Irena Cosic

Macromolecular bioactivity: is it resonant interaction between macromolecules?-theory and applications

Biological processes in any living organism are based on selective interactions between particular biomolecules. In most cases, these interactions involve and are driven by proteins which are the main conductors of any living process within the organism. The physical nature of these interactions is still not well known. The authors represent a whole new view to biomolecular interactions, in particular protein-protein and protein-DNA interactions, based on the assumption that these interactions are electromagnetic in their nature. This new approach is incorporated in the Resonant Recognition Model (RRM), which was developed over the last 10 years. It has been shown initially that certain periodicities within the distribution of energies of delocalized electrons along a protein molecule are critical for protein biological function, i.e., interaction with its target. If protein conductivity was introduced, then a charge moving through protein backbone can produce electromagnetic irradiation or absorption with spectral characteristics corresponding to energy distribution along the protein. The RRM enables these spectral characteristics, which were found to be in the range of infrared and visible light, to be calculated. These theoretically calculated spectra were proved using experimentally obtained frequency characteristics of some light-induced biological processes. Furthermore, completely new peptides with desired spectral characteristics, and consequently corresponding biological activities, were designed.<<ETX>>

Wavelet transform feature extraction from human PPG, ECG, and EEG signal responses to ELF PEMF exposures: A pilot study

This paper presents the experimental pilot study to investigate the effects of pulsed electromagnetic field (PEMF) at extremely low frequency (ELF) in response to photoplethysmographic (PPG), electrocardiographic (ECG), electroencephalographic (EEG) activity. The assessment of wavelet transform (WT) as a feature extraction method was used in representing the electrophysiological signals. Considering that classification is often more accurate when the pattern is simplified through representation by important features, the feature extraction and selection play an important role in classifying systems such as neural networks. The PPG, ECG, EEG signals were decomposed into time-frequency representations using discrete wavelet transform (DWT) and the statistical features were calculated to depict their distribution. Our pilot study investigation for any possible electrophysiological activity alterations due to ELF PEMF exposure, was evaluated by the efficiency of DWT as a feature extraction method in representing the signals. As a result, this feature extraction has been justified as a feasible method.

Is it Possible to Analyze DNA and Protein Sequences by the Methods of Digital Signal Processing

Informational content of linear macromolecules (DNA, RNA, and proteins) is analyzed by the method of digital signal processing. Each element (amino acid or nucleotide) of macromolecules is represented by the corresponding value of electron-ion interaction potential. This numerical representation of the primary structure of macromolecules is subjected to digital signal processing in order to extract the information corresponding to biological function. It is shown that the multiple cross spectrum of functionally related sequences exhibits significant peak frequencies. In the case of functionally unrelated sequences such peaks were not found. Peak frequencies are different for different biological functions. Based on this, we conjecture that the peak frequencies in the multiple cross spectrum of sequences with the same boilogical function are related to this biological function.

Human brain wave activity during exposure to radiofrequency field emissions from mobile phones

The aim of this study was to determine whether there is an effect of mobile phone electromagnetic field emissions on the human electroencephalograph (EEG). EEG recordings from ten awake subjects were taken during exposure to radiofrequency (RF) emissions from a mobile phone positioned behind the head. Two experimental trials were conducted. In the first trial, RF exposures were generated by a GSM mobile phone with the speaker disabled and configured to transmit at full-radiated power. During the second trial, exposures were generated by a non-modified GSM mobile phone in activestandby mode. For each trial, subjects were exposed in five minute intervals to a randomized, interrupted sequence of five active and five sham exposures. The experiment was conducted under single-blind conditions. The average EEG band power in active exposure recordings was compared to corresponding sham recordings. Statistical tests indicated significant difference in thefull-power mode trial within the EEG alpha (8–13 Hz) and beta (13–32 Hz) bands. A subsequent statistical analysis of median spectral power in discrete EEG rhythms revealed significant differences in 7 of the 32 distinct frequencies overall. In conclusion, the results of this study lend support to EEG effects from mobile phones activated in talk-mode.

ECG R-R Peak Detection on Mobile Phones

Mobile phones have become an integral part of modern life. Due to the ever increasing processing power, mobile phones are rapidly expanding its arena from a sole device of telecommunication to organizer, calculator, gaming device, web browser, music player, audio/video recording device, navigator etc. The processing power of modern mobile phones has been utilized by many innovative purposes. In this paper, we are proposing the utilization of mobile phones for monitoring and analysis of biosignal. The computation performed inside the mobile phones processor will now be exploited for healthcare delivery. We performed literature review on RR interval detection from ECG and selected few PC based algorithms. Then, three of those existing RR interval detection algorithms were programmed on JavaTM platform. Performance monitoring and comparison studies were carried out on three different mobile devices to determine their application on a realtime telemonitoring scenario.

In vitro inhibition of the actions of basic FGF by a novel 16 amino acid peptide

A composite procedure involving molecular modelling and a property-pattern algorithm, the Resonant Recognition Model (RRM), has been applied to structure-function studies with basic fibroblast growth factor (bFGF). Propertypattern characteristics for biological activity and receptor recognition for a group of FGF-related proteins were de fined and then used to aid the design of a set of peptides which can act as bFGF antagonists. Molecular modelling techniques were then employed to identify the peptide within this set with the greatest conformational similarity to the putative receptor domain of bFGF. This 16 amino acid residue peptide (16mer), which exhibits no sequence homology to bFGF, antagonised the stimulatory effect of bFGF on fibroblast [3H]thymidine incorporation and cell proliferation, but exerted no effect itself in thesein vitro bioassays.

Developing a Wireless Implantable Body Sensor Network in MICS Band

Through an integration of wireless communication and sensing technologies, the concept of a body sensor network (BSN) was initially proposed in the early decade with the aim to provide an essential technology for wearable, ambulatory, and pervasive health monitoring for elderly people and chronic patients. It has become a hot research area due to big opportunities as well as great challenges it presents. Though the idea of an implantable BSN was proposed in parallel with the on-body sensor network, the development in this area is relatively slow due to the complexity of human body, safety concerns, and some technological bottlenecks such as the design of ultralow-power implantable RF transceiver. This paper describes a new wireless implantable BSN that operates in medical implant communication service (MICS) frequency band. This system innovatively incorporates both sensing and actuation nodes to form a closed-control loop for physiological monitoring and drug delivery for critically ill patients. The sensing node, which is designed using system-on-chip technologies, takes advantage of the newly available ultralow-power Zarlink MICS transceiver for wireless data transmission. Finally, the specific absorption rate distribution of the proposed system was simulated to determine the in vivo electromagnetic field absorption and the power safety limits.

FACTORS INFLUENCING UNCERTAINTY IN MEASUREMENT OF ELECTRIC FIELDS CLOSE TO THE BODY IN PERSONAL RF DOSIMETRY

This paper provides an insight into factors that can influence uncertainty in measurements at 900 MHz of electric fields close to the body for use in personal dosimetry. Computational simulations using the finite difference time domain method were used to determine the total electric field near the surface of the torso of heterogeneous (adult and child) human body models for a set of exposure scenarios that simulated both spatially constant and randomly varying incident fields. Modelling has shown that a properly responding isotropic electric field dosemeter mounted between 10 and 50 mm of the torso will on average underestimate the incident field strength by up to 6.45 dB. In the worst case (i.e. spatially constant field), the standard deviation or uncertainty reached 6.42 dB. Uncertainty was reduced to <2.17 dB by combining the simultaneous outputs of a pair of body-worn dosemeters (mounted front and rear of torso).

Biological Effects of Electromagnetic Radiation

There are a considerable number of peer-reviewed publications which show that electromagnetic EMR can result in physiologically beneficial in vivo, in vitro and in situ biological effects. These bioeffects are already used for therapeutic applications of nonthermal and non-ionizing EMR such as bone repair, nerve stimulation, wound healing, osteoarthritis treatment, electro-acupuncture, tissue regeneration, immune system stimulation, and neuroendocrine modulations.

A Mobile Phone Based Intelligent Telemonitoring Platform

In this paper, we propose a generic smart telemonitoring platform in which the computation power of the mobile phone is highly utilized. In this approach, compression of ECG is done in real-time by the mobile phone for the very first time. The fast and effective compression scheme, designed for the proposed telemonitoring system, outperforms most of the real-time lossless ECG compression algorithms. This mobile phone based computation platform is a promising solution for privacy issues in telemonitoring through encryptions. Moreover, the mobile phones used in this platform performs preliminary detection of abnormal biosignal in realtime. Apart from the usage of mobile phones, this platform supports background biosignal abnormality surveillance using data mining agent.