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The implementation of novel analytic methodologies in cancer and biomedical research has enabled the quantification of parameters that were previously disregarded only a few decades ago. A notable example of this paradigm shift is the widespread integration of atomic force microscopy (AFM) into biomedical laboratories, significantly advancing our understanding of cancer cell biology and treatment response. AFM allows for the meticulous monitoring of different parameters at the molecular and nanoscale levels, encompassing critical aspects such as cell morphology, roughness, adhesion, stiffness, and elasticity. These parameters can be systematically investigated in correlation with specific cell treatment, providing important insights into morpho-mechanical properties during normal and treated conditions. The resolution of this system holds the potential for its systematic adoption in clinics; its application could produce useful diagnostic information regarding the aggressiveness of cancer and the efficacy of treatment. This review endeavors to analyze the current literature, underscoring the pivotal role of AFM in biomedical research, especially in cancer cases, while also contemplating its prospective application in a clinical context.
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http://dx.doi.org/10.3390/cancers17050858 | DOI Listing |
Int J Biol Macromol
September 2025
Shenzhen College of Advanced Technology, University of Chinese Academy of Sciences, Beijing, 100049, China; Research Center for Cancer Immunology, Institute of Biomedicine and Biotechnology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China. Electronic
Within the bone microenvironment, the intricate interplay and regulation among matrix components form a complex network. Disentangling this network is crucial for uncovering potential therapeutic targets in bone pathology. Osteocalcin (OCN), the most abundant non-collagenous bone protein, is an essential node within this network.
View Article and Find Full Text PDFJ Chem Theory Comput
September 2025
Materials DX Research Center, National Institute of Advanced Industrial Science and Technology, Tsukuba Central 2, 1-1-1 Umezono, Tsukuba, Ibaraki 305-8568, Japan.
The quantum mechanics/molecular mechanics (QM/MM) method is a powerful approach for investigating solid surfaces in contact with various types of media, since it allows for flexible modeling of complex interfaces while maintaining an all-atom representation. The mean-field QM/MM method is an average reaction field model within the QM/MM framework. The method addresses the challenges associated with the statistical sampling of interfacial atomic configurations of a medium and enables efficient calculation of free energies.
View Article and Find Full Text PDFLangmuir
September 2025
Department of Materials Science and Engineering, Drexel University, Philadelphia, Pennsylvania 19104, United States.
The surfaces of 1D layered lepidocrocite-structured titanates (1DLs) are negatively charged due to an oxygen-to-titanium atomic ratio >2. This, and their layered structure, allow for facile ion exchange and high colloidal stability, demonstrated by ζ-potentials of ≈ -85 mV at their unadjusted pH of ≈10.4.
View Article and Find Full Text PDFACS Nano
September 2025
CINBIO and Departamento de Química Orgánica. Campus Lagoas-Marcosende, Universidade de Vigo, Vigo E-36310, Spain.
Archimedean spirals are architectural motifs that are found in nature. The facial asymmetry of amphiphilic molecules or macromolecules has been a key parameter in the preparation of these well-organized two-dimensional nanostructures in the laboratory. This facial asymmetry is also present in the helical grooves of chiral helical substituted poly(phenylacetylene)s (PPAs) and poly(diphenylacetylene)s (PDPAs), making them excellent candidates for self-assembly into 2D Archimedean nanospirals or nanotoroids.
View Article and Find Full Text PDFJ Chem Theory Comput
September 2025
Department of Materials Science and Engineering, City University of Hong Kong, Kowloon 999077, Hong Kong China.
Coarse-grained (CG) lipid models enable efficient simulations of large-scale membrane events. However, achieving both speed and atomic-level accuracy remains challenging. Graph neural networks (GNNs) trained on all-atom (AA) simulations can serve as CG force fields, which have demonstrated success in CG simulations of proteins.
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