Fibroblasts Accelerate Formation and Improve Reproducibility of 3D Cellular Structures Printed with Magnetic Assistance
Bibliographic record
Abstract
Fibroblasts (mouse, NIH/3T3) are combined with MDA-MB-231 cells to accelerate the formation and improve the reproducibility of 3D cellular structures printed with magnetic assistance. Fibroblasts and MDA-MB-231 cells are cocultured to produce 12.5 : 87.5, 25 : 75, and 50 : 50 total population mixtures. These mixtures are suspended in a cell medium containing a paramagnetic salt, Gd-DTPA, which increases the magnetic susceptibility of the medium with respect to the cells. A 3D monotypic MDA-MB-231 cellular structure is printed within 24 hours with magnetic assistance, whereas it takes 48 hours to form a similar structure through gravitational settling alone. The maximum projected areas and circularities, and cellular ATP levels of the printed structures are measured for 336 hours. Increasing the relative amounts of the fibroblasts mixed with the MDA-MB-231 cells decreases the time taken to form the structures and improves their reproducibility. Structures produced through gravitational settling have larger maximum projected areas and cellular ATP, but are deemed less reproducible. The distribution of individual cell lines in the cocultured 3D cellular structures shows that printing with magnetic assistance yields 3D cellular structures that resemble in vivo tumors more closely than those formed through gravitational settling. The results validate our hypothesis that (1) fibroblasts act as a “glue” that supports the formation of 3D cellular structures, and (2) the structures are produced more rapidly and with greater reproducibility with magnetically assisted printing than through gravitational settling alone. Printing of 3D cellular structures with magnetic assistance has applications relevant to drug discovery, lab-on-chip devices, and tissue engineering.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".