ArticleBiophysical journal2024
Enhancing micropipette aspiration with artificial-intelligence analysis.
Article in Biophysical journal, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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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.
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Who cites it
3 citing papers in PubMed.
- Resilient Biophysical Phenotype of Memory CD4Immunology · 2026Article
- Materials and measurement in mechanobiology.Biophysical journal · 2025Article
- Machine learning tools advance biophysics.Biophysical journal · 2024Article
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The micropipette-aspiration technique is commonly used in the field of mechanobiology, offering a variety of measurement types. To extract biophysical parameters from the experiments, numerical analysis is required. Although previous works have developed techniques for the partial automation of these analyses, these approaches are relatively time consuming for the researchers. In this article, we describe the development and application of an artificial-intelligence tool for the completely automatic analysis of micropipette-aspiration experiments. The use of this tool is compared with previous methods and the impressive reduction in the time required for these analyses is discussed. The new tool opens new possibilities for the micropipette-aspiration technique by enabling dealing with large numbers of experiments and real-time measurements.
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Registered trials
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