Abstract
Atomic force microscopy (AFM) is powerful nanobiotechnology for characterizing the nanotopographic and nanobiomechanical properties of live cells. Current limitations in AFM analysis of nanomechanobiology include the unjustified selection of nesting indices and filters, leading to the inaccurate reporting of waviness and roughness parameters, and inadequacies in the selection of the mathematical model for the Young's modulus. Critical biomechanical factors such as total deformation energy, elastic energy, and plastic energy are often overlooked. Here we refine and optimize the selection of the nesting index and filters for cellular analysis and develop an artificial intelligence-based classifier that can differentiate between normal and cancer cells. The application of AFM for detecting surface waviness and roughness, further enhanced by artificial intelligence (AI), represents a substantial advancement in cancer diagnostics. Although still in the experimental phase, AFM holds the potential to revolutionize cell biology and oncology by facilitating early cancer detection and advancing precision medicine. Moreover, this study's innovative exploration of the relationship between cellular nanomechanobiology and thermodynamics introduces important perspectives on cancer cell behavior at the nanoscale, unlocking opportunities for therapeutic interventions and cutting-edge oncological research. This paradigm shift may significantly influence the future trajectory of cancer biology and therapy.