Self-filling microwell arrays (SFMAs) for tumor spheroid formation
Bibliographic record
Abstract
Tumor spheroid formation in microwell arrays is a promising approach for high-throughput screening of chemotherapeutic agents. This method offers the advantage of better mimicking the complexities of tumors as compared to conventional monolayer culture systems. However, using these technologies to their full potential is hindered by the inability to seed the cells within the wells uniformly and with high yield and reproducibility. Moreover, standard manufacturing approaches for fabrication of microwell arrays rely on lithography and etching techniques, which are costly, labor-intensive, and time-consuming. Herein, we report on the development of self-filling microwell arrays (SFMAs) in which cells are directed from a loading chamber to microwells using inclined guiding channels. The SFMAs are fabricated by replica molding of three-dimensionally (3D) printed molds in agarose. We characterize the fabrication process, demonstrate the ability to culture breast adenocarcinoma MCF-7 and glioma U87 in SFMAs and perform drug toxicity studies. We envision that the proposed innovative approach opens avenues of opportunities for high-throughput three-dimensional cell culture for drug screening and disease modeling.
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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 | 0.001 |
| 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".