Label-Free Study of the Global Cell Behavior during Exposure to Environmental Radiofrequency Fields in the Presence or Absence of Pro-Apoptotic or Pro-Autophagic Treatments

It remains controversial whether exposure to environmental radiofrequency signals (RF) impacts cell status or response to cellular stress such as apoptosis or autophagy. We used two label-free techniques, cellular impedancemetry and Digital Holographic Microscopy (DHM), to assess the overall cellular response during RF exposure alone, or during co-exposure to RF and chemical treatments known to induce either apoptosis or autophagy. Two human cell lines (SH-SY5Y and HCT116) and two cultures of primary rat cortex cells (astrocytes and co-culture of neurons and glial cells) were exposed to RF using an 1800 MHz carrier wave modulated with various environmental signals (GSM: Global System for Mobile Communications, 2G signal), UMTS (Universal Mobile Telecommunications System, 3G signal), LTE (Long-Term Evolution, 4G signal, and Wi-Fi) or unmodulated RF (continuous wave, CW). The specific absorption rates (S.A.R.) used were 1.5 and 6 W/kg during DHM experiments and ranged from 5 to 24 W/kg during the recording of cellular impedance. Cells were continuously exposed for three to five consecutive days while the temporal phenotypic signature of cells behavior was recorded at constant temperature. Statistical analysis of the results does not indicate that RF-EMF exposure impacted the global behavior of healthy, apoptotic, or autophagic cells, even at S.A.R. levels higher than the guidelines, provided that the temperature was kept constant.

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.