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Affective temperaments show potential for aggressive behavior (AB) preventive strategies in bipolar disorder (BD). We aim to define intra-diagnostic subgroups of patients with BD based on homogeneous behaviors related to AB. Subsequently, to assess whether affective temperament dimensions may contribute to the presence and severity of AB.Patients with BD were recruited. AB was evaluated through the modified overt aggression scale (MOAS); affective temperaments were assessed with the TEMPS-A. A cluster analysis was conducted based on TEMPS-A and MOAS scores. Stepwise backward logistic regression models were used to identify the predictive factors of cluster membership.799 patients with BD were enrolled. Three clusters were determined: non-aggressive (55.5 %), self-aggressive (18 %), and hetero-aggressive (26.5 %). Depressive, irritable, and anxious temperament scores significantly increased from the non-aggressive (lower) to the self-aggressive (intermediate) and the hetero-aggressive group (highest). A positive history of a suicide attempt (B = 5.131; OR = 169.2, 95 % CI 75.9; 377) and rapid cycling (B = -0.97; OR = 0.40, 95 % CI 0.17; 0.95) predicted self-aggressive cluster membership. Atypical antipsychotics (B = 1.19; OR = 3.28, 95 % CI 2.13; 5.06) or SNRI treatment (B = 1.09; OR = 3, 95 % CI 1.57; 5.71), psychotic symptoms (B = 0.73; OR = 2.09, 95 % CI 1.34; 3.26), and history of a suicide attempt (B = -1.56; OR = 0.20, 95 % CI 0.11; 0.38) predicted hetero-aggressive cluster membership.Recall bias might have affected the recollection of AB.Clinical factors orientate the prevention of different ABs in BD. Affective temperaments might play a role in preventing AB since patients with more pronounced affective temperaments might have an increased risk of showing AB, in particular hetero-AB.

Several psychotropic drugs, including antidepressants (AD), mood stabilizers, and antipsychotics (AP) have been suggested to have favorable effects in the treatment of COVID-19. The aim of this systematic review and meta-analysis was to collect evidence from studies concerning the scientific evidence for the repurposing of psychotropic drugs in COVID-19 treatment. Two independent authors searched PubMed-MEDLINE, Scopus, PsycINFO, and ClinicalTrials.gov databases, and reviewed the reference lists of articles for eligible articles published up to 13th December 2021. All computational, preclinical and clinical (observational and/or RCTs) studies on the effect of any psychotropic drug on Sars-CoV-2 or patients with COVID-19 were considered for inclusion. We conducted random effect meta-analyses on clinical studies reporting the effect of AD or AP on COVID-19 outcomes. 29 studies were included in the synthesis: 15 clinical, 9 preclinical, and 5 computational studies. 9 clinical studies could be included in the quantitative analyses. AD did not increase the risk of severe COVID-19 (RR= 1.71; CI 0.65-4.51) or mortality (RR=0.94; CI 0.81-1.09). Fluvoxamine was associated with a reduced risk of mortality for COVID-19 (OR=0.15; CI 0.02-0.95). AP increased the risk of severe COVID-19 (RR=3.66; CI 2.76-4.85) and mortality (OR=1.53; CI 1.15-2.03). Fluvoxamine might be a possible candidate for psychotropic drug repurposing in COVID-19 due to its anti-inflammatory and antiviral potential, while evidence on other AD is still controversial. Although AP are associated with worse COVID-19 outcomes, their use should be evaluated case to case and ongoing treatment with antipsychotics should be not discontinued in psychiatric patients.

Treatment Resistant Schizophrenia (TRS) is the persistence of significant symptoms despite adequate antipsychotic treatment. Although consensus guidelines are available, this condition remains often unrecognized and an average delay of 4-9 years in the initiation of clozapine, the gold standard for the pharmacological treatment of TRS, has been reported. We aimed to determine through a machine learning approach which domain of the Positive and Negative Syndrome Scale (PANSS) 5-factor model was most associated with TRS.In a cross-sectional design, 128 schizophrenia patients were classified as TRS (n = 58) or non-TRS (n = 60) after a structured retrospective-prospective analysis of treatment response. The random forest algorithm (RF) was trained to analyze the relationship between the presence/absence of TRS and PANSS-based psychopathological factor scores (positive, negative, disorganization, excitement, and emotional distress). As a complementary strategy to identify the variables most associated with the diagnosis of TRS, we included the variables selected by the RF algorithm in a multivariate logistic regression model.according to the RF model, patients with higher disorganization, positive, and excitement symptom scores were more likely to be classified as TRS. The model showed an accuracy of 67.19%, a sensitivity of 62.07%, and a specificity of 71.43%, with an area under the curve (AUC) of 76.56%. The multivariate model including disorganization, positive, and excitement factors showed that disorganization was the only factor significantly associated with TRS. Therefore, the disorganization factor was the variable most consistently associated with the diagnosis of TRS in our sample.

Impairment of insulin action and metabolic dysregulation have traditionally been associated with schizophrenia, although the molecular basis of such association remains still elusive. The present meta-analysis aims to assess the impact of insulin action manipulations (i.e., hyperinsulinemia, hypoinsulinemia, systemic or brain insulin resistance) on glutamatergic, dopaminergic, γ-aminobutyric acid (GABA)ergic, and serotonergic pathways in the central nervous system. More than one hundred outcomes, including transcript or protein levels, kinetic parameters, and other components of the neurotransmitter pathways, were collected from cultured cells, animals, or humans, and meta-analyzed by applying a random-effects model and adopting Hedges’g to compare means. Two hundred fifteen studies met the inclusion criteria, of which 180 entered the quantitative synthesis. Significant impairments in key regulators of synaptic plasticity processes were detected as the result of insulin handlings. Specifically, protein levels of N-methyl-D-aspartate receptor (NMDAR) subunits including type 2A (NR2A) (Hedges’ g = −0.95, 95%C.I. = −1.50, −0.39; p = 0.001; I2 = 47.46%) and 2B (NR2B) (Hedges’g = −0.69, 95%C.I. = −1.35, −0.02; p = 0.043; I2 = 62.09%), and Postsynaptic density protein 95 (PSD-95) (Hedges’g = −0.91, 95%C.I. = −1.51, −0.32; p = 0.003; I2 = 77.81%) were found reduced in insulin-resistant animal models. Moreover, insulin-resistant animals showed significantly impaired dopamine transporter activity, whereas the dopamine D2 receptor mRNA expression (Hedges’g = 3.259; 95%C.I. = 0.497, 6.020; p = 0.021; I2 = 90.61%) increased under insulin deficiency conditions. Insulin action modulated glutamate and GABA release, as well as several enzymes involved in GABA and serotonin synthesis. These results suggest that brain neurotransmitter systems are susceptible to insulin signaling abnormalities, resembling the discrete psychotic disorders’ neurobiology and possibly contributing to the development of neurobiological hallmarks of treatment-resistant schizophrenia.

The prevalence, correlates, and management of tobacco use disorder (TUD) or nicotine dependence (ND) among people with severe mental illness (SMI), namely schizophrenia, bipolar disorder (BD), and major depressive disorder (MDD), remain unclear. Therefore, a systematic review and meta-analysis was conducted. Electronic databases were systematically searched from inception to July 12, 2020, for observational studies documenting the prevalence, odds, and correlates of TUD/ND among people with SMI; randomized controlled trials (RCTs) informing the management of TUD/ND in people with SMI were also included. Random-effects meta-analyses were conducted. Sources of heterogeneity were explored. Nineteen observational studies, including 7527 participants with SMI met inclusion criteria. TUD/ND co-occurred in 33.4-65% of people with SMI. Rates were higher among males. While bupropion and varenicline represent promising treatment opportunities for schizophrenia with TUD/ND, non-pharmacological interventions require further research, mainly for people with primary mood disorders. TUD/ND represent prevalent co-occurring conditions among people with SMI. Further well-designed RCTs are warranted to inform their management.

Few longitudinal studies have so far investigated the impact of sustained COVID-19 among people with pre-existing psychiatric disorders. We conducted a prospective study involving people with serious mental illness (n = 114) and healthy controls (n = 41) to assess changes in the Perceived Stress Scale, Generalized Anxiety Disorder Scale, Patient Health Questionnaire, and Specific Psychotic Experiences Questionnaire scores 18 months after the COVID-19 pandemic outset. Subjects underwent interviews with a mental health professional in April 2020 and at the end of the local third wave (October 2021). A significant increase in perceived stress was found in healthy controls, especially females. Psychiatric patients showed a significant worsening of anxiety symptoms compared to baseline records (t = -2.3, p = 0.036). Patients who rejected vaccination had significantly higher paranoia scores compared to those willing to get vaccinated (U = 649.5, z = -2.02, p = 0.04). These findings indicate that COVID-19's sustained emergency may cause enduring consequences on mental health, soliciting further investigations.

INTRODUCTION: The COVID-19 (coronavirus disease 2019)-related pandemic represents a global source of societal and health burden. Yet, the impact of the pandemic on people with severe mental illness, including bipolar disorder (BD), remains unclear, warranting scoping review on the matter. METHODS: The MEDLINE and EMBASE databases were systematically searched from inception up to April 24, 2021, adopting broad inclusion criteria to assess a variety of clinical and public health themes related to people with a primary diagnosis of BD during the COVID-19 pandemics. The present work complying with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for scoping reviews (PRISMA-ScR) registered in the Open Science Framework (OSF) repository (https://osf.io/7evpx/). RESULTS: Fourteen papers informed the present scoping review. Four major themes were identified: (i) impact of COVID-19-related stressors on BD; (ii) impact of COVID-19 on mental health service utilization among people with BD; (iii) impact of BD on the risk of acquiring SARS-CoV-2 infection; (iv) engagement in preventative behaviors among people with BD. Additional themes warranting further research were nonetheless detected. LIMITATIONS: Further original studies are needed. CONCLUSION: The present study confirmed the high-vulnerability hypothesis concerning people with BD versus the general population, reinforcing the need for further research related to the COVID-19 pandemic. Additional information is warranted to compare the impact of the pandemic period among BD people against pre-pandemic records, the general population, and other severe mental illnesses, namely people with schizophrenia or major depressive disorder, to inform the public health and the delivery of patient-tailored interventions.

BACKGROUND Depressive and manic episodes within bipolar disorder (BD) and major depressive disorder (MDD) involve altered mood, sleep, and activity, alongside physiological alterations wearables can capture. OBJECTIVE Firstly, we explored whether physiological wearable data could predict (aim 1) the severity of an acute affective episode at the intra-individual level and (aim 2) the polarity of an acute affective episode and euthymia among different individuals. Secondarily, we explored which physiological data were related to prior predictions, generalization across patients, and associations between affective symptoms and physiological data. METHODS We conducted a prospective exploratory observational study including patients with BD and MDD on acute affective episodes (manic, depressed, and mixed) whose physiological data were recorded using a research-grade wearable (Empatica E4) across 3 consecutive time points (acute, response, and remission of episode). Euthymic patients and healthy controls were recorded during a single session (approximately 48 h). Manic and depressive symptoms were assessed using standardized psychometric scales. Physiological wearable data included the following channels: acceleration (ACC), skin temperature, blood volume pulse, heart rate (HR), and electrodermal activity (EDA). Invalid physiological data were removed using a rule-based filter, and channels were time aligned at 1-second time units and segmented at window lengths of 32 seconds, as best-performing parameters. We developed deep learning predictive models, assessed the channels’ individual contribution using permutation feature importance analysis, and computed physiological data to psychometric scales’ items normalized mutual information (NMI). We present a novel, fully automated method for the preprocessing and analysis of physiological data from a research-grade wearable device, including a viable supervised learning pipeline for time-series analyses. RESULTS Overall, 35 sessions (1512 hours) from 12 patients (manic, depressed, mixed, and euthymic) and 7 healthy controls (mean age 39.7, SD 12.6 years; 6/19, 32% female) were analyzed. The severity of mood episodes was predicted with moderate (62%-85%) accuracies (aim 1), and their polarity with moderate (70%) accuracy (aim 2). The most relevant features for the former tasks were ACC, EDA, and HR. There was a fair agreement in feature importance across classification tasks (Kendall W=0.383). Generalization of the former models on unseen patients was of overall low accuracy, except for the intra-individual models. ACC was associated with “increased motor activity” (NMI>0.55), “insomnia” (NMI=0.6), and “motor inhibition” (NMI=0.75). EDA was associated with “aggressive behavior” (NMI=1.0) and “psychic anxiety” (NMI=0.52). CONCLUSIONS Physiological data from wearables show potential to identify mood episodes and specific symptoms of mania and depression quantitatively, both in BD and MDD. Motor activity and stress-related physiological data (EDA and HR) stand out as potential digital biomarkers for predicting mania and depression, respectively. These findings represent a promising pathway toward personalized psychiatry, in which physiological wearable data could allow the early identification and intervention of mood episodes.

BACKGROUND Many people attending primary care (PC) have anxiety-depressive symptoms and work-related burnout compounded by a lack of resources to meet their needs. The COVID-19 pandemic has exacerbated this problem, and digital tools have been proposed as a solution. OBJECTIVE We aimed to present the development, feasibility, and potential effectiveness of Vickybot, a chatbot aimed at screening, monitoring, and reducing anxiety-depressive symptoms and work-related burnout, and detecting suicide risk in patients from PC and health care workers. METHODS Healthy controls (HCs) tested Vickybot for reliability. For the simulation study, HCs used Vickybot for 2 weeks to simulate different clinical situations. For feasibility and effectiveness study, people consulting PC or health care workers with mental health problems used Vickybot for 1 month. Self-assessments for anxiety (Generalized Anxiety Disorder 7-item) and depression (Patient Health Questionnaire-9) symptoms and work-related burnout (based on the Maslach Burnout Inventory) were administered at baseline and every 2 weeks. Feasibility was determined from both subjective and objective user-engagement indicators (UEIs). Potential effectiveness was measured using paired 2-tailed t tests or Wilcoxon signed-rank test for changes in self-assessment scores. RESULTS Overall, 40 HCs tested Vickybot simultaneously, and the data were reliably transmitted and registered. For simulation, 17 HCs (n=13, 76% female; mean age 36.5, SD 9.7 years) received 98.8% of the expected modules. Suicidal alerts were received correctly. For the feasibility and potential effectiveness study, 34 patients (15 from PC and 19 health care workers; 76% [26/34] female; mean age 35.3, SD 10.1 years) completed the first self-assessments, with 100% (34/34) presenting anxiety symptoms, 94% (32/34) depressive symptoms, and 65% (22/34) work-related burnout. In addition, 27% (9/34) of patients completed the second self-assessment after 2 weeks of use. No significant differences were found between the first and second self-assessments for anxiety (t8=1.000; P=.34) or depressive (t8=0.40; P=.70) symptoms. However, work-related burnout scores were moderately reduced (z=−2.07, P=.04, r=0.32). There was a nonsignificant trend toward a greater reduction in anxiety-depressive symptoms and work-related burnout with greater use of the chatbot. Furthermore, 9% (3/34) of patients activated the suicide alert, and the research team promptly intervened with successful outcomes. Vickybot showed high subjective UEI (acceptability, usability, and satisfaction), but low objective UEI (completion, adherence, compliance, and engagement). Vickybot was moderately feasible. CONCLUSIONS The chatbot was useful in screening for the presence and severity of anxiety and depressive symptoms, and for detecting suicidal risk. Potential effectiveness was shown to reduce work-related burnout but not anxiety or depressive symptoms. Subjective perceptions of use contrasted with low objective-use metrics. Our results are promising but suggest the need to adapt and enhance the smartphone-based solution to improve engagement. A consensus on how to report UEIs and validate digital solutions, particularly for chatbots, is required.

Mood disorders are severe and chronic mental conditions exacting high costs from society. The lack of reliable biomarkers to aid clinicians in tailoring pharmacotherapy based on distinguishable patient-specific traits means that the current prescribing paradigm is largely one of trial and error. Previous studies showed that different biological signatures, such as patterns of heart rate variability or electro-dermal reactivity, are associated with clinically meaningful outcomes. Against this backdrop, the advances in machine learning and the spread of wearable devices capable of providing continuous and ecological monitoring of patients may unlock great opportunities in mental healthcare. We herewith present a pilot study on mania and depression where we moved beyond the simple disease state binary classification but pursued the more informative and clinically meaningful task of differentiating between levels of disease severity. While most previous similar endeavours used recording segments extracted from the same subjects for both training and testing, we explicitly carried out model development and evaluation on segments from different groups of patients, in order to have a fair assessment of the model out-of-sample generalisation. This illustrated how individuals heterogeneity and non-disease-related dimensions of variations (e.g. sex, age, physical fitness) may dominate the signal so that in low sample size regimes a model might learn and overfit subject-specific patterns rather than capturing disease-relevant traits generalisable across disorders. Lastly, we developed a viable baseline for pre-processing raw data from wristband recordings and compared three classical and two deep-learning models to identify levels of disease severity.

Emotion dysregulation (ED) is characterized by rigid and frequent use of maladaptive emotion regulation (ER) strategies. Conceptualized as a transdiagnostic feature, ED may occur in both clinical and non-clinical populations, including people diagnosed with bipolar disorder (BD) and their first-degree relatives (FDRs), though expected to manifest with differential clinical features. To this end, we conducted a systematic review and meta-analysis of the literature comparing people with BD to healthy controls (HCs) or FDRs, from inception up to November 25, 2021, across major databases. Random-effects meta-analyses considered twenty-eight studies assessing ER/ED with a validated scale. Patients with BD differed from HCs in adopting more maladaptive ER strategies, such as rumination, risk-taking behaviors, negative focus, and less adaptive ones. Unaffected FDRs differed from people with BD, yet to a lower extent, suggesting that ED may span a continuum. ED in BD should be widely explored to better understand its course and management, with specific interventions aimed at reducing its burden on both high-risk and full-threshold populations.

Substance use disorder (SUD) is a common comorbidity in individuals with bipolar disorder (BD), and it is associated with a severe course of illness, making early identification of the risk factors for SUD in BD warranted. We aimed to identify, through machine-learning models, the factors associated with different types of SUD in BD. We recruited 508 individuals with BD from a specialized unit. Lifetime SUDs were defined according to the DSM criteria. Random forest (RF) models were trained to identify the presence of (i) any (SUD) in the total sample, (ii) alcohol use disorder (AUD) in the total sample, (iii) AUD co-occurrence with at least another SUD in the total sample (AUD+SUD), and (iv) any other SUD among BD patients with AUD. Relevant variables selected by the RFs were considered as independent variables in multiple logistic regressions to predict SUDs, adjusting for relevant covariates. AUD+SUD could be predicted in BD at an individual level with a sensitivity of 75% and a specificity of 75%. The presence of AUD+SUD was positively associated with having hypomania as the first affective episode (OR = 4.34 95% CI = 1.42–13.31), and the presence of hetero-aggressive behavior (OR = 3.15 95% CI = 1.48–6.74). Machine-learning models might be useful instruments to predict the risk of SUD in BD, but their efficacy is limited when considering socio-demographic or clinical factors alone.

Parenthood age may affect the risk for the development of different psychiatric disorders in the offspring, including bipolar disorder (BD). The present systematic review and meta-analysis aimed to appraise the relationship between paternal age and risk for BD and to explore the eventual relationship between paternal age and age at onset of BD. We searched the MEDLINE, Scopus, Embase, PsycINFO online databases for original studies from inception, up to December 2021. Random-effects meta-analyses were conducted. Sixteen studies participated in the qualitative synthesis, of which k = 14 fetched quantitative data encompassing a total of 13,424,760 participants and 217,089 individuals with BD. Both fathers [adjusted for the age of other parent and socioeconomic status odd ratio - OR = 1.29(95%C.I. = 1.13-1.48)] and mothers aged ≤ 20 years [(OR = 1.23(95%C.I. = 1.14-1.33)] had consistently increased odds of BD diagnosis in their offspring compared to parents aged 25-29 years. Fathers aged ≥ 45 years [adjusted OR = 1.29 (95%C.I. = 1.15-1.46)] and mothers aged 35-39 years [OR = 1.10(95%C.I. = 1.01-1.19)] and 40 years or older [OR = 1.2(95% C.I. = 1.02-1.40)] likewise had inflated odds of BD diagnosis in their offspring compared to parents aged 25-29 years. Early and delayed parenthood are associated with an increased risk of BD in the offspring. Mechanisms underlying this association are largely unknown and may involve a complex interplay between psychosocial, genetic and biological factors, and with different impacts according to sex and age range. Evidence on the association between parental age and illness onset is still tentative but it points towards a possible specific effect of advanced paternal age on early BD-onset.

Thirty naturally infected dogs were divided into three groups (10 animals each): group I (canine leishmaniosis), group II (canine ehrlichiosis) and group III (dogs co-infected by both infectious agents). Ten clinically healthy dogs were used as controls. A haematological profile was obtained and dog sera were assessed for the presence of platelet-bound IgM and IgG antibodies (PBIgM, PBIgG) using flow cytometry. Eight of the naturally co-infected dogs (80%), five of the leishmaniotic dogs (50%) and six of the ehrlichiotic dogs (60%) had detectable levels of platelet-bound antibodies (PBIg). No PBIg were observed in the control group. The hypothesis that Leishmania and Ehrlichia co-infection could more severely affect the immune response of infected dogs is discussed. Copyright © 2010 Elsevier Ltd. All rights reserved.

Plasma atrial natriuretic peptide (proANP 31–67), B-type natriuretic peptide (Nt-proBNP) and endothelin-1 (ET-1) concentrations in dogs with chronic degenerative valvular disease (CDVD) D. Piantedosi & L. Cortese & A. Di Loria & G. Brignola & M. Prisco & A. Persechino & P. Ciaramella Published online: 8 July 2009 # Springer Science + Business Media B.V. 2009