ANN-aided Mechanophenotyping of Biological Cells Using AFM

Abstract

Mechanical properties of biological cells can serve as biomarkers for indicating various diseases like cancer and sickle cell disease. Hertzian model-based prediction of mechanical properties of biological cells, although most widely used, has shown to have limited potential in determining constitutive parameters of cells of uneven shape and non-linear force-indentation responses of AFM-based cell nanoindentation. We report a new artificial neural network-aided approach, which takes into account, the variation in cell shapes and their effect on the predictions in cell mechanophenotyping. We have developed an artificial neural network (ANN) model which could predict the mechanical properties of biological cells by utilizing the force vs. indentation curve of AFM and we obtained a recall of 0.98 ± 0.03 and 1 ± 0.0 for hyperelastic and elastic cells respectively for the prediction error of less than 10%. We envisage that the developed technique can be used for the validation of quantitative biomechanical markers for diagnoses of diseases like cancer and sickle cell disease which could help to improve clinical decision-making.

Liquid drops on compliant and non-compliant substrates: An ellipsoid-based fitting for approximating drop shape and volume

“…Thus, there is a need for a simpler method for contact angle measurement. Here, artificial neural networks (ANNs) can be of benefit due to their simplicity and accurate forecasting capabilities (Ozerdem and Kolukisa 2009;Kamble, Y.;Raj, A.;Thakur 2015;Abiodun et al 2018;Alotaibi et al 2018;Toghraie et al 2019;Graybill et al 2021). Toward this, we developed an ANN which takes into account the various lengths associated with the droplet and predicts the contact angle hysteresis (CAH).…”