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The hybrid K+/Ca2+ sensor based on laser scanned silicon transducer for multi-component analysis
(2002)
Frequency mixing magnetic detection (FMMD) is a sensitive and selective technique to detect magnetic nanoparticles (MNPs) serving as probes for binding biological targets. Its principle relies on the nonlinear magnetic relaxation dynamics of a particle ensemble interacting with a dual frequency external magnetic field. In order to increase its sensitivity, lower its limit of detection and overall improve its applicability in biosensing, matching combinations of external field parameters and internal particle properties are being sought to advance FMMD. In this study, we systematically probe the aforementioned interaction with coupled Néel–Brownian dynamic relaxation simulations to examine how key MNP properties as well as applied field parameters affect the frequency mixing signal generation. It is found that the core size of MNPs dominates their nonlinear magnetic response, with the strongest contributions from the largest particles. The drive field amplitude dominates the shape of the field-dependent response, whereas effective anisotropy and hydrodynamic size of the particles only weakly influence the signal generation in FMMD. For tailoring the MNP properties and parameters of the setup towards optimal FMMD signal generation, our findings suggest choosing large particles of core sizes dc > 25 nm nm with narrow size distributions (σ < 0.1) to minimize the required drive field amplitude. This allows potential improvements of FMMD as a stand-alone application, as well as advances in magnetic particle imaging, hyperthermia and magnetic immunoassays.
Magnetic nanoparticle relaxation in biomedical application: focus on simulating nanoparticle heating
(2021)
Dual frequency magnetic excitation of magnetic nanoparticles (MNP) enables enhanced biosensing applications. This was studied from an experimental and theoretical perspective: nonlinear sum-frequency components of MNP exposed to dual-frequency magnetic excitation were measured as a function of static magnetic offset field. The Langevin model in thermodynamic equilibrium was fitted to the experimental data to derive parameters of the lognormal core size distribution. These parameters were subsequently used as inputs for micromagnetic Monte-Carlo (MC)-simulations. From the hysteresis loops obtained from MC-simulations, sum-frequency components were numerically demodulated and compared with both experiment and Langevin model predictions. From the latter, we derived that approximately 90% of the frequency mixing magnetic response signal is generated by the largest 10% of MNP. We therefore suggest that small particles do not contribute to the frequency mixing signal, which is supported by MC-simulation results. Both theoretical approaches describe the experimental signal shapes well, but with notable differences between experiment and micromagnetic simulations. These deviations could result from Brownian relaxations which are, albeit experimentally inhibited, included in MC-simulation, or (yet unconsidered) cluster-effects of MNP, or inaccurately derived input for MC-simulations, because the largest particles dominate the experimental signal but concurrently do not fulfill the precondition of thermodynamic equilibrium required by Langevin theory.
Heating efficiency of magnetic nanoparticles decreases with gradual immobilization in hydrogels
(2019)
Many efforts are made worldwide to establish magnetic fluid hyperthermia (MFH) as a treatment for organ-confined tumors. However, translation to clinical application hardly succeeds as it still lacks of understanding the mechanisms determining MFH cytotoxic effects. Here, we investigate the intracellular MFH efficacy with respect to different parameters and assess the intracellular cytotoxic effects in detail. For this, MiaPaCa-2 human pancreatic tumor cells and L929 murine fibroblasts were loaded with iron-oxide magnetic nanoparticles (MNP) and exposed to MFH for either 30 min or 90 min. The resulting cytotoxic effects were assessed via clonogenic assay. Our results demonstrate that cell damage depends not only on the obvious parameters bulk temperature and duration of treatment, but most importantly on cell type and thermal energy deposited per cell during MFH treatment. Tumor cell death of 95% was achieved by depositing an intracellular total thermal energy with about 50% margin to damage of healthy cells. This is attributed to combined intracellular nanoheating and extracellular bulk heating. Tumor cell damage of up to 86% was observed for MFH treatment without perceptible bulk temperature rise. Effective heating decreased by up to 65% after MNP were internalized inside cells.
Biomedical applications of magnetic nanoparticles (MNP) fundamentally rely on the particles’ magnetic relaxation as a response to an alternating magnetic field. The magnetic relaxation complexly depends on the interplay of MNP magnetic and physical properties with the applied field parameters. It is commonly accepted that particle core size is a major contributor to signal generation in all the above applications, however, most MNP samples comprise broad distribution spanning nm and more. Therefore, precise knowledge of the exact contribution of individual core sizes to signal generation is desired for optimal MNP design generally for each application. Specifically, we present a magnetic relaxation simulation-driven analysis of experimental frequency mixing magnetic detection (FMMD) for biosensing to quantify the contributions of individual core size fractions towards signal generation. Applying our method to two different experimental MNP systems, we found the most dominant contributions from approx. 20 nm sized particles in the two independent MNP systems. Additional comparison between freely suspended and immobilized MNP also reveals insight in the MNP microstructure, allowing to use FMMD for MNP characterization, as well as to further fine-tune its applicability in biosensing.
Magnetic nanoparticles (MNPs) are used as therapeutic and diagnostic agents for local delivery of heat and image contrast enhancement in diseased tissue. Besides magnetization, the most important parameter that determines their performance for these applications is their magnetic relaxation, which can be affected when MNPs immobilize and agglomerate inside tissues. In this letter, we investigate different MNP agglomeration states for their magnetic relaxation properties under excitation in alternating fields and relate this to their heating efficiency and imaging properties. With focus on magnetic fluid hyperthermia, two different trends in MNP heating efficiency are measured: an increase by up to 23% for agglomerated MNP in suspension and a decrease by up to 28% for mixed states of agglomerated and immobilized MNP, which indicates that immobilization is the dominant effect. The same comparatively moderate effects are obtained for the signal amplitude in magnetic particle spectroscopy.
Reconstructive surgery and tissue replacements like ureters or bladders reconstruction have been recently studied, taking into account growth and remodelling of cells since living cells are capable of growing, adapting, remodelling or degrading and restoring in order to deform and respond to stimuli. Hence, shapes of ureters or bladders and their microstructure change during growth and these changes strongly depend on external stimuli such as training. We present the mechanical stimulation of smooth muscle cells in a tubular fibrin-PVDFA scaffold and the modelling of the growth of tissue by stimuli. To this end, mechanotransduction was performed with a kyphoplasty balloon catheter that was guided through the lumen of the tubular structure. The bursting pressure was examined to compare the stability of the incubated tissue constructs. The results showed the significant changes on tissues with training by increasing the burst pressure as a characteristic mechanical property and the smooth muscle cells were more oriented with uniformly higher density. Besides, the computational growth models also exhibited the accurate tendencies of growth of the cells under different external stimuli. Such models may lead to design standards for the better layered tissue structure in reconstructing of tubular organs characterized as composite materials such as intestines, ureters and arteries.
Influence of refrigerated storage on tensile mechanical properties of porcine liver and spleen
(2015)
This paper considers a paired data framework and discusses the question of marginal homogeneity of bivariate high-dimensional or functional data. The related testing problem can be endowed into a more general setting for paired random variables taking values in a general Hilbert space. To address this problem, a Cramér–von-Mises type test statistic is applied and a bootstrap procedure is suggested to obtain critical values and finally a consistent test. The desired properties of a bootstrap test can be derived that are asymptotic exactness under the null hypothesis and consistency under alternatives. Simulations show the quality of the test in the finite sample case. A possible application is the comparison of two possibly dependent stock market returns based on functional data. The approach is demonstrated based on historical data for different stock market indices.
A nonparametric goodness-of-fit test for random variables with values in a separable Hilbert space is investigated. To verify the null hypothesis that the data come from a specific distribution, an integral type test based on a Cramér-von-Mises statistic is suggested. The convergence in distribution of the test statistic under the null hypothesis is proved and the test's consistency is concluded. Moreover, properties under local alternatives are discussed. Applications are given for data of huge but finite dimension and for functional data in infinite dimensional spaces. A general approach enables the treatment of incomplete data. In simulation studies the test competes with alternative proposals.
This book provides a compact introduction to the bootstrap method. In addition to classical results on point estimation and test theory, multivariate linear regression models and generalized linear models are covered in detail. Special attention is given to the use of bootstrap procedures to perform goodness-of-fit tests to validate model or distributional assumptions. In some cases, new methods are presented here for the first time.
The text is motivated by practical examples and the implementations of the corresponding algorithms are always given directly in R in a comprehensible form. Overall, R is given great importance throughout. Each chapter includes a section of exercises and, for the more mathematically inclined readers, concludes with rigorous proofs. The intended audience is graduate students who already have a prior knowledge of probability theory and mathematical statistics.
Based on an identifying Volterra type integral equation for randomly right censored observations from a lifetime distribution function F, we solve the corresponding estimating equation by an explicit and implicit Euler scheme. While the first approach results in some known estimators, the second one produces new semi-parametric and pre-smoothed Kaplan–Meier estimators which are real distribution functions rather than sub-distribution functions as the former ones are. This property of the new estimators is particular useful if one wants to estimate the expected lifetime restricted to the support of the observation time.
Specifically, we focus on estimation under the semi-parametric random censorship model (SRCM), that is, a random censorship model where the conditional expectation of the censoring indicator given the observation belongs to a parametric family. We show that some estimated linear functionals which are based on the new semi-parametric estimator are strong consistent, asymptotically normal, and efficient under SRCM. In a small simulation study, the performance of the new estimator is illustrated under moderate sample sizes. Finally, we apply the new estimator to a well-known real dataset.
Weak Representation of the Cumulative Hazard Function under Semiparametric Random Censorship Models
(2001)
We study the estimation of some linear functionals which are based on an unknown lifetime distribution. The observations are assumed to be generated under the semi-parametric random censorship model (SRCM), that is, a random censorship model where the conditional expectation of the censoring indicator given the observation belongs to a parametric family. Under this setup a semi-parametric estimator of the survival function was introduced by the author. If the parametric model assumption is correct, it is known that the estimated functional which is based on this semi-parametric estimator is asymptotically at least as efficient as the corresponding one which rests on the nonparametric Kaplan–Meier estimator.
In this paper we show that the estimated functional which is based on this semi-parametric estimator is asymptotically efficient with respect to the class of all regular estimators under this semi-parametric model.
Visual Virology
(2012)
Suspension depletion approach for exemption of infected Solanum jasminoides cells from pospiviroids
(2018)
Despite numerous studies, viroid elimination from infected plants remains a very challenging task. This study introduces for the first time a novel ‘suspension depletion’ approach for exemption of Solanum jasminoides plants from viroids. The proposed method implies initial establishment of suspension cultures of the infected plant cells. The suspended cells were then physically treated (mild thermotherapy, 33 °C), which presumably delayed the replication of the viroid. The viroid concentration in the treated biomass was monitored weekly using pospiviroid-specific PCR. After 10–12 weeks of continuous treatment, a sufficient decrease in viroid concentration was observed such that the infection became undetectable by PCR. The treated single cells then gave rise to microcolonies on a solid culture medium and the obtained viroid-negative clones were further promoted to regenerate into viroid-free plants. Three years of accumulated experimental data suggests feasibility, broad applicability, and good efficacy of the proposed approach.
Changes in intestinal microflora in rats induced by oral exposure to low lead (II) concentrations
(2015)
Heterogeneous Composites on the Basis of Microbial Cells and Nanostructured Carbonized Sorbents
(2012)
The fact that microorganisms prefer to grow on liquid/solid phase surfaces rather than in the surrounding aqueous phase was noticed long time ago [1]. Virtually any surface – animal, mineral, or vegetable – is a subject for microbial colonization and subsequent biofilm formation. It would be adequate to name just a few notorious examples on microbial colonization of contact lenses, ship hulls, petroleum pipelines, rocks in streams and all kinds of biomedical implants. The propensity of microorganisms to become surface-bound is so profound and ubiquitous that it vindicates the advantages for attached forms over their free-ranging counterparts [2]. Indeed, from ecological and evolutionary standpoints, for many microorganisms the surface-bound state means dwelling in nutritionally favorable, non-hostile environments [3]. Therefore, in most of natural and artificial ecosystems surface-associated microorganisms vastly outnumber organisms in suspension and often organize into complex communities with features that differ dramatically from those of free cells [4].
With the variety of toothbrushes on the market, the question arises, which toothbrush is best suited to maintain oral health? This thematic review focuses first on plaque formation mechanisms and then on the plaque removal effectiveness of ultrasonic toothbrushes and their potential in preventing oral diseases like periodontitis, gingivitis, and caries. We overviewed the physical effects that occurred during brushing and tried to address the question of whether ultrasonic toothbrushes effectively reduced the microbial burden by increasing the hydrodynamic forces. The results of published studies show that electric toothbrushes, which combine ultrasonic and sonic (or acoustic and mechanic) actions, may have the most promising effect on good oral health. Existing ultrasonic/sonic toothbrush models do not significantly differ regarding the removal of dental biofilm and the reduction of gingival inflammation compared with other electrically powered toothbrushes, whereas the manual toothbrushes show a lower effectiveness.
Sampling of dry surfaces for microorganisms is a main component of microbiological safety and is of critical importance in many fields including epidemiology, astrobiology as well as numerous branches of medical and food manufacturing. Aspects of biofilm formation, analysis and removal in aqueous solutions have been thoroughly discussed in literature. In contrast, microbial communities on air-exposed (dry) surfaces have received significantly less attention. Diverse surface sampling methods have been developed in order to address various surfaces and microbial groups, but they notoriously show poor repeatability, low recovery rates and suffer from lack of mutual consistency. Quantitative sampling for viable microorganisms represents a particular challenge, especially on porous and irregular surfaces. Therefore, it is essential to examine in depth the factors involved in microorganisms’ recovery efficiency and accuracy depending on the sampling technique used. Microbial colonization, retention and community composition on different dry surfaces are very complex and rely on numerous physicochemical and biological factors. This study is devoted to analyze and review the (a) physical phenomena and intermolecular forces relevant for microbiological surface sampling; (b) challenges and problems faced by existing sampling methods for viable microorganisms and (c) current directions of engineering and research aimed at improvement of quality and efficiency of microbiological surface sampling.
A light-addressable potentiometric sensor (LAPS) is a field-effect-based potentiometric sensor with an electrolyte/insulator/semiconductor (EIS) structure, which is able to monitor analyte concentrations of (bio-)chemical species in aqueous solutions in a spatially resolved way. Therefore, it is also an appropriate tool to record 2D-chemical images of concentration variations on the sensor surface. In the present work, two differential, LAPS-based measurement principles are introduced to determine the metabolic activity of Escherichia coli (E. coli) K12 and Chinese hamster ovary (CHO) cells as test microorganisms. Hereby, we focus on i) the determination of the extracellular acidification rate (ΔpH/min) after adding glucose solutions to the cell suspensions; and ii) recording the amplitude increase of the photocurrent (Iph) related to the produced acids from E. coli K12 bacteria and CHO cells on the sensor surface by 2D-chemical imaging. For this purpose, 3D-printed multi-chamber structures were developed and mounted on the planar sensor-chip surface to define four independent compartments, enabling differential measurements with varying cell concentrations. The differential concept allows eliminating unwanted drift effects and, with the four-chamber structures, measurements on the different cell concentrations were performed simultaneously, thus reducing also the overall measuring time.
On-line monitoring of the metabolic activity of microorganisms involved in intermediate stages of biogas production plays an important role to avoid undesirable “down times” during the biogas production. In order to control this process, an on-chip differential measuring system based on the light-addressable potentiometric sensor (LAPS) principle combined with a 3D-printed multi-chamber structure has been realized. As a test microorganism, Escherichia coli K12 (E. coli K12) were used for cell-based measurements. Multi-chamber structures were developed to determine the metabolic activity of E. coli K12 in suspension for a different number of cells, responding to the addition of a constant or variable amount of glucose concentrations, enabling differential and simultaneous measurements.
A light-addressable potentiometric sensor (LAPS) is a field-effect-based potentiometric device, which detects concentration changes of an analyte solution on the sensor surface in a spatially resolved way. It uses a light source to generate electron–hole pairs inside the semiconductor, which are separated in the depletion region due to an applied bias voltage across the sensor structure and hence, a surface-potential-dependent photocurrent can be read out. However, depending on the beam angle of the light source, scattering effects can occur, which influence the recorded signal in LAPS-based differential measurements. To solve this problem, a novel illumination unit based on a field programmable gate array (FPGA) consisting of 16 small-sized tunable infrared laser-diode modules (LDMs) is developed. Due to the improved focus of the LDMs with a beam angle of only 2 mrad, undesirable scattering effects are minimized. Escherichia coli (E. coli) K12 bacteria are used as a test microorganism to study the extracellular acidification on the sensor surface. Furthermore, a salt bridge chamber is built up and integrated with the LAPS system enabling multi-chamber differential measurements with a single Ag/AgCl reference electrode.
Monitoring the cellular metabolism of bacteria in (bio)fermentation processes is crucial to control and steer them, and to prevent undesired disturbances linked to metabolically inactive microorganisms. In this context, cell-based biosensors can play an important role to improve the quality and increase the yield of such processes. This work describes the simultaneous analysis of the metabolic behavior of three different types of bacteria by means of a differential light-addressable potentiometric sensor (LAPS) set-up. The study includes Lactobacillus brevis, Corynebacterium glutamicum, and Escherichia coli, which are often applied in fermentation processes in bioreactors. Differential measurements were carried out to compensate undesirable influences such as sensor signal drift, and pH value variation during the measurements. Furthermore, calibration curves of the cellular metabolism were established as a function of the glucose concentration or cell number variation with all three model microorganisms. In this context, simultaneous (bio)sensing with the multi-organism LAPS-based set-up can open new possibilities for a cost-effective, rapid detection of the extracellular acidification of bacteria on a single sensor chip. It can be applied to evaluate the metabolic response of bacteria populations in a (bio)fermentation process, for instance, in the biogas fermentation process.
LAPS-based monitoring of metabolic responses of bacterial cultures in a paper fermentation broth
(2020)
As an alternative renewable energy source, methane production in biogas plants is gaining more and more attention. Biomass in a bioreactor contains different types of microorganisms, which should be considered in terms of process-stability control. Metabolically inactive microorganisms within the fermentation process can lead to undesirable, time-consuming and cost-intensive interventions. Hence, monitoring of the cellular metabolism of bacterial populations in a fermentation broth is crucial to improve the biogas production, operation efficiency, and sustainability. In this work, the extracellular acidification of bacteria in a paper-fermentation broth is monitored after glucose uptake, utilizing a differential light-addressable potentiometric sensor (LAPS) system. The LAPS system is loaded with three different model microorganisms (Escherichia coli, Corynebacterium glutamicum, and Lactobacillus brevis) and the effect of the fermentation broth at different process stages on the metabolism of these bacteria is studied. In this way, different signal patterns related to the metabolic response of microorganisms can be identified. By means of calibration curves after glucose uptake, the overall extracellular acidification of bacterial populations within the fermentation process can be evaluated.
LAPS are field-effect-based potentiometric sensors which are able to monitor analyte concentrations in a spatially resolved manner. Hence, a LAPS sensor system is a powerful device to record chemical imaging of the concentration of chemical species in an aqueous solution, chemical reactions, or the growth of cell colonies on the sensor surface, to record chemical images. In this work, multi-chamber 3D-printed structures made out of polymer (PP-ABS) were combined with LAPS chips to analyse differentially and simultaneously the metabolic activity of Escherichia coli K12 and Chinese hamster ovary (CHO) cells, and the responds of those cells to the addition of glucose solution.
Superparamagnetic iron oxide nanoparticles (SPION) are extensively used for magnetic resonance imaging (MRI) and magnetic particle imaging (MPI), as well as for magnetic fluid hyperthermia (MFH). We here describe a sequential centrifugation protocol to obtain SPION with well-defined sizes from a polydisperse SPION starting formulation, synthesized using the routinely employed co-precipitation technique. Transmission electron microscopy, dynamic light scattering and nanoparticle tracking analyses show that the SPION fractions obtained upon size-isolation are well-defined and almost monodisperse. MRI, MPI and MFH analyses demonstrate improved imaging and hyperthermia performance for size-isolated SPION as compared to the polydisperse starting mixture, as well as to commercial and clinically used iron oxide nanoparticle formulations, such as Resovist® and Sinerem®. The size-isolation protocol presented here may help to identify SPION with optimal properties for diagnostic, therapeutic and theranostic applications.
Impaired cerebral autoregulation and neurovascular coupling (NVC) contribute to delayed cerebral ischemia after subarachnoid hemorrhage (SAH). Retinal vessel analysis (RVA) allows non-invasive assessment of vessel dimension and NVC hereby demonstrating a predictive value in the context of various neurovascular diseases. Using RVA as a translational approach, we aimed to assess the retinal vessels in patients with SAH. RVA was performed prospectively in 24 patients with acute SAH (group A: day 5–14), in 11 patients 3 months after ictus (group B: day 90 ± 35), and in 35 age-matched healthy controls (group C). Data was acquired using a Retinal Vessel Analyzer (Imedos Systems UG, Jena) for examination of retinal vessel dimension and NVC using flicker-light excitation. Diameter of retinal vessels—central retinal arteriolar and venular equivalent—was significantly reduced in the acute phase (p < 0.001) with gradual improvement in group B (p < 0.05). Arterial NVC of group A was significantly impaired with diminished dilatation (p < 0.001) and reduced area under the curve (p < 0.01) when compared to group C. Group B showed persistent prolonged latency of arterial dilation (p < 0.05). Venous NVC was significantly delayed after SAH compared to group C (A p < 0.001; B p < 0.05). To our knowledge, this is the first clinical study to document retinal vasoconstriction and impairment of NVC in patients with SAH. Using non-invasive RVA as a translational approach, characteristic patterns of compromise were detected for the arterial and venous compartment of the neurovascular unit in a time-dependent fashion. Recruitment will continue to facilitate a correlation analysis with clinical course and outcome.