Right frontal cingulate cortex mediates the effect of prenatal complications on youth internalizing behaviors.

Prenatal and perinatal complications represent well-known risk factors for the future development of psychiatric disorders. Such influence might become manifested during childhood and adolescence, as key periods for brain and behavioral changes. Internalizing and externalizing behaviors in adolescence have been associated with the risk of psychiatric onset later in life. Both brain morphology and behavior seem to be affected by obstetric complications, but a clear link among these three aspects is missing. Here, we aimed at analyzing the association between prenatal and perinatal complications, behavioral issues, and brain volumes in a group of children and adolescents. Eighty-two children and adolescents with emotional-behavioral problems underwent clinical and 3 T brain magnetic resonance imaging (MRI) assessments. The former included information on behavior, through the Child Behavior Checklist/6-18 (CBCL/6-18), and on the occurrence of obstetric complications. The relationships between clinical and gray matter volume (GMV) measures were investigated through multiple generalized linear models and mediation models. We found a mutual link between prenatal complications, GMV alterations in the frontal gyrus, and withdrawn problems. Specifically, complications during pregnancy were associated with higher CBCL/6-18 withdrawn scores and GMV reductions in the right superior frontal gyrus and anterior cingulate cortex. Finally, a mediation effect of these GMV measures on the association between prenatal complications and the withdrawn dimension was identified. Our findings suggest a key role of obstetric complications in affecting brain structure and behavior. For the first time, a mediator role of frontal GMV in the relationship between prenatal complications and internalizing symptoms was suggested. Once replicated on independent cohorts, this evidence will have relevant implications for planning preventive interventions.

Theory of Mind: A Brief Review of Candidate Genes.

Deficits in theory of mind (ToM), known as the ability to understand the other's mind, have been associated with several psychopathological outcomes. The present systematic review aims to summarize the results of genetic studies that investigated gene polymorphisms associated with mentalization performance tasks in children and adults. The systematic review was carried out following PRISMA guidelines, and the literature search was conducted in PubMed and EBSCOhost using the following keywords: 'theory of mind, mentalizing, mindreading' and 'gene, genetic basis'. Nineteen studies met the eligibility criteria for inclusion. Most of the literature focused on the role of DRD4, DAT1, OXTR, OXT, COMT, ZNF804A, AVP, AVPR, SCL6A4, EFHC2, MAO-A, and the family of GTF2I genes in influencing ToM. However, controversial results emerged in sustaining the link between specific genetic polymorphisms and mentalization abilities in children and adults. Available data show heterogeneous outcomes, with studies reporting an association between the same family genes in subjects of the same age and other studies reporting no correlation. This does not allow us to draw any solid conclusions but paves the way for exploring genes involved in ToM tasks.

Prevalence of perfluoroalkyl substances in paired batches of precooked and canned bovine meat and their implication on consumer safety.

Meat is a highly nutritious food but there is a lot of significant evidence of negative health outcomes related to its excessive consumption, especially for processed one. Among the variety of emerging contaminants of concern for human health, a key role is played by poly- and per-fluoroalkyl substances (PFASs), which show adverse effects in humans who are exposed to them through diet. In the present study, for the first time, 70 paired batches of pre-cooked and canned bovine meat were analysed by Liquid Chromatography coupled to High Resolution Mass Spectrometry to evaluate the presence and concentration of 18 PFASs. These data were used to assess Italian consumers' health risks by performing the PFAS intake evaluation. PFBA and PFOS were detected in the precooked and canned meat samples, with PFBA mean concentration of 0.22 ± 0.36 ng g-1, and

The EU Interreg Project "ADRINET": Assessment of Well-Known and Emerging Pollutants in Seafood and Their Potential Effects for Food Safety.

Anthropogenic activities lead to the spread of chemicals and biological materials, including plastic waste, toxic metals, and pharmaceuticals, of which the impact on the Mediterranean Sea is of high concern. In this context, the EU Interreg Italy-Albania-Montenegro Project "ADRINET (Adriatic Network for Marine Ecosystem) _244" (2018-2020) arises. It aims to carry out biomonitoring campaigns in the main commercial interest of fish and cephalopod species, such as Sparus aurata, Dicentrarchus labrax, Sepia spp., and Loligo spp. sampled in three different subregions of the Mediterranean Sea. The presence of the main environmental contaminants, such as cadmium, microplastics, and antibiotics was investigated in these seafood samples. Contamination by cadmium and antibiotics in the seafood investigated in our study was negligible. However, a high value of microplastics was detected in the stomach and gut of Sparus aurata and Dicentrarchus labrax. Overall, even though the presence of microplastics needs to be investigated by further studies, the results confirmed that the environmental conditions of the three bays investigated by the ADRINET project partners (Italy, Albania, Montenegro) are positive and not affected by intensive anthropogenic activity.

Preliminary Investigation towards the Use of Infrared Technology for Raw Milk Treatment.

Infrared (IR) technology offers a promising solution for reducing microbiological loads in various food types while preserving their quality traits, such as flavour. However, research on IR's application in complex matrices is limited. Therefore, our preliminary study aimed to evaluate its effectiveness in sanitizing bovine raw milk. We assessed the bacterial count before and after IR treatment by comparing volatile organic compound profiles via headspace extraction and GC/MS analysis. Our findings showed that higher energy levels led to a greater bacterial reduction. IR85 was the most effective in reducing Coliforms and Enterobacteriaceae in non-homogenised samples, with a reduction ranging from -1.01 to >-2.99 and from -1.66 to -3.09 Log CFU/mL, respectively. IR60 and 70 showed no efficacy, while IR80 had intermediate but still satisfactory effect. IR85 notably affected volatile compounds, particularly increasing hexanal (from 0.08 to 4.21 ng g-1) and dimethyl sulphone (from 10.76 to 26.40 ng g-1), while IR80 better preserved the aroma profile. As a result, only IR80 was tested with homogenised raw milk, demonstrating significant bacterial reduction (from >2.39 to 3.06 Log CFU/mL for Coliforms and from 1.90 to >2.45 Log CFU/mL for Enterobacteriaceae) and maintaining the aroma profile quality.

Evaluation of the Risk from Potentially Toxic Elements (PTEs) in Italy's Most Consumed Processed Fish Products.

In a balanced diet, regular fish consumption provides positive outcomes for human health. On the other hand, the seafood supply chain faces a significant food safety risk due to the presence of potentially toxic elements (PTEs). In the present study, to assess the risk for Italian consumers, the concentrations of five PTEs, namely lead, chromium, cadmium, mercury, nickel, and aluminum, were determined in the three most consumed preserved fish in Italy: tuna (Thunnus albacares, Katsuwonus pelamis), mackerel (Scomber scombrus) and anchovy (Engraulis encrasicolus). Samples were collected from the national market, and the instrumental analysis was performed by inductively coupled plasma mass spectrometry (ICP-MS). The analyzed PTEs were found in all the species that were investigated. However, after considering the target hazard quotient (THQ) and the hazard index (HI), it was observed that the three fish preserves did not pose any risk of chronic toxicity for the average consumer, even at the highest concentrations detected. However, for significant consumers, mercury detected in tuna samples represented almost 90% of the tolerable weekly intake (TWI) reported by the European Food Safety Authority (EFSA), representing a matter of concern for consumers, particularly regarding developmental neurotoxicity, whose HI exceeded 111%. The acute toxicity of nickel was also considered for significant consumers at the highest concentration detected, and the margin of exposure (MOE) calculated was above 7000, much higher than the value of 30 indicated by EFSA. Due to the lack of data on non-professional carcinogenicity or human intake through foods with low cancer risk, this toxicity was not considered in the analysis of PTEs.

Use of Machine Learning Models to Differentiate Neurodevelopment Conditions Through Digitally Collected Data: Cross-Sectional Questionnaire Study.

BACKGROUND: Diagnosis of child and adolescent psychopathologies involves a multifaceted approach, integrating clinical observations, behavioral assessments, medical history, cognitive testing, and familial context information. Digital technologies, especially internet-based platforms for administering caregiver-rated questionnaires, are increasingly used in this field, particularly during the screening phase. The ascent of digital platforms for data collection has propelled advanced psychopathology classification methods such as supervised machine learning (ML) into the forefront of both research and clinical environments. This shift, recently called psycho-informatics, has been facilitated by gradually incorporating computational devices into clinical workflows. However, an actual integration between telemedicine and the ML approach has yet to be fulfilled. OBJECTIVE: Under these premises, exploring the potential of ML applications for analyzing digitally collected data may have significant implications for supporting the clinical practice of diagnosing early psychopathology. The purpose of this study was, therefore, to exploit ML models for the classification of attention-deficit/hyperactivity disorder (ADHD) and autism spectrum disorder (ASD) using internet-based parent-reported socio-anamnestic data, aiming at obtaining accurate predictive models for new help-seeking families. METHODS: In this retrospective, single-center observational study, socio-anamnestic data were collected from 1688 children and adolescents referred for suspected neurodevelopmental conditions. The data included sociodemographic, clinical, environmental, and developmental factors, collected remotely through the first Italian internet-based screening tool for neurodevelopmental disorders, the Medea Information and Clinical Assessment On-Line (MedicalBIT). Random forest (RF), decision tree, and logistic regression models were developed and evaluated using classification accuracy, sensitivity, specificity, and importance of independent variables. RESULTS: The RF model demonstrated robust accuracy, achieving 84% (95% CI 82-85; P<.001) for ADHD and 86% (95% CI 84-87; P<.001) for ASD classifications. Sensitivities were also high, with 93% for ADHD and 95% for ASD. In contrast, the DT and LR models exhibited lower accuracy (DT 74%, 95% CI 71-77; P<.001 for ADHD; DT 79%, 95% CI 77-82; P<.001 for ASD; LR 61%, 95% CI 57-64; P<.001 for ADHD; LR 63%, 95% CI 60-67; P<.001 for ASD) and sensitivities (DT: 82% for ADHD and 88% for ASD; LR: 62% for ADHD and 68% for ASD). The independent variables considered for classification differed in importance between the 2 models, reflecting the distinct characteristics of the 3 ML approaches. CONCLUSIONS: This study highlights the potential of ML models, particularly RF, in enhancing the diagnostic process of child and adolescent psychopathology. Altogether, the current findings underscore the significance of leveraging digital platforms and computational techniques in the diagnostic process. While interpretability remains crucial, the developed approach might provide valuable screening tools for clinicians, highlighting the significance of embedding computational techniques in the diagnostic process.

Exploring the effects of family and life events on genetic and environmental architecture of schizotypal and hypomanic dimensions: Insights from a twin study.

BACKGROUND: Strategies of prevention for psychiatric disorders need a deep understanding of the aetiological factors involved in the psychopathological processes. Our twin study aims at disentangling the contributions of genes and environment to schizotypal and hypomanic dimensions, considering the role of stressful life events (LEs) and the quality of family relationships. METHODS: The Magical Ideation Scale (MIS) and Perceptual Aberration Scale (PAS) were used to assess positive schizotypy, while Hypomanic Personality Scale (HPS) and its sub-scales were used to investigate proneness to affective disorders. 268 twins (54.5 % female; aged 18.0 ± 6.68) were included. Participants filled out a questionnaire on LEs and their parents provided an evaluation of intra-family relationship (Relationship Quality Index, RQI). Classic univariate twin models for quantitative traits were fitted for scales, and the effects of covariates (LEs and RQI) were assessed. RESULTS: For MIS, HPS and its sub-scales, significant common and unique environmental effects were detected, with genetic factors affecting only HPS Social Vitality sub-scale. Unique environment was the only source of variance of PAS score. The number of recent LEs influenced MIS and PAS models, while RQI score affected MIS model. LIMITATIONS: The main limitation of the study is the small sample size, which reduces statistical power and may potentially lead to an underestimation of heritability. Additionally, the cross-sectional design limits the possibility to draw causal considerations. CONCLUSIONS: Findings provide preliminary evidence for a significant environmental role in modulating states of vulnerability. Moreover, the expression of positive schizotypy resulted influenced by recent stressors and intra-family relationships.

Brain Alteration Patterns in Children with Duchenne Muscular Dystrophy: A Machine Learning Approach to Magnetic Resonance Imaging.

Background: Duchenne Muscular Dystrophy (DMD) is a genetic disease in which lack of the dystrophin protein causes progressive muscular weakness, cardiomyopathy and respiratory insufficiency. DMD is often associated with other cognitive and behavioral impairments, however the correlation of abnormal dystrophin expression in the central nervous system with brain structure and functioning remains still unclear. Objective: To investigate brain involvement in patients with DMD through a multimodal and multivariate approach accounting for potential comorbidities. Methods: We acquired T1-weighted and Diffusion Tensor Imaging data from 18 patients with DMD and 18 age- and sex-matched controls with similar cognitive and behavioral profiles. Cortical thickness, structure volume, fractional anisotropy and mean diffusivity measures were used in a multivariate analysis performed using a Support Vector Machine classifier accounting for potential comorbidities in patients and controls. Results: the classification experiment significantly discriminates between the two populations (97.2% accuracy) and the forward model weights showed that DMD mostly affects the microstructural integrity of long fiber bundles, in particular in the cerebellar peduncles (bilaterally), in the posterior thalamic radiation (bilaterally), in the fornix and in the medial lemniscus (bilaterally). We also reported a reduced cortical thickness, mainly in the motor cortex, cingulate cortex, hippocampal area and insula. Conclusions: Our study identified a small pattern of alterations in the CNS likely associated with the DMD diagnosis.

Psychosocial resources and psychopathology among persons with neuromuscular disorders during the COVID-19 pandemic.

BACKGROUND: The COVID-19 pandemic substantially affected the lives of persons with inherited neuromuscular disorders (INMD), causing disruption in clinical and support services. While several studies have investigated mental health, distress and psychosocial resources in the general population during the pandemic, little is known about the experience of persons with INMD. METHODS: This study was aimed to fill this gap by jointly investigating both psychopathological symptoms and psychosocial resources - specifically, resilience and perceived social support - among persons with INMD during the pandemic, taking into account demographic and clinical factors. Between April and December 2020, 59 participants with INMD (aged 15-59, 71.2% M) completed a questionnaire collecting demographic and clinical data, the Multidimensional Scale of Perceived Social Support, the Resilience Scale for Adults, and the Achenbach System of Empirically Based Assessment. RESULTS: Overall, participants showed good levels of resilience and perceived social support. A minority of participants reported clinically relevant psychopathological symptoms, 28.81% for anxiety and depression. Most psychopathological symptoms were negatively correlated with resilience (-0.347 < r < - .420), but not significantly associated with social support. Consistent with previous studies, regression analyses highlighted that participants with Duchenne muscular dystrophy were more prone to report anxious and depressive symptoms (B = 1.748, p = .028, OR = 5.744), and participants with myotonic dystrophy, attention problems (B = 2.339, p = .006, OR = 10.376). Resilience emerged as a potential predictor of lower anxious-depressive symptoms (B=-1.264, p = .012, OR = 0.283). CONCLUSIONS: The findings suggest the importance to investigate psychosocial resources in addition to psychopathology among persons with INMD, and to design interventions supporting resilience as a protective factor for mental health promotion.

Metabolic Syndrome in people treated with Antipsychotics (RISKMet): A multimethod study protocol investigating genetic, behavioural, and environmental risk factors.

INTRODUCTION: The RISKMet project aims to: (1) identify risk factors for metabolic syndrome (MetS) by comparing patients with and without MetS; (2) characterise patients treated with second-generation antipsychotics (SGAs) about MetS diagnosis; (3) study behavioural patterns, including physical activity (PA) and dietary habits, in patients and healthy individuals using a prospective cohort design. METHOD: The RISKMet project investigates MetS in individuals treated with SGAs, focusing on both adult and paediatric populations. The study utilizes a case-control design to examine potential risk factors for MetS, categorizing participants as MetS+ considered as "Cases" and MetS- considered as "Controls" matched by sex and age. The evaluation of factors such as MetS, lifestyle habits, and environmental influences is conducted at two time points, T0 and T3, after 3 months. Subsequently, the project aims to assess body parameters, including physical examinations, and blood, and stool sample collection, to evaluate metabolic markers and the impact of SGAs. The analysis includes pharmacological treatment data and genetic variability. Behavioural markers related to lifestyle, eating behaviour, PA, and mood are assessed at both T0 and T3 using interviews, accelerometers, and a mobile app. The study aims to improve mental and physical well-being in SGA-treated individuals, establish a biobank for MetS research, build an evidence base for physical health programs, and develop preventive strategies for SGA-related comorbidities. CONCLUSIONS: This project innovates MetS monitoring in psychiatry by using intensive digital phenotyping, identifying biochemical markers, assessing familial risks, and including genetically similar healthy controls. STUDY REGISTRATION NUMBER: ISRCTN18419418 at www.isrctn.com.