Orthorexia nervosa symptoms are highly prevalent among patients with AN and BN, and tend to increase after treatment. ON seems associated both with the clinical improvement of AN and BN and the migration towards less severe forms of EDs. It is necessary to clarify if ON residual symptomatology can be responsible for a greater number of relapses and recurrences of EDs.

BackgroundSeveral studies have investigated the cognitive profile in patients with Anorexia Nervosa (AN) and Bulimia Nervosa (BN); on the contrary few studies have evaluated it in patients with Binge Eating Disorder (BED). The purpose of this study was to compare decision making, central coherence and set-shifting between BED and AN patients.MethodsA battery of neuropsychological tests including the Iowa Gambling Task (IGT), the Rey-Osterrieth Complex Figure Test (RCFT), the Wisconsin Card Sorting Test (WCST), the Trial Making Task (TMT) and the Hayling Sentence Completion Task (HSCT) were administered in a sample of 135 women (45 AN, 45 BED, 45 Healthy Controls [HC]). Furthermore, Beck Depression Inventory (BDI) was administered to evaluate depressive symptoms. Years of education, age, Body Mass Index (BMI) and depression severity were considered as covariates in statistical analyses.ResultsBED and AN patients showed high rates of cognitive impairment compared to HC on the domains investigated; furthermore, the cognitive profile of BED patients was characterised by poorer decision making and cognitive flexibility compared to patients with AN. Cognitive performance was strongly associated with depressive symptoms.ConclusionsIn the present sample, two different neurocognitive profiles emerged: a strong cognitive rigidity and a central coherence based on the details was predominant in patients with AN, while a lack of attention and difficulty in adapting to changes in a new situation seemed to better describe patients with BED. The knowledge of the different cognitive profiles of EDs patients may be important for the planning their psychotherapeutic intervention.

Background Diagnosis of schizophrenia (SCZ) is made exclusively clinically, since specific biomarkers that can predict the disease accurately remain unknown. Machine learning (ML) represents a promising approach that could support clinicians in the diagnosis of mental disorders. Objectives A systematic review, according to the PRISMA statement, was conducted to evaluate its accuracy to distinguish SCZ patients from healthy controls. Methods We systematically searched PubMed, Embase, MEDLINE, PsychINFO and the Cochrane Library through December 2018 using generic terms for ML techniques and SCZ without language or time restriction. Thirty-five studies were included in this review: eight of them used structural neuroimaging, twenty-six used functional neuroimaging and one both, with a minimum accuracy >60% (most of them 75–90%). Sensitivity, Specificity and accuracy were extracted from each publication or obtained directly from authors. Results Support vector machine, the most frequent technique, if associated with other ML techniques achieved accuracy close to 100%. The prefrontal and temporal cortices appeared to be the most useful brain regions for the diagnosis of SCZ. ML analysis can efficiently detect significantly altered brain connectivity in patients with SCZ (eg, default mode network, visual network, sensorimotor network, frontoparietal network and salience network). Conclusion The greater accuracy demonstrated by these predictive models and the new models resulting from the integration of multiple ML techniques will be increasingly decisive for early diagnosis and evaluation of the treatment response and to establish the prognosis of patients with SCZ. To achieve a real benefit for patients, the future challenge is to reach an accurate diagnosis not only through clinical evaluation but also with the aid of ML algorithms.

ObjectiveIndividuals suffering from dementia are affected by a progressive and significant global deterioration and, consequently, might require longer assistance in the advanced stage of the illness. The illness is a great burden on the person who takes care of a patient, namely, the caregiver. This study aims to analyze the presence and relationship of specific sociodemographic variables, subjective burden, and depressive symptoms among caregivers of patients with dementia.MethodsThe participants of this study were caregivers at a health care unit for the elderly in southern Italy. An evaluation of the burden of patients with dementia on caregivers was carried out using the Caregiver Burden Inventory (CBI) and depressive symptoms using the Self-Rating Depression Scale (SDS).ResultsA total of 150 caregivers completed the study. In all, 83 (55%) caregivers showed a total CBI score ≥36, of whom 70% showed pathological depression scores in SDS. According to SDS, 28 (19%) caregivers showed a total CBI score from 24 to 36, of whom 32% were depressed. Depression was present in 5% of the caregivers whose CBI score was <24. Hence, an association between burden and depression was evident (χ2=47.446, P<0.001). A multiple linear regression analysis showed that depression (adjusted R2=0.622, F=50.123, P<0.001) was associated with higher physical (β=0.666, P=0.001) and developmental (β=0.712, P<0.001) burdens, lower socioeconomic status (β=−4.282; P=0.002), higher level of urbanicity (β=3.070; P=0.012), and advanced age (β=2.132; P=0.08).ConclusionOur study confirms the presence of depressive symptoms in a large number of caregivers with high burden. Nevertheless, this study demonstrates that depressive symptoms are mainly associated with sociodemographic variables and, to a lesser degree, physical and developmental burdens.

The COVID‐19 pandemic has had a strong impact on healthcare workers (HCWs), affecting their physical and mental health. In Italy, HCWs have been among the first exposed to unprecedented pressure, dealing with large numbers of infections during the first pandemic wave. However, the severe psychological consequences on HCWs find little evidence in the literature, especially in terms of comparison to the status quo ante pandemic. The aim of this study was to provide an assessment of the mental health burden in a cohort of Italian HCWs during the COVID‐19 pandemic, comparing their condition with that before the emergency, to direct the promotion of mental well‐being among HCWs worldwide. In this retrospective study, we included physicians, physical therapists, and nurses working in the Respiratory Intensive Care Unit, Neurology Unit, and Rehabilitation Unit from a Southern Italy University Hospital. All study participants underwent a battery of psychological tests, aimed at verifying their state of mental health during the COVID‐19 emergency and before it. Depressive, anxiety, and burnout symptoms were assessed using the following questionnaires: Maslach Burnout Inventory, Patient Health Questionnaire‐9 (PHQ‐9), and General Anxiety Disorder‐7. Depressive, anxiety, and burnout clinical relevance symptoms were present in HCWs during the COVID‐19 pandemic more than those before the emergency. Fifty percent of the HCWs obtained a score clinically significant during the emergency. Moreover, a depersonalization factor showed a statistically significant increase in average scores ( p < 0.0001). The PHQ‐9 scale showed that 47.1% of the operators reported depressive state presence. The number of operators scoring above the cut‐off for the anxiety scale tripled during the emergency ( p < 0.0001). The female gender conferred greater risks for depression. Taken together, the findings of this study showed that our sample of Italian HCWs showed a greater risk for depression, anxiety, and stress during the COVID‐19 pandemic. These data might be a starting point to plan mental health monitoring and prevention programs for HCWs, thus ensuring patients receive the best possible care performances even during healthcare crises such as the current pandemic.