Radiofrequency radiation at 1950 MHz (UMTS) does not affect key cellular endpoints in neuron-like PC12 cells

In this study, rat pheochromocytoma (PC12) cells were exposed, as a model of neuron-like cells, to 1950 MHz radiofrequency (RF) radiation with a signal used by the 3G wireless technology of the Universal Mobile Telecommunications System (UMTS) to assess possible adverse effects. RF exposure for 24 h at a specific absorption rate (SAR) of 10 W/kg was carried out in a waveguide system under accurately controlled environmental and dosimetric parameters. DNA integrity, cell viability, and apoptosis were investigated as cellular endpoints relevant for carcinogenesis and other diseases of the central nervous system. Very sensitive biological assays were employed to assess the effects immediately after RF exposure and 24 h later, as demonstrated by the cellular response elicited in PC12 cells using positive control treatments provided for each assay. In our experimental conditions, 24 h of RF exposure at a carrier frequency and modulation scheme typical of a UMTS signal was not able to elicit any effect in the selected cellular endpoints in undifferentiated PC12 cells, despite the application of a higher SAR value than those applied in the majority of the studies reported in the literature.

Biomedical signal and image processing

Generally, physiological modeling and biomedical signal processing constitute two important paradigms of biomedical engineering (BME): their fundamental concepts are taught starting from undergraduate studies and are more completely dealt with in the last years of graduate curricula, as well as in Ph.D. courses. Traditionally, these two cultural aspects were separated, with the first one more oriented to physiological issues and how to model them and the second one more dedicated to the development of processing tools or algorithms to enhance useful information from clinical data. A practical consequence was that those who did models did not do signal processing and vice versa. However, in recent years,the need for closer integration between signal processing and modeling of the relevant biological systems emerged very clearly [1], [2]. This is not only true for training purposes(i.e., to properly prepare the new professional members of BME) but also for the development of newly conceived research projects in which the integration between biomedical signal and image processing (BSIP) and modeling plays a crucial role. Just to give simple examples, topics such as brain–computer machine or interfaces,neuroengineering, nonlinear dynamical analysis of the cardiovascular (CV) system,integration of sensory-motor characteristics aimed at the building of advanced prostheses and rehabilitation tools, and wearable devices for vital sign monitoring and others do require an intelligent fusion of modeling and signal processing competences that are certainly peculiar of our discipline of BME.