  An imbalance between bodily heat production and heat dissipation leads to
heat stress in organisms. In addition to diminished animal well-being, heat
stress is detrimental to the poultry industry as poultry entails fast growth
and high yield, resulting in greater metabolic activity and higher body heat
production. When stressed, cells overexpress heat shock proteins (such as
HSP70, a well-established intracellular stress indicator) and may undergo
changes in their mechanical properties. Liquid crystals (LCs, fluids with
orientational order) have been recently employed to rapidly characterize
changes in mechanical properties of cells enabling a means of optically
reporting the presence of disease in organisms. In this work, we explore the
difference in the expression of HSP70 to a change in the LC response pattern
via the use of convolutional neural networks (CNNs). The machine-learning (ML)
models were trained on hundreds of such LC-response micrographs of chicken red
blood cells with and without heat stress. Trained models exhibited remarkable
accuracy of up to 99% on detecting the presence of heat stress in unseen
microscope samples. We also show that crosslinking the chicken and human RBCs
using glutaraldehyde in order to simulate a diseased cell was an efficient
strategy for planning, building, training, and evaluating ML models. Overall,
our efforts build towards the rapid detection of disease in organisms, which is
accompanied by a distinct change in the mechanical properties of cells. We aim
to eventuate CNN-enabled LC-sensors can rapidly report the presence of disease
in scenarios where human judgment could be prohibitively difficult or slow.
