosan (CHT) polymer led to the complexation of the polyelectrolytes. When directed through a polydimethylsiloxane (PDMS) channel, the polyelectrolytes formed a hierarchical fibrous hydrogel demonstrating nano-scale periodic light/dark bands similar to D-periodic bands in native collagen and aligned parallel fibrils at micro-scale. Importantly, collagen-mimicking hydrogel fibers exhibited robust mechanical properties (MPa scale) at a single fiber bundle level and enabled encapsulation of cells inside the fibers under cell-friendly mild conditions. Presence of carboxyl- (in gellan gum) or amino- (in chitosan) functionalities further enabled controlled peptide functionalization such as RGD for biochemical mimicry (cell adhesion sites) of native collagen. This biomimetic aligned fibrous hydrogel system can potentially be used as a scaffold for tissue engineering as well as a drug/gene delivery vehicle.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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