Biological Impact of γ-Fe2O3 Magnetic Nanoparticles Obtained by Laser Target Evaporation: Focus on Magnetic Biosensor Applications

The biological activity of γ-Fe2O3 magnetic nanoparticles (MNPs), obtained by the laser target evaporation technique, was studied, with a focus on their possible use in biosensor applications. The biological effect of the MNPs was investigated in vitro on the primary cultures of human dermal fibroblasts. The effects of the MNPs contained in culture medium or MNPs already uptaken by cells were evaluated for the cases of the fibroblast’s proliferation and secretion of cytokines and collagen. For the tests related to the contribution of the constant magnetic field to the biological activity of MNPs, a magnetic system for the creation of the external magnetic field (having no commercial analogues) was designed, calibrated, and used. It was adapted to the size of standard 24-well cell culture plates. At low concentrations of MNPs, uptake by fibroblasts had stimulated their proliferation. Extracellular MNPs stimulated the release of pro-inflammatory cytokines (Interleukin-6 (IL-6) and Interleukin-8 (IL-8) or chemokine (C-X-C motif) ligand 8 (CXCL8)) in a concentration-dependent manner. However, the presence of MNPs did not increase the collagen secretion. The exposure to the uniform constant magnetic field (H ≈ 630 or 320 Oe), oriented in the plane of the well, did not cause considerable changes in fibroblasts proliferation and secretion, regardless of presence of MNPs. Statistically significant differences were detected only in the levels of IL-8/CXCL8 release.The study was supported by the program of the Ministry of Health of the Russian Federation (project 121032300335-1). This work was financially supported, in part, by the Ministry of Science and Higher Education of the RF (grant FEUZ-2020-0051) (G.Yu. Melnikov) and University of the Basque Country Research Groups Funding (grant IT1245-19) (G.V. Kurlyandskaya)

Polyacrylamide Ferrogels with Magnetite or Strontium Hexaferrite: Next Step in the Development of Soft Biomimetic Matter for Biosensor Applications

Magnetic biosensors are an important part of biomedical applications of magnetic materials. As the living tissue is basically a " soft matter." this study addresses the development of ferrogels (FG) with micron sized magnetic particles of magnetite and strontium hexaferrite mimicking the living tissue. The basic composition of the FG comprised the polymeric network of polyacrylamide, synthesized by free radical polymerization of monomeric acrylamide (AAm) in water solution at three levels of concentration (1.1 M, 0.85 M and 0.58 M) to provide the FG with varying elasticity. To improve FG biocompatibility and to prevent the precipitation of the particles, polysaccharide thickeners-guar gum or xanthan gum were used. The content of magnetic particles in FG varied up to 5.2 wt % depending on the FG composition. The mechanical properties of FG and their deformation in a uniform magnetic field were comparatively analyzed. FG filled with strontium hexaferrite particles have larger Young's modulus value than FG filled with magnetite particles, most likely due to the specific features of the adhesion of the network's polymeric subchains on the surface of the particles. FG networks with xanthan are stronger and have higher modulus than the FG with guar. FG based on magnetite, contract in a magnetic field 0.42 T, whereas some FG based on strontium hexaferrite swell. Weak FG with the lowest concentration of AAm shows a much stronger response to a field, as the concentration of AAm governs the Young's modulus of ferrogel. A small magnetic field magnetoimpedance sensor prototype with Co68.6Fe3.9Mo3.0Si12.0B12.5 rapidly quenched amorphous ribbon based element was designed aiming to develop a sensor working with a disposable stripe sensitive element. The proposed protocol allowed measurements of the concentration dependence of magnetic particles in gels using magnetoimpedance responses in the presence of magnetite and strontium hexaferrite ferrogels with xanthan. We have discussed the importance of magnetic history for the detection process and demonstrated the importance of remnant magnetization in the case of the gels with large magnetic particles.This work was supported in part within the framework of the state task of the Ministry of Education and Science of Russia 3.6121.2017/8.9; RFBR grant 16-08-00609 and by the ACTIMAT grant of the Basque Country Government. Selected studies were made at SGIKER Common Services of UPV-EHU and URFU Common Services. We thank I.V. Beketov, A.A. Svalova, Burgoa Beitia, A. Amirabadizadeh, A. Garcia-Arribas and I. Orue for their special support

Validity of ejection fraction as a measure of myocardial functional state: impact of asynchrony

Aims The goal of this study was to test whether peculiarities of left ventricular (LV) regional function place limits on the validity of ejection fraction (EF) as a measure of the myocardial functional state. Methods and results Transthoracic and transoesophageal data from patients with a variety of cardiac conditions were used for analysis of LV regional function. The focus was on the effects of mechanical asynchrony. Ejection fraction was calculated on the basis of LV end-diastolic volume and end-systolic volume obtained by two different ways: (i) end-systolic volume as a whole; and (ii) the sum of all regional end-systolic volumes (which may occur at different times). The relative difference, D-EF, between EFs obtained by (i) and (ii) was taken as the ‘merit ’ of EF. A value of zero is the highest merit. Irrespective of the examination method, we found that D-EF was always higher than zero, and that its value depended on the extent of mechanical asynchrony. Conclusions Ejection fraction is not the arithmetic average of regional EFs. An increase of asynchrony increases D-EF, i.e. it reduces the merit of EF as a measure of cardiac function

Mechanical, Electrical and Magnetic Properties of Ferrogels with Embedded Iron Oxide Nanoparticles Obtained by Laser Target Evaporation: Focus on Multifunctional Biosensor Applications

Hydrogels are biomimetic materials widely used in the area of biomedical engineering and biosensing. Ferrogels (FG) are magnetic composites capable of functioning as magnetic field sensitive transformers and field assisted drug deliverers. FG can be prepared by incorporating magnetic nanoparticles (MNPs) into chemically crosslinked hydrogels. The properties of biomimetic ferrogels for multifunctional biosensor applications can be set up by synthesis. The properties of these biomimetic ferrogels can be thoroughly controlled in a physical experiment environment which is much less demanding than biotests. Two series of ferrogels (soft and dense) based on polyacrylamide (PAAm) with different chemical network densities were synthesized by free-radical polymerization in aqueous solution with N, N'-methylene-diacrylamide as a cross-linker and maghemite Fe2O3 MNPs fabricated by laser target evaporation as a filler. Their mechanical, electrical and magnetic properties were comparatively analyzed. We developed a giant magnetoimpedance (MI) sensor prototype with multilayered FeNi-based sensitive elements deposited onto glass or polymer substrates adapted for FG studies. The MI measurements in the initial state and in the presence of FG with different concentrations of MNPs at a frequency range of 1-300 MHz allowed a precise characterization of the stray fields of the MNPs present in the FG. We proposed an electrodynamic model to describe the MI in multilayered film with a FG layer based on the solution of linearized Maxwell equations for the electromagnetic fields coupled with the Landau-Lifshitz equation for the magnetization dynamics.This work was supported in part within the framework of the state task of the Ministry of Education and Science of Russia 3.6121.2017/8.9; RFBR grants 16-08-00609-a, 18-08-00178, and by the ACTIMAT ELKARTEK grant of the Basque Country Government. Selected studies were made at SGIKER Common Services of UPV-EHU and URFU Common Services. We thank I.V. Beketov, A.A. Chlenova, S.O. Volchkov, V.N. Lepalovskij, A.M. Murzakaev and A.A. Svalova for special support

Assessing the Mass Transfer Coefficient in Jet Bioreactors with Classical Computer Vision Methods and Neural Networks Algorithms

Development of energy-efficient and high-performance bioreactors requires progress in methods for assessing the key parameters of the biosynthesis process. With a wide variety of approaches and methods for determining the phase contact area in gas–liquid flows, the question of obtaining its accurate quantitative estimation remains open. Particularly challenging are the issues of getting information about the mass transfer coefficients instantly, as well as the development of predictive capabilities for the implementation of effective flow control in continuous fermentation both on the laboratory and industrial scales. Motivated by the opportunity to explore the possibility of applying classical and non-classical computer vision methods to the results of high-precision video records of bubble flows obtained during the experiment in the bioreactor vessel, we obtained a number of results presented in the paper. Characteristics of the bioreactor’s bubble flow were estimated first by classical computer vision (CCV) methods including an elliptic regression approach for single bubble boundaries selection and clustering, image transformation through a set of filters and developing an algorithm for separation of the overlapping bubbles. The application of the developed method for the entire video filming makes it possible to obtain parameter distributions and set dropout thresholds in order to obtain better estimates due to averaging. The developed CCV methodology was also tested and verified on a collected and labeled manual dataset. An onwards deep neural network (NN) approach was also applied, for instance the segmentation task, and has demonstrated certain advantages in terms of high segmentation resolution, while the classical one tends to be more speedy. Thus, in the current manuscript both advantages and disadvantages of the classical computer vision method (CCV) and neural network approach (NN) are discussed based on evaluation of bubbles’ number and their area defined. An approach to mass transfer coefficient estimation methodology in virtue of obtained results is also represented