Fabrication of Porous Collagen Scaffolds Containing Embedded Channels with Collagen Membrane Linings
Bibliographic record
Abstract
Tissues and organs contain an extracellular matrix (ECM). In the case of blood vessels, endothelium cells are anchored to a specialized basement membrane (BM) embedded inside the interstitial matrix (IM). We introduce a multi-structural collagen-based scaffold with embedded microchannels that mimics in vivo structures within vessels. Our scaffold consists of two parts, each containing two collagen layers, i.e., a 3D porous collagen layer analogous to IM lined with a thin 2D collagen film resembling the BM. Enclosed microchannels were fabricated using contact microprinting. Microchannel test structures with different sizes ranging from 300 to 800 µm were examined for their fabrication reproducibility. The heights and perimeters of the fabricated microchannels were ~20% less than their corresponding values in the replication PDMS mold; however, microchannel widths were significantly closer to their replica dimensions. The stiffness, permeability, and pore size properties of the 2D and 3D collagen layers were measured. The permeability of the 2D collagen film was negligible, making it suitable for mimicking the BM of large blood vessels. A leakage test at various volumetric flow rates applied to the microchannels showed no discharge, thereby verifying the reliability of the proposed integrated 2D/3D collagen parts and the contact printing method used for bonding them in the scaffold. In the future, multi-cell culturing will be performed within the 3D porous collagen and against the 2D membrane inside the microchannel, hence preparing this scaffold for studying a variety of blood vessel-tissue interfaces. Also, thicker collagen scaffold tissues will be fabricated by stacking several layers of the proposed scaffold.
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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) | 0.000 | 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 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".