Magnetic assembly of 3D cell clusters: visualizing the formation of an engineered tissue
Bibliographic record
Abstract
OBJECTIVES: Contactless magnetic assembly of cells into 3D clusters has been proposed as a novel means for 3D tissue culture that eliminates the need for artificial scaffolds. However, thus far its efficacy has only been studied by comparing expression levels of generic proteins. Here, it has been evaluated by visualizing the evolution of cell clusters assembled by magnetic forces, to examine their resemblance to in vivo tissues. MATERIALS AND METHODS: Cells were labeled with magnetic nanoparticles, then assembled into 3D clusters using magnetic force. Scanning electron microscopy was used to image intercellular interactions and morphological features of the clusters. RESULTS: When cells were held together by magnetic forces for a single day, they formed intercellular contacts through extracellular fibers. These kept the clusters intact once the magnetic forces were removed, thus serving the primary function of scaffolds. The cells self-organized into constructs consistent with the corresponding tissues in vivo. Epithelial cells formed sheets while fibroblasts formed spheroids and exhibited position-dependent morphological heterogeneity. Cells on the periphery of a cluster were flattened while those within were spheroidal, a well-known characteristic of connective tissues in vivo. CONCLUSIONS: Cells assembled by magnetic forces presented visual features representative of their in vivo states but largely absent in monolayers. This established the efficacy of contactless assembly as a means to fabricate in vitro tissue models.
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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 | 0.001 | 0.000 |
| 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".