An isolated anterior mitral leaflet cleft: a case report

<p>Abstract</p> <p>Introduction</p> <p>The anterior mitral leaflet cleft is an unusual congenital lesion most often encountered in association with other congenital heart defects. The isolated anterior leaflet cleft is quite a rare anomaly and is usually cause of mitral valve regurgitation. The importance of the lesion is that it is often correctable. When feasible, cleft suture and, eventually, annuloplasty are preferable to valve replacement. Echocardiography is the first choice technique in the evaluation of mitral valve disease, providing useful information about valve anatomy and hemodynamic parameters.</p> <p>Case presentation</p> <p>We present a case of an isolated anterior mitral leaflet cleft producing moderate-severe mitral regurgitation correctly identified by echocardiography and successfully surgically corrected.</p> <p>Conclusion</p> <p>Isolated cleft is a rare aberration, that has to be known in order to be diagnosed. Transthoracic and transesophageal echocardiography is the most useful non invasive technique for cleft diagnosis and to indicate the right surgical correction.</p

From single gene analysis to single cell profiling: a new era for precision medicine

Molecular profiling of DNA and RNA has provided valuable new insights into the genetic basis of non-malignant and malignant disorders, as well as an increased understanding of basic mechanisms that regulate human disease. Recent technological advances have enabled the analyses of alterations in gene-based structure or function in a comprehensive, high-throughput fashion showing that each tumor type typically exhibits distinct constellations of genetic alterations targeting one or more key cellular pathways that regulate cell growth and proliferation, evasion of the immune system, and other aspects of cancer behavior. These advances have important implications for future research and clinical practice in areas as molecular diagnostics, the implementation of gene or pathway-directed targeted therapy, and the use of such information to drive drug discovery. The 1st international and 32nd Annual Conference of Italian Association of Cell Cultures (AICC) conference wanted to offer the opportunity to match technological solutions and clinical needs in the era of precision medicine

Skeletal Muscle Pathophysiology: The Emerging Role of Spermine Oxidase and Spermidine

Skeletal muscle comprises approximately 40% of the total body mass. Preserving muscle health and function is essential for the entire body in order to counteract chronic diseases such as type II diabetes, cardiovascular diseases, and cancer. Prolonged physical inactivity, particularly among the elderly, causes muscle atrophy, a pathological state with adverse outcomes such as poor quality of life, physical disability, and high mortality. In murine skeletal muscle C2C12 cells, increased expression of the spermine oxidase (SMOX) enzyme has been found during cell differentiation. Notably, SMOX overexpression increases muscle fiber size, while SMOX reduction was enough to induce muscle atrophy in multiple murine models. Of note, the SMOX reaction product spermidine appears to be involved in skeletal muscle atrophy/hypertrophy. It is effective in reactivating autophagy, ameliorating the myopathic defects of collagen VI-null mice. Moreover, spermidine treatment, if combined with exercise, can affect D-gal-induced aging-related skeletal muscle atrophy. This review hypothesizes a role for SMOX during skeletal muscle differentiation and outlines its role and that of spermidine in muscle atrophy. The identification of new molecular pathways involved in the maintenance of skeletal muscle health could be beneficial in developing novel therapeutic lead compounds to treat muscle atrophy

Reprogramming Tumor-Immune Cell Interface in Solid and Hematological Malignancies to Enhance Response to Therapy

The 30thAnnual Conference of Italian Association of Cell Cultures (AICC) was held at Fondazione IRCCS Istituto Nazionale dei Tumori, in Milan, on November 27\u201328, 2017, with the Scientific Coordination of Claudia Chiodoni and Mario Paolo Colombo, from the same Institute. The conference was opened by Katia Scotlandi, President of the Italian Association of Cell Cultures. The main objective of the conference was to discuss the present knowledge on the role of immune cells in the tumor microenvironment, shaped by cells promoting tumor survival that could be reeducated to treat cancer. The meeting was organized in two days with 5 thematic sessions, an opening and a closing lecture, with national and international speakers. The organizers truly thank those who took part in the conference and made the Conference a success

A recurrent Hopfield network for estimating meso-scale effective connectivity in MEG

: The architecture of communication within the brain, represented by the human connectome, has gained a paramount role in the neuroscience community. Several features of this communication, e.g., the frequency content, spatial topology, and temporal dynamics are currently well established. However, identifying generative models providing the underlying patterns of inhibition/excitation is very challenging. To address this issue, we present a novel generative model to estimate large-scale effective connectivity from MEG. The dynamic evolution of this model is determined by a recurrent Hopfield neural network with asymmetric connections, and thus denoted Recurrent Hopfield Mass Model (RHoMM). Since RHoMM must be applied to binary neurons, it is suitable for analyzing Band Limited Power (BLP) dynamics following a binarization process. We trained RHoMM to predict the MEG dynamics through a gradient descent minimization and we validated it in two steps. First, we showed a significant agreement between the similarity of the effective connectivity patterns and that of the interregional BLP correlation, demonstrating RHoMM's ability to capture individual variability of BLP dynamics. Second, we showed that the simulated BLP correlation connectomes, obtained from RHoMM evolutions of BLP, preserved some important topological features, e.g, the centrality of the real data, assuring the reliability of RHoMM. Compared to other biophysical models, RHoMM is based on recurrent Hopfield neural networks, thus, it has the advantage of being data-driven, less demanding in terms of hyperparameters and scalable to encompass large-scale system interactions. These features are promising for investigating the dynamics of inhibition/excitation at different spatial scales