Collagen Hybridizing Peptides Promote Collagen Fibril Growth <i>In Vitro</i>
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
High Resolution Image Download MS PowerPoint Slide Recreating the structural and mechanical properties of native tissues in vitro presents significant challenges, particularly in mimicking the dense fibrillar network of extracellular matrixes such as skin and tendons. This study develops a reversible collagen film through cycling collagen self-assembly and disassembly, offering an innovative approach to address these challenges. We first generated an engineered collagen scaffold by applying plastic compression to the collagen hydrogel. The reversibility of the collagen assembly was explored by treating the scaffold with lactic acid, leading to its breakdown into an amorphous gel─a process termed defibrillogenesis. Subsequent immersion of this gel in phosphate buffer facilitated the reassembly of collagen into fibrils larger than those in the original scaffold yet with the D-banding pattern characteristic of collagen fibrils. Transfer learning of the mobileNetV2 convolutional neural network trained on atomic force microscope images of collagen nanoscale D-banding patterns was created with 99% training and testing accuracy. In addition, extensive external validation was performed, and the model achieved high robustness and generalization with unseen data sets. Further innovation was introduced by applying collagen hybridizing peptides, which significantly accelerated and directed the assembly of collagen fibrils, promoting a more organized and aligned fibrillar structure. This study not only demonstrates the feasibility of creating a reversible collagen film that closely mimics the density and structural properties of the native matrix but also highlights the potential of using collagen hybridizing peptides to control and enhance collagen fibrillogenesis. Our findings offer promising tissue engineering and regenerative medicine strategies by enabling precise manipulation of collagen structures in vitro .
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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