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Gastrointestinal (GI) symptoms have been described in a conspicuous percentage of coronavirus disease 2019 (COVID-19) patients. This clinical evidence is supported by the detection of viral RNA in stool, which also supports the hypothesis of a possible fecal-oral transmission route. The involvement of GI tract in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection is corroborated by the theoretical assumption that angiotensin converting enzyme 2, which is a SARS-CoV-2 target receptor, is present along the GI tract. Studies have pointed out that gut dysbiosis may occur in COVID-19 patients, with a possible correlation with disease severity and with complications such as multisystem inflammatory syndrome in children. However, the question to be addressed is whether dysbiosis is a consequence or a contributing cause of SARS-CoV-2 infection. In such a scenario, pharmacological therapies aimed at decreasing GI permeability may be beneficial for COVID-19 patients. Considering the possibility of a fecal-oral transmission route, water and environmental sanitation play a crucial role for COVID-19 containment, especially in developing countries.

Background The gut microbiota plays a central role in host physiology and in several pathological mechanisms in humans. Antibiotics compromise the composition and functions of the gut microbiota inducing long-lasting detrimental effects on the host. Recent studies suggest that the efficacy of different clinical therapies depends on the action of the gut microbiota. Here, we investigated how different antibiotic treatments affect the ability of the gut microbiota to control intestinal inflammation upon fecal microbiota transplantation in an experimental colitis model and in ex vivo experiments with human intestinal biopsies. Results Murine fecal donors were pre-treated with different antibiotics, i.e., vancomycin, streptomycin, and metronidazole before FMT administration to colitic animals. The analysis of the gut microbiome, fecal metabolome, and the immunophenotyping of colonic lamina propria immune cells revealed that antibiotic pre-treatment significantly influences the capability of the microbiota to control intestinal inflammation. Streptomycin and vancomycin-treated microbiota failed to control intestinal inflammation and were characterized by the blooming of pathobionts previously associated with IBD as well as with metabolites related to the presence of oxidative stress and metabolism of simple sugars. On the contrary, the metronidazole-treated microbiota retained its ability to control inflammation co-occurring with the enrichment of Lactobacillus and of innate immune responses involving iNKT cells. Furthermore, ex vivo cultures of human intestinal lamina propria mononuclear cells and iNKT cell clones from IBD patients with vancomycin pre-treated sterile fecal water showed a Th1/Th17 skewing in CD4+ T-cell populations; metronidazole, on the other hand, induced the polarization of iNKT cells toward the production of IL10. Conclusions Diverse antibiotic regimens affect the ability of the gut microbiota to control intestinal inflammation in experimental colitis by altering the microbial community structure and microbiota-derived metabolites.

Background Congenital malformations of the central nervous system (CNS) consist of a wide range of birth defects of multifactorial origin. Methods Concentrations of 44 metals were determined by Inductively Coupled Plasma Mass Spectrometry in serum of 111 mothers in the second trimester of pregnancy who carried a malformed fetus and compared them with serum concentrations of the same metals in 90 mothers with a normally developed fetus at the same week of pregnancy. Data are reported as means ± standard deviations. Results We found a direct relationship between congenital defects of the CNS and maternal serum concentration of aluminum: it was statistically higher in women carrying a fetus with this class of malformation, compared both to mothers carrying a fetus with another class of malformation (6.45 ± 15.15 μg/L Vs 1.44 ± 4.21 μg/L, p Conclusion CAluminum may play a role in the onset of central nervous system malformations, although the exact Aluminum species and related specific type of malformation needs further elucidation.

Background:Bisphenol A (BPA) is one of the highest volume chemicals produced worldwide. It has recognized activity as an endocrine-disrupting chemical and has suspected roles as a neurological and reproductive toxicant. It interferes in steroid signaling, induces oxidative stress, and affects gene expression epigenetically. Gestational, perinatal and neonatal exposures to BPA affect developmental processes, including brain development and gametogenesis, with consequences on brain functions, behavior, and fertility.Methods:This review critically analyzes recent findings on the neuro-toxic and reproductive effects of BPA (and its analogues), with focus on neuronal differentiation, synaptic plasticity, glia and microglia activity, cognitive functions, and the central and local control of reproduction.Results:BPA has potential human health hazard associated with gestational, peri- and neonatal exposure. Beginning with BPA’s disposition, this review summarizes recent findings on the neurotoxicity of BPA and its analogues, on neuronal differentiation, synaptic plasticity, neuroinflammation, neuro-degeneration, and impairment of cognitive abilities. Furthermore, it reports the recent findings on the activity of BPA along the HPG axis, effects on the hypothalamic Gonadotropin Releasing Hormone (GnRH), and the associated effects on reproduction in both sexes and successful pregnancy.Conclusion:BPA and its analogues impair neuronal activity, HPG axis function, reproduction, and fertility. Contrasting results have emerged in animal models and human. Thus, further studies are needed to better define their safety levels. This review offers new insights on these issues with the aim to find the “fil rouge”, if any, that characterize BPA’s mechanism of action with outcomes on neuronal function and reproduction.

Although metabolomics studies are recently spreading and have allowed the characterization of putative biomarkers in many diseases, they are relatively scanty in anorexia nervosa (AN). In this explorative study we analyzed the fecal metabolomics profiles of women with AN in the underweight phase (n = 24) and after short-term weight restoration (n = 16) and compared them with 20 healthy women. An untargeted metabolomic procedure allowed the characterization of 224 metabolites involved in energy, lipid, and amino acid metabolism. A partial least square discriminant analysis identified 14 metabolites with a variable importance in projection score >1.5 that clearly differentiated underweight from weight-restored patients from healthy women. Compared with healthy women, fecal concentrations of valeric acid and 3-methyl,2-ketobutyric acid were increased in both underweight and weight-restored patients; fecal concentrations of propionic acid, stearic acid, linolenic acid, methyl-galactoside, coprosterol, cycloserine, and lauric acid were increased while fecal levels of xylose, fucose, and rhamnose were decreased in underweight patients and normalized after weight-restoration; fecal concentrations of piperine, phenylalanine, butyric acid, and meso-erythritol-1 were decreased while fecal levels of hydroxystearic acid were increased in weight-restored but normal in underweight AN patients. All these changes point to peculiar fecal metabolomics profiles of acute and short-term weight restored AN patients. The value of these changes to improve our understanding of the pathophysiology of AN and to characterize potential biomarker targets for developing new treatment strategies needs further studies to be clarified.

Objectives: The aims of the study were to understand how education relates to contraceptive choice and how sexual function can vary in relation to the use of a contraceptive method. Methods: We surveyed female medical students and women attending a family planning service (FPS) in Italy. Participants completed an online questionnaire which asked for information on sociodemographics, lifestyle, sexuality and contraceptive use and also included items of the Female Sexual Function Index (FSFI). Results: The questionnaire was completed by 413 women (362 students and 51 women attending the FPS) between the ages of 18 and 30 years. FSFI scores revealed a lower risk of sexual dysfunction among women in the control group who did not use oral hormonal contraception. The differences in FSFI total scores between the two study groups, when subdivided by the primary contraceptive method used, was statistically significant (p

Background Polycystic ovarian syndrome (PCOS) is a highly variable syndrome and one of the most common female endocrine disorders. Although the association inositols-glucomannan may represent a good therapeutic strategy in the treatment of PCOS women with insulin resistance, the effect of inositols on the metabolomic profile of these women has not been described yet. Results Fifteen PCOS-patients and 15 controls were enrolled. Patients were treated with myo-inositol (1.75 g/day), D-chiro-inositol (0.25 g/day) and glucomannan (4 g/day) for 3 months. Blood concentrations of glucose, insulin, triglycerides and cholesterol, and ovary volumes and antral follicles count, as well as metabolomic profiles, were evaluated for control subjects and for cases before and after treatment. PCOS-patients had higher BMI compared with Controls, BMI decreased significantly after 3 months of treatment although it remained significantly higher compared to controls. 3-methyl-1-hydroxybutyl-thiamine-diphosphate, valine, phenylalanine, ketoisocapric, linoleic, lactic, glyceric, citric and palmitic acid, glucose, glutamine, creatinine, arginine, choline and tocopherol emerged as the most relevant metabolites for distinguishing cases from controls. Conclusion Our pilot study has identified a complex network of serum molecules that appear to be correlated with PCOS, and with a combined treatment with inositols and glucomannan. Trial registration ClinicalTial.gov, NCT03608813. Registered 1st August 2018 - Retrospectively registered, . Electronic supplementary material The online version of this article (10.1186/s13048-019-0500-x) contains supplementary material, which is available to authorized users.

Non-Alcoholic Fatty Liver Disease encompasses a spectrum of diseases ranging from simple steatosis to steatohepatitis (or NASH), up to cirrhosis and hepatocellular carcinoma (HCC). The challenge is to recognize the more severe and/or progressive pathology. A reliable non-invasive method does not exist. Untargeted metabolomics is a novel method to discover biomarkers and give insights on diseases pathophysiology.We applied metabolomics to understand if simple steatosis, steatohepatitis and cirrhosis in NAFLD patients have peculiar metabolites profiles that can differentiate them among each-others and from controls.Metabolomics signatures were obtained from 307 subjects from two separated enrollments. The first collected samples from 69 controls and 144 patients (78 steatosis, 23 NASH, 15 NASH-cirrhosis, 8 HCV-cirrhosis, 20 cryptogenic cirrhosis). The second, used as validation-set, enrolled 44 controls and 50 patients (34 steatosis, 10 NASH and 6 NASH-cirrhosis).The "Partial-Least-Square Discriminant-Analysis"(PLS-DA) was used to reveal class separation in metabolomics profiles between patients and controls and among each class of patients, and to reveal the metabolites contributing to class differentiation.Several metabolites were selected as relevant, in particular:Glycocholic acid, Taurocholic acid, Phenylalanine, branched-chain amino-acids increased at the increase of the severity of the disease from steatosis to NASH, NASH-cirrhosis, while glutathione decreased (p0.001 for each). Moreover, an ensemble machine learning (EML) model was built (comprehending 10 different mathematical models) to verify diagnostic performance, showing an accuracy80% in NAFLD clinical stages prediction.Metabolomics profiles of NAFLD patients could be a useful tool to non-invasively diagnose NAFLD and discriminate among the various stages of the disease, giving insights into its pathophysiology.

The microbiota comprises the microorganisms that live in close contact with the host, with mutual benefit for both counterparts. The contribution of the gut microbiota to the emergence of castration-resistant prostate cancer (CRPC) has not yet been addressed. We found that androgen deprivation in mice and humans promotes the expansion of defined commensal microbiota that contributes to the onset of castration resistance in mice. Specifically, the intestinal microbial community in mice and patients with CRPC was enriched for species capable of converting androgen precursors into active androgens. Ablation of the gut microbiota by antibiotic therapy delayed the emergence of castration resistance even in immunodeficient mice. Fecal microbiota transplantation (FMT) from CRPC mice and patients rendered mice harboring prostate cancer resistant to castration. In contrast, tumor growth was controlled by FMT from hormone-sensitive prostate cancer patients and Prevotella stercorea administration. These results reveal that the commensal gut microbiota contributes to endocrine resistance in CRPC by providing an alternative source of androgens.

Background: Parkinson’s disease is the second most frequent neurodegenerative disorder. Its diagnosis is challenging and mainly relies on clinical aspects. At present, no biomarker is available to obtain a diagnosis of certainty in vivo. Objective: The present review aims at describing machine learning algorithms as they have been variably applied to different aspects of Parkinson’s disease diagnosis and characterization. Methods: A systematic search was conducted on PubMed in December 2019, resulting in 230 publications obtained with the following search query: “Machine Learning” “AND” “Parkinson Disease”. Results: The obtained publications were divided into 6 categories, based on different application fields: “Gait Analysis - Motor Evaluation”, “Upper Limb Motor and Tremor Evaluation”, “Handwriting and typing evaluation”, “Speech and Phonation evaluation”, “Neuroimaging and Nuclear Medicine evaluation”, “Metabolomics application”, after excluding the papers of general topic. As a result, a total of 166 articles were analyzed after elimination of papers written in languages other than English or not directly related to the selected topics. Conclusion: Machine learning algorithms are computer-based statistical approaches that can be trained and are able to find common patterns from big amounts of data. The machine learning approaches can help clinicians in classifying patients according to several variables at the same time.

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.

Objective Heart anomalies represent nearly one-third of all congenital anomalies. They are currently diagnosed using ultrasound. However, there is a strong need for a more accurate and less operator-dependent screening method. Here we report a metabolomics characterization of maternal serum in order to describe a metabolomic fingerprint representative of heart congenital anomalies. Methods Metabolomic profiles were obtained from serum of 350 mothers (280 controls and 70 cases). Nine classification models were built and optimized. An ensemble model was built based on the results from the individual models. Results The ensemble machine learning model correctly classified all cases and controls. Malonic, 3-hydroxybutyric and methyl glutaric acid, urea, androstenedione, fructose, tocopherol, leucine and putrescine were determined as the most relevant metabolites in class separation. Conclusion The metabolomic signature of second trimester maternal serum from pregnancies affected by a fetal heart anomaly is quantifiably different from that of a normal pregnancy. Maternal serum metabolomics is a promising tool for the accurate and sensitive screening of such congenital defects. Moreover, the revelation of the associated metabolites and their respective biochemical pathways allows a better understanding of the overall pathophysiology of affected pregnancies. This article is protected by copyright. All rights reserved.

Background: One of the most challenging aspects related to Covid-19 is to establish the presence of infection in an early phase of the disease. Texture analysis might be an additional tool for the evaluation of Chest X-ray in patients with clinical suspicion of Covid-19 related pneumonia. Objective: To evaluate the diagnostic performance of texture analysis and machine learning models for the diagnosis of Covid-19 interstitial pneumonia in Chest X-ray images. Methods: Chest X-ray images were accessed from a publicly available repository(https://www.kaggle. com/tawsifurrahman/covid19-radiography-database). Lung areas were manually segmented using a polygonal region of interest covering both lung areas, using MaZda, a freely available software for texture analysis. A total of 308 features per ROI was extracted. One hundred-ten Covid-19 Chest X-ray images were selected for the final analysis. Results: Six models, namely NB, GLM, DL, GBT, ANN, and PLS-DA were selected and ensembled. According to Youden’s index, the Covid-19 Ensemble Machine Learning Score showing the highest area under the curve (0.971±0.015) was 132.57. Assuming this cut-off the Ensemble model performance was estimated by evaluating both true and false positive/negative, resulting in 91.8% accuracy with 93% sensitivity and 90% specificity. Moving the cut-off value to -100, although the accuracy resulted lower (90.6%), the Ensemble Machine Learning showed 100% sensitivity, with 80% specificity. Conclusion: Texture analysis of Chest X-ray images and machine learning algorithms may help in differentiating patients with Covid-19 pneumonia. Despite several limitations, this study can lay the ground for future research works in this field and help to develop more rapid and accurate screening tools for these patients.

Summary Background & aims We have recently reported specific fecal metabolomic changes in acute and short-term weight restored patients with anorexia nervosa (AN). In this study we explored the association between those metabolomic changes and patients’ gut microbiome composition. Methods The gut microbiome of AN women was sequenced in both the underweight phase (n = 21) and after short-term weight restoration (n = 16) and compared to that of 20 healthy women. According to a multi-omics approach, microbiome data were correlated with 49 relevant fecal metabolites previously characterized in our participants by an untargeted metabolomic procedure. Results Compared to healthy women, AN patients showed a decreased intra-individual bacterial richness, an increased Bacteroidetes-to-Firmicutes abundance ratio and significant changes in the relative abundances of several bacteria at phylum, class, order, family and genus levels. These changes were observed in both the underweight and weight-restored condition. Moreover, the relationships among the 49 previously selected fecal metabolites and bacteria genera showed structures of different complexity among the 3 groups. In particular, a quarter of those relationships showed a divergent direction in the acutely ill patients with respect to the weight-restored ones or normal controls. Finally, in acutely ill patients 70% of those correlations showed a negative sign suggesting a prevalent metabolites consummation by gut microbiome. Conclusions These data confirm a profound perturbation in the gut microbiome composition of AN patients. Moreover, for the first time, they provide the evidence that in AN gut bacteria are connected with several fecal metabolites in a different way from normal controls and with divergent directions in the acute phase with respect to the weight-restored phase.

Key Points Question Is combining the blood metabolomic signature of endometrial carcinoma with an ensemble machine learning algorithm a useful system for building a screening test for endometrial cancer? Findings In this diagnostic study that included 1550 postmenopausal women, the proposed screening test correctly identified all 16 women with endometrial cancer, with 2 false-positive results and 0 false-negative results. Meaning The results of this study suggest that the metabolomic profile of a blood sample could provide a noninvasive and accurate screening test with high sensitivity and specificity for endometrial cancer., This diagnostic test validates a metabolomics-based classification algorithm as a screening test for endometrial carcinoma., Importance Endometrial carcinoma (EC) is the most commonly diagnosed gynecologic cancer. Its early detection is advisable because 20% of women have advanced disease at the time of diagnosis. Objective To clinically validate a metabolomics-based classification algorithm as a screening test for EC. Design, Setting, and Participants This diagnostic study enrolled 2 cohorts. A multicenter prospective cohort, with 50 cases (postmenopausal women with EC; International Federation of Gynecology and Obstetrics stage I-III and grade G1-G3) and 70 controls (no EC but matched on age, years from menopause, tobacco use, and comorbidities), was used to train multiple classification models. The accuracy of each trained model was then used as a statistical weight to produce an ensemble machine learning algorithm for testing, which was validated with a subsequent prospective cohort of 1430 postmenopausal women. The study was conducted at the San Giovanni di Dio e Ruggi d’Aragona University Hospital of Salerno (Italy) and Lega Italiana per la Lotta contro i Tumori clinic in Avellino (Italy). Data collection was conducted from January 2018 to February 2019, and analysis was conducted from January to March 2019. Main Outcomes and Measures The presence or absence of EC based on evaluation of the blood metabolome. Metabolites were extracted from dried blood samples from all participants and analyzed by gas chromatography–mass spectrometry. A confusion matrix was used to summarize test results. Performance indices included sensitivity, specificity, positive and negative predictive values, positive and negative likelihood ratios, and accuracy. Confirmation or exclusion of EC in women with a positive test result was by means of hysteroscopy. Participants with negative results were followed up 1 year after enrollment to investigate the appearance of EC signs. Results The study population consisted of 1550 postmenopausal women. The mean (SD) age was 68.2 (11.7) years for participants with no EC in the training cohort, 69.4 (13.8) years for women with EC in the training cohort, and 59.7 (7.7) years for women in the validation cohort. Application of the ensemble machine learning to the validation cohort resulted in 16 true-positives, 2 false-positives, and 0 false-negatives, and it correctly classified more than 99% of samples. Disease prevalence was 1.12% (16 of 1430). Conclusions and Relevance In this study, dried blood metabolomic profile was used to assess the presence or absence of EC in postmenopausal women not receiving hormonal therapy with greater than 99% accuracy.

Introduction Endometrial cancer (EC) is the most common gynecological malignancy in the developed world. The prognosis of EC strongly depends on tumor stage, hence the importance of improving diagnosis. Metabolomics has recently appeared as a promising test for a non-invasive diagnosis of several diseases. Nevertheless, no metabolic marker has been approved for use in the routine practice. We aimed to provide an overview of metabolomics findings in the diagnosis of EC. Material and methods A systematic review was performed by searching eight electronic databases from their inception to October 2019 for studies assessing metabolomics in EC diagnosis. Extracted data included characteristics of patients and EC, serum concentration of metabolites in women with and without EC and its association with EC diagnosis, tumor behavior and pathological characteristics. Results Six studies with 732 women (356 cases and 376 controls) were included. Several metabolites were found able to predict the presence of EC, tumor behavior (progression and recurrence) and pathological characteristics (histotype, myometrial invasion and lymph vascular space invasion). Conclusions Metabolomics might be suitable for a non-invasive diagnosis and screening of EC, offering the possibility to predict tumor behavior and pathological characteristics. Further studies are necessary to validate these results.

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.

IntroductionThe intestinal microbiota has been indicated to have a role in the pathophysiology of AN.ObjectivesAim of this study was to analyze fecal microbiome profiles of AN women before and after weight restoration and to combine them with fecal metabolomic profiles according to a multi-omics approach.MethodsThe gut microbiome and fecal metabolites were characterized in 21 underweight AN women and after weight restoration and compared with those of 20 healthy women. Microbiome data were correlated with the relevant fecal metabolites.ResultsAN subjects showed a decreased intra-individual bacterial richness, an increased Bacteroidetes-to-Firmicutes abundance ratio and significant changes in the relative abundances of several bacteria at different order levels in both the underweight and weight-restored condition compared to healthy women. The untargeted metabolomic procedure allowed the characterization of 224 metabolites involved in energy, lipid and amino acid metabolism. A genetic algorithm identified 49 relevant metabolites. The relationships among these fecal metabolites and bacteria genera showed structures of different complexity in the 3 groups. In particular, a quarter of those relationships showed a divergent direction in the acute phase of AN than in the weight-restored phase or normal controls. Finally, in acute AN 70% of those correlations showed a negative sign suggesting a prevalent metabolites consummation by gut microbiome.ConclusionsOur results provide a picture of the connections between gut bacteria and fecal metabolites in both the acute phase of AN and after short-term weight restoration. Further studies should aim to investigate the significance of gut microbiome perturbations in development and treatment of AN.

Bladder cancer has a high incidence and is marked by high morbidity and mortality. Early diagnosis is still challenging. The objective of this study was to create a metabolomics-based profile of bladder cancer in order to provide a novel approach for disease screening and stratification. Moreover, the study characterized the metabolic changes associated with the disease. Serum metabolomic profiles were obtained from 149 bladder cancer patients and 81 healthy controls. Different ensemble machine learning models were built in order to: (1) differentiate cancer patients from controls, (2) stratify cancer patients according to grading, (3) stratify patients according to cancer muscle invasiveness. Ensemble machine learning models were able to discriminate well between cancer patients and controls, between high grade (G3) and low grade (G1-2) cancers and between different degrees of muscle invasivity, ensemble model accuracies were ≥80%. Relevant metabolites, selected using the partial least square discriminant analysis (PLS-DA) algorithm, were included in a metabolite-set enrichment analysis, showing perturbations primarily associated with cell glucose metabolism. The metabolomic approach may be useful as a non-invasive screening tool for bladder cancer. Furthermore, metabolic pathway analysis can increase understanding of cancer pathophysiology. Studies conducted on larger cohorts, and including blind trials, are needed to validate results.

Summary Background Gut–liver axis (GLA) dysfunction appears to play a role in obesity and obesity-related hepatic complications. Objectives This study sought to concurrently explore several GLA components in a paediatric obese population with/without liver disease. Methods Thirty-two children (mean age 11.2 years) were enrolled: nine controls with normal weight and 23 patients with obesity (OB+). Of the 23 patients OB(+), 12 had not steatosis (ST−), and 11 had steatosis (ST+) (associated [n = 8] or not [n = 3] with hypertransaminasaemia [ALT +/−]). Subjects were characterized by using auxologic, ultrasonographic and laboratory parameters. A glucose hydrogen breath test was performed to test for small intestinal bacterial overgrowth, a urinary lactulose/mannitol ratio (LMR) was obtained to assess intestinal permeability, and tests for transaminases, blood endogenous ethanol, endotoxin and faecal calprotectin were also conducted. Results Eleven out of 23 patients OB(+) (p 90th percentile of the control group values) LMR, with values paralleling the grade of liver involvement (normal weight

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.

Female cancers represent one of the major causes of morbidity and mortality in the adult population.

About 90% of cases of Parkinson’s disease (PD) are idiopathic and attempts to understand pathogenesis typically assume a multifactorial origin. Multifactorial diseases can be studied using metabolomics, since the cellular metabolome reflects the interplay between genes and environment. The aim of our case–control study is to compare metabolomic profiles of whole blood obtained from treated PD patients, de-novo PD patients and controls, and to study the perturbations correlated with disease duration, disease stage and motor impairment. We collected blood samples from 16 drug naive parkinsonian patients, 84 treated parkinsonian patients, and 42 age matched healthy controls. Metabolomic profiles have been obtained using gas chromatography coupled to mass spectrometry. Multivariate statistical analysis has been performed using supervised models; partial least square discriminant analysis and partial least square regression. This approach allowed separation between discrete classes and stratification of treated patients according to continuous variables (disease duration, disease stage, motor score). Analysis of single metabolites and their related metabolic pathways revealed unexpected possible perturbations related to PD and underscored existing mechanisms that correlated with disease onset, stage, duration, motor score and pharmacological treatment. Metabolomics can be useful in pathogenetic studies and biomarker discovery. The latter needs large-scale validation and comparison with other neurodegenerative conditions.

Pediatric obesity-related metabolic syndrome (MetS) and nonalcoholic fatty liver disease (NAFLD) are increasingly frequent conditions with a still-elusive diagnosis and low-efficacy treatment and monitoring options. In this study, we investigated the salivary metabolomic signature, which has been uncharacterized to date. In this pilot-nested case-control study over a transversal design, 41 subjects (23 obese patients and 18 normal weight (NW) healthy controls), characterized based on medical history, clinical, anthropometric, and laboratory data, were recruited. Liver involvement, defined according to ultrasonographic liver brightness, allowed for the allocation of the patients into four groups: obese with hepatic steatosis ([St+], n = 15) and without hepatic steatosis ([St&ndash, ], n = 8), and with (n = 10) and without (n = 13) MetS. A partial least squares discriminant analysis (PLS-DA) model was devised to classify the patients&rsquo, classes based on their salivary metabolomic signature. Pediatric obesity and its related liver disease and metabolic syndrome appear to have distinct salivary metabolomic signatures. The difference is notable in metabolites involved in energy, amino and organic acid metabolism, as well as in intestinal bacteria metabolism, possibly reflecting diet, fatty acid synthase pathways, and the strict interaction between microbiota and intestinal mucins. This information expands the current understanding of NAFLD pathogenesis, potentially translating into better targeted monitoring and/or treatment strategies in the future.

Alteration of the gut microbiota has been associated with different gastrointestinal disorders. Normobiosis restoration by faecal microbiota transplantation (FMT) is considered a promising therapeutic approach, even if the mechanisms underlying its efficacy are at present largely unknown. Here we sought to elucidate the functional effects of therapeutic FMT administration during experimental colitis on innate and adaptive immune responses in the intestinal mucosa. We show that therapeutic FMT reduces colonic inflammation and initiates the restoration of intestinal homeostasis through the simultaneous activation of different immune-mediated pathways, ultimately leading to IL-10 production by innate and adaptive immune cells, including CD4+ T cells, iNKT cells and Antigen Presenting Cells (APC), and reduces the ability of dendritic cells, monocytes and macrophages to present MHCII-dependent bacterial antigens to colonic T cells. These results demonstrate the capability of FMT to therapeutically control intestinal experimental colitis and poses FMT as a valuable therapeutic option in immune-related pathologies., Faecal microbiota transplantation (FMT) is becoming a therapeutic option in several gastrointestinal disorders. Here, Burrello et al. study the immunological mechanisms by which FMT reduces colonic inflammation and initiates the restoration of intestinal homeostasis in a mouse model of colitis.

Endometrial cancer (EC) is the most common cancer of the female reproductive tract in developed countries. At the moment, no effective screening system is available. Here, we evaluate the diagnostic performance of a serum metabolomic signature. Two enrollments were carried out, one consisting of 168 subjects: 88 with EC and 80 healthy women, was used for building the classification models. The second (used to establish the performance of the classification algorithm) was consisted of 120 subjects: 30 with EC, 30 with ovarian cancer, 10 with benign endometrial disease, and 50 healthy controls. Two ensemble models were built, one with all EC versus controls (Model I) and one in which EC patients were aggregated according to their histotype (Model II). Serum metabolomic analysis was conducted via gas chromatography-mass spectrometry, while classification was done by an ensemble learning machine. Accuracy ranged from 62% to 99% for the Model I and from 67% to 100% for the Model II. Ensemble model showed an accuracy of 100% both for Model I and II. The most important metabolites in class separation were lactic acid, progesterone, homocysteine, 3-hydroxybutyrate, linoleic acid, stearic acid, myristic acid, threonine, and valine. The serum metabolomics signature of endometrial cancer patients is peculiar because it differs from that of healthy controls and from that of benign endometrial disease and from other gynecological cancers (such as ovarian cancer).

Bisphenol A (BPA) is a widely distributed estrogen-mimetic molecule, with well-established effects on the dopaminergic system. It can be found in canned food, dental sealants, thermal paper, etc. BPA undergoes liver conjugation with glucuronic acid and is subsequently excreted in the urine.In the present study we quantified the concentration of free and conjugated Bisphenol A in blood of patients affected by Parkinson Disease, using their spouses as controls.An interview was performed to determine possible confounders in BPA exposure. Free and conjugated BPA were quantified by gas chromatography coupled with mass spectrometry.Parkinson's Disease patients carried a statistically significant lower amount of conjugated Bisphenol A compared to controls. The two populations were mostly homogeneous in terms of exposure to possible Bisphenol A sources. The only exceptions were exposure to canned tuna and canned tomatoes PD patients consumed significantly more of both (p0.05). Moreover, no difference in Bisphenol A glucuronidation was found after stratification by typology of anti-Parkinson's drug taken and after conversion to the Levodopa Equivalent Daily Dose.BPA glucuronidation was decreased in patients with Parkinson disease. The possible unique mechanisms underlying Bisphenol A metabolism in PD patients deserve further elucidation. Moreover, further study is needed to assess a possible BPA role in Parkinson's Disease pathogenesis, due to its documented dopaminergic toxicity.

Chromosomal anomalies (CA) are the most frequent fetal anomalies. To evaluate the diagnostic performance of a machine learning ensemble model based on the maternal serum metabolomic fingerprint of fetal aneuploidies during the second trimester . This is a case-control pilot study. Metabolomic profiles have been obtained on serum of 328 mothers (220 controls and 108 cases), using gas chromatography coupled to mass spectrometry. Eight machines learning and classification models were built and optimized. An ensemble model was built using a voting scheme. All samples were randomly divided into two sets. One was used as training set, the other one for diagnostic performance assessment. Ensemble machine learning model correctly classified all cases and controls. The accuracy was the same for trisomy 21 and 18; also, the other CA were correctly detected. Elaidic, stearic, linolenic, myristic, benzoic, citric and glyceric acid, mannose, 2-hydroxy butyrate, phenylalanine, proline, alanine and 3-methyl histidine were selected as the most relevant metabolites in class separation. The proposed model, based on the maternal serum metabolomic fingerprint of fetal aneuploidies during the second trimester, correctly identifies all the cases of chromosomal abnormalities. Overall, this preliminary analysis appeared suggestive of a metabolic environment conductive to increased oxidative stress and a disturbance in the fetal central nervous system development. Maternal serum metabolomics can be a promising tool in the screening of chromosomal defects. Moreover, metabolomics allows to extend our knowledge about biochemical alterations caused by aneuploidies and responsible for the observed phenotypes.

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.

Introduction Infants born below 2500 g are classified as low birth weight. Excess in utero exposure to cortisol has been linked to restricted fetal growth. Placental production of 11β-hydroxysteroid dehydrogenase type 2 (11β-HSD2) inactivates cortisol before passage into the fetus. The present study tested the hypothesis that placental 11β-HSD2 expression is positively correlated with an individualized birth weight centile and raw birth weight, and examines the relationship between metal concentrations in placental tissue and 11β-HSD2 expression. Methods Placentae from 191 births were collected and samples preserved to maintain mRNA profile. Placental 11β-HSD2 expression was measured via qRT-PCR. Addition samples were collected from placental tissues and uniformly dried in order to quantify 18 metals via ICP-MS (n = 160). Results A significant, positive correlation between 11β-HSD2 expression and individualized birth weight centile (p = 0.0321) and birth weight (p = 0.0243) was found. Additionally, maternal age and gestational age were positivity correlated with each other (p = 0.0321). Birth weight was significantly different with race, marital status, education and maternal tobacco use. Four metals (Co, Mn, Ni, Zn) demonstrated significant positive correlations (p 11β-HSD2 expression. Sex specific differences were found; Co, Cu, Fe, Zn, and Ni were positively correlated with 11β-HSD2 expression in males only, no significant correlations were found in the female only sample. Conclusion These data indicate that the growth potential of a fetus is related to the 11β-HSD2 expression in the placenta, and that 11β-HSD2 expression is related to the trace metals status of the mother.

Objective Overweight/obesity prevalence has increased dramatically worldwide. Recent evidence suggests sleep deprivation/fragmentation, fructose-exceedingly rich diets, and exposure to endocrine disruptors (eg, bisphenol A, BPA) as emerging additional factors involved in pathomechanisms and in the treatment resistance of obesity and its complications. Our study focuses on these factors for further preventive/therapeutic approaches in paediatric obesity. Methods Fifty-four Italian children (cases: n = 31 overweight/obese; controls: n = 23 normal weight) were clinically/anthropometrically characterised. Parents completed questionnaires on the relation between obesogenic factors and childhood obesity. BPA was measured by gas chromatography/tandem mass spectrometry on early morning urine samples. Correlations between the continuous variables were analysed using Spearman rank correlation. Results Sleep deprivation/fragmentation, nocturnal breathing problems, and daytime sleepiness increased with increasing body mass index, correlating with the presence of clinical markers of metabolic syndrome (eg, acanthosis nigricans). Frequency of sugar-enriched drink consumption and the amount of fructose per portion and/or per week increased, paralleling the ponderal excess and all the other anthropometric parameters. In the entire sample population, free and total BPA levels increased paralleling the body mass index increase (r > 0.8), whereas the conjugate demonstrated the opposite trend. The re-use of disposable plastic showed a positive correlation with urinary BPA levels. Conclusions Despite its exploratory nature, the results of our pilot study confirm the close relation between certain factors and paediatric obesity, underscoring their role as emerging targets for prevention and therapy.

Introduction: Bisphenol A (BPA) is a weakly estrogenic compound that has been detected in a wide variety of food products and biological matrices (saliva, blood, urine, etc). Despite the potential risk of human exposure to BPA, little information exists concerning maternal and fetal exposure to BPA during pregnancy. The aim of this study is to evaluate the correlation between placental BPA concentration, infant birth weight and calculated birth weight centile, and several other maternal and infant parameters. Methods: Placental sample were collected from 200 subjects. BPA levels were measured by isotope dilution GCeMS. Additional maternal and infant data were gathered from medical charts and were potential correlates with placental BPA levels. Results: Placental BPA concentrations ranged from 4.4 ng/g to 273.9 ng/g in oven-dried tissue (average 103.4 ± 61.8 ng/g). There was a significant negative correlation between calculated birth weight centile and levels of placental BPA (p < 0.05). Low birth weight and small for gestational age infants also had significantly greater placental BPA concentrations as compared to normal weight infants and average/ large for gestational age infants. Infants born to African American mothers also had greater placental BPA concentrations as compared to infants born to Hispanic mothers. Discussion: Placental BPA concentrations are correlated with the growth potential of the fetus and may play a role in reduced fetal growth.

s / Digestive and Liver Disease 47S (2015) e237–e276 e253 retardation bilateral cataract with visual impairment and hypertension). In one of them, three doses of Rtx (375mg/m2) allowed to suspend immunosuppressive drugs and reduce PDN, maintaining remission for 25 months. PDN associated with Rapamycin was restarted, for a relapsewith only a partial remission. Twoadditional doses of Rtx again induced and maintained remission, which still persists on PDN 0.12mg/kg/day and azathioprine 2.38mg/kg/day. Treatment with seven doses of Rtx induced remission in the other patient, with PDN sparring up to 0.26mg/kg/48h associated with 1.31mg/kg/day of azathioprine. Treatment with Rtx was well tolerated, with, as only side effect a persistent and asymptomatic hypogammaglobulinaemia. In conclusion Rtx proved effective in inducing and maintain remission in refractory GCH-AHA with a steroid sparring effect; however the optimal frequency and duration of RTx treatment remain to be defined and must be individualized due to interpatient variability. http://dx.doi.org/10.1016/j.dld.2015.07.090

// Maria Ravo 1 , Angela Cordella 2 , Antonio Rinaldi 1 , Giuseppina Bruno 1 , Elena Alexandrova 1, 3 , Pasquale Saggese 1 , Giovanni Nassa 1 , Giorgio Giurato 1 , Roberta Tarallo 1 , Giovanna Marchese 1, 3 , Francesca Rizzo 1 , Claudia Stellato 1 , Rossella Biancardi 4 , Jacopo Troisi 4 , Attilio Di Spiezio Sardo 5 , Fulvio Zullo 5 , Alessandro Weisz 1, 6 , Maurizio Guida 4 1 Laboratory of Molecular Medicine and Genomics, Department of Medicine and Surgery, University of Salerno, Baronissi, Italy 2 Fondazione IRCCS SDN, Napoli, Italy 3 Genomix4Life Srl, Spin-Off of the Laboratory of Molecular Medicine and Genomics, University of Salerno, Baronissi, Italy 4 Department of Medicine and Surgery and Division of Gynecology and Obstetrics, “SS. Giovanni di Dio e Ruggi d'Aragona - Schola Medica Salernitana”, University of Salerno Hospital, Salerno, Italy 5 Department of Gynecology and Obstetrics and Pathophysiology of Human Reproduction, University of Naples “Federico II”, Napoli, Italy 6 Division of Molecular Pathology and Medical Genomics, “SS. Giovanni di Dio e Ruggi d'Aragona - Schola Medica Salernitana”, University of Salerno Hospital, Salerno, Italy Correspondence to: Maurizio Guida, e-mail: maguida@unisa.it Alessandro Weisz, e-mail: aweisz@unisa.it Keywords: small non-coding RNA, endometrial cancer, miRNAs, piRNAs, snoRNAs Received: November 02, 2014 Accepted: December 11, 2014 Published: February 28, 2015 ABSTRACT Small non-coding RNAs (sncRNAs) represent a heterogeneous group of

Aim: Exposure to the ubiquitous endocrine disrupter Bisphenol A (BPA) has been associated, in pregnancy, with low birth weight. The aim of our study is the identification of the damage caused by Bisphenol A on placental tissue through the evaluation of its effects on micro-vessel density and apoptosis. Methods: After fertilization, we exposed 3 female rats to oral BPA, by means of a free access to a beverage solution containing 100 μg/L of BPA. Three female rats were used as controls. Placentas underwent histological examination and immunohistochemistry for von Willebr and factor (F-VIII) and caspase-9. Results: Sixty-seven fetuses have been produced, 30 from control rats and 37 from exposed rats. Exposed fetuses showed a lower longitudinal/transverse diameter ratio than controls (2.57 ± 0.29 vs. 2.78 ± 0.38, p < 0.05). Also, exposed fetuses showed a significant reduction in the number of placental vessels per field (124.86 ± 19.15 vs. 143.54 ± 22.09, p < 0.05). On the other hand, apoptosis is not increased by exposure, as shown by caspase-9 levels. Conclusion: Exposure to BPA during pregnancy may affect placental vascularization, and this phenomenon may explain the lower birth weight reported. However, our results do not show the increase in apoptosis observed in vitro.

Autism is a group of neurodevelopmental disorders, characterized by early onset difficulties in social communication and restricted, repetitive behaviors and interests. It is characterized by familial aggregation, suggesting that genetic factors play a role in disease development, in addition to developmentally early environmental factors. Here, we review the role of the gut microbiome in autism, as it has been characterized in case-control studies. We discuss how methodological differences may have led to inconclusive or contradictory results, even though a disproportion between harmful and beneficial bacteria is generally described in autism. Furthermore, we review the studies concerning the effects of gut microbial-based and dietary interventions on autism symptoms. Also, in this case, the results are not comparable due to the lack of standardized methods. Therefore, autism-specific microbiome signatures and, consequently, possible microbiome-oriented interventions are far from being recognized. We argue that a multi-omic longitudinal implementation may be useful to study metabolic changes connected to microbiome changes.