Development of digital organ-on-a-chip to assess hepatotoxicity and extracellular vesicle-based anti-liver cancer immunotherapy
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it