Quantitative Magnetic Resonance Imaging of the Liver and Heart In Iron Overload

Systemic iron overload (IO) is considered a principal determinant in the clinical outcome of different forms of IO and in allogeneic hematopoietic stem cell transplantation (alloSCT). However, indirect markers for iron do not provide exact quantification of iron burden, and the evidence of iron-induced adverse effects in hematological diseases has not been established. Hepatic iron concentration (HIC) has been found to represent systemic IO, which can be quantified safely with magnetic resonance imaging (MRI), based on enhanced transverse relaxation. The iron measurement methods by MRI are evolving. The aims of this study were to implement and optimise the methodology of non-invasive iron measurement with MRI to assess the degree and the role of IO in the patients. An MRI-based HIC method (M-HIC) and a transverse relaxation rate (R2*) from M-HIC images were validated. Thereafter, a transverse relaxation rate (R2) from spin-echo imaging was calibrated for IO assessment. Two analysis methods, visual grading and rSI, for a rapid IO grading from in-phase and out-of-phase images were introduced. Additionally, clinical iron indicators were evaluated. The degree of hepatic and cardiac iron in our study patients and IO as a prognostic factor in patients undergoing alloSCT were explored. In vivo and in vitro validations indicated that M-HIC and R2* are both accurate in the quantification of liver iron. R2 was a reliable method for HIC quantification and covered a wider HIC range than M-HIC and R2*. The grading of IO was able to be performed rapidly with the visual grading and rSI methods. Transfusion load was more accurate than plasma ferritin in predicting transfusional IO. In patients with hematological disorders, the prevalence of hepatic IO was frequent, opposite to cardiac IO. Patients with myelodysplastic syndrome were found to be the most susceptible to IO. Pre-transplant IO predicted severe infections during the early post-transplant period, in contrast to the reduced risk of graft-versus-host disease. Iron-induced, poor transplantation results are most likely to be mediated by severe infections.

From Unspecific Quenching to Specific Signaling: Functional GTPase Assays Utilizing Quenching Resonance Energy Transfer (QRET) Technology

The aim of the work presented in this study was to demonstrate the wide applicability of a single-label quenching resonance energy transfer (QRET) assay based on time-resolved lanthanide luminescence. QRET technology is proximity dependent method utilizing weak and unspecific interaction between soluble quencher molecule and lanthanide chelate. The interaction between quencher and chelate is lost when the ligand binds to its target molecule. The properties of QRET technology are especially useful in high throughput screening (HTS) assays. At the beginning of this study, only end-point type QRET technology was available. To enable efficient study of enzymatic reactions, the QRET technology was further developed to enable measurement of reaction kinetics. This was performed using proteindeoxyribonuclei acid (DNA) interaction as a first tool to monitor reaction kinetics. Later, the QRET was used to study nucleotide exchange reaction kinetics and mutation induced effects to the small GTPase activity. Small GTPases act as a molecular switch shifting between active GTP bound and inactive GDP bound conformation. The possibility of monitoring reaction kinetics using the QRET technology was evaluated using two homogeneous assays: a direct growth factor detection assay and a nucleotide exchange monitoring assay with small GTPases. To complete the list, a heterogeneous assay for monitoring GTP hydrolysis using small GTPases, was developed. All these small GTPase assays could be performed using nanomolar protein concentrations without GTPase pretreatment. The results from these studies demonstrated that QRET technology can be used to monitor reaction kinetics and further enable the possibility to use the same method for screening.

Parameter estimation for Chaotic or Stochastic Dynamics

Since its discovery, chaos has been a very interesting and challenging topic of research. Many great minds spent their entire lives trying to give some rules to it. Nowadays, thanks to the research of last century and the advent of computers, it is possible to predict chaotic phenomena of nature for a certain limited amount of time. The aim of this study is to present a recently discovered method for the parameter estimation of the chaotic dynamical system models via the correlation integral likelihood, and give some hints for a more optimized use of it, together with a possible application to the industry. The main part of our study concerned two chaotic attractors whose general behaviour is diff erent, in order to capture eventual di fferences in the results. In the various simulations that we performed, the initial conditions have been changed in a quite exhaustive way. The results obtained show that, under certain conditions, this method works very well in all the case. In particular, it came out that the most important aspect is to be very careful while creating the training set and the empirical likelihood, since a lack of information in this part of the procedure leads to low quality results.

Stochastic particle models: mean reversion and burgers dynamics. An application to commodity markets

The aim of this study is to propose a stochastic model for commodity markets linked with the Burgers equation from fluid dynamics. We construct a stochastic particles method for commodity markets, in which particles represent market participants. A discontinuity in the model is included through an interacting kernel equal to the Heaviside function and its link with the Burgers equation is given. The Burgers equation and the connection of this model with stochastic differential equations are also studied. Further, based on the law of large numbers, we prove the convergence, for large N, of a system of stochastic differential equations describing the evolution of the prices of N traders to a deterministic partial differential equation of Burgers type. Numerical experiments highlight the success of the new proposal in modeling some commodity markets, and this is confirmed by the ability of the model to reproduce price spikes when their effects occur in a sufficiently long period of time.