A Versatile Method to Create Perfusable, Capillary‐Scale Channels in Cell‐Laden Hydrogels Using Melt Electrowriting
Bibliographic record
Abstract
Abstract A major obstacle toward creating human‐scale artificial tissue models is supplying encapsulated cells with oxygen and other nutrients throughout the construct. In particular, creating channels in hydrogels that match the resolution and density of the smallest blood capillaries (≤10 µm) remains highly challenging. Here, a novel method is demonstrated where polycaprolactone fibers printed using melt‐electrowriting are encapsulated in cell‐laden hydrogels and then physically removed to produce hollow, perfusable channels. This technique produces a range of channel diameters (10–41 µm) with circular cross‐sections and in hydrogels representing various crosslinking mechanisms. The channels can be formed as interconnected grids, hierarchically branched patterns, or stacked in layers with ≈200 µm channel spacing, thus matching average capillary density in the human body. Alternatively, selective removal of fibers from a melt electrowriting grid can generate perfusable channels within a reinforcing fiber network. This method can be performed in the presence of cells, with human fibroblasts exhibiting encapsulated in gelatin methacryloyl showing no detectable cytotoxic effects. This technique is a promising approach for creating perfusable channels with very small diameters within cell‐laden hydrogel matrices, with potential applications including in vitro tissue models and hydrogel microfluidics.
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.
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".