Transcribing In Vivo Blood Vessel Networks into In Vitro Perfusable Microfluidic Devices
Bibliographic record
Abstract
Abstract The 3D architecture of blood vessel networks dictates how nutrients, waste, and drugs are transported. These transport processes are difficult to study in vivo, leading researchers to develop methods to construct vessel networks in vitro. However, existing methods require expensive, customized equipment and cannot create large (>1 cm3) constructs. This makes them inaccessible to many researchers or educators. Here, a method that transcribes 3D images of blood vessel networks into physical microfluidic devices is developed. The method takes 3D images of blood vessel networks and uses fused‐filament 3D fabrication with standard polylactic acid (PLA) filament to print the imaged vessel network. The 3D printout is cast in polydimethylsiloxane (PDMS) and dissolved, producing vessel channels that are lined with endothelial cells. Devices imprinted with different vessel networks including small intestinal villi, pancreatic islets, and tumors from mice and humans are created. The method replicates the complex geometries of blood vessel networks in an in vitro device with commonly available equipment and materials. This increases the accessibility of this technology by allowing researchers or educators without access to expensive laser ablation microscope set‐ups or custom 3D printers to be able to create vasculature network devices.
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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".