Hydrophilic Copolymers with Hydroxamic Acid Groups as a Protective Biocompatible Coating of Maghemite Nanoparticles: Synthesis, Physico-Chemical Characterization and MRI Biodistribution Study.

Superparamagnetic iron oxide nanoparticles (SPION) with a "non-fouling" surface represent a versatile group of biocompatible nanomaterials valuable for medical diagnostics, including oncology. In our study we present a synthesis of novel maghemite (γ-Fe2O3) nanoparticles with positive and negative overall surface charge and their coating by copolymer P(HPMA-co-HAO) prepared by RAFT (reversible addition-fragmentation chain-transfer) copolymerization of N-(2-hydroxypropyl)methacrylamide (HPMA) with N-[2-(hydroxyamino)-2-oxo-ethyl]-2-methyl-prop-2-enamide (HAO). Coating was realized via hydroxamic acid groups of the HAO comonomer units with a strong affinity to maghemite. Dynamic light scattering (DLS) demonstrated high colloidal stability of the coated particles in a wide pH range, high ionic strength, and the presence of phosphate buffer (PBS) and serum albumin (BSE). Transmission electron microscopy (TEM) images show a narrow size distribution and spheroid shape. Alternative coatings were prepared by copolymerization of HPMA with methyl 2-(2-methylprop-2-enoylamino)acetate (MMA) and further post-polymerization modification with hydroxamic acid groups, carboxylic acid and primary-amino functionalities. Nevertheless, their colloidal stability was worse in comparison with P(HPMA-co-HAO). Additionally, P(HPMA-co-HAO)-coated nanoparticles were subjected to a bio-distribution study in mice. They were cleared from the blood stream by the liver relatively slowly, and their half-life in the liver depended on their charge; nevertheless, both cationic and anionic particles revealed a much shorter metabolic clearance rate than that of commercially available ferucarbotran.

The Effect of Face Masks on Physiological Data and the Classification of Rehabilitation Walking.

Gait analysis and the assessment of rehabilitation exercises are important processes that occur during fitness level monitoring and the treatment of neurological disorders. This paper presents the possibility of using oximetric, heart rate (HR), accelerometric, and global navigation satellite systems (GNSSs) to analyse signals recorded during uphill and downhill walking without and with a face mask to find its influence on physiological functions during selected walking patterns. The experimental dataset includes 86 signal segments acquired under different conditions. The proposed methodology is based on signal analysis in both the time and frequency domains. The results indicate that face mask use has a minimal effect on blood oxygen concentration and heart rate, with the average mean changes of these parameters being less than 2%. The support vector machine, a Bayesian method, the k -nearest neighbour method, and a two-layer neural network showed very good separation abilities and successfully classified different walking patterns only in the case when the effect of face mask wearing was not included in the classification process. Our methodology suggests that artificial intelligence and machine learning tools are efficient methods for the assessment of motion patterns in different motion conditions and that face masks have a negligible effect for short-duration experiments.

Motion Assessment for Accelerometric and Heart Rate Cycling Data Analysis.

Motion analysis is an important topic in the monitoring of physical activities and recognition of neurological disorders. The present paper is devoted to motion assessment using accelerometers inside mobile phones located at selected body positions and the records of changes in the heart rate during cycling, under different body loads. Acquired data include 1293 signal segments recorded by the mobile phone and the Garmin device for uphill and downhill cycling. The proposed method is based upon digital processing of the heart rate and the mean power in different frequency bands of accelerometric data. The classification of the resulting features was performed by the support vector machine, Bayesian methods, k-nearest neighbor method, and neural networks. The proposed criterion is then used to find the best positions for the sensors with the highest discrimination abilities. The results suggest the sensors be positioned on the spine for the classification of uphill and downhill cycling, yielding an accuracy of 96.5% and a cross-validation error of 0.04 evaluated by a two-layer neural network system for features based on the mean power in the frequency bands 〈 3 , 8 〉 and 〈 8 , 15 〉 Hz. This paper shows the possibility of increasing this accuracy to 98.3% by the use of more features and the influence of appropriate sensor positioning for motion monitoring and classification.

Machine Learning in Rehabilitation Assessment for Thermal and Heart Rate Data Processing.

Multimodal signal analysis based on sophisticated noninvasive sensors, efficient communication systems, and machine learning, have a rapidly increasing range of different applications. The present paper is devoted to pattern recognition and the analysis of physiological data acquired by heart rate and thermal camera sensors during rehabilitation. A total number of 56 experimental data sets, each 40 min long, of the heart rate and breathing temperature recorded on an exercise bike have been processed to determine the fitness level and possible medical disorders. The proposed general methodology combines machine learning methods for the detection of the changing temperature ranges of the thermal camera and adaptive image processing methods to evaluate the frequency of breathing. To determine the individual temperature values, a neural network model with the sigmoidal and the probabilistic transfer function in the first and the second layers are applied. Appropriate statistical methods are then used to find the correspondence between the exercise activity and selected physiological functions. The evaluated mean delay of 21 s of the heart rate drop related to the change of the activity level corresponds to results obtained in real cycling conditions. Further results include the average value of the change of the breathing temperature (167 s) and breathing frequency (49 s).

Breathing Analysis Using Thermal and Depth Imaging Camera Video Records.

The paper is devoted to the study of facial region temperature changes using a simple thermal imaging camera and to the comparison of their time evolution with the pectoral area motion recorded by the MS Kinect depth sensor. The goal of this research is to propose the use of video records as alternative diagnostics of breathing disorders allowing their analysis in the home environment as well. The methods proposed include (i) specific image processing algorithms for detecting facial parts with periodic temperature changes; (ii) computational intelligence tools for analysing the associated videosequences; and (iii) digital filters and spectral estimation tools for processing the depth matrices. Machine learning applied to thermal imaging camera calibration allowed the recognition of its digital information with an accuracy close to 100% for the classification of individual temperature values. The proposed detection of breathing features was used for monitoring of physical activities by the home exercise bike. The results include a decrease of breathing temperature and its frequency after a load, with mean values -0.16 °C/min and -0.72 bpm respectively, for the given set of experiments. The proposed methods verify that thermal and depth cameras can be used as additional tools for multimodal detection of breathing patterns.