Development of digital organ-on-a-chip to assess hepatotoxicity and extracellular vesicle-based anti-liver cancer immunotherapy
Bibliographic record
Abstract
Abstract Organ-on-a-chip systems have been increasingly recognized as attractive platforms to assess toxicity and to develop new therapeutic agents. However, current organ-on-a-chip platforms are limited by a “single pot” design, which inevitably requires holistic analysis and limits parallel processing. Here, we developed a digital organ-on-a-chip by combining a microwell array with cellular microspheres, which significantly increased the parallelism over traditional organ-on-a-chip for drug development. Up to 127 uniform liver cancer microspheres in this digital organ-on-a-chip format served as individual analytical units, allowing for analysis with high consistency and quick response. Our platform displayed evident anti-cancer efficacy at a concentration of 10 μM for sorafenib, and had greater alignment than the “single pot” organ-on-a-chip with a previous in vivo study. In addition, this digital organ-on-a-chip demonstrated the treatment efficacy of natural killer cell-derived extracellular vesicles for liver cancer at 50 μg/mL. The successful development of this digital organ-on-a-chip platform provides high-parallelism and a low-variability analytical tool for toxicity assessment and the exploration of new anticancer modalities, thereby accelerating the joint endeavor to combat cancer. Graphic abstract
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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 | 0.001 | 0.000 |
| 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".