Integrating spheroid‐on‐a‐chip with tubeless rocker platform: A high‐throughput biological screening platform
Bibliographic record
Abstract
Spheroid-on-a-chip platforms are emerging as promising in vitro models that enable screening of the efficacy of biologically active ingredients. Generally, the supply of liquids to spheroids occurs in the steady flow mode with the use of syringe pumps; however, the utilization of tubing and connections, especially for multiplexing and high-throughput screening applications, makes spheroid-on-a-chip platforms labor- and cost-intensive. Gravity-induced flow using rocker platforms overcomes these challenges. Here, a robust gravity-driven technique was developed to culture arrays of cancer cell spheroids and dermal fibroblast spheroids in a high-throughput manner using a rocker platform. The efficiency of the developed rocker-based platform was benchmarked to syringe pumps for generating multicellular spheroids and their use for screening biologically active ingredients. Cell viability, internal spheroid structure as well as the effect of vitamin C on spheroids' protein synthesis was studied. The rocker-based platform not only offers comparable or enhanced performance in terms of cell viability, spheroids formation, and protein production by dermal fibroblast spheroids but also, from a practical perspective, offers a smaller footprint, requires a lower cost, and offers an easier method for handling. These results support the application of rocker-based microfluidic spheroid-on-a-chip platforms for in vitro screening in a high-throughput manner with industrial scaling-up opportunities.
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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 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".