Evidence map›Paper›PMID 41040796›Full record

ArticleArXiv2025

Viscoelastic properties of tumor spheroids revealed by a microfluidic compression device and a modified power law model.

Mrinal Pandey, Bangguo Zhu, Kaitlyn Roach, Young Joon Suh, Jeffrey E Segall, Chung-Yuen Hui, Mingming Wu

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In one paragraph

Article in ArXiv, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

7 authors.

Mrinal PandeyBiological and Environmental Engineering Department, Cornell University, Ithaca, NY, USA.
Bangguo ZhuSibley School of Mechanical and Aerospace Engineering, Cornell University, Ithaca, NY. USA.
Kaitlyn RoachMeinig School of Biomedical Engineering, Cornell University, Ithaca, NY. USA.
Young Joon SuhBiological and Environmental Engineering Department, Cornell University, Ithaca, NY, USA.
Jeffrey E SegallDepartment of Pathology, Albert Einstein College of Medicine, 1300 Morris Park Avenue, Bronx, NY, USA 10461.
Chung-Yuen HuiSibley School of Mechanical and Aerospace Engineering, Cornell University, Ithaca, NY. USA.
Mingming WuBiological and Environmental Engineering Department, Cornell University, Ithaca, NY, USA.

Funding

Microfluidic platform for tumor cell invasionR01CA221346 · NCI · CORNELL UNIVERSITY · PI Mingming Wu · 2017 to 2026
$3.4M
NCI NIH HHS R01 CA221346
6 · The paper itself

Abstract

Clinically, palpation is one of the important diagnostic methods to assess tumor malignancy. In laboratory research, it is well accepted that the bulk stiffness of the tumor and the surrounding tissue is closely correlated with the malignant state of the tumor. Here, we postulate that, in addition to tumor stiffness, tumor viscoelasticity - the fact that tumor tissue takes time to bounce back after compression, can also be used to evaluate the tumor malignancy state. In this work, we characterized the viscoelastic properties of breast tumor spheroids using a recently developed microfluidic compression device and a theoretical power law model. Breast tumor cells at varying malignant levels; a non-tumorigenic epithelial (MCF10A), moderately malignant tumor (MCF7) and triple negative metastatic tumor (MDA-MB-231) cells were used. Spheroids embedded within a 3D extracellular matrix were periodically compressed, and their strain responses were recorded using microscopic imaging. Our results revealed that the measured strain relaxation curves can be successfully described by a modified power law model, demonstrated that non-tumorigenic tumor spheroids were more elastic, exhibited shorter relaxation time and less plasticity than those of tumorigenic spheroids. Together, these results highlight that viscoelastic properties in addition to bulk stiffness of the tumor spheroids can serve as a complementary mechanical biomarker of tumor malignancy and demonstrate the validity of a modified power law model for the mechanical characterization of a living tissue.

Identifiers

PMID41040796
PMCPMC12486062

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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.