ArticleAdvanced intelligent systems (Weinheim an der Bergstrasse, Germany)2025
High-Throughput Nanorheology of Living Cells Powered by Supervised Machine Learning.
Article in Advanced intelligent systems (Weinheim an der Bergstrasse, Germany), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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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.
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.
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Who cites it
2 citing papers in PubMed.
- Unveiling Van Der Waals Forces and Associated Hamaker Constants Across the Nanomaterial Landscape With Advanced Atomic Force Microscopy.Small methods · 2026Review
- Advances in nanomechanical property mapping by atomic force microscopy.Nanoscale advances · 2025Review
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Atomic force microscopy (AFM) is extensively applied to measure the nanomechanical properties of living cells. Despite its popularity, some applications on mechanobiology are limited by the low throughput of the technique. Currently, the analysis of AFM-nanoindentation data is performed by model fitting. Model fitting is slow, data intensive, and prone to error. Herein, a supervised machine-learning regressor is developed for transforming AFM force-distance curves into nanorheological behavior. The method reduces the computational time required to process a force volume of a cell made of 2.62 × 10
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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.