Structural and practical identifiability of contrast transport models for DCE-MRI.

Contrast transport models are widely used to quantify blood flow and transport in dynamic contrast-enhanced magnetic resonance imaging. These models analyze the time course of the contrast agent concentration, providing diagnostic and prognostic value for many biological systems. Thus, ensuring accuracy and repeatability of the model parameter estimation is a fundamental concern. In this work, we analyze the structural and practical identifiability of a class of nested compartment models pervasively used in analysis of MRI data. We combine artificial and real data to study the role of noise in model parameter estimation. We observe that although all the models are structurally identifiable, practical identifiability strongly depends on the data characteristics. We analyze the impact of increasing data noise on parameter identifiability and show how the latter can be recovered with increased data quality. To complete the analysis, we show that the results do not depend on specific tissue characteristics or the type of enhancement patterns of contrast agent signal.

A stochastic hierarchical model for low grade glioma evolution.

A stochastic hierarchical model for the evolution of low grade gliomas is proposed. Starting with the description of cell motion using a piecewise diffusion Markov process (PDifMP) at the cellular level, we derive an equation for the density of the transition probability of this Markov process based on the generalised Fokker-Planck equation. Then, a macroscopic model is derived via parabolic limit and Hilbert expansions in the moment equations. After setting up the model, we perform several numerical tests to study the role of the local characteristics and the extended generator of the PDifMP in the process of tumour progression. The main aim focuses on understanding how the variations of the jump rate function of this process at the microscopic scale and the diffusion coefficient at the macroscopic scale are related to the diffusive behaviour of the glioma cells and to the onset of malignancy, i.e., the transition from low-grade to high-grade gliomas.

Structural and practical identifiability of contrast transport models for DCE-MRI.

Compartment models are widely used to quantify blood flow and transport in dynamic contrast-enhanced magnetic resonance imaging. These models analyze the time course of the contrast agent concentration, providing diagnostic and prognostic value for many biological systems. Thus, ensuring accuracy and repeatability of the model parameter estimation is a fundamental concern. In this work, we analyze the structural and practical identifiability of a class of nested compartment models pervasively used in analysis of MRI data. We combine artificial and real data to study the role of noise in model parameter estimation. We observe that although all the models are structurally identifiable, practical identifiability strongly depends on the data characteristics. We analyze the impact of increasing data noise on parameter identifiability and show how the latter can be recovered with increased data quality. To complete the analysis, we show that the results do not depend on specific tissue characteristics or the type of enhancement patterns of contrast agent signal.

Multi-Cue Kinetic Model with Non-Local Sensing for Cell Migration on a Fiber Network with Chemotaxis.

Cells perform directed motion in response to external stimuli that they detect by sensing the environment with their membrane protrusions. Precisely, several biochemical and biophysical cues give rise to tactic migration in the direction of their specific targets. Thus, this defines a multi-cue environment in which cells have to sort and combine different, and potentially competitive, stimuli. We propose a non-local kinetic model for cell migration in which cell polarization is influenced simultaneously by two external factors: contact guidance and chemotaxis. We propose two different sensing strategies, and we analyze the two resulting transport kinetic models by recovering the appropriate macroscopic limit in different regimes, in order to observe how the cell size, with respect to the variation of both external fields, influences the overall behavior. This analysis shows the importance of dealing with hyperbolic models, rather than drift-diffusion ones. Moreover, we numerically integrate the kinetic transport equations in a two-dimensional setting in order to investigate qualitatively various scenarios. Finally, we show how our setting is able to reproduce some experimental results concerning the influence of topographical and chemical cues in directing cell motility.

Modeling invasion patterns in the glioblastoma battlefield.

Glioblastoma is the most aggressive tumor of the central nervous system, due to its great infiltration capacity. Understanding the mechanisms that regulate the Glioblastoma invasion front is a major challenge with preeminent potential clinical relevances. In the infiltration front, the key features of tumor dynamics relate to biochemical and biomechanical aspects, which result in the extension of cellular protrusions known as tumor microtubes. The coordination of metalloproteases expression, extracellular matrix degradation, and integrin activity emerges as a leading mechanism that facilitates Glioblastoma expansion and infiltration in uncontaminated brain regions. We propose a novel multidisciplinary approach, based on in vivo experiments in Drosophila and mathematical models, that describes the dynamics of active and inactive integrins in relation to matrix metalloprotease concentration and tumor density at the Glioblastoma invasion front. The mathematical model is based on a non-linear system of evolution equations in which the mechanisms leading chemotaxis, haptotaxis, and front dynamics compete with the movement induced by the saturated flux in porous media. This approach is able to capture the relative influences of the involved agents and reproduce the formation of patterns, which drive tumor front evolution. These patterns have the value of providing biomarker information that is related to the direction of the dynamical evolution of the front and based on static measures of proteins in several tumor samples. Furthermore, we consider in our model biomechanical elements, like the tissue porosity, as indicators of the healthy tissue resistance to tumor progression.

Pregnancy, oral health and dental education: an overview on the northeast of Italy.

Objectives To evaluate the level of knowledge of pregnant women and puerpers about oral health and prevention during and after gestation. Methods One hundred women aged 18-49 years (mean age 33±6 years) were included in this cross-sectional study. An anonymous questionnaire with 24 items related to oral health has been administered during or just after pregnancy. Firstly, answers have been analyzed on the full population and then subdividing the sample on the base of age ranges (G1: 18-25 years, G2: 26-35 years and G3: >35 years) and number of pregnancies (FP: first pregnancy; SP: second or more pregnancies). Parametric tests have been chosen for the statistical analysis; in particular, Anova test for independent samples was used to evaluate differences of baseline demographic characteristics among subgroups G1, G2, G3 while chi-square test was used for FP and SP subgroups. Anova test was also used to intercept differences on answers given to the questionnaire among G1, G2 and G3 group; for FP and SP group was used t-test. Results Level of information and knowledge of the full sample was medium-low and no significant differences have been observed between groups regarding awareness of the own level of the oral hygiene and knowledge of oral care. Conclusions Results of this survey underline the high necessity of educational programs regarding oral care in pregnant and puerpers women. A strict collaboration between medical figures (dentist, oral hygienist, gynecologist and obstetric) is strongly encouraged to spread the concept of prevention.

Glioma invasion and its interplay with nervous tissue and therapy: A multiscale model.

A multiscale mathematical model for glioma cell migration and proliferation is proposed, taking into account a possible therapeutic approach. Starting with the description of processes occurring at the subcellular level, the equation for the mesoscopic level is formulated and a macroscopic model is derived, via parabolic limit and Hilbert expansions in the moment equations. After the model set up and the study of the well-posedness of this macroscopic setting, we investigate the role of the fibers in the tumor dynamics. In particular, we focus on the fiber density function, with the aim of comparing some common choices present in the literature and understanding which differences arise in the description of the actual fiber density and orientation. Finally, some numerical simulations, based on real data, highlight the role of each modelled process in the evolution of the solution of the macroscopic equation.

Psychological care of caregivers, nurses and physicians: a study of a new approach.

There is much evidence demonstrating that psychosocial interventions in caregivers and oncological staff produce an improvement in their patients' quality of life. The aim of this explorative study was to evaluate the effect of a new approach in promoting more functional ways to face stressful situations in the constellation of people around patients: caregivers, physicians and nurses. Thirty-four subjects were divided into three groups: 10 caregivers, 11 physicians, and 13 nurses. A "Balint Group" method modified according to a mindfulness technique was used as the intervention. Three assessment tools were administered to the participants at baseline, during, and after completion of the study: the Response Evaluation Measure (REM-71), the Satisfaction Profile (SAT-P), and the Group Climate Questionnaire (GCQ). Mean values of defense mechanisms determined by the REM-71 were compared with those of the standard population. At baseline, we observed a prevalence of immature defenses in the three groups, with mean values above those in the standard population. After the psychological intervention, a tendency to normalization of the mean values was observed, indicating the development of more adaptive ways of using defense mechanisms and the effectiveness of the intervention. Group climate, assessed through the GCQ, showed an increase in the "Engagement" factor and a decline in the "Conflict" factor in all groups. This study suggests that group treatment focused on changing personal responses to stressful situations can induce more adaptive strategies enabling caregivers, hematologists, and nurses to help patients better and thereby improve their quality of life.