Ortsaufgelöste Messung von Zellgrößen in biologischem Gewebe mit Methoden der diffusionsgewichteten Magnetresonanztomographie

Die diffusionsgewichtete Magnetresonanztomographie (MRT) ermöglicht die ortsaufgelöste Messung der Selbstdiffusion, also der Brownschen Molekularbewegung, beispielsweise von Wasser im untersuchten Objekt. In biologischem Gewebe oder porösen Medien beeinflussen die Zell- oder Porenwände die freie Diffusion der Moleküle: Abhängig vom Abstand und der Permeabilität dieser Barrieren verringert sich die Wahrscheinlichkeit für große Diffusionsstrecken gegenüber der freien Diffusion. Aus diesem Grund mißt man in derartigen Objekten für längere Diffusionszeiten tau erniedrigte effektive Diffusionskoeffizienten D_eff und kann aus dem funktionellen Zusammenhang D_eff(tau) auf die Zellgröße und die Membranpermeabilität zurückschließen. Diese Arbeit beschreibt ein Verfahren, mit dem auf dieser Grundlage die Zellgrößen in biologischem Gewebe ortsaufgelöst gemessen werden können. Ein eindimensionales mathematisches Modell der Diffusion in einem unendlichen System permeabler Membranen wurde in Simulationsrechnungen analysiert, und eine Methode wurde entwickelt, um aus Meßwerten D_eff(tau) mit vergleichsweise wenig Rechenaufwand die Zellgröße und die Permeabilität zu berechnen. Dieses Verfahren wurde an Karotten erprobt. Es wurden erstmals Parameterbilder der Zellgröße berechnet, und die Resultate wurden mit lichtmikroskopisch gemessenen Zellgrößen verglichen; dabei ergab sich eine sehr gute Übereinstimmung der Ergebnisse aus beiden Verfahren. Außerdem wurde der Einfluß des Rauschens auf die Signalintensität in MRT-Bildern und auf die daraus berechneten Diffusionskoeffizienten untersucht. Zwei Korrekturverfahren, die den Einfluß des Rauschens beseitigen, wurden vorgeschlagen

Understanding the Ageing Consumer: Exploring Strategies for Overcoming Innovation Resistance

This thesis deals with the trend of an ageing population in Germany and the opportunities and challenges that it presents for the consumer goods industry. The goal of the research is to provide a more nuanced understanding of ageing consumers and to suggest strategies to overcome innovation resistance. It departs from the traditional product-oriented research perspective and explores domestic practices of everyday life. Using this approach, it investigates the role of household appliances in facilitating the wish of older adults to age-in-place. Due to the interdisciplinary nature of the research, a synthetic framework was created that melds and extends distinct conceptual elements from separate theories. While previous studies have largely failed to provide a detailed description of user segments, this research applies a novel market segmentation approach that assists in developing more effective innovation strategies. It has extended the Use Diffusion model (Shih & Venkatesh, 2004) by creating a number of novel sub-determinants which direct household technology use in different directions. It posits that different user segments exhibit different levels of interest in future technology acquisition. Based on an advanced understanding of use patterns, the research intends to clarify a possible application of disruptive innovations, which suggest simpler, more familiar and affordable products and services. The research followed a sequential approach to data generation. It begins with interviews conducted during home visits using the task of ‘doing the laundry’ as a focal practice, interviews with care workers, and medical practitioners. It is supplemented with focus groups comprised of the intended product users in order to generate innovation ideas. A final focus group of industry experts followed and centred on the operationalization of those ideas within an established company. Finally, the thesis developed a synthetic model to support innovation management that is not present in current conceptions

Investigations on correlations between changes of optical tissue properties and NMR relaxation times

Background: Accurate light dosimetry is a complex remaining challenge in interstitial photodynamic therapy (iPDT) for malignant gliomas. The light dosimetry should ideally be based on the tissue morphology and the individual optical tissue properties of each tissue type in the target region. First investigations are reported on using NMR information to estimate changes of individual optical tissue properties. Methods: Porcine brain tissue and optical tissue phantoms were investigated. To the porcine brain, supplements were added to simulate an edema or high blood content. The tissue phantoms were based on agar, Lipoveneous, ink, blood and gadobutrol (Gd-based MRI contrast agent). The concentrations of phantom ingredients and tissue additives are varied to compare concentration-dependent effects on optical and NMR properties. A 3-tesla wholebody MRI system was used to determine T1 and T2 relaxation times. Optical tissue properties, i.e., the spectrally resolved absorption and reduced scattering coefficient, were obtained using a single integrating sphere setup. The observed changes of NMR and optical properties were compared to each other. Results: By adjusting the NMR relaxation times and optical tissue properties of the tissue phantoms to literature values, recipes for human brain tumor, white matter and grey matter tissue phantoms were obtained that mimic these brain tissues simultaneously in both properties. For porcine brain tissue, it was observed that with increasing water concentration in the tissue, both NMR-relaxation times increased, while µa decreased and µs’ increased at 635 nm. The addition of blood to porcine brain samples showed a constant T1, while T2 shortened and the absorption coefficient at 635 nm increased. Conclusions: In this investigation, by changing sample contents, notable changes of both NMR relaxation times and optical tissue properties have been observed and their relations examined. The developed dual NMR/optical tissue phantoms can be used in iPDT research, clinical training and demonstrations

Clinically Approved MRI Contrast Agents as Imaging Labels for a Porous Iron‐Based MOF Nanocarrier: A Systematic Investigation in a Clinical MRI Setting

Metal‐organic framework nanoparticles (MOF NPs) are a promising class of NP systems that offer versatile and tunable properties. Creating a magnetic resonance imaging (MRI)‐MOF NP platform as a basis for a theranostic drug delivery system is considered an auspicious approach, as MRI is a routinely used clinical method allowing real‐time imaging. So far clinically approved MRI contrast agents (CAs) have not been investigated systematically for the visualization of loading and release from MOF NPs. Here, loading and release of six clinically approved CAs from the MOF MIL‐100(Fe) are investigated in a clinical MRI setting. Standard procedures, beginning with sample preparation up to MRI methods, are established for that purpose. Results are reproduced and verified by Inductively Coupled Plasma Atomic Emission Spectrometry (ICP‐AES) and thiocyanate testing. The macrocyclic CA gadoterate meglumine is identified as the best CA candidate for labeling MIL‐100(Fe). The CA is successfully loaded after 1 h, and also effectively released within the first hour. The MR‐active CA and iron residuals in supernatants are differentiable based on MRI only and concentrations can be successfully calculated. The presented systematic approach suggests procedures and MRI‐methodology that can be used as blueprint strategy when labeling porous NPs with clinically approved MRI CAs

Bayesian pharmacokinetic modeling of dynamic contrast-enhanced magnetic resonance imaging: validation and application

Tracer-kinetic analysis of dynamic contrast-enhanced magnetic resonance imaging data is commonly performed with the well-known Tofts model and nonlinear least squares (NLLS) regression. This approach yields point estimates of model parameters, uncertainty of these estimates can be assessed e.g. by an additional bootstrapping analysis. Here, we present a Bayesian probabilistic modeling approach for tracer-kinetic analysis with a Tofts model, which yields posterior probability distributions of perfusion parameters and therefore promises a robust and information-enriched alternative based on a framework of probability distributions. In this manuscript, we use the quantitative imaging biomarkers alliance (QIBA) Tofts phantom to evaluate the Bayesian tofts model (BTM) against a bootstrapped NLLS approach. Furthermore, we demonstrate how Bayesian posterior probability distributions can be employed to assess treatment response in a breast cancer DCE-MRI dataset using Cohen's d. Accuracy and precision of the BTM posterior distributions were validated and found to be in good agreement with the NLLS approaches, and assessment of therapy response with respect to uncertainty in parameter estimates was found to be excellent. In conclusion, the Bayesian modeling approach provides an elegant means to determine uncertainty via posterior distributions within a single step and provides honest information about changes in parameter estimates

Psychiatric disorders and health service utilization in unemployed youth

Aim: Youth unemployment is associated with increased levels of anxiety, depression, alcohol abuse, reduced self-esteem and satisfaction with life. Up to date data based on standardized psychiatric diagnostic assessments in adolescent or young adult unemployment is very scarce. To our knowledge, this study has, for the first time, assessed both Axis-I (non-personality) and Axis-II (personality) psychiatric disorders and related constructs in a preselected sample of unemployed individuals. Subjects and methods: Subjects were aged 16–24 and lived in the large urban region of Essen, Germany. They were referred by case managers of the vocational services centre to the on-site psychiatric out-patient unit. SCID-I and II were administered to assess DSM-IV diagnoses. Symptom Checklist 90 R, Beck Depression Inventory and Client Sociodemographic Service Receipt Inventory were used to measure severity of psychopathology and health service utilization. Results: In all, 98% of the unemployed young adults suffered from mental disorders. Mood and anxiety disorders were the most common Axis-I disorders (47.9 and 33.4%). Altogether, 58.2% of probands met diagnostic criteria for a personality disorder; a borderline personality disorder accounted for one half of these disorders. Despite a 49% rate of Axis-I and II comorbidity and severe psychopathology, the majority of subjects were untreated and mental health service utilization in general was low. The diagnosis of a personality disorder was related to a 2.7-fold risk of dropping out of job reintegration programmes. Conclusion: Unemployed young adults referred for a psychiatric assessment have a high rate of both Axis-I and II disorders, which need to be considered upon planning individual-based vocational rehabilitation programs

Emergent spatial patterns of competing benthic and pelagic algae in a river network: A parsimonious basin-scale modeling analysis.

Algae, as primary producers in riverine ecosystems, are found in two distinct habitats: benthic and pelagic algae typically prevalent in shallow/small and deep/large streams, respectively. Over an entire river continuum, spatiotemporal patterns of the two algal communities reflect specificity in habitat preference determined by geomorphic structure, hydroclimatic controls, and spatiotemporal heterogeneity in nutrient loads from point- and diffuse-sources. By representing these complex interactions between geomorphic, hydrologic, geochemical, and ecological processes, we present here a new river-network-scale dynamic model (CnANDY) for pelagic (A) and benthic (B) algae competing for energy and one limiting nutrient (phosphorus, P). We used the urbanized Weser River Basin in Germany (7th-order; ~8.4 million population; ~46 K km2) as a case study and analyzed simulations for equilibrium mass and concentrations under steady median river discharge. We also examined P, A, and B spatial patterns in four sub-basins. We found an emerging pattern characterized by scaling of P and A concentrations over stream-order ω, whereas B concentration was described by three distinct phases. Furthermore, an abrupt algal regime shift occurred in intermediate streams from B dominance in ω≤3 to exclusive A presence in ω≥6. Modeled and long-term basin-scale monitored dissolved P concentrations matched well for ω>4, and with overlapping ranges in ω<3. Power-spectral analyses for the equilibrium P, A, and B mass distributions along hydrological flow paths showed stronger clustering compared to geomorphological attributes, and longer spatial autocorrelation distance for A compared to B. We discuss the implications of our findings for advancing hydro-ecological concepts, guiding monitoring, informing management of water quality, restoring aquatic habitat, and extending CnANDY model to other river basins

Monitoring Early Response to Anti-Angiogenic Therapy: Diffusion-Weighted Magnetic Resonance Imaging and Volume Measurements in Colon Carcinoma Xenografts

Objectives: To evaluate the use of diffusion-weighted MRI (DW-MRI) and volume measurements for early monitoring of antiangiogenic therapy in an experimental tumor model. Materials and Methods: 23 athymic nude rats, bearing human colon carcinoma xenografts (HT-29) were examined before and after 6 days of treatment with regorafenib (n=12) or placebo (n=11) in a clinical 3-Tesla MRI. For DW-MRI, a single-shot EPI sequence with 9 b-values (10-800 s/mm(2)) was used. The apparent diffusion coefficient (ADC) was calculated voxelwise and its median value over a region of interest, covering the entire tumor, was defined as the tumor ADC. Tumor volume was determined using T2-weighted images. ADC and volume changes between first and second measurement were evaluated as classifiers by a receiver-operator-characteristic (ROC) analysis individually and combined using Fisher's linear discriminant analysis (FLDA). Results: All ADCs and volumes are stated as median +/- standard deviation. Tumor ADC increased significantly in the therapy group (0.76 +/- 0.09x10(-3) mm(2)/s to 0.90 +/- 0.12x10(-3) mm(2)/s;p<0.001), with significantly higher changes of tumor ADC than in the control group (0.10 +/- 0.11x10(-3) mm(2)/s vs. 0.03 +/- 0.09x10(-3) mm(2)/s;p = 0.027). Tumor volume increased significantly in both groups (therapy: 347.8 +/- 449.1 to 405.3 +/- 823.6 mm(3);p = 0.034;control: 219.7 +/- 79.5 to 443.7 +/- 141.5 mm(3);p<0.001), however, the therapy group showed significantly reduced tumor growth (33.30 +/- 47.30% vs. 96.43 +/- 31.66%;p<0.001). Area under the curve and accuracy of the ADC-based ROC analysis were 0.773 and 78.3%;and for the volume change 0.886 and 82.6%. The FLDA approach yielded an AUC of 0.985 and an accuracy of 95.7%. Conclusions: Regorafenib therapy significantly increased tumor ADC after 6 days of treatment and also significantly reduced tumor growth. However, ROC analyses using each parameter individually revealed a lack of accuracy in discriminating between therapy and control group. The combination of both parameters using FLDA substantially improved diagnostic accuracy, thus highlighting the potential of multi-parameter MRI as an imaging biomarker for non-invasive early tumor therapy monitoring