AIMC Topic: Microscopy, Atomic Force

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Machine learning-powered single-molecule cancer diagnosis using DNA origami tags.

Science advances
Single-molecule detection (SMD) holds considerable promise in biomedical research. Although atomic force microscopy (AFM) provides an important technique with nanoscale resolution for SMD, its broader application is limited by labeling challenges and...

Deep learning-powered high-efficient atomic force microscopy single-cell nanomechanical analysis on diverse biointerfaces.

Biochemical and biophysical research communications
The extracellular matrix (ECM) is crucial in tuning cellular behavior, and quantifying cellular mechanical changes in response to ECM stimuli can help reveal the underlying physical mechanisms of cell-ECM interactions for a comprehensive understandin...

Deep Learning-Based Classification of NSCLC-Derived Extracellular Vesicles Using AFM Nanomechanical Signatures.

Analytical chemistry
Nonsmall cell lung cancer (NSCLC) remains a leading cause of cancer-related mortality, with liquid biopsy emerging as a promising tool for noninvasive diagnostics. Extracellular vesicles (EVs) serve as molecular messengers of the tumor microenvironme...

Quantifying complexity in DNA structures with high resolution Atomic Force Microscopy.

Nature communications
DNA topology is essential for regulating cellular processes and maintaining genome stability, yet it is challenging to quantify due to the size and complexity of topologically constrained DNA molecules. By combining high-resolution Atomic Force Micro...

Advancing High-Throughput Cellular Atomic Force Microscopy with Automation and Artificial Intelligence.

ACS nano
Atomic force microscopy (AFM) has reached a significant level of maturity in biology, demonstrated by the diversity of modes for obtaining not only topographical images but also insightful mechanical and adhesion data by performing force measurements...

The Influence of Surface Treatment on the Color of Enamel and Dentin: An In Vitro Study Using Machine Learning-Based Analysis.

Journal of esthetic and restorative dentistry : official publication of the American Academy of Esthetic Dentistry ... [et al.]
OBJECTIVE: To investigate how surface treatment affects the color of enamel and dentin, and to evaluate whether the color differences are acceptable.

Detection of Human Bladder Epithelial Cancerous Cells with Atomic Force Microscopy and Machine Learning.

Cells
The development of noninvasive methods for bladder cancer identification remains a critical clinical need. Recent studies have shown that atomic force microscopy (AFM), combined with pattern recognition machine learning, can detect bladder cancer by ...

Determining structures of RNA conformers using AFM and deep neural networks.

Nature
Much of the human genome is transcribed into RNAs, many of which contain structural elements that are important for their function. Such RNA molecules-including those that are structured and well-folded-are conformationally heterogeneous and flexible...

Deep learning-based denoising for unbiased analysis of morphology and stiffness in amyloid fibrils.

Computers in biology and medicine
Understanding the morphology of amyloid fibrils is crucial for comprehending the aggregation and degradation mechanisms of abnormal proteins implicated in various diseases, such as Alzheimer's disease, Parkinson's disease, type II diabetes, and vario...

Automated High-Throughput Atomic Force Microscopy Single-Cell Nanomechanical Assay Enabled by Deep Learning-Based Optical Image Recognition.

Nano letters
Mechanical forces are essential for life activities, and the mechanical phenotypes of single cells are increasingly gaining attention. Atomic force microscopy (AFM) has been a standard method for single-cell nanomechanical assays, but its efficiency ...