ArticleScientific reports2025
A mathematical phase field model predicts superparamagnetic nanoparticle accelerated fusion of HeLa spheroids for field guided biofabrication.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
4 citing papers in PubMed.
- Self-buoyant cell-spheroid culture programming multi-spheroid assembly.Science advances · 2026Article
- Magnetically Driven Biofabrication for Tissue Engineering: From Nanoparticle Design to Mag-ATMP Translation.Advanced healthcare materials · 2026Review
- Article
- Unconventional bioprinting modalities for advanced tissue biofabrication.Biomaterials · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
Abstract
In vitro tissue models are crucial for regenerative medicine, drug discovery, and the reduction of animal testing. 3D bioprinting, particularly when utilizing magnetic manipulation of cell spheroids, provides precise control over tissue architecture. However, existing mathematical models lack the precision to capture the interplay between biological dynamics and magnetic forces during spheroid fusion. This study developed and validated a novel mathematical model that simulates magnetically assisted spheroid fusion, taking into account cell migration, adhesion, and the effects of external magnetic fields. The model integrates principles of cell mechanics, fluid dynamics, and magnetostatics, implemented in COMSOL Multiphysics. Experimental validation used HeLa cell spheroids bioprinted with superparamagnetic iron oxide nanoparticles (SPIONs). Spheroid fusion was monitored with and without an external magnetic field using confocal microscopy. Rigorous statistical analysis (MAE, RMSE, MAPE, R², Chi-Square, Bland-Altman, and variance-weighted metrics) was used to evaluate model performance. The model accurately predicted accelerated fusion under magnetic manipulation, reducing fusion time from approximately 7 days (without field) to 2 days. High R² values (> 0.99 for two-spheroid fusion and > 0.97 for multi-spheroid systems) and narrow confidence intervals demonstrated strong agreement between the simulation and the experiment. Increased system complexity introduced slightly higher error variability, but the model maintained robust predictive capabilities. Spheroid disassembly was observed in the four-spheroid case, highlighting the complex interplay of magnetic forces and cellular reorganization. This validated, high-precision model represents a significant advancement in tissue engineering, providing a powerful tool for optimizing bioprinting protocols, designing complex tissue constructs, and advancing in vitro model development. This breakthrough has implications for regenerative medicine and drug discovery while also highlighting the importance of addressing nanoparticle safety concerns.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.