Beauty and health: an intriguing liaison?

The exposure to different form of beauty coming from visual art, music, nature, architecture, improves mental health and exerts neurotrophic effects on different parts of the brain. This in turn improves physical health, prolongs life expectancy, and reduces the risk of serious degenerative diseases such as Alzheimer's and cancer. These beneficial actions would not be understandable and plausible if one did not accept the mind-body unity. The 'hegemonic' role of the brain in health and illness can be discerned, for example, in the effect of emotions on vital physiological parameters, in the relationships between stress and many medical-clinical pathologies, in the control exercised by the brain over the immune system reflecting also in the inhibition of tumour progression.

The impact of unmet treatment need on oral health related quality of life: a questionnaire survey.

BACKGROUND: Based on the present global burden of oral diseases, unmet dental needs affect a more significant population worldwide. It is characterised by the need for dental care but receiving delayed or no care. The contributing factors include lack of knowledge about oral health, its consequences, and the availability of dental services. We need to find out the scale of the problem of unmet dental needs for the south Indian population. Therefore, the objective was to determine the relationship between the presence of oral disease and the quality of life-related to oral health using the OHIP-14 tool. METHODS: The unmet dental requirements of the south Indian population were determined using a cross-sectional questionnaire survey. Close-ended questions were used to obtain data from two investigators trained to record the answers from the patients. The data was collected using the OHIP-14 questionnaire, which consists of 14 items divided into seven domains with two questions each. Physical pain, psychological impairment, physical disability, psychological disability, social disability, and disability were all considered. An additional analysis of artificial neural network (ANN) was done. RESULTS: The response rate was 100 per cent. N = 1029 people replied to the questionnaire about their unmet dental needs. N = 497 (48.3%) were men, whereas N = 532 (51.7%) were women. The average age was 31.7811.72. As their current occupation, most of the included subjects (60.1%) were students. The respondents had no known personal habits and a mixed diet (94.93%). The average BMI was 24.022.59 (14-30.9). OHIP was present in 62.3% of the population. The average OHIP-14 severity score was 10.97. (8.54). The severity and degree of unmet dental need were substantial (p0.01) due to pain in the mouth/teeth/gums, malocclusion, and gum bleeding. The most common OHIP-14 domains affected by unmet oral needs were psychological discomfort, psychological limitation, social limitation, and feeling handicapped. The analysis of ANN revealed that high OHIP scores were primarily attributed to dental caries, poor oral health, and dental aesthetics. CONCLUSION: The severity and degree of unmet dental needs were significant among the south Indian population. The most common oral health status that impacted OHIP-14 domains were pain, malocclusion, and bleeding gums. These patients were significantly impacted by psychological discomfort and social limitations and felt handicapped.

Do artificial neural networks love sex? How the combination of artificial neural networks with evolutionary algorithms may help to identify gender influence in rheumatic diseases.

Although medical research has been performed predominantly on men both in preclinical and clinical studies, continuous efforts have been made to overcome this gender bias. Examining retrospectively 21 data sets containing sex as one of the descriptive variables, it was possible to verify how many times our AI protocol decided to keep gender information in the predictive model. The data sets pertained a vast array of diseases such as dyspeptic syndrome, atrophic gastritis, venous thrombosis, gastroesophageal reflux disease, irritable bowel syndrome, Alzheimer diseases and mild cognitive impairment, myocardial infarction, gastrointestinal bleeding, gastric cancer, hypercortisolism, AIDS, COVID diagnosis, extracorporeal membrane oxygenation in intensive therapy, among others. The sample size of these data sets ranged between 80 and 3147 (average 600). The number of variables ranged from 19 to 101 (average 41). Gender resulted to be part of the heuristic predictive model 19 out of 21 times. This means that also for highly adaptive and potent tools like Artificial Neural Networks, information on sex carries a specific value. In the field of rheumatology, there is a specific example in psoriatic arthritis that shows that the presence of gender information allows a significantly better accuracy of ANNs in predicting diagnosis from clinical data (from 87.7% to 94.47%). The results of this study confirm the importance of gender information in building high performance predictive model in the field of Artificial Intelligence (AI). Therefore, also for AI, sex counts.

Hyperandrogenism and menstrual imbalance are the best predictors of metformin response in PCOS patients.

BACKGROUND: Moving from the correlation between insulin-resistance and PCOS, metformin has been administered in some PCOS women improving ovulatory and metabolic functions and decreasing androgen levels. Inconsistency and unpredictability of response to metformin limit its extensive use. Aim of this study was to identify reliable predictors of response to metformin therapy for weight loss and reduction in plasma androgen levels using ANNs (artificial neural networks). METHODS: One hundred eight consecutive women with PCOS (ESHRE/ASRM 2003 Rotterdam criteria) treated with metformin 1500 mg/day, at inclusion and every 6 months underwent to a complete clinical, endocrine/metabolic assessment and ultrasonographic evaluation. Therapy outcomes were BMI reduction (≥1 kg/m2) in overweight/obese and free-androgen-index (FAI) decrease (≥1%) in hyperandrogenemic women. Semantic connectivity maps (SCMs) were obtained through Auto-CM, a fourth generation ANN, to compare patients' baseline clinical features to the treatment outcomes. Multivariate logistic regression analysis was used to assess the major predictor in drop-out patients and the associated risk. RESULTS: At 6 months 54 out of 103 (52,4%) obese patients showed BMI reduction and 45 out of 89 (50,6%) hyperandrogenemic women showed FAI decrease. The further response rates at 12 months were 30,6 and 47%, respectively. SCMs showed a clear polarization for both the outcomes with elevated accuracy. Treatment responsiveness resulted strictly related to oligo-amenorrhea and hyperandrogenemia at baseline. In addition, lower serum testosterone levels at baseline were found to be the major predictor of treatment discontinuation. CONCLUSIONS: In women with PCOS, menstrual pattern imbalance and ovarian androgens excess are the best predictors of metformin response. They may pave the way for a rethinking of the criteria for evaluating hyperandrogenism in order to better define the large population included in the diagnosis of PCOS. Baseline plasma testosterone level can serve as a sensitive marker to predict treatment compliance.

Communication improvement reduces BPSD: a music therapy study based on artificial neural networks.

BACKGROUND: Literature showed the effects of music therapy on behavioral disturbances, cognitive functions, and on quality of life in people with dementia. Especially, relational active music therapy approach is oriented to reduce behavioral disturbances increasing communication, especially non-verbal communication. OBJECTIVE: This study aimed at exploring the connection between the baseline characteristics of responders and the positive outcome of the intervention, but also the close relationship between the behavioral disturbances and the core of the therapeutic intervention (the relationship/communication improvement). METHOD: Linear correlation index between input variables and the presence of a critical improvement of behavioral symptoms according Neuropsychiatric Inventory and a semantic connectivity map were used to determine, respectively, variables predictive of the response and complex connections between clinical variables and the relational nature of active music therapy intervention. The dataset was composed of 27 variables and 70 patients with a moderate-severe stage of dementia and behavioral disturbances. RESULTS: The main predictive factor is the Barthel Index, followed by NPI and some of its sub-items (mainly, Disinhibition, Depression, Hallucinations, Irritability, Aberrant Motor Activity, and Agitation). Moreover, the semantic map underlines how the improvement in communication/relationship is directly linked to "responder" variable. "Responder" variable is also connected to "age," "Mini Mental State Examination," and sex ("female"). CONCLUSIONS: The study confirms the appropriateness of active music therapy in the reduction of behavioral disturbances and also highlights how unsupervised artificial neural networks models can support clinical practice in defining predictive factors and exploring the correlation between characteristics of therapeutic-rehabilitative interventions and related outcomes.

Possible tics diagnosed as stereotypies in patients with severe autism spectrum disorder: a video-based evaluation.

BACKGROUND: The association of stereotypies and tics is not rare in children with severe autism spectrum disorder (ASD). The differential diagnosis between stereotypies and tics in this patient population can be difficult; however, it could be clinically relevant because of treatment implications. METHODS: A total of 108 video recordings of repetitive behaviors in young patients with stereotypies in the context of ASD were reviewed by a movement disorders expert and a trainee, in order to assess the prevalence of possible co-morbid tics. The Modified Rush Videotape Rating Scale (MRVS) was used to rate tic frequency and severity. RESULTS: Out of 27 patients with stereotypies (24 males; mean age 14 years), 18 (67%) reported possible tics. The most frequently observed tics were eye blinking, shoulder shrugging, neck bending, staring, and throat clearing. The mean MRVS score was 5, indicating mild tic severity. The only significant difference between patients with tics and patients without tics was the total number of stereotypies, which was higher in the subgroup of patients without tics (p = 0.01). CONCLUSIONS: Expert review of video-recordings of repetitive behaviors in young patients with ASD and stereotypies suggests the possibility of a relatively high rate of co-morbid tics. These findings need to be integrated with a comprehensive clinical assessment focusing on the diagnostic re-evaluation of heterogeneous motor manifestations.

Predicting Secukinumab Fast-Responder Profile in Psoriatic Patients: Advanced Application of Artificial-Neural-Networks (ANNs).

BACKGROUND: Drug resistance to biologics in psoriasis therapy can occur – it may be acquired during a treatment or else present itself from the beginning. To date, no biomarkers are known that may reliably guide clinicians in predicting responsiveness to biologics. Biologics may pose a substantial economic burden. Secukinumab efficiently targets IL-17 in the treatment of psoriasis. OBJECTIVE: To assess the “fast responder” patient profile, predicting it from the preliminary complete blood count (CBC) and clinical examination. MATERIALS AND METHODS: From November 2016 to May 2017 we performed a multicenter prospective open label pilot study in three Italian reference centers enrolling bio-naive plaque psoriasis patients, undergoing the initiation phase secukinumab treatment (300mg subcutaneous at week 0,1,2,3,4). We define fast responders as patients having achieved at least PASI 75 at the end of secukinumab induction phase. Clinical and CBC data at week 0 and at week 4 were analyzed with linear statistics, principal component analysis, and artificial neural networks (ANNs), also known as deep learning. Two different ANNs were employed: Auto Contractive Map (Auto-CM), an unsupervised ANNs, to study how this variables cluster and a supervised ANNs, Training with Input Selection and Testing (TWIST), to build the predictive model. RESULTS: We enrolled 23 plaque psoriasis patients: 19 patients were responders and 4 were non-responders. 30 attributes were examined by Auto-CM, creating a semantic map for three main profiles: responders, non-responders and an intermediate profile. The algorithm yielded 5 of the 30 attributes to describe the 3 profiles. This allowed us to set up the predictive model. It displayed after training testing protocol an overall accuracy of 91.88% (90% for responders and 93,75% for non-responders). CONCLUSIONS: The present study is possibly the first approach employing ANNs to predict drug efficacy in dermatology; a wider use of ANNs may be conducive to useful both theoretical and clinical insight. J Drugs Dermatol. 2020;19(12) doi:10.36849/JDD.2020.5006.

Bacterial network community in fecal and endoluminal Microbiota after colonoscopy.

The gut microbiota is a complex and dynamic ecosystem with a strong influence on the host's health. Several factors can modify the gut's bacterial composition, often leading to the onset of intestinal dysbiosis. Therefore, it is essential not only to evaluate the quantitative bacterial changes occurring in the human microbiota but also to characterize relationships existing among all the microorganisms. This study aimed to evaluate the impact of bowel cleansing on the fecal microbiota network by highlighting differences between fecal microflora before and after colonoscopy, and luminal samples during colonoscopy. Fecal and luminal samples, previously analyzed by mean of Next-Generation Sequencing (NGS) for their bacterial abundance, were further processed by a method based on Artificial Neural Network (ANN) architecture. The bowel lavage had a strong effect on the intestinal microbiota network, leading to significant changes in the distribution of different bacterial hubs potentially involved in the microbiota homeostasis. Furthermore, the fecal and luminal microbiota showed a different bacterial network, characterized by distinct microbial hubs. In particular, the latter seemed to be rich in potentially pathogenic bacteria which, in physiological conditions, are counteracted by fecal microorganisms.

COVID-19 in Italy and extreme data mining.

In this article we want to show the potential of an evolutionary algorithm called Topological Weighted Centroid (TWC). This algorithm can obtain new and relevant information from extremely limited and poor datasets. In a world dominated by the concept of big (fat?) data we want to show that it is possible, by necessity or choice, to work profitably even on small data. This peculiarity of the algorithm means that even in the early stages of an epidemic process, when the data are too few to have sufficient statistics, it is possible to obtain important information. To prove our theory, we addressed one of the most central issues at the moment: the COVID-19 epidemic. In particular, the cases recorded in Italy have been selected. Italy seems to have a central role in this epidemic because of the high number of measured infections. Through this innovative artificial intelligence algorithm, we have tried to analyze the evolution of the phenomenon and to predict its future steps using a dataset that contained only geospatial coordinates (longitude and latitude) of the first recorded cases. Once the coordinates of the places where at least one case of contagion had been officially diagnosed until February 26th, 2020 had been collected, research and analysis was carried out on: outbreak point and related heat map (TWC alpha); probability distribution of the contagion on February 26th (TWC beta); possible spread of the phenomenon in the immediate future and then in the future of the future (TWC gamma and TWC theta); how this passage occurred in terms of paths and mutual influence (Theta paths and Markov Machine). Finally, a heat map of the possible situation towards the end of the epidemic in terms of infectiousness of the areas was drawn up. The analyses with TWC confirm the assumptions made at the beginning.

Directional Relationship Between Vitamin D Status and Prediabetes: A New Approach from Artificial Neural Network in a Cohort of Workers with Overweight-Obesity.

Objective: Despite the increasing literature on the association of diabetes with inflammation, cardiovascular risk, and vitamin D (25(OH)D) concentrations, strong evidence on the direction of causality among these factors is still lacking. This gap could be addressed by means of artificial neural networks (ANN) analysis.Methods: Retrospective observational study was carried out by means of an innovative data mining analysis-known as auto-contractive map (AutoCM)-and semantic mapping followed by Activation and Competition System on data of workers referring to an occupational-health outpatient clinic. Parameters analyzed included weight, height, waist circumference, body mass index (BMI), percentage of fat mass, glucose, insulin, glycated hemoglobin (HbA1c), creatinine, total cholesterol, low- and high-density lipoprotein cholesterol, triglycerides, uric acid, fibrinogen, homocysteine, C-reactive protein (CRP), diastolic and systolic blood pressure, and 25(OH)D.Results: The study included 309 workers. Of these, 23.6% were overweight, 40.5% were classified into the first class of obesity, 23.3% were in the second class, and 12.6% were in the third class (BMI > 40 kg/m ). All mean biochemical values were in normal range, except for total cholesterol, low- and high-density lipoprotein cholesterol, CRP, and 25(OH)D. HbA1c was between 39 and 46 mmol/mol in 51.78%. 25(OH)D levels were sufficient in only 12.6%. Highest inverse correlation for hyperglycemia onset was with BMI and waist circumference, suggesting a protective role of 25(OH)D against their increase. AutoCM processing and the semantic map evidenced direct association of 25(OH)D with high link strength (0.99) to low CRP levels and low high-density lipoprotein cholesterol levels. Low 25(OH)D led to changes in glucose, which affected metabolic syndrome biomarkers, first of which was homeostatic model assessment index and blood glucose, but not 25(OH)D.Conclusions: The use of ANN suggests a key role of 25(OH)D respect to all considered metabolic parameters in the development of diabetes and evidences a causation between low 25(OH)D and high glucose concentrations.

Application of artificial neural network analysis in the evaluation of cardiovascular risk in primary Sjögren's syndrome: a novel pathogenetic scenario?

OBJECTIVES: The aim of the present study was to verify whether artificial neural networks (ANNs) might help to elucidate the mechanisms underlying the increased prevalence of cardiovascular events (CV) in primary Sjögren's syndrome (pSS). METHODS: 408 pSS patients (395 F: 13 M), with a mean age of 61 (±14) years and mean disease duration of 8.8 (±7.8) years were retrospectively included. CV risk factors and events were analysed and correlated with the other pSS clinical and serological manifestations by using both a traditional statistical approach (i.e. Agglomerative Hierarchical Clustering (AHC)) and Auto-CM, a data mining tool based on ANNs. RESULTS: Five percent of pSS patients experienced one or more CV events, including heart failure (8/408), transient ischaemic attack (6/408), stroke (4/408), angina (4/408), myocardial infarction (3/408) and peripheral obliterative arteriopathy (2/408). The AHC provided a dendrogram with at least three clusters that did not allow us to infer specific differential associations among variables (i.e. CV comorbidity and pSS manifestations). On the other hand, Auto-CM identified two different patterns of distributions in CV risk factors, pSS-related features, and CV events. The first pattern, centered on "non-ischaemic CV events/generic condition of HF", was characterised by the presence of traditional CV risk factors and by a closer link with pSS glandular features rather than to pSS extra-glandular manifestations. The second pattern included "ischaemic neurological, cardiac events and peripheral obliterative arteriopathy" and appeared to be strictly associated with extra-glandular disease activity and longer disease duration. CONCLUSIONS: This study represents the first application of ANNs to the analysis of factors contributing to CV events in pSS. When compared to AHC, ANNs had the advantage of better stratifying CV risk in pSS, opening new avenues for planning specific interventions to prevent long-term CV complications in pSS patients.

Magic Moments: Determinants of Stress Relief and Subjective Wellbeing from Visiting a Cultural Heritage Site.

We provide an experimental evaluation of the impact of aesthetic experiences in terms of stress reduction (cortisol levels) and wellbeing increase. The test experience is a visit to the vault of the Sanctuary of Vicoforte, Italy. Data have been collected using a double step method. A structured interview in relation to the individual subjective well-being has been administered to a sample of 100 subjects. In addition, a sample of their saliva has been taken, and its cortisol level measured, before and after the experience, and likewise for momentary wellbeing measured on a Visual Analogous Scale. Subjects reported an average increase of 40% in wellbeing and a decrease of the 60% in the cortisol level. The recorded cortisol level values dropped on average well beyond the decrease normally associated to its circadian cycle. The modulating role of various variables has been appreciated, and profiling of the typical subjects who are wellbeing respondents/non-respondents and cortisol respondents/non-respondents has been carried out. We conclude that aesthetic experience seems to have a noticeable impact on individual physical and mental health. In both dominions, cultural participation intensity is significantly correlated to the response. The study underlines the potential of the arts and culture as a new platform for public health practices and new approaches to welfare policy design.

Determinants of metabolic syndrome in obese workers: gender differences in perceived job-related stress and in psychological characteristics identified using artificial neural networks.

OBJECTIVE: The metabolic syndrome (MS) is a multifactorial disorder associated with a higher risk of developing cardiovascular diseases and type 2 diabetes. However, its pathophysiology and risk factors are still poorly understood. In this study, we investigated the associations among gender, psychosocial variables, job-related stress and the presence of MS in a cohort of obese Caucasian workers. METHODS: A total of 210 outpatients (142 women, 68 men) from an occupational medicine service was enrolled in the study. Age, BMI, waist circumference, fasting glucose, blood pressure, triglycerides and HDL cholesterol were collected to define MS. In addition, we evaluated eating behaviors, depressive symptoms, and work-related stress. Data analyses were performed with an artificial neural network algorithm called Auto Semantic Connectivity Map (AutoCM), using all available variables. RESULTS: MS was diagnosed in 54.4 and 33.1% of the men and women, respectively. AutoCM evidenced gender-specific clusters associated with the presence or absence of MS. Men with a moderate occupational physical activity, obesity, older age and higher levels of decision-making freedom at work were more likely to have a diagnosis of MS than women. Women with lower levels of decision-making freedom, and higher levels of psychological demands and social support at work had a lower incidence of MS but showed higher levels of binge eating and depressive symptomatology. CONCLUSION: We found a complex gender-related association between MS, psychosocial risk factors and occupational determinants. The use of these information in surveillance workplace programs might prevent the onset of MS and decrease the chance of negative long-term outcomes. LEVEL OF EVIDENCE: Level V, observational study.

Artificial neural networks help to identify disease subsets and to predict lymphoma in primary Sjögren's syndrome.

OBJECTIVES: Primary Sjögren's syndrome (pSS) is a complex chronic systemic disorder, for which specific and effective therapeutic interventions are still lacking. In this era of precision medicine, there is a clear need for a better definition of disease phenotypes to foster the research of novel specific biomarkers and new therapeutic targets. The main objectives of this work are: 1) to compare Auto Contractive Map (AutoCM), a data mining tool based on an artificial neural network (ANN) versus conventional Principal Component Analysis (PCA) in discriminating different pSS subsets and 2) to specifically focus on variables predictive of MALT-NHL development, assessing the previsional gain of the predictive models developed. METHODS: Out of a historic cohort of 850 patients, we selected 542 cases of pSS fulfilling the AECG criteria 2002. Thirty-seven variables were analysed including: patient demographics, glandular symptoms, systemic features, biological abnormalities and MALT-NHLs. AutoCM was used to compute the association of strength of each variable with all other variables in the dataset. PCA was applied to the same data set. RESULTS: Both PCA and AutoCM confirmed the associations between autoantibody positivity and several pSS clinical manifestations, highlighting the importance of serological biomarkers in pSS phenotyping. However, AutoCM allowed us to clearly distinguish pSS patients presenting with predominant glandular manifestations and no or mild extra-glandular features from those with a more severe clinical presentation. Out of 542 patients, we had 27 cases of MALT-NHLs. The AutoCM highlighted that, besides other traditional lymphoproliferative risk factors (i.e. salivary gland enlargement, low C4, leukocytopenia, cryoglobulins, monoclonal gammopathy, disease duration), rheumatoid factor was strongly associated to MALT-NHLs development. By applying data mining analysis, we obtained a predictive model characterised by a sensitivity of 92.5% and a specificity of 98%. If we restricted the analysis to the seven most significant variables, the sensitivity of the model was 96.2% and its specificity 96%. CONCLUSIONS: Our study has shed new light on the possibility of using novel tools to extract hidden, previously unknown and potentially useful information in complex diseases like pSS, facing the challenge of disease phenotyping as a prerequisite for discovering novel specific biomarkers and new therapeutic targets.

Current Knowledge on Endocrine Disrupting Chemicals (EDCs) from Animal Biology to Humans, from Pregnancy to Adulthood: Highlights from a National Italian Meeting.

Wildlife has often presented and suggested the effects of endocrine disrupting chemicals (EDCs). Animal studies have given us an important opportunity to understand the mechanisms of action of many chemicals on the endocrine system and on neurodevelopment and behaviour, and to evaluate the effects of doses, time and duration of exposure. Although results are sometimes conflicting because of confounding factors, epidemiological studies in humans suggest effects of EDCs on prenatal growth, thyroid function, glucose metabolism and obesity, puberty, fertility, and on carcinogenesis mainly through epigenetic mechanisms. This manuscript reviews the reports of a multidisciplinary national meeting on this topic.

Impact of delivery mode on the colostrum microbiota composition.

BACKGROUND: Breast milk is a rich nutrient with a temporally dynamic nature. In particular, numerous alterations in the nutritional, immunological and microbiological content occur during the transition from colostrum to mature milk. The objective of our study was to evaluate the potential impact of delivery mode on the microbiota of colostrum, at both the quantitative and qualitative levels (bacterial abundance and microbiota network). METHODS: Twenty-nine Italian mothers (15 vaginal deliveries vs 14 Cesarean sections) were enrolled in the study. The microbiota of colostrum samples was analyzed by next generation sequencing (Ion Torrent Personal Genome Machine). The colostrum microbiota network associated with Cesarean section and vaginal delivery was evaluated by means of the Auto Contractive Map (AutoCM), a mathematical methodology based on Artificial Neural Network (ANN) architecture. RESULTS: Numerous differences between Cesarean section and vaginal delivery colostrum were observed. Vaginal delivery colostrum had a significant lower abundance of Pseudomonas spp., Staphylococcus spp. and Prevotella spp. when compared to Cesarean section colostrum samples. Furthermore, the mode of delivery had a strong influence on the microbiota network, as Cesarean section colostrum showed a higher number of bacterial hubs if compared to vaginal delivery, sharing only 5 hubs. Interestingly, the colostrum of mothers who had a Cesarean section was richer in environmental bacteria than mothers who underwent vaginal delivery. Finally, both Cesarean section and vaginal delivery colostrum contained a greater number of anaerobic bacteria genera. CONCLUSIONS: The mode of delivery had a large impact on the microbiota composition of colostrum. Further studies are needed to better define the meaning of the differences we observed between Cesarean section and vaginal delivery colostrum microbiota.

Alcohol consumption pattern and risk of Barrett's oesophagus and erosive oesophagitis: an Italian case-control study.

Knowledge about the association between alcohol and Barrett's oesophagus and reflux oesophagitis is conflicting. In this case-control study we evaluated the role of specific alcoholic beverages (red and white wine, beer and liquors) in 339 Barrett's oesophagus and 462 oesophagitis patients compared with 619 endoscopic controls with other disorders, recruited in twelve Italian endoscopic units. Data on alcohol and other individual characteristics were obtained from structured questionnaires. No clear, monotonic significant dose-response relationship was pointed out for red wine. However, a generalised U-shaped trend of Barrett's oesophagus/oesophagitis risk due to red wine consumption particularly among current drinkers was found. Similar results were also found for white wine. Liquor/spirit consumption seemed to bring about a 1·14-2·30 risk excess, although statistically non-significant, for current Barrett's oesophagus/oesophagitis drinkers. Statistically significant decreasing dose-response relationships were found in Barrett's oesophagus for frequency and duration of beer consumption. Similar, but less clear downward tendencies were also found for oesophagitis patients. In conclusion, although often not statistically significant, our data suggested a reduced risk of Barrett's oesophagus and oesophagitis with a low/moderate intake of wine and beer consumption. A non-significant increased risk of Barrett's oesophagus/oesophagitis was observed with a higher intake of any type of heavy alcohol consumption, but no conclusion can be drawn owing to the high number of non-spirit drinkers and to the small number of drinkers at higher alcohol intake levels.

Effects of Improvisational Music Therapy vs Enhanced Standard Care on Symptom Severity Among Children With Autism Spectrum Disorder: The TIME-A Randomized Clinical Trial.

Importance: Music therapy may facilitate skills in areas affected by autism spectrum disorder (ASD), such as social interaction and communication. Objective: To evaluate effects of improvisational music therapy on generalized social communication skills of children with ASD. Design, Setting, and Participants: Assessor-blinded, randomized clinical trial, conducted in 9 countries and enrolling children aged 4 to 7 years with ASD. Children were recruited from November 2011 to November 2015, with follow-up between January 2012 and November 2016. Interventions: Enhanced standard care (n = 182) vs enhanced standard care plus improvisational music therapy (n = 182), allocated in a 1:1 ratio. Enhanced standard care consisted of usual care as locally available plus parent counseling to discuss parents' concerns and provide information about ASD. In improvisational music therapy, trained music therapists sang or played music with each child, attuned and adapted to the child's focus of attention, to help children develop affect sharing and joint attention. Main Outcomes and Measures: The primary outcome was symptom severity over 5 months, based on the Autism Diagnostic Observation Schedule (ADOS), social affect domain (range, 0-27; higher scores indicate greater severity; minimal clinically important difference, 1). Prespecified secondary outcomes included parent-rated social responsiveness. All outcomes were also assessed at 2 and 12 months. Results: Among 364 participants randomized (mean age, 5.4 years; 83% boys), 314 (86%) completed the primary end point and 290 (80%) completed the last end point. Over 5 months, participants assigned to music therapy received a median of 19 music therapy, 3 parent counseling, and 36 other therapy sessions, compared with 3 parent counseling and 45 other therapy sessions for those assigned to enhanced standard care. From baseline to 5 months, mean ADOS social affect scores estimated by linear mixed-effects models decreased from 14.08 to 13.23 in the music therapy group and from 13.49 to 12.58 in the standard care group (mean difference, 0.06 [95% CI, -0.70 to 0.81]; P = .88), with no significant difference in improvement. Of 20 exploratory secondary outcomes, 17 showed no significant difference. Conclusions and Relevance: Among children with autism spectrum disorder, improvisational music therapy, compared with enhanced standard care, resulted in no significant difference in symptom severity based on the ADOS social affect domain over 5 months. These findings do not support the use of improvisational music therapy for symptom reduction in children with autism spectrum disorder. Trial Registration: isrctn.org Identifier: ISRCTN78923965.

Pregnancy risk factors in autism: a pilot study with artificial neural networks.

BACKGROUND: Autism is a multifactorial condition in which a single risk factor can unlikely provide comprehensive explanation for the disease origin. Moreover, due to the complexity of risk factors interplay, traditional statistics is often unable to explain the core of the problem due to the strong inherent nonlinearity of relationships. The aim of this study was to assess the frequency of 27 potential risk factors related to pregnancy and peri-postnatal period. METHODS: The mothers of 45 autistic children and of 68 typical developing children completed a careful interview. Twenty-four siblings of 19 autistic children formed an internal control group. RESULTS: A higher prevalence of potential risk factors was observed in 22 and 15 factors in external control and internal control groups, respectively. For six of them, the difference in prevalence was statistically significant. Specialized artificial neural networks (ANNs) discriminated between autism and control subjects with 80.19% global accuracy when the dataset was preprocessed with TWIST (training with input selection and testing) system selecting 16 out of 27 variables. Logistic regression applied to 27 variables gave unsatisfactory results with global accuracy of 46%. CONCLUSION: Pregnancy factors play an important role in autism development. ANNs are able to build up a predictive model, which could represent the basis for a diagnostic screening tool.