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The female carriers of BRCA1/2 pathogenic variants (mutations) face a high lifetime risk of developing breast and/or ovarian cancer. However, the risk may differ depending on various genetic and non-genetic elements, including metabolic and hormonal factors. We previously showed that a 6-month Mediterranean dietary intervention trial reduced body weight and the levels of insulin-like growth factor I and other metabolic factors in BRCA mutation carriers. We also found that higher baseline levels of glucose and insulin were significantly associated with BRCA loss-of-function (LOF) variants. In this study, we evaluated whether the BRCA mutation type influences in a different way the metabolic and hormonal response to the dietary intervention in 366 female carriers. The LOF variant carriers randomized in the intervention group (IG) showed significantly higher changes in most considered parameters compared to the control group (CG). The nonsynonymous variant carriers in the IG showed similar changes, but none of them were statistically significant. Performing the "delta" analysis of differences (intention-to-treat analysis), we observed that in LOF variant carriers, the reduction of insulin levels was significantly more pronounced that in nonsynonymous variant carriers. These findings suggest that the changes in insulin levels might be modulated by a different response to the dietary intervention mediated by BRCA LOF variants., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Oliverio, Radice, Colombo, Paradiso, Tommasi, Daniele, Terribile, Magno, Guarino, Manoukian, Peissel, Bruno and Pasanisi.)

Obesity is reaching epidemic proportions globally and represents a major cause of comorbidities, mostly related to cardiovascular disease. The autonomic nervous system (ANS) dysfunction has a two-way relationship with obesity. Indeed, alterations of the ANS might be involved in the pathogenesis of obesity, acting on different pathways. On the other hand, the excess weight induces ANS dysfunction, which may be involved in the haemodynamic and metabolic alterations that increase the cardiovascular risk of obese individuals, i.e., hypertension, insulin resistance and dyslipidemia. This article will review current evidence about the role of the ANS in short-term and long-term regulation of energy homeostasis. Furthermore, an increased sympathetic activity has been demonstrated in obese patients, particularly in the muscle vasculature and in the kidneys, possibily contributing to increased cardiovascular risk. Selective leptin resistance, obstructive sleep apnea syndrome, hyperinsulinemia and low ghrelin levels are possible mechanisms underlying sympathetic activation in obesity. Weight loss is able to reverse metabolic and autonomic alterations associated with obesity. Given the crucial role of autonomic dysfunction in the pathophysiology of obesity and its cardiovascular complications, vagal nerve modulation and sympathetic inhibition may serve as therapeutic targets in this condition.

Neuroscientists use many different software tools to acquire, analyze and visualize electrophysiological signals. However, incompatible data models and file formats make it difficult to exchange data between these tools. This reduces scientific productivity, renders potentially useful analysis methods inaccessible and impedes collaboration between labs. A common representation of the core data would improve interoperability and facilitate data-sharing. To that end, we propose here a language-independent object model, named "Neo," suitable for representing data acquired from electroencephalographic, intracellular, or extracellular recordings, or generated from simulations. As a concrete instantiation of this object model we have developed an open source implementation in the Python programming language. In addition to representing electrophysiology data in memory for the purposes of analysis and visualization, the Python implementation provides a set of input/output (IO) modules for reading/writing the data from/to a variety of commonly used file formats. Support is included for formats produced by most of the major manufacturers of electrophysiology recording equipment and also for more generic formats such as MATLAB. Data representation and data analysis are conceptually separate: it is easier to write robust analysis code if it is focused on analysis and relies on an underlying package to handle data representation. For that reason, and also to be as lightweight as possible, the Neo object model and the associated Python package are deliberately limited to representation of data, with no functions for data analysis or visualization. Software for neurophysiology data analysis and visualization built on top of Neo automatically gains the benefits of interoperability, easier data sharing and automatic format conversion; there is already a burgeoning ecosystem of such tools. We intend that Neo should become the standard basis for Python tools in neurophysiology.