ArticleCell regeneration (London, England)2025
Image-based evaluation of single-cell mechanics using deep learning.
Article in Cell regeneration (London, England), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 3 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
3 citing papers in PubMed.
- Multiscale Architecture and Mechanics of the Cell Nucleus: Implications for Disease, Bioengineering and Nanomedicine.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Review
- Correction: Image-based evaluation of single-cell mechanics using deep learning.Cell regeneration (London, England) · 2025Article
Corrections and comments
- Erratum issued
Authors and funding
10 authors.
Funding
Abstract
Mechanical properties of cells have been proposed as potential biophysical markers for cell phenotypes and functions since they are vital for maintaining biological activities. However, current approaches used to measure single-cell mechanics suffer from low throughput, high technical complexity, and stringent equipment requirements, which cannot satisfy the demand for large-scale cell sample testing. In this study, we proposed to evaluate cell stiffness at the single-cell level using deep learning. The image-based deep learning models could non-invasively predict the stiffness ranges of mesenchymal stem cells (MSCs) and macrophages in situ with high throughput and high sensitivity. We further applied the models to evaluate MSC functions including senescence, stemness, and immunomodulatory capacity as well as macrophage diversity in phenotypes and functions. Our image-based deep learning models provide potential techniques and perspectives for cell-based mechanobiology research and clinical translation.
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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.