Rapid Magnetic 3D Printing of Cellular Structures with MCF-7 Cell Inks
Bibliographic record
Abstract
A contactless label-free method using a diamagnetophoretic ink to rapidly print three-dimensional (3D) scaffold-free multicellular structures is described. The inks consist of MCF-7 cells that are suspended in a culture medium to which a paramagnetic salt, diethylenetriaminepentaacetic acid gadolinium (III) dihydrogen salt hydrate (Gd-DTPA), is added. When a magnetic field is applied, the host fluid containing the paramagnetic salt is attracted towards regions of high magnetic field gradient, displacing the ink towards regions with a low gradient. Using this method, 3D structures are printed on ultra-low attachment (ULA) surfaces. On a tissue culture treated (TCT) surface, a 3D printed spheroid coexists with a two-dimensional (2D) cell monolayer, where the composite is termed as a 2.5D structure. The 3D structures can be magnetically printed within 6 hours in a medium containing 25 mM Gd-DTPA. The influence of the paramagnetic salt on MCF-7 cell viability, cell morphology, and ability of cells to adhere to each other to stabilize the printed structures on both ULA and TCT surfaces is investigated. Gene expressions of hypoxia-inducible factor 1-alpha (HIF1α) and vascular endothelial growth factor (VEGF) allow comparison of the relative stresses for the printed 3D and 2.5D cell geometries with those for 3D spheroids formed without magnetic assistance. This magnetic printing method can be potentially scaled to a higher throughput to rapidly print cells into 3D heterogeneous cell structures with variable geometries with repeatable dimensions for applications such as tissue engineering and tumour formation for drug discovery.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".