Media additives to promote spheroid circularity and compactness in hanging drop platform
Bibliographic record
Abstract
Three-dimensional spheroid cultures have become increasingly popular as drug screening platforms, especially with the advent of different high throughput spheroid forming technologies. However, comparing drug efficacy across different cell types in spheroid culture can be difficult due to variations in spheroid morphologies and transport characteristics. Improving the reproducibility of compact, circular spheroids contributes to standardizing and increasing the fidelity of the desired gradient profiles in these drug screening three-dimensional tissue cultures. In this study we discuss the role that circularity and compaction has on spheroids, and demonstrate the impact methylcellulose (MethoCel) and collagen additives in the culture media can contribute to more compact and circular spheroid morphology. We demonstrate that improved spheroid formation is not a simple function of increased viscosity of the different macromolecule additives, suggesting that other macromolecular characteristics contribute to improved spheroid formation. Of the various macromolecular additives tested for hanging drop culture, MethoCel provided the most desirable spheroid formation. Additionally, the higher viscosity of MethoCel-containing media improved the ease of imaging of cellular spheroids within hanging drop cultures by reducing motion-induced image blur.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".