In-House Manufacturing of 3D Culture Chips via Vacuum Thermoforming for Enhanced Imaging Applications
Bibliographic record
Abstract
Abstract Engineered 3D in vitro cancer models, particularly those that facilitate image-based readouts capable of distinguishing the behavior of different cell populations, have become crucial tools in the discovery process. One such model, 96-GLAnCE (Gels for Live Analysis of Compartmentalized Environments), allows for longitudinal imaging of tumor cell dynamics and therapy response. However, the widespread adoption of 96-GLAnCE has been limited by the need for expensive, specialized fabrication equipment. To overcome this challenge, we have optimized a desktop vacuum thermoforming technique for the in-house production of 96- GLAnCE bottom chips using thin polystyrene films. This optimization has led to the reliable and consistent fabrication of devices. Notably, using thin polystyrene films reduces the overall thickness of the chips, enabling high-magnification imaging for studying primary tumor cell phenotypes in 3D with single-cell and subcellular resolution. Our thermoformed devices offer a flexible, cost-effective solution for addressing a wide range of biological questions across various time scales.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".