High throughput hemogram of T cells using digital holographic microscopy and deep learning

T cells of the adaptive immune system provide effective protection to the human body against numerous pathogenic challenges. Current labelling methods of detecting these cells, such as flow cytometry or magnetic bead labelling, are time consuming and expensive. To overcome these limitations, the label-free method of digital holographic microscopy (DHM) combined with deep learning has recently been introduced which is both time and cost effective. In this study, we demonstrate the application of digital holographic microscopy with deep learning to classify the key CD4+ and CD8+ T cell subsets. We show that combining DHM of varying fields of view, with deep learning, can potentially achieve a classification throughput rate of 78,000 cells per second with an accuracy of 76.2% for these morphologically similar cells. This throughput rate is 100 times faster than the previous studies and proves to be an effective replacement for labelling methods.

Digital Holographic Microscopy as Identifier of Ultrafine Particles Emitted during Fused Deposition Modelling

Additive manufacturing has acquired a global industrial panorama for being an alternative to redirect the industry towards sustainability. However, previous studies have indicated that fused deposition modelling (FDM) techniques are potential sources of particles that are harmful to health. For this reason, this work is focused on exploring the behaviour and distribution of FDM resultant nanoparticles from the most commonly used printable materials through alternative methods as digital holographic microscopy (DHM). In this paper, we present the feasibility of using DHM to determine the presence of nanoparticles in the FDM process. Experimental results validate this technology’s precision and provide extensive knowledge about the implications of the FDM on health. The measure of the thin films deposited in glass substrates was between a minimum of 9 nm to a maximum of 200 nm, in agreement with the previous studies.