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.

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.

Electroencephalography (EEG) 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 one 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 results suggest that the present approach is comparable or superior to BEM and to the recent FEM based subtraction approach regarding both accuracy, computation time and accessibility of implementation.

Solar flares originate from magnetically active regions but not all solar active regions give rise to a flare. Therefore, the challenge of solar flare prediction benefits by an intelligent computational analysis of physics-based properties extracted from active region observables, most commonly line-ofsight or vector magnetograms of the active-region photosphere. For the purpose of flare forecasting, this study utilizes an unprecedented 171 flare-predictive active region properties, mainly inferred by the Helioseismic and Magnetic Imager onboard the Solar Dynamics Observatory (SDO/HMI) in the course of the European Union Horizon 2020 FLARECAST project. Using two different supervised machine learning methods that allow feature ranking as a function of predictive capability, we show that: i) an objective training and testing process is paramount for the performance of every supervised machine learning method; ii) most properties include overlapping information and are therefore highly redundant for flare prediction; iii) solar flare prediction is still -and will likely remain -a predominantly probabilistic challenge. Nishizuka et al 2018) within the recent field of space weather forecasting that relies on the availability of two ingredients; one observational and one computational. First, it is well-established that solar active regions (ARs) exclusively host major flares and therefore flare prediction needs experimental data on AR properties, associated to the photospheric and coronal magnetic field; however, coronal information has only recently started being used in the form of EUV images given as input to a deep learning network by Nishizuka et al (2018). Second, this information on AR magnetic properties can be processed for prediction purposes by means of a computational method for data analysis; machine learning has recently offered strong candidates for such methods.Since February 2010, the Helioseismic and Magnetic Imager onboard the Solar Dynamics Observatory (SDO/HMI) (Scherrer et al 2012) is providing both line-of-sight and vector magnetograms of the full solar disk at a (vector magnetogram) cadence of 12 minutes. SDO/HMI magnetograms can be used for solar flare prediction according to two different approaches. First, HMI magnetograms are utilized to calculate a variety of properties either from the line-of-sight component only, from the radial component only, or from all three vector components. Various single-valued quantities, hereafter referred to as features, can be calculated from these property images through a variety of techniques (e.g., thresholding, feature recognition, etc), such that calculation of one physical property may provide multiple features as inputs to machine learning (i.e., image maximum, total, and moments). Of course, additional features that are not derived from property images may also contribute to the input dataset. Second, from a deep learning perspective, HMI images can be given as input to Convolutional Neural Networks (CNNs) that automatically perform a probabilistic f...

Our computational analysis of PET/CT images provides a first estimation of the extension and metabolism of the BM in a population of adult patients without haematooncological disorders. This information might represent a new window to explore pathophysiology the BM and the response of BM diseases to chemotherapy.