Uniform Tumor Spheroids on Surface-Optimized Microfluidic Biochips for Reproducible Drug Screening and Personalized Medicine
Bibliographic record
Abstract
Spheroids are recognized for resembling the important characteristics of natural tumors in cancer research. However, the lack of controllability of the spheroid size, form, and density in conventional spheroid culture methods reduces the reproducibility and precision of bioassay results and the assessment of drug-dose responses in spheroids. Nonetheless, the accurate prediction of cellular responses to drug compounds is crucial for developing new efficient therapeutic agents and optimizing existing therapeutic strategies for personalized medicine. We developed a surface-optimized PDMS microfluidic biochip to produce uniform and homogenous multicellular spheroids in a reproducible manner. This platform is surface optimized with 10% bovine serum albumin (BSA) to provide cell-repellent properties. Therefore, weak cell-surface interactions lead to the promotion of cell self-aggregations and the production of compact and uniform spheroids. We used a lung cancer cell line (A549), a co-culture model of lung cancer cells (A549) with (primary human osteoblasts, and patient-derived spine metastases cells (BML, bone metastasis secondary to lung). We observed that the behavior of cells cultured in three-dimensional (3D) spheroids within this biochip platform more closely reflects in vivo-like cellular responses to a chemotherapeutic drug, Doxorubicin, rather than on 24-well plates (two-dimensional (2D) model). It was also observed that the co-culture and patient-derived spheroids exhibited resistance to anti-cancer drugs more than the mono-culture spheroids. The repeatability of drug test results in this optimized platform is the hallmark of the reproducibility of uniform spheroids on a chip. This surface-optimized biochip can be a reliable platform to generate homogenous and uniform spheroids to study and monitor the tumor microenvironment and for drug screening.
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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 | 0.000 | 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 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".