21 resultados para Bio medical Applications
Resumo:
A series of the most common chelators used in magnetic resonance imaging ( MRI) and in radiopharmaceuticals for medical diagnosis and tumour therapy, H(4)dota, H(4)teta, H(8)dotp and H(8)tetp, is examined from a chemical point of view. Differences between 12- and 14-membered tetraazamacrocyclic derivatives with methylcarboxylate and methylphosphonate pendant arms and their chelates with divalent first-series transition metal and trivalent lanthanide ions are discussed on the basis of their thermodynamic stability constants, X- ray structures and theoretical studies.
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Biological models of an apoptotic process are studied using models describing a system of differential equations derived from reaction kinetics information. The mathematical model is re-formulated in a state-space robust control theory framework where parametric and dynamic uncertainty can be modelled to account for variations naturally occurring in biological processes. We propose to handle the nonlinearities using neural networks.
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Methods have recently been developed that make use of electromagnetic radiation at terahertz (THz) frequencies, the region of the spectrum between millimetre wavelengths and the infrared, for imaging purposes. Radiation at these wavelengths is non-ionizing and subject to far less Rayleigh scatter than visible or infrared wavelengths, making it suitable for medical applications. This paper introduces THz pulsed imaging and discusses its potential for in vivo medical applications in comparison with existing modalities.
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Introduction Facing the challenging treatment of neurodegenerative diseases as well as complex craniofacial injuries such as those common after cancer therapy, the field of regenerative medicine increasingly relies on stem cell transplantation strategies. Here, neural crest-derived stem cells (NCSCs) offer many promising applications, although scale up of clinical-grade processes prior to potential transplantations is currently limiting. In this study, we aimed to establish a clinical-grade, cost-reducing cultivation system for NCSCs isolated from the adult human nose using cGMP-grade Afc-FEP bags. Methods We cultivated human neural crest-derived stem cells from inferior turbinate (ITSCs) in a cell culture bag system using Afc-FEP bags in human blood plasma-supplemented medium. Investigations of viability, proliferation and expression profile of bag-cultured ITSCs were followed by DNA-content and telomerase activity determination. Cultivated ITSCs were introduced to directed in vitro differentiation assays to assess their potential for mesodermal and ectodermal differentiation. Mesodermal differentiation was determined using an enzyme activity assay (alkaline phosphatase, ALP), respective stainings (Alizarin Red S, Von Kossa and Oil Red O), and RT-PCR, while immunocytochemistry and synaptic vesicle recycling were applied to assay neuroectodermal differentiation of ITSCs. Results When cultivated within Afc-FEP bags, ITSCs grew three-dimensionally in a human blood plasma-derived matrix, thereby showing unchanged morphology, proliferation capability, viability and expression profile in comparison to three dimensionally-cultured ITSCs growing in standard cell culture plastics. Genetic stability of bag-cultured ITSCs was further accompanied by unchanged telomerase activity. Importantly, ITSCs retained their potential to differentiate into mesodermal cell types, particularly including ALP-active, Alizarin Red S-, and Von Kossa-positive osteogenic cell types, as well as adipocytes positive in Oil Red O assays. Bag culture further did not affect the potential of ITSCs to undergo differentiation into neuroectodermal cell types coexpressing β-III-tubulin and MAP2 and exhibiting the capability for synaptic vesicle recycling. Conclusions Here, we report for the first time the successful cultivation of human NCSCs within cGMP-grade Afc-FEP bags using a human blood plasma-supplemented medium. Our findings particularly demonstrate the unchanged differentiation capability and genetic stability of the cultivated NCSCs, suggesting the great potential of this culture system for future medical applications in the field of regenerative medicine.
Resumo:
Foams are cellular structures, produced by gas bubbles formed during the polyurethane polymerization mixture. Flexible PU foams meet the following two criteria: have a limited resistance to an applied load, being both permeable to air and reversibly deformable. There are two main types of flexible foams, hot and cold cure foams differing in composition and processing temperatures. The hot cure foams are widely applied and represent the main composition of actual foams, while cold cure foams present several processing and property advantages, e.g, faster demoulding time, better humid aging properties and more versatility, as hardness variation with index changes are greater than with hot cure foams. The processing of cold cure foams also is attractive due to the low energy consumption (mould temperature from 30 degrees to 65 degrees C) comparatively to hot cure foams (mould temperature from 30 degrees to 250 degrees C). Another advantage is the high variety of soft materials for low temperature processing moulds. Cold cure foams are diphenylmethane diisocyanate (MDI) based while hot cure foams are toluene diisocyanate (TDI) based. This study is concerned with Viscoelastic flexible foams MDI based for medical applications. Differential Scanning Calorimetry (DSC) was used to characterize the cure kinetics and Dynamical Mechanical Analisys to collect mechanical data. The data obtained from these two experimental procedures were analyzed and associated to establish processing/properties/operation conditions relationships. These maps for the selection of optimized processing/properties/operation conditions are important to achieve better final part properties at lower costs and lead times.
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Data assimilation algorithms are a crucial part of operational systems in numerical weather prediction, hydrology and climate science, but are also important for dynamical reconstruction in medical applications and quality control for manufacturing processes. Usually, a variety of diverse measurement data are employed to determine the state of the atmosphere or to a wider system including land and oceans. Modern data assimilation systems use more and more remote sensing data, in particular radiances measured by satellites, radar data and integrated water vapor measurements via GPS/GNSS signals. The inversion of some of these measurements are ill-posed in the classical sense, i.e. the inverse of the operator H which maps the state onto the data is unbounded. In this case, the use of such data can lead to significant instabilities of data assimilation algorithms. The goal of this work is to provide a rigorous mathematical analysis of the instability of well-known data assimilation methods. Here, we will restrict our attention to particular linear systems, in which the instability can be explicitly analyzed. We investigate the three-dimensional variational assimilation and four-dimensional variational assimilation. A theory for the instability is developed using the classical theory of ill-posed problems in a Banach space framework. Further, we demonstrate by numerical examples that instabilities can and will occur, including an example from dynamic magnetic tomography.
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The ‘soft’ ionization technique matrix-assisted laser desorption/ionization (MALDI) is without doubt one of the great success stories of modern mass spectrometry (MS). In particular, the further development of MALDI and in general ‘soft’ laser ionization, focusing on their unique characteristics and advantages in areas such as speed, spatial resolution, sample preparation and low spectral complexity, have led to great advances in mass spectral profiling and imaging with an extremely auspicious future in (bio)medical analyses.
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The absorption coefficient of a substance distributed as discrete particles in suspension is less than that of the same material dissolved uniformly in a medium—a phenomenon commonly referred to as the flattening effect. The decrease in the absorption coefficient owing to flattening effect depends on the concentration of the absorbing pigment inside the particle, the specific absorption coefficient of the pigment within the particle, and on the diameter of the particle, if the particles are assumed to be spherical. For phytoplankton cells in the ocean, with diameters ranging from less than 1 µm to more than 100 µm, the flattening effect is variable, and sometimes pronounced, as has been well documented in the literature. Here, we demonstrate how the in vivo absorption coefficient of phytoplankton cells per unit concentration of its major pigment, chlorophyll a, can be used to determine the average cell size of the phytoplankton population. Sensitivity analyses are carried out to evaluate the errors in the estimated diameter owing to potential errors in the model assumptions. Cell sizes computed for field samples using the model are compared qualitatively with indirect estimates of size classes derived from high performance liquid chromatography data. Also, the results are compared quantitatively against measurements of cell size in laboratory cultures. The method developed is easy-to-apply as an operational tool for in situ observations, and has the potential for application to remote sensing of ocean colour data.
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With the fast development of wireless communications, ZigBee and semiconductor devices, home automation networks have recently become very popular. Since typical consumer products deployed in home automation networks are often powered by tiny and limited batteries, one of the most challenging research issues is concerning energy reduction and the balancing of energy consumption across the network in order to prolong the home network lifetime for consumer devices. The introduction of clustering and sink mobility techniques into home automation networks have been shown to be an efficient way to improve the network performance and have received significant research attention. Taking inspiration from nature, this paper proposes an Ant Colony Optimization (ACO) based clustering algorithm specifically with mobile sink support for home automation networks. In this work, the network is divided into several clusters and cluster heads are selected within each cluster. Then, a mobile sink communicates with each cluster head to collect data directly through short range communications. The ACO algorithm has been utilized in this work in order to find the optimal mobility trajectory for the mobile sink. Extensive simulation results from this research show that the proposed algorithm significantly improves home network performance when using mobile sinks in terms of energy consumption and network lifetime as compared to other routing algorithms currently deployed for home automation networks.