InVADE: Integrated Vasculature for Assessing Dynamic Events
Bibliographic record
Abstract
Abstract Drug screening with simplified 2D cell culture and relevant animal testing fail to predict clinical outcomes. With the rising cost of drug development, predictive 3D tissue models with human cells are in urgent demand. Establishing vascular perfusion of 3D tissues has always been a challenge, but it is necessary to mimic drug transport and to capture complex interorgan crosstalk. Here, a versatile multiwell plate is presented empowered by built‐in microfabricated vascular scaffolds that define the vascular space and support self‐assembly of various parenchymal tissues. In this configuration, assembly and organ‐specific function of a metabolically active liver, a free‐contracting cardiac muscle, and a metastatic solid tumor are demonstrated, tracking organ function using noninvasive analysis techniques. By linking the 3D tumor and the liver tissue in series, it is demonstrated that the presence of liver tissue is crucial to correctly reveal the efficacy of a chemotherapeutic drug, Tegafur. Furthermore, the complete cancer metastasis cascade is demonstrated across multiple organs, where cancer cells escaping from the solid tumor can invade a distant liver tissue connected through a continuous vascular interface. This combinatory use of microfabricated scaffold onto a standard cell culturing platform can offer important insights into the mechanics of complex interorgan biological events.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".