Self‐Assembled Hydrogel Fiber Bundles from Oppositely Charged Polyelectrolytes Mimic Micro‐/Nanoscale Hierarchy of Collagen
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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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".