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Autistic Spectrum Disorder (ASD) is a neurodevelopmental condition affecting approximately 1 out of 70 (range 1:59 – 1:89) children worldwide. It is characterized by a delay in cognitive capabilities, repetitive and restricted behaviors and deficit in communication and social interaction. Several factors seem to be associated with ASD development; its heterogeneous nature makes the diagnosis difficult and slow since it is essentially based on screening tools focused on stereotypical and repetitive behaviors, gait, facial emotion expression and speech assessments. Recently, artificial intelligence (AI) has been widely used to investigate ASD with the overall goal of simplifying and speeding up the diagnostic process as well as making earlier access to therapies possible. The aim of this review is to provide an overview of the state-of-the-art research in the ASD field, identifying and describing machine learning (ML) approaches in ASD literature that could be used by clinicians to improve diagnostic capability and treatment efficiency. A systematic search was conducted and the resulting articles were subdivided into several categories reflecting the different fields of study associated with ASD research. The existing literature has widely demonstrated the potential of ML in several types of ASD study analyses: behavior, gait, speech, facial emotion expression, neuroimaging, genetics, and metabolomics. Therefore, AI techniques are becoming increasingly implemented and accepted, so highlighting the power of ML approaches to extract and obtain knowledge from a large volume of data. This makes ML a promising tool for future ASD research and clinical endeavors suggesting possible avenues for improving ASD screening, diagnostic and therapeutic tools.

Parkinson's disease (PD) is a multifactorial neurodegenerative disorder in which environmental (lifestyle, dietary, infectious disease) factors as well as genetic make-up play a role. Metabolomics, an evolving research field combining biomarker discovery and pathogenetics, is particularly useful in studying complex pathophysiology in general and Parkinson's disease (PD) specifically. PD, the second most frequent neurodegenerative disorder, is characterized by the loss of dopaminergic neurons in the substantia nigra and the presence of intraneural inclusions of α-synuclein aggregates. Although considered a predominantly movement disorder, PD is also associated with number of non-motor features. Metabolomics has provided useful information regarding this neurodegenerative process with the aim of identifying a disease-specific fingerprint. Unfortunately, many disease variables such as clinical presentation, motor system involvement, disease stage and duration substantially affect biomarker relevance. As such, metabolomics provides a unique approach to studying this multifactorial neurodegenerative disorder.

Non-alcoholic fatty liver disease (NAFLD) is a challenging disease caused by multiple factors, which may partly explain why it remains still orphan of an adequate therapeutic strategy. Herein we focus on the interplay between oxidative stress (OS) and the other causal pathogenetic factors. Different reactive oxygen species (ROS) generators contribute to NAFLD inflammatory and fibrotic progression, which is quite strictly linked to the lipotoxic liver injury from fatty acids and/or a wide variety of their biologically active metabolites in the context of either a two-hit or a (more recent) multiple parallel hits theory. An antioxidant defense system is usually able to protect hepatic cells from damaging effects caused by ROS, including those produced into the gastrointestinal tract, i.e., by-products generated by usual cellular metabolic processes, normal or dysbiotic microbiota, and/or diet through an enhanced gut–liver axis. Oxidative stress originating from the imbalance between ROS generation and antioxidant defenses is under the influence of individual genetic and epigenetic factors as well. Healthy diet and physical activity have been shown to be effective on NAFLD also with antioxidant mechanisms, but compliance to these lifestyles is very low. Among several considered antioxidants, vitamin E has been particularly studied; however, data are still contradictory. Some studies with natural polyphenols proposed for NAFLD prevention and treatment are encouraging. Probiotics, prebiotics, diet, or fecal microbiota transplantation represent new therapeutic approaches targeting the gut microbiota dysbiosis. In the near future, precision medicine taking into consideration genetic or environmental epigenetic risk factors will likely assist in further selecting the treatment that could work best for a specific patient.

Parkinson's disease (PD) is a multifactorial neurodegenerative disorder in which environmental (lifestyle, dietary, infectious disease) factors as well as genetic make-up play a role. Metabolomics, an evolving research field combining biomarker discovery and pathogenetics, is particularly useful in studying complex pathophysiology in general and Parkinson's disease (PD) specifically. PD, the second most frequent neurodegenerative disorder, is characterized by the loss of dopaminergic neurons in the substantia nigra and the presence of intraneural inclusions of ?-synuclein aggregates. Although considered a predominantly movement disorder, PD is also associated with number of non-motor features. Metabolomics has provided useful information regarding this neurodegenerative process with the aim of identifying a disease-specific fingerprint. Unfortunately, many disease variables such as clinical presentation, motor system involvement, disease stage and duration substantially affect biomarker relevance. As such, metabolomics provides a unique approach to studying this multifactorial neurodegenerative disorder.

Autistic Spectrum Disorder (ASD) is a neurodevelopmental condition affecting approximately 1 out of 70 (range 1:59 - 1:89) children worldwide. It is characterized by a delay in cognitive capabilities, repetitive and restricted behaviors and deficit in communication and social interaction. Several factors seem to be associated with ASD development; its heterogeneous nature makes the diagnosis difficult and slow since it is essentially based on screening tools focused on stereotypical and repetitive behaviors, gait, facial emotion expression and speech assessments. Recently, artificial intelligence (AI) has been widely used to investigate ASD with the overall goal of simplifying and speeding up the diagnostic process as well as making earlier access to therapies possible. The aim of this review is to provide an overview of the state-of-the-art research in the ASD field, identifying and describing machine learning (ML) approaches in ASD literature that could be used by clinicians to improve diagnostic capability and treatment efficiency. A systematic search was conducted and the resulting articles were subdivided into several categories reflecting the different fields of study associated with ASD research. The existing literature has widely demonstrated the potential of ML in several types of ASD study analyses: behavior, gait, speech, facial emotion expression, neuroimaging, genetics, and metabolomics. Therefore, AI techniques are becoming increasingly implemented and accepted, so highlighting the power of ML approaches to extract and obtain knowledge from a large volume of data. This makes ML a promising tool for future ASD research and clinical endeavors suggesting possible avenues for improving ASD screening, diagnostic and therapeutic tools.

Childhood obesity is reaching alarming rates in many countries, posing an urgent and serious challenge. Adipose tissue is a key endocrine organ that releases several pro- or anti-inflammatory adipokines that are involved in the pathogenesis of obesity complications. Moreover, it is suggested that gut microbiota is also involved in the development of these complications, including obesity-related liver disease and metabolic syndrome.

Background The pediatric obesity epidemic calls for the noninvasive detection of individuals at higher risk of complications. Aims To investigate the diagnostic role of combined salivary uric acid (UA), glucose and insulin levels to screen noninvasively for metabolic syndrome (MetS) and nonalcoholic fatty liver disease. Methods Medical history, clinical, anthropometric, and laboratory data including serum triglyceride, glucose, insulin, HOMA, HDL-cholesterol, and UA levels of 23 obese children (15 with [St+] and 8 without [St−] ultrasonographic hepatic steatosis) and 18 normal weight controls were considered. Results Serum and salivary UA (p Conclusions Salivary testing together with selected anthropometric parameters helps to identify noninvasively obese children with hepatic steatosis and/or having MetS components.

Gut epithelia and other cell types contacted by enteric commensal bacteria generate reactive oxygen species (ROS) in response to microbial signals. Physiologically generated ROS also serve as second messengers in multiple signal transduction pathways stimulated by cytokines and growth factors, and participate in cellular signaling. The imbalance between ROS production and the abilities of biological systems for detoxification and secondary damage repair are called oxidative stress. This represents a most important mediator involved in inflammation in several conditions such as metabolic syndrome (including nonalcoholic fatty liver disease), mental/neurological diseases (e.g., autism, depression, Alzheimer’s, and Parkinson’s disease), inflammatory bowel diseases, and cancer. In addition to these harms, ROS may also be beneficial by assisting the immune system to attack/kill pathogens, by inducing apoptosis for destroying malignant cells during chemo- and radiotherapy, and by prompting adaptive reactions within the mitochondria, which may become therefore more stress-resistant. Here we present an update on gut microbiota, oxidative stress, and focus on their interaction in health and disease.

To get insight into still elusive pathomechanisms of pediatric obesity and non-alcoholic fatty liver disease (NAFLD) we explored the interplay among GC-MS studied urinary metabolomic signature, gut liver axis (GLA) abnormalities, and food preferences (Kid-Med). Intestinal permeability (IP), small intestinal bacterial overgrowth (SIBO), and homeostatic model assessment-insulin resistance were investigated in forty children (mean age 9.8 years) categorized as normal weight (NW) or obese (body mass index 95th percentile, respectively) ± ultrasonographic bright liver and hypertransaminasemia (NAFLD). SIBO was increased in all obese children (p = 0.0022), IP preferentially in those with NAFLD (p = 0.0002). The partial least-square discriminant analysis of urinary metabolome correctly allocated children based on their obesity, NAFLD, visceral fat, pathological IP and SIBO. Compared to NW, obese children had (1) higher levels of glucose/1-methylhistidine, the latter more markedly in NAFLD patients; and (2) lower levels of xylitol, phenyl acetic acid and hydroquinone, the latter especially in children without NAFLD. The metabolic pathways of BCAA and/or their metabolites correlated with excess of visceral fat centimeters (leucine/oxo-valerate), and more deranged IP and SIBO (valine metabolites). Urinary metabolome analysis contributes to define a metabolic fingerprint of pediatric obesity and related NAFLD, by identifying metabolic pathways/metabolites reflecting typical obesity dietary habits and GLA perturbations.

Adulthood and childhood obesity is rapidly becoming an epidemic problem and it has a short and long-term impact on health. Short-term consequences are mostly represented by psychological effects; in fact obese children have more chances to develop psychological or psychiatric problems than non-obese children. The main long-term effect is represented by the fact that childhood obesity continues into adulthood obesity and this results in negative effects in young adult life, since obesity increases the risk to develop morbidity and premature mortality. The obesity-related diseases are mostly represented by hypertension, type 2 diabetes, dyslipidemia, cardiovascular diseases. Medical treatment should be discouraged in childhood because of the side effects and it should be only reserved for obese children with related medical complications. Lifestyle changes should be encouraged in both adulthood and childhood obesity. This review focuses on the management of obesity both in adulthood and in childhood, paying particular attention to lifestyle changes that should be recommended.

Background Celiac disease (CD) is the most common autoimmune enteropathy. Clinical manifestations may range from a typical malabsorption syndrome to several apparently unrelated extra-intestinal symptoms. Aim Here we specifically focus on the spectrum of CD-related liver disorders and the underlying pathomechanisms. Methods A computer-based search up to August 2015 was completed using appropriate keywords. References from selected papers were also reviewed and used if relevant. Results An unexplained hypertransaminasemia with nonspecific histologic hepatic changes is the most common hepatic presentation. CD however can coexist with a number of liver disorders such as Autoimmune Hepatitis, Autoimmune Cholangitis, Primary Biliary Cirrhosis and Primary Sclerosing Cholangitis requiring a specific treatment in addition to gluten-free diet. CD has also been associated with Viral Hepatitis, Fatty Liver, Non-Alcoholic Steatohepatitis and some severe cryptogenic hepatopaties in the liver transplantation list. Pathomechanisms underlying hepatic injury in CD are multiple, appear still not completely defined and may probably co-occur. Conclusions An ever-increasing number of CD-related liver injuries exist, probably representing a continuum of a same disorder where genetic predisposition, timing, and duration of previous gluten exposure might influence the reversibility of liver damage. Evidences, although not conclusive, support therefore testing for CD also in cryptogenic hepatobiliary conditions where the relationship with CD has not yet been fully investigated.

Background and aims Berberine (BBR) is an isoquinoline derivative alkaloid isolated from Rhizoma Coptidis traditionally used as anti diarrheic and, more recently, as hypolipidemic and insulin sensitizer agent. Thus, BBR could represent a potential therapeutic option for patients with polycystic ovary syndrome (PCOS). The aim of this study was to evaluate the clinical, metabolic and hormonal effects of BBR in PCOS women. Methods Fifty oligoamenorrheic PCOS obese women and 50 age and Body Mass Index (BMI) matched healthy controls were enrolled. PCOS women received BBR treatment (500 mg, 2 times daily) for 6 months. Clinical and biochemical parameters were assessed before and after the treatment period. Results Total testosterone (p Conclusions BBR improves clinical, metabolic and reproductive features in PCOS women. Its mechanism of actions need to be elucidated in further studies.