Patterning Collagen/Poloxamine-Methacrylate Hydrogels for Tissue-Engineering-Inspired Microfluidic and Laser Lithography Applications
Bibliographic record
Abstract
The ability to pattern semi-synthetic collagen/poloxamine-methacrylate hydrogels into straight-channel flow circuits and sub-millimeter-sized rectangular blocks for tissue-engineering applications was evaluated. Endothelial cells, grown on the surface of flat collagen/poloxamine-methacrylate hydrogels, proliferated, expressed ICAM-1 (but not VCAM-1) and began to detach after 6 days. Seeding endothelial cells onto the lumen surface of straight collagen/poloxamine-methacrylate flow channels increased ICAM-1 and VCAM-1 expression, and exposure to laminar shear stress (0.3-10 dyn/cm(2)) was unable to attenuate activation on the relatively few cells that were able to withstand flow associated ablation. The enrichment of poloxamine-methacrylate at the lumen surface during fabrication likely caused the decrease in cell attachment and increased activation. To micropattern more complex structures, confocal microscopy UV laser lithography was used to selectively cross-link a HepG2-containing pre-polymer solution of collagen/poloxamine-methacrylate. Turbidity (caused by suspended cells and the incomplete miscibility of collagen and poloxamine-methacrylate) scattered the UV laser energy and necessitated the optimization of exposure times with respect to cross-linking extent and cell viability. Free radical diffusion beyond the bounds of the initial photopattern reduced the resolution of the structures and created a weakly cross-linked periphery around the original pattern. Over time, HepG2 cells migrated towards the less cross-linked periphery and proliferated, creating a non-uniform distribution of cells.
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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".