Cristina Campi

Direct inhibition of hexokinase activity by metformin at least partially impairs glucose metabolism and tumor growth in experimental breast cancer

Emerging evidence suggests that metformin, a widely used anti-diabetic drug, may be useful in the prevention and treatment of different cancers. In the present study, we demonstrate that metformin directly inhibits the enzymatic function of hexokinase (HK) I and II in a cell line of triple-negative breast cancer (MDA-MB-231). The inhibition is selective for these isoforms, as documented by experiments with purified HK I and II as well as with cell lysates. Measurements of 18F-fluoro-deoxyglycose uptake document that it is dose- and time-dependent and powerful enough to virtually abolish glucose consumption despite unchanged availability of membrane glucose transporters. The profound energetic imbalance activates phosphorylation and is subsequently followed by cell death. More importantly, the “in vivo” relevance of this effect is confirmed by studies of orthotopic xenografts of MDA-MB-231 cells in athymic (nu/nu) mice. Administration of high drug doses after tumor development caused an evident tumor necrosis in a time as short as 48 h. On the other hand, 1 mo metformin treatment markedly reduced cancer glucose consumption and growth. Taken together, our results strongly suggest that HK inhibition contributes to metformin therapeutic and preventive potential in breast cancer.

Dynamical MEG Source Modeling with Multi-Target Bayesian Filtering

We present a Bayesian filtering approach for automatic estimation of dynamical source models from magnetoencephalographic data. We apply multi‐target Bayesian filtering and the theory of Random Finite Sets in an algorithm that recovers the life times, locations and strengths of a set of dipolar sources. The reconstructed dipoles are clustered in time and space to associate them with sources. We applied this new method to synthetic data sets and show here that it is able to automatically estimate the source structure in most cases more accurately than either traditional multi‐dipole modeling or minimum current estimation performed by uninformed human operators. We also show that from real somatosensory evoked fields the method reconstructs a source constellation comparable to that obtained by multi‐dipole modeling. Hum Brain Mapp, 2009.

A Rao–Blackwellized particle filter for magnetoencephalography

A Rao-Blackwellized particle filter for the tracking of neural sources from biomagnetic data is described. A comparison with a sampling importance resampling particle filter performed in the case of both simulated and real data shows that the use of Rao-Blackwellization is highly recommended since it produces more accurate reconstructions within a lower computational effort.

Forward simulation and inverse dipole localization with the lowest order Raviart—Thomas elements for electroencephalography

Electroencephalography is a non-invasive imaging modality in which a primary current density generated by the neural activity in the brain is to be reconstructed based on external electric potential measurements. This paper focuses on the finite element method (FEM) from both forward and inverse aspects. The goal is to establish a clear correspondence between the lowest order Raviart?Thomas basis functions and dipole sources as well as to show that the adopted FEM approach is computationally effective. Each basis function is associated with a dipole moment and a location. Four candidate locations are tested. Numerical experiments cover two different spherical multilayer head models, four mesh resolutions and two different forward simulation approaches, one based on FEM and another based on the boundary element method (BEM) with standard dipoles as sources. The forward simulation accuracy is examined through column- and matrix-wise relative errors as well as through performance in inverse dipole localization. A closed-form approximation of dipole potential was used as the reference forward simulation. The present approach is compared to the BEM and indirectly also to the recent FEM-based subtraction approach regarding both accuracy, computation time and accessibility of implementation.

Profile Detection in Medical and Astronomical Images by Means of the Hough Transform of Special Classes of Curves

We develop a formal procedure for the automated recognition of rational and elliptic curves in medical and astronomical images. The procedure is based on the extension of the Hough transform concept to the definition of Hough transform of special classes of algebraic curves. We first introduce a catalogue of curves that satisfy the conditions to be automatically extracted from an image and the recognition algorithm, then we illustrate the power of this method to identify skeleton profiles in clinical X-ray tomography maps and front ends of solar eruptions in astronomical images provided by the NASA solar dynamics observatory satellite.

Allogeneic cell transplant expands bone marrow distribution by colonizing previously abandoned areas: an FDG PET/CT analysis.

Mechanisms of hematopoietic reconstitution after bone marrow (BM) transplantation remain largely unknown. We applied a computational quantification software application to hybrid 18F-fluorodeoxyglucose positron emission tomography (PET)/computed tomography (CT) images to assess activity and distribution of the hematopoietic system throughout the whole skeleton of recently transplanted patients. Thirty-four patients underwent PET/CT 30 days after either adult stem cell transplantation (allogeneic cell transplantation [ACT]; n = 18) or cord blood transplantation (CBT; n = 16). Our software automatically recognized compact bone volume and trabecular bone volume (IBV) in CT slices. Within IBV, coregistered PET data were extracted to identify the active BM (ABM) from the inactive tissue. Patients were compared with 34 matched controls chosen among a published normalcy database. Whole body ABM increased in ACT and CBT when compared with controls (12.4 ± 3 and 12.8 ± 6.8 vs 8.1 ± 2.6 mL/kg of ideal body weight [IBW], P < .001). In long bones, ABM increased three- and sixfold in CBT and ACT, respectively, compared with controls (0.9 ± 0.9 and 1.7 ± 2.5 vs 0.3 ± 0.3 mL/kg IBW, P < .01). These data document an unexpected distribution of transplanted BM into previously abandoned BM sites.

Adult Advanced Chronic Lymphocytic Leukemia: Computational Analysis of Whole-Body CT Documents a Bone Structure Alteration

PURPOSE To assess the presence of alteration of bone structure and bone marrow metabolism in adult patients who were suspected of having advanced chronic lymphocytic leukemia (ACLL) by using a computational prognostic model that was based on computational analysis of positron emission tomography (PET)/computed tomography (CT) images. MATERIALS AND METHODS In this retrospective study, all patients signed written informed consent as a requisite to undergo PET/CT examination. However, due to its observational nature, approval from the ethical committee was not deemed necessary. Twenty-two previously untreated chronic lymphocytic leukemia patients underwent PET/CT for disease progression. PET/CT images were analyzed by using dedicated software, capable of recognizing an external 2-pixel bone ring whose Hounsfield coefficient served as cutoff to recognize trabecular and compact bone. PET/CT data from 22 age- and sex-matched control subjects were used as comparison. All data are reported as means ± standard deviations. The Student t test, log-rank, or Cox proportional hazards model were used as appropriate, considering a difference with a P value of less than .05 as significant. RESULTS Trabecular bone was expanded in ACLL patients and occupied a larger fraction of the skeleton with respect to control subjects (mean, 39% ± 5 [standard deviation] vs 31% ± 7; ie, 32 of 81 mL/kg of ideal body weight vs 27 of 86 mL/kg of ideal body weight, respectively; P < .001). After stratification according to median value, patients with a ratio of trabecular to skeletal bone volume of more than 37.3% showed an actuarial 2-year survival of 18%, compared with 82% for those with a ratio of less than 37.3% (P < .001), independent from age, sex, biological markers, and disease duration. CONCLUSION These data suggest that computational assessment of skeletal alterations might represent a new window for prediction of the clinical course of the disease.

Highly Automated Dipole EStimation (HADES)

Automatic estimation of current dipoles from biomagnetic data is still a problematic task. This is due not only to the ill-posedness of the inverse problem but also to two intrinsic difficulties introduced by the dipolar model: the unknown number of sources and the nonlinear relationship between the source locations and the data. Recently, we have developed a new Bayesian approach, particle filtering, based on dynamical tracking of the dipole constellation. Contrary to many dipole-based methods, particle filtering does not assume stationarity of the source configuration: the number of dipoles and their positions are estimated and updated dynamically during the course of the MEG sequence. We have now developed a Matlab-based graphical user interface, which allows nonexpert users to do automatic dipole estimation from MEG data with particle filtering. In the present paper, we describe the main features of the software and show the analysis of both a synthetic data set and an experimental dataset.

Spinal Canal and Spinal Marrow Segmentation by Means of the Hough Transform of Special Classes of Curves

In this paper we present a Hough Transform-based method for the detection of the spinal district in X-ray Computed Tomography (CT) images in order to build binary masks that can be applied to functional images to infer information on the metabolic activity of the spinal marrow. This kind of information may be of particular interest for the study of the spinal marrow physiology in both health and disease.

Non-linear canonical correlation for joint analysis of MEG signals from two subjects

Traditional stimulus-based analysis methods of magnetoencephalography (MEG) data are often dissatisfactory when applied to naturalistic experiments where two or more subjects are measured either simultaneously or sequentially. To uncover the commonalities in the brain activity of the two subjects, we propose a method that searches for linear transformations that output maximally correlated signals between the two brains. Our method is based on canonical correlation analysis (CCA), which provides linear transformations, one for each subject, such that the temporal correlation between the transformed MEG signals is maximized. Here, we present a non-linear version of CCA which measures the correlation of energies and allows for a variable delay between the time series to accommodate, e.g., leader–follower changes. We test the method with simulations and with MEG data from subjects who received the same naturalistic stimulus sequence. The method may help analyse future experiments where the two subjects are measured simultaneously while engaged in social interaction.