IFlowPlate—A Customized 384‐Well Plate for the Culture of Perfusable Vascularized Colon Organoids
Bibliographic record
Abstract
Despite the complexity and structural sophistication that 3D organoid models provide, their lack of vascularization and perfusion limit the capability of these models to recapitulate organ physiology effectively. A microfluidic platform named IFlowPlate is engineered, which can be used to culture up to 128 independently perfused and vascularized colon organoids in vitro. Unlike traditional microfluidic devices, the vascularized organoid-on-chip device with an "open-well" design does not require any external pumping systems and allows tissue extraction for downstream analyses, such as histochemistry or even in vivo transplantation. By optimizing both the extracellular matrix (ECM) and the culture media formulation, patient-derived colon organoids are co-cultured successfully within a self-assembled vascular network, and it is found that the colon organoids grow significantly better in the platform under constant perfusion versus conventional static condition. Furthermore, a colon inflammation model with an innate immune function where circulating monocytes can be recruited from the vasculature, differentiate into macrophage, and infiltrate the colon organoids in response to tumor necrosis factor (TNF)- inflammatory cytokine stimulation is developed using the platform. With the ability to grow vascularized colon organoids under intravascular perfusion, the IFlowPlate platform could unlock new possibilities for screening potential therapeutic targets or modeling relevant diseases.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".