Fachbereich Medizintechnik und Technomathematik
Refine
Year of publication
Institute
- Fachbereich Medizintechnik und Technomathematik (2092)
- IfB - Institut für Bioengineering (537)
- INB - Institut für Nano- und Biotechnologien (527)
- Fachbereich Chemie und Biotechnologie (40)
- Fachbereich Energietechnik (7)
- Fachbereich Luft- und Raumfahrttechnik (7)
- Institut fuer Angewandte Polymerchemie (7)
- Nowum-Energy (6)
- Fachbereich Wirtschaftswissenschaften (3)
- Fachbereich Elektrotechnik und Informationstechnik (2)
Document Type
- Article (1585)
- Conference Proceeding (253)
- Book (98)
- Part of a Book (63)
- Doctoral Thesis (28)
- Patent (17)
- Report (15)
- Other (9)
- Conference: Meeting Abstract (5)
- Habilitation (4)
Keywords
- Biosensor (25)
- Finite-Elemente-Methode (16)
- CAD (15)
- civil engineering (14)
- Bauingenieurwesen (13)
- Einspielen <Werkstoff> (13)
- shakedown analysis (9)
- FEM (6)
- Limit analysis (6)
- Shakedown analysis (6)
Magnetic nanoparticles (MNP) are widely investigated for biomedical applications in diagnostics (e.g. imaging), therapeutics (e.g. hyperthermia) and general biosensing. For all these applications, the MNPs’ unique magnetic relaxation mechanism in an alternating magnetic field (AFM) is stimulated to induce desired effects. Whereas magnetic fluid hyperthermia (MFH) and magnetic particle imaging (MPI) are the most prominent examples for biomedical application, we investigate the relatively new biosensing application of frequency mixing magnetic detection (FMMD) from a fundamental perspective. Generally, we ask how specific MNP parameters (core size, magnetic anisotropy) influence the signal, specifically we predict the most effective MNP core size for signal generation. In FMMD, simultaneously two AFM are applied: a low-frequency magnetic driving field, driving MNP close to saturation, and a high-frequency excitation field that probes MNP susceptibility: . Resulting from the nonlinear magnetization of the MNP, harmonics of both individual incident frequencies as well as intermodulation products of these frequencies are generated. In this work, we present numerical Monte-Carlo(MC)-based simulations of the MNP relaxation process, solving the Landau-Lifshitz-Gilbert (LLG) equation to predict FMMD signals: As Figure 1 shows for the first four intermodulation signals , with , we can clearly see that larger core sizes generally increase the signal intensity. Same trend is predicted by a simple Langevin-function based thermal equilibrium model. Both predictions include a lognormal size distribution. The effect of core size distribution presumably dominates the effect of magnetic anisotropy. The findings are supported by comparison with experimental data and help to identify which MNP are best suited for magnetic biosensing applications using FMMD.
Frequency mixing magneticdetection(FMMD) has been widely utilized as a measurement technique in magnetic immunoassays. It can also be used for characterization[1]and distinction[2](also known as “colorization”) ofdifferent types of magnetic nanoparticlesaccording totheircore sizes.It is well known that the large particles contribute most of the FMMD signal. Typically, 90% of the signal stems from the largest 10% of the particles [1]. This leads to ambiguities in core size fitting since thecontribution of thesmall sized particles is almostundetectable among the strong responses from the large ones. In this work, we report on how this ambiguity can be overcome. Magnetic nanoparticle samples from Micromod (Rostock, Germany) were prepared in liquid and filterbound state. Their FMMD response at mixing frequencies f1 ± nf2 to magnetic excitation H(t)=H0+H1sin(2 f1t)+H2sin(2 f2t),with H1=1.3mT/μ0 at f1=40.5kHzandH2=16mT/μ0 at f2=63Hz,was measured as a function ofoffset field strength H0= (0,…,24) mT/μ0.The signal calculated fromLangevin model in thermodynamic equilibrium[1]with a lognormal core size distribution fL(dc,d0, ,A) = Aexp(–ln²(dc/d0)/(2 ²))/(dc (2 )1/2)was fitted to the experimental data. For each choice of median diameter d0, pairs of parameters ( ,A) are found which yield excellent fit results with R²>0.99.All the lognormal core size distributions shown in Figure (a) are compatible with the measurements because their large-size tails are almost equal. However, all distributions have different number of particles and different total iron content. We determined the samples’ total iron mass with inductively coupled plasma optical emission spectrometry(ICP-OES) and, out of all possible lognormal distributions, determined the one with the same amount of iron. With this additional externally measured parameter, we resolved the ambiguity in core size distribution and determined the parameters (d0, ,A).
Hyperthermia with the use of magnetic nanoparticles (MNP) is a challenging but most promising approach for cancer therapy. After being magnetically trapped at the tumor site, MNP are heated in alternating magnetic fields (AMF) to approx. 43 °C, which causes tumor cell apoptosis. For an effective and controllable hyperthermia application, two parameters are most important: the amount of internalized MNP in tumor cells and their heating characteristics in AMF. In this study, we evaluated if a sufficient temperature could be achieved by cell internalized MNP heated up in AMF and if cell death could be induced in this way. The heating of pancreatic tumor cell lines MiaPaCa-2 and BxPC-3 loaded with different amounts of selfsynthesized magnetoliposomes nanoparticles (MLs) was measured with a custom-built setup. The MLs consisted of a fluorescent bi-layer of phospholipids and multiple magnetite (Fe3O4) cores with a diameter of (10.0 ± 0.5) nm each. The hydrodynamic diameter of the MLs was (90 ± 5) nm. Cell loading was performed by incubation of tumor cells for up to 24 h at 37 °C in a DMEM cell medium with MLs, which had an iron concentration of 150 μg/mL. Transmission electron microscopy and fluorescence microscopy were used to depict the uptake of MLs into the tumor cells (see Figure). The internalized iron-content per cell was determined with a magnetic particle spectrometer (MPS). After application of AMF for approx. 30 min, cell viability was assessed by clonogenic assay. The cellular uptake of MLs was time-dependent, cell line-specific and saturated: For both MiaPaCa-2 and BxPC-3 cell lines, the MLs cell internalization followed an exponential growth function which saturated after about 24 h cell incubation time at an iron load of (110 ± 6) pg/cell and (30 ± 2) pg/cell, respectively. The time constants of the exponential growth were (7.2 ± 1.4) h and (4.0 ± 0.6) h, respectively. In AMF, cells with the saturated MLs loading reached temperatures of approx. 44 °C and 43.5 °C, which caused the cell survival fraction to drop to approx. one third compared to untreated tumor cells for both MiaPaCa-2 and BxPC-3 cell lines. These results demonstrate the feasibility of hyperthermia in pancreatic cancer treatment by confirming cell death of pancreatic tumor cells at temperatures of approx. 43 °C. Further investigations are planned, which aim for the optimization of MNP dosage in targeting experiments as well as the assessment of incubation times and AMF parameters needed for a successful hyperthermic therapy.
In the innovative tumor treatment approach of magnetic fluid hyperthermia (MFH), magnetic nanoparticles (MNP) are accumulated at the tumor site and heated in a time-varying magnetic field to substantially damage the tumor (1). This tumor damage depends mainly on the rate and amount of heat delivered via the MNP locally, which is in turn governed by a multitude of variables including the applied field amplitude and frequency, particle size and size distribution. In this study, we compare measured heating rates of MNP with sizes ranging from 21 nm to 28 nm with those obtained from Monte Carlo simulations of non-equilibrium Langevin dynamics to predict particle sizes and field amplitude /frequency settings for optimized MFH within medically safe tolerances. We have synthesized monodisperse iron-oxide MNP via thermal decomposition, coated with poly(ethylene glycol) methyl ether amine (mPEG NH2) as reported in (2). Transmission electron microscopy analysis yielded core sizes (and log-normal distribution width) of 21.9 nm (0.04), 23.1 nm (0.05), 25.3 nm (0.08) and 27.7 nm (0.07). These MNP were subjected to magnetic fields with amplitudes h0 = (6...20) mT/ 0-1 and frequencies f = (176...993) kHz in a magneTherm hyperthermia device (nanoTherics Ltd., Newcastle under Lyme, UK). From the recorded timetemperature curves we calculated the specific loss power (SLP) as a measure of the heating rate: SLP values increased generally with size and frequency (Fig. 1a), as well as with the field amplitude (not shown here). Monte Carlo based stochastic Langevin equation simulations combining Néel and Brownian rotation relaxation and thermal activation (based on (3)) verified this trend (Fig. 1b). Under the assumption of an upper field limitation of f[kHz] · h0[mT/ 0-1] 1758 imposed by medical safety requirements (4), we simulated a heat map based on the parameters obtained from fitting simulation to experiment (Fig. 1c). This map shows maximum SLP values for frequencies f ~ 100 kHz (equivalent to h0 ~ 17.5 mT/ 0-1) at particle sizes of 29 nm and greater. These results can provide a pivotal and integral tool for predicting particle sizes and applied field settings for optimized MFH.
The heat generated by magnetic nanoparticles (MNP) forms the basis of magnetic fluid hyperthermia (MFH) tumor therapy and arises from MNP magnetic moments relaxing in an alternating magnetic field. In physiological environments MNP strongly interact with cells, binding to their membranes as well as internalizing inside lysosomes, which alters the MNP magnetic relaxation. In the present study, we investigate the heating behavior of MNP in-vitro for different binding states and compare it to the heating of trapped MNP in dedicated hydrogels of different mesh size, mimicking different immobilization states. We used iron-oxide MNP (mean core size 10 nm) with a biocompatible phospholipid coating, referred to as magnetoliposomes (ML), for in-vitro studies, and with citric acid coating (CA-MNP) for studies in hydrogels. All samples were subjected to an AMF (40 kA/m, 270 kHz) for 30 min, and from the recorded time-temperature curve, the specific loss power (SLP) value was calculated. In-vitro experiments were performed with L929 cells, which were incubated for 24 h with 225 μg(Fe)/mL ML dispersed in RPMI cell medium. The results of the SLP values were analyzed regarding the internalized ML amount with respect to ML residuals in RMPI medium and compared to fully immobilized ML after freeze-drying (FD): The SLP value of 10.1 % intracellular ML decreased by 20 %, the SLP value of 100 % intracellular ML decreased by 60 % and that of FD-ML decreased by 70 % (Fig 1a). The influence of gradual immobilization of MNP on the heating was investigated by mixing CA-MNP in low-melting agarose and polyacrylamide hydrogels. In agarose and polyacrylamide gels the mean mesh size can be tuned via the amount of monomers and cross-linkers, respectively, and in this way the state of MNP immobilization is influenced. SLP values decreased by up to 40 % in agarose gels for mesh sizes smaller than the hydrodynamic size dH = 20.6 nm. A comparable decrease was observed in polyacrylamide gels (Fig 1b & c). We attribute this drop in SLP values to a gradual immobilization of MNP trapped in the hydrogels, which blocks particle relaxation and therefore decreases heating efficiency. This agrees very well with the results of the in-vitro measurements. The relative difference in the SLP drop in agarose and polyacrylamide hydrogels for similar mesh sizes might be explained by their gel-specific microstructures, which influence the MNP freedom of movement. For validation of these results, further investigations of the relaxation behavior of such trapped MNP via magnetic particle spectroscopy are currently under progress.
Easy-read and large language models: on the ethical dimensions of LLM-based text simplification
(2024)
The production of easy-read and plain language is a challenging task, requiring well-educated experts to write context-dependent simplifications of texts. Therefore, the domain of easy-read and plain language is currently restricted to the bare minimum of necessary information. Thus, even though there is a tendency to broaden the domain of easy-read and plain language, the inaccessibility of a significant amount of textual information excludes the target audience from partaking or entertainment and restricts their ability to live life autonomously. Large language models can solve a vast variety of natural language tasks, including the simplification of standard language texts to easy-read or plain language. Moreover, with the rise of generative models like GPT, easy-read and plain language may be applicable to all kinds of natural language texts, making formerly inaccessible information accessible to marginalized groups like, a.o., non-native speakers, and people with mental disabilities. In this paper, we argue for the feasibility of text simplification and generation in that context, outline the ethical dimensions, and discuss the implications for researchers in the field of ethics and computer science.
The quest for scientifically advanced and sustainable solutions is driven by growing environmental and economic issues associated with coal mining, processing, and utilization. Consequently, within the coal industry, there is a growing recognition of the potential of microbial applications in fostering innovative technologies. Microbial-based coal solubilization, coal beneficiation, and coal dust suppression are green alternatives to traditional thermochemical and leaching technologies and better meet the need for ecologically sound and economically viable choices. Surfactant-mediated approaches have emerged as powerful tools for modeling, simulation, and optimization of coal-microbial systems and continue to gain prominence in clean coal fuel production, particularly in microbiological co-processing, conversion, and beneficiation. Surfactants (surface-active agents) are amphiphilic compounds that can reduce surface tension and enhance the solubility of hydrophobic molecules. A wide range of surfactant properties can be achieved by either directly influencing microbial growth factors, stimulants, and substrates or indirectly serving as frothers, collectors, and modifiers in the processing and utilization of coal. This review highlights the significant biotechnological potential of surfactants by providing a thorough overview of their involvement in coal biodegradation, bioprocessing, and biobeneficiation, acknowledging their importance as crucial steps in coal consumption.
Dieses Buch lädt dazu ein, die Welt um uns herum aus einem neuen Blickwinkel zu betrachten und dabei die spannende Verbindung zwischen der Mathematik und unserem täglichen Leben zu entdecken – denn um die Technologien und Entwicklungen unserer modernen Gesellschaft zu verstehen, benötigen wir ein intuitives Verständnis grundlegender mathematischer Ideen. In diesem Buch geht es um diese Grundlagen, vor allem aber um ihre praktische Anwendung im Alltag: Gemeinsam begeben wir uns auf eine unterhaltsame Reise und entdecken dabei, wie Mathematik in vielfältiger Weise allgegenwärtig ist. Anschauliche Beispiele zeigen, wie wir täglich – oft unbewusst – mathematische Ideen nutzen und wie wir mit Hilfe von Mathematik bessere Entscheidungen treffen können.
Nach einer Einführung in Algorithmen und Optimierungsprobleme, geht es im weiteren Verlauf um die Modellierung von Zufall und Unsicherheiten. Zum Ende des Buchs werden die Themen zusammengeführt und Algorithmen für Anwendungen besprochen, bei denen der Zufall eine entscheidende Rolle spielt.
Sexism in online media comments is a pervasive challenge that often manifests subtly, complicating moderation efforts as interpretations of what constitutes sexism can vary among individuals. We study monolingual and multilingual open-source text embeddings to reliably detect sexism and misogyny in Germanlanguage online comments from an Austrian newspaper. We observed classifiers trained on text embeddings to mimic closely the individual judgements of human annotators. Our method showed robust performance in the GermEval 2024 GerMS-Detect Subtask 1 challenge, achieving an average macro F1 score of 0.597 (4th place, as reported on Codabench). It also accurately predicted the distribution of human annotations in GerMS-Detect Subtask 2, with an average Jensen-Shannon distance of 0.301 (2nd place). The computational efficiency of our approach suggests potential for scalable applications across various languages and linguistic contexts.