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By testing recombinants between "high tumor" (inducing a high incidence of tumors) and "low tumor" (inducing a low incidence of tumors) strains of polyomavirus, we have previously shown that the key determinant(s) for induction of a high tumor profile resides in coding regions of the high tumor strain (R.

In the last decades, obesity has become a major concern for clinical and public health. Despite the variety of available treatments, the outcomes remain—by and large—still unsatisfactory, owing to high rates of nonresponse and relapse. Interestingly, obesity is being associated with a growing surge of neuropsychiatric problems, certainly related to the pathogenesis of this condition, and likely to be of great consequence as for its treatment and prognosis. In a neurobiologic direction, a sturdy body of evidence has recently shown that the immune–metabolic–endocrine dyscrasias, notoriously attached to excess body weight/adiposity, affect and impair the morpho-functional integrity of the brain, thus possibly contributing to neuroprogressive/degenerative processes and behavioral deviances. Likewise, in a neuropsychiatric perspective, obesity displays complex associations with mood disorders and affective temperamental dimensions (namely cyclothymia), eating disorders characterized by overeating/binge-eating behaviors, ADHD-related executive dysfunctions, emotional dysregulation and motivational–addictive disturbances. With this review, we attempt to provide the clinician a synoptic, yet exhaustive, tool for a more conscious approach to that subset of this condition, which could be reasonably termed “psychiatric” obesity.

BackgroundObesity is unanimously regarded as a global epidemic and a major contributing factor to the development of many common illnesses. Laparoscopic Adjustable Gastric Banding (LAGB) is one of the most popular surgical approaches worldwide. Yet, substantial variability in the results and significant rate of failure can be expected, and it is still debated which categories of patients are better suited to this type of bariatric procedure. The aim of this study was to build a statistical model based on both psychological and physical data to predict weight loss in obese patients treated by LAGB, and to provide a valuable instrument for the selection of patients that may benefit from this procedure.Methodology/Principal FindingsThe study population consisted of 172 obese women, with a mean±SD presurgical and postsurgical Body Mass Index (BMI) of 42.5±5.1 and 32.4±4.8 kg/m2, respectively. Subjects were administered the comprehensive test of psychopathology Minnesota Multiphasic Personality Inventory-2 (MMPI-2). Main goal of the study was to use presurgical data to predict individual therapeutical outcome in terms of Excess Weight Loss (EWL) after 2 years. Multiple linear regression analysis using the MMPI-2 scores, BMI and age was performed to determine the variables that best predicted the EWL. Based on the selected variables including age, and 3 psychometric scales, Artificial Neural Networks (ANNs) were employed to improve the goodness of prediction. Linear and non linear models were compared in their classification and prediction tasks: non linear model resulted to be better at data fitting (36% vs. 10% variance explained, respectively) and provided more reliable parameters for accuracy and mis-classification rates (70% and 30% vs. 66% and 34%, respectively).Conclusions/SignificanceANN models can be successfully applied for prediction of weight loss in obese women treated by LAGB. This approach may constitute a valuable tool for selection of the best candidates for surgery, taking advantage of an integrated multidisciplinary approach.

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