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Introduction: Bipolar disorder (BD) is a recurrent disorder, causing functional impairment and raised mortality, particularly due to suicide. However, the difficulty in predicting suicidal behaviors relies in the lack of clear biomarkers. Machine learning (ML) has emerged as a promising tool to enhance suicidal prediction. However, most ML studies focused on lifetime attempts , without having a predictive time window, and did not employ time-dependent variables. Moreover, most studies lie on cross-sectional databases, without including more than one time-point. Objectives: First, we aimed to predict 12-months suicide attempts in a naturalistic sample of BD patients, using clinical and demographic data. Second, we aimed to improve the prediction by including information from intermediate visits (1, 3, and 6 months), mimicking more closely the clinician's way of thinking and the multiple observations a patient receives. Methods: A sample of 163 BD patients (53% females, mean age 44.7, SD 15.3) were recruited. Based on EHR, 56 clinical and demographic features were extracted , including hospitalizations, suicidal behaviors lifetime and in the last 12 months , along with comorbidity, family history, work, and therapies. Patients were followed up for 12 months. Support Vector Machine (SVM) was used to differentiate subjects who attempted suicide versus those who did not in a 12-month time window , within a repeated nested Cross-Validation. The SVM was optimized weighting the hyperplane for uneven group sizes. Then, we repeated the analysis including information from intermediate visits (1, 3, 6 months after the first contact). For each visit, we created a composite score based on current therapy, new admissions, and ER presentations. To avoid circularity, all the information (ER, admission etc.) related to a suicide attempt were not included. Results: During the 12-months follow-up, 9.8% of patients attempted suicide. The results from the 12-months suicide prediction model obtained an Area Under the Curve of 0.71(with a Balanced Accuracy (BAC) of 68%). After incorporating the composite scores based on intermediate visits in the model, the prediction raised to an Area Under the Curve of 0.78 (BAC 73%), suggesting that including intermediate visits is a valid method to improve prediction. The features that contributed the most to the prediction were the composite score at 6-month visit, lifetime number of suicide attempts, suicide attempts in the last 12 months, substance of abuse (other than cannabis), and antipsychotics. Conclusions: ML proved a good prediction accuracy for suicide in a 12-months time window, and the prediction was improved by including data from intermediate visits. The model showed the importance of time-dependent features, such as attempts in the last 12 months. Our analysis might help in identifying early clinical risk factors and underlies the importance of multiple evaluations in populations at risk. Disclosure of Interest: None Declared [ABSTRACT FROM AUTHOR]

Introduction: Discriminating between bipolar disorder (BD) and behavioral variant Frontotemporal Dementia (bvFTD) is a clinical challenge as it is still based on clinical judgement, which often leads to misdiagnosis. This challenge is particularly pronounced in cases involving the C9orf72 hexanucleotide expansion, a genetic factor responsible for a substantial portion of familial FTD cases, as in these patients the development of late psychoses is particularly frequent. Moreover, individuals with C9orf72 bvFTD are also characterized by behavioral changes that resemble those seen in late-life BD, especially during the early stages of the disease. This raises questions about whether the clinical similarities between BD and bvFTD are rooted in specific alterations within the brain networks involved in cognitive processing or in selective genetic and epigenetic mutations. In light of this, our recently published neuroimaging study has shed light on the presence of distinctive structural and metabolic characteristics in elderly individuals with BD and bvFTD. These findings offer valuable neurobiological insights that may lead to differentiate between bvFTD and elderly BD patients. Objectives: Building on our previous research, this study further explores the existence of similar epigenetic expression patterns in plasma neural derived extra cellular vesicles (NDEs), such as miRNA and lncRNA, and seeks to correlate these epigenetic data with shared or distinct biological markers obtained through structural Magnetic Resonance Imaging and [18F]-fluorodeoxyglucose (FDG)-Positron Emission Tomography (PET). Methods: We will plan to conduct statistical analyses on epigenetic and neuroimaging data on C9orf72 and sporadic bvFTD as well as on late-and early-onset BD patients and on healthy controls. Additionally, A PET study will be also performed on a subpopulation of these patients. Results: Our hypothesis posits that selective epigenetic modifications may impact the brain's structure and function, in a way that can change the glutamatergic neurotransmission in prefrontal regions, with subsequent indirect effects on subcortical areas. Conclusions: Our findings will not only help identifying the specific biological signatures of BD and bvFTD, which might have important implications not only in prevention but also in differential diagnosis and treatment, but also offer insights into potential targets for slowing the onset and progression of the structural alterations characterizing these disorders. Disclosure of Interest: None Declared [ABSTRACT FROM AUTHOR]

BackgroundSchizophrenia (SZ) and bipolar disorder (BD) may overlap in etiology and phenomenology but differ with regard to emotional processing. We used facial affect as a probe for emotional processing to determine whether there are diagnosis-related differences between SZ and BD in the function of the underlying neural circuitry.MethodFunctional magnetic resonance imaging (fMRI) studies published up to 30 April 2012 investigating facial affect processing in patients with SZ or BD were identified through computerized and manual literature searches. Activation foci from 29 studies encompassing 483 healthy individuals, 268 patients with SZ and 267 patients with BD were subjected to voxel-based quantitative meta-analysis using activation likelihood estimation (ALE).ResultsCompared to healthy individuals, when emotional facial stimuli were contrasted to neutral stimuli, patients with BD showed overactivation within the parahippocampus/amygdala and thalamus and reduced engagement within the ventrolateral prefrontal cortex (PFC) whereas patients with SZ showed underactivation throughout the entire facial affect processing network and increased activation in visual processing regions within the cuneus. Patients with BD showed greater thalamic engagement compared to patients with SZ; in the reverse comparison, patients with SZ showed greater engagement in posterior associative visual cortices.ConclusionsDuring facial affect processing, patients with BD show overactivation in subcortical regions and underactivation in prefrontal regions of the facial affect processing network, consistent with the notion of reduced emotional regulation. By contrast, overactivation within visual processing regions coupled with reduced engagement of facial affect processing regions points to abnormal visual integration as the core underlying deficit in SZ. [ABSTRACT FROM AUTHOR]

Introduction: Current literature has described psychosis as a pathological condition affecting multiple system, including a relevant involvement of immune system, possibly varying in different stages of psychotic disorders. Objectives: We carried out a Machine Learning (ML) analysis aiming at differentiating FEP patients, chronic psychosis patients and health controls, based on transcriptomic of genes linked to immune response and inflammation. Methods: We extracted mRNA from peripheral mononucleate blood cells of a population of 348 subjects, including FEP patients (n=148), patients affected by chronic psychosis (CRO, n=65), and healthy controls (HC, N=129). Then, we performed a rtPCR to measure the mRNA levels of 56 genes involved in immune response, applying a ML classification algorithm basing on these. Results: The results of ML analysis carried out in our sample predict the diagnosis of FEP, CRO and HC with a mean accuracy of 79.1%. Moreover, the total gain feature importance method reveals that the transcript levels of just 11 immunological genes are sufficient to predict the three groups reaching a total mean accuracy of 77.2%. Conclusions: Our study is the first trying to classify different phases of psychotic disorders and healthy subjects with transcriptomic of immunological genes, by means of ML technology. The accuracy value resulting from our classification model strongly supports current evidence reporting the involvement of the immune system in the pathogenesis and course of psychotic disorders. Moreover, our results suggest the need to define an "immunological print" of subjects affected by psychosis, with the final aim of translating it in clinical practice. [ABSTRACT FROM AUTHOR]

Deals with a study which aimed at reducing the number of subareas needed for studying a large grounding grid by the Maxwell's subareas method. Calculation of the earthing resistance(R[subE]); Calculation of R[subE] for similar grounding electrodes; Numerical examples.

Background: Although symptoms of depressive episodes in patients with bipolar depressive episodes (BDE) and major depressive disorder (MDD) are similar, the treatment strategies for these disorders are completely different, suggesting that BDE and MDD have different neurobiological backgrounds. In this study, we examined the relationship between brain function and clinical symptoms, particularly cognitive function, in female individuals with bipolar disorder and MDD experiencing depressive episodes. Methods: Regional homogeneity (ReHo) was analyzed in 51 medication-free female patients with BDE, 63 medication-free female patients with MDD, and 45 female healthy controls (HCs). Depressive symptom severity was assessed using the 24-item Hamilton Depression Rating Scale (HAMD-24), and multidimensional cognitive function was evaluated using the MATRICS Consensus Cognition Battery. Partial correlation analysis was used to explore the links between the brain regions and clinical characteristics. A support vector machine (SVM) was used to assess the classification accuracy. Results: Compared with HCs, patients with BDE and MDD had decreased ReHo in the left lobule VI of the cerebellum and increased ReHo in the left precuneus. Patients with BDE also had reduced ReHo in the left lobules IV–V of the cerebellum and increased ReHo in the right putamen, unlike patients with MDD who had no significant differences in these regions. Patients with BDE exhibited more severe cognitive deficits in processing speed, attention, word learning, and overall cognitive function than those with MDD. In patients with BDE, a significant negative correlation was found between the right putamen and HAMD-24 scores. However, no significant association was observed between abnormal ReHo levels and cognitive function. The SVM effectively differentiated between patients with BDE, MDD, and HCs. Conclusion: Cognitive impairment was more severe in female patients with BDE than in those with MDD. Changes in the ReHo values of the right putamen and left lobules IV–V may serve as unique neuroimaging markers for BDE. Alterations in the ReHo values of the left precuneus and left lobule VI could serve as common pathophysiological mechanisms for BDE and MDD in women and indicate depressive states. [ABSTRACT FROM AUTHOR]

Multimodal brain network analysis enables a comprehensive understanding of neurological disorders by integrating information from multiple neuroimaging modalities. However, existing methods often struggle to effectively model the complex structures of multimodal brain networks. In this paper, we propose a novel tensor-based graph convolutional network (TGNet) framework that combines tensor decomposition with multi-layer GCNs to capture both the homogeneity and intricate graph structures of multimodal brain networks. We evaluate TGNet on four datasets—HIV, Bipolar Disorder (BP), and Parkinson's Disease (PPMI), Alzheimer's Disease (ADNI)—demonstrating that it significantly outperforms existing methods for disease classification tasks, particularly in scenarios with limited sample sizes. The robustness and effectiveness of TGNet highlight its potential for advancing multimodal brain network analysis. The code is available at https://github.com/rongzhou7/TGNet. [ABSTRACT FROM AUTHOR]

Objective: Vitamin D is thought to be deficient in patients with bipolar disorder. The purpose of this study is to use latent profile analysis to identify the patterns of vitamin D levels in patients with episodes of bipolar depression, and to examine the relationship among these latent profiles and demographic and clinical characteristics. Methods: A total of 149 patients diagnosed with bipolar depression were selected in Guangzhou, China. Depression was evaluated by Zung Self-Rating Depression Scale. Serum 25-hydroxyvitamin D levels tested at baseline and after two weeks of psychiatric treatment were included in the latent profile analysis to identify subgroups. P-trend analysis was used to assess the association between subgroups and depression improvement. Multinomial logistic regression analysis was used to assess the influencing factors of subgroups. Results: A three-profiles solution was found to demonstrate the best fit [low-level profile (32.9%), medium-level profile (51.0%), and high-level profile (16.1%)]. There was a significant nonlinear relationship between depression improvement and vitamin D high-level profile, compared to medium-level profile (P for trend <0.05). In multinomial logistic regression analysis, baseline and post-treatment SDS scores, admission season, age, and body mass index significantly affect the profile membership. Conclusions: This study found that individuals with high levels of vitamin D showed a significant improvement in depression severity. However, those with low levels of vitamin D remained deficient, indicating a need for targeted vitamin D supplementation. Our findings may provide valuable insights for designing tailored vitamin D supplement interventions to address vitamin D deficiency in bipolar depression. [ABSTRACT FROM AUTHOR]

Background: Past research has highlighted that bipolar I disorder is associated with significant changes in brain structure and function. Notably, the manifestation and progression of bipolar I disorder have been known to differ between males and females. However, the relationship between sex-related differences and bipolar I disorder diagnosis affecting these changes was not fully understood. This study aimed to investigate the sex-by-diagnosis interactions concerning the structural and functional features of the brain in individuals with bipolar I disorder. Methods: Both structural and functional MRI data were obtained from 105 individuals with bipolar I disorder (36 males and 69 females) and 210 healthy controls (72 males and 138 females). Voxel-wise analyses of gray matter volume and functional connectivity were conducted using a general linear regression model. This model included age, sex, diagnosis, and a sex-by-diagnosis interaction as predictors to explore potential sex-related differences in the brain features of participants with bipolar I disorder. Results: The gray matter volume analysis revealed significant sex-by-diagnosis interactions in six brain regions: the left caudate (p < 0.001), left thalamus (p < 0.001), right caudate (p = 0.003), right thalamus (p < 0.001), left anterior cingulate gyrus (p = 0.022), and left middle/posterior cingulate gyrus (p = 0.015). Using these regions as seeds, we detected a significant sex-by-diagnosis interaction in the functional connectivity alteration between the left thalamus and right angular gyrus (p = 0.019). Conclusions: Our findings revealed a noteworthy sex-by-diagnosis interaction, with male individuals with bipolar I disorder displaying larger gray matter volume and altered functional connectivity in the limbic system compared to female individuals with bipolar I disorder and healthy participants. These results hint at potential sex-related differences in the pathophysiology of the limbic system in bipolar I disorder, which may have significant implications for understanding the underlying mechanisms in bipolar I disorder. Our findings could contribute to developing more personalized treatment approaches for individuals with bipolar I disorder. [ABSTRACT FROM AUTHOR]

Background: Suicidal behaviors, which may lead to death (suicide) or survival (suicide attempt), are influenced by various factors. Identifying the specific risk factors for suicidal behavior mortality is critical for improving prevention strategies and clinical interventions. Predicting the outcomes of suicidal behaviors can help identify individuals at higher risk of death, enabling timely and targeted interventions. This study aimed to determine the critical risk factors associated with suicidal behavior mortality and identify an effective classification model for predicting suicidal behavior outcomes. Materials and methods: This study utilized data recorded in the suicidal behavior registry system of hospitals in Ilam Province. In the first phase, duplicate records were removed, and the data was numerically encoded via Python version 3.11; then, the data was analyzed using chi-square and Fisher's exact tests in SPSS version 22 software to identify the factors influencing suicidal behavior mortality. In the second phase, missing data were removed, and the dataset was standardized. Five binary classification algorithms were utilized, including Random Forest, Logistic Regression, and Decision Trees, with hyperparameters optimized using the area under the receiver operating characteristic curve (AUC) and F1 score metrics. These models were compared based on accuracy, recall, precision, F1 score, and AUC. Results: Among 3833 cases of suicidal behavior in various hospitals in Ilam Province, the results indicated that the method of suicidal behavior (P < 0.001), reason for suicidal behavior (P < 0.001), age group (P < 0.001), education level (P < 0.001), marital status (P = 0.004), and employment status (P = 0.042) were significantly associated with suicide. Variables such as the season of suicidal behavior, gender, father's education, and mother's education were not significantly related to suicidal behavior mortality. Furthermore, the random forest model demonstrated the highest area under the ROC curve (0.79) and the highest classification accuracy and F1 score on both the training data (0.85 and 0.2, respectively) and test data (0.86 and 0.31, respectively) for predicting suicidal behaviors outcomes among the models tested. Conclusion: This study identified key factors such as older age, lower education, divorce or widowhood, employment, physical methods, and socioeconomic issues as significant predictors of suicidal behavior outcomes. A combination of statistical models for feature selection and machine learning algorithms for prediction was used, with Random Forest showing the best performance. This approach highlights the potential of integrating statistical methods with machine learning to improve suicide risk prediction and intervention strategies. [ABSTRACT FROM AUTHOR]

Autistic people and those with attention deficit hyperactivity disorder are at a high risk of developing an eating disorder. While there is limited evidence on the relationship between other forms of neurodivergence and eating disorders, research suggests associations between giftedness, intellectual disability, obsessive–compulsive disorder, psychosis, Tourette's syndrome, and disordered eating. Factors underlying disordered eating and/or eating disorder risk for neurodivergent people are multifaceted and complex, encompassing a wide range of intertwined psychosocial, environmental, and biological processes. Moreover, research shows that neurodivergent individuals experience poorer treatment outcomes compared to neurotypical individuals. However, there is a paucity of research in this area overall. More specifically, lived experience-led research remains rare, despite its critical role for improving individualised eating disorder care, as well as mental healthcare more broadly. Indeed, the importance of eating disorder care individuation is increasingly being recognised, particularly within the context of neurodivergence, given the heterogeneous experiences and support needs of neurodivergent people affected by disordered eating and/or eating disorders. Furthermore, despite documented overlaps between various forms of neurodivergence (e.g., co-occurring autism and attention deficit hyperactivity disorder), research looking at eating disorders in the context of neurodivergence through a transdiagnostic perspective is scarce. This lived experience-led narrative review aims to shed light on the intersectional factors underlying elevated disordered eating and/or eating disorder risk for neurodivergent individuals. First, an overview of prevalence data is provided, followed by a thematic framework identifying factors underlying disordered eating and/or eating disorder risk in relation to neurodivergence. A critical appraisal of current eating disorder research and care is then offered before suggestions for neurodiversity-affirming eating disorder care are made. In this view, this paper offers a foundation for future empirical work in this nascent field of inquiry by providing a lived experience-led, transdiagnostic, and intersectional account of eating disorders in the context of neurodivergence. [ABSTRACT FROM AUTHOR]

Background: Nutritional metals (NM) are essential for neurodevelopment and cognitive performance during growth. Nevertheless, epidemiological evidence regarding the associations between NM and brain function remains understudied, particularly among adolescents. Therefore, the objective of this pilot study was to examine the effects of NM biomarkers such as iron (Fe), selenium (Se), zinc (Zn), magnesium (Mg), and copper (Cu) on neurobehavioral functions among a group of rural Bangladeshi adolescents. Methodology: We conducted a cross-sectional study involving 105 adolescents aged 13–17 from Araihazar, Bangladesh. Cognitive function was assessed using the computer-based Behavioral Assessment and Research System (BARS), focusing attention, memory, and executive function, and blood NM levels (Fe, Se, Zn, Mg, and Cu) were measured. Associations between individual minerals, NM composite scores, and cognition were analyzed using multiple linear regressions. Results: This study included 47 boys and 58 girls with an average age of 15 years. Fe levels were correlated with Continuous Performance Test (CPT) latency (r = −0.42, p < 0.05) and Se levels correlated with Match-to-Sample (MTS) correct count (r = 0.32, p < 0.01). Linear regressions showed that Se was associated with MTS correct count (b = 0.02, 95%CI: 0.01, −0.04), reflecting visual memory, and Fe was associated with CPT latency (b = −0.68, 95%CI: −1.11, −0.26), reflecting improved attention. The same BARS measures were also significantly associated with the 3-NM composite score. Conclusions: Our findings suggest that NM, particularly Fe, Se, and NM mixtures, could play a crucial role in brain development and neurocognitive function during adolescence. Further studies will help design national public health policies and strategies to address and mitigate brain health deficiencies among adolescents. [ABSTRACT FROM AUTHOR]

Background: Schizophrenia (SZ) is a severe mental disorder with complex origins. Observational studies suggested that inflammatory factors may play a role in the pathophysiology of SZ and we aim to investigate the potential genetic connection between them by examining the causal impact of circulating inflammatory proteins on SZ. Methods: We utilized Mendelian randomization (MR) analysis to assess the causal relationship between circulating inflammatory proteins and SZ and the GWAS summary datasets were sourced from public databases. The SZ dataset comprised 74,776 cases and 101,023 controls, while the summary results for 91 plasma proteins in 14,824 participants were obtained through the Olink Target platform. Moreover, to identify and evaluate potential drug targets, we searched the Drug-Gene Interaction Database (DGIdb). Results: The results of the MR study confirmed that nine inflammatory proteins had a causal effect on SZ. Among these proteins, IL1A (OR: 0.93), TNFB (OR: 0.94), TNFSF14 (OR: 0.96), and CD40 (OR: 0.95) exhibited protective effects against SZ. Conversely, CCL23 (OR: 1.04), CCL19 (OR: 1.04), 4EBP1 (OR: 1.06), TWEAK (OR: 1.08), and DNER (OR: 1.10) were associated with an increased risk of SZ. The MR-Egger and weighted median methods also supported the direction of these effects. According to the Gene-Drug analysis, LTA, IL1A, CD40, and 4EBP1 can serve as drug targets. Conclusions: Our study established causal relationships between circulating inflammatory proteins and SZ. It may be beneficial to personalize the treatment of SZ by incorporating inflammation management into the treatment regimen. [ABSTRACT FROM AUTHOR]

Background: Schizophrenia (SZ) is a severe mental disorder with complex origins. Observational studies suggested that inflammatory factors may play a role in the pathophysiology of SZ and we aim to investigate the potential genetic connection between them by examining the causal impact of circulating inflammatory proteins on SZ. Methods: We utilized Mendelian randomization (MR) analysis to assess the causal relationship between circulating inflammatory proteins and SZ and the GWAS summary datasets were sourced from public databases. The SZ dataset comprised 74,776 cases and 101,023 controls, while the summary results for 91 plasma proteins in 14,824 participants were obtained through the Olink Target platform. Moreover, to identify and evaluate potential drug targets, we searched the Drug-Gene Interaction Database (DGIdb). Results: The results of the MR study confirmed that nine inflammatory proteins had a causal effect on SZ. Among these proteins, IL1A (OR: 0.93), TNFB (OR: 0.94), TNFSF14 (OR: 0.96), and CD40 (OR: 0.95) exhibited protective effects against SZ. Conversely, CCL23 (OR: 1.04), CCL19 (OR: 1.04), 4EBP1 (OR: 1.06), TWEAK (OR: 1.08), and DNER (OR: 1.10) were associated with an increased risk of SZ. The MR-Egger and weighted median methods also supported the direction of these effects. According to the Gene-Drug analysis, LTA, IL1A, CD40, and 4EBP1 can serve as drug targets. Conclusions: Our study established causal relationships between circulating inflammatory proteins and SZ. It may be beneficial to personalize the treatment of SZ by incorporating inflammation management into the treatment regimen. [ABSTRACT FROM AUTHOR]

Introduction: Reproductive endocrine disorders (RED), including polycystic ovary syndrome (PCOS), endometriosis (EMs), and female infertility (FI), significantly affect women's health globally, with varying prevalence across different regions. These conditions can be addressed through medication, surgical interventions, and lifestyle modifications. However, the limited understanding of RED's etiology and the substantial economic burden of its treatment highlight the importance of investigating its pathogenesis. Metabolites play a critical role in metabolic processes and are potentially linked to the development of RED. Despite existing studies suggesting correlations between metabolites and RED, conclusive evidence remains scarce, primarily due to the observational nature of these studies, which are prone to confounding factors. Methods: This study utilized Mendelian Randomization (MR) to explore the causal relationship between metabolites and RED, leveraging genetic variants associated with metabolite levels as instrumental variables to minimize confounding and reverse causality. Data were obtained from the Metabolomics GWAS Server and the IEU OpenGWAS project. Instrumental variables were selected based on their association with the human gut microbiota composition, and the GWAS summary statistics for metabolites, PCOS, EMs, and FI were analyzed. The MR-Egger regression and random-effects inverse-variance weighted (IVW) methods were employed to validate the causal relationship. Cochran's Q test was employed to evaluate heterogeneity, sensitivity analysis was performed using leave-one-out analysis, and for pleiotropy analysis, the intercept term of MR-Egger's method was investigated. Results: The MR analysis revealed significant associations between various metabolites and RED conditions. For instance, a positive association was found between 1-palmitoylglycerophosphocholine and PCOS, while a negative association was noted between phenylacetate and FI. The study identified several metabolites associated with an increased risk and others with protective effects against PCOS, EMs, and FI. These findings highlight the complex interplay between metabolites and RED, suggesting potential pathways through which these conditions could be influenced or treated. Conclusion: This MR study provides valuable insights into the causal relationship between metabolites and female reproductive endocrine disorders, suggesting that metabolic alterations play a significant role in the pathogenesis of PCOS, EMs, and FI, and offering a foundation for future research and therapeutic development. [ABSTRACT FROM AUTHOR]

Background: There has been recent evidence to suggest that people who suffer from eating disorders (EDs) or other eating behavior disturbances, such as emotional eating (EE), are particularly vulnerable post-COVID-19. This narrative literature review aims to comprehensively analyze and scrutinize the existing clinical studies regarding the effects of the COVID-19 pandemic on people with EDs or EE. Moreover, due to the COVID-19 pandemic, it is very important to find out whether a person's emotional state may lead them to wrongly confront their emotional behavior with food consumption. Methods: A thorough search of several databases (PubMed, Scopus, Web of Science, Public Library of Science, Google Scholar, CINAHL Complete, PsycINFO, and Academic Search Complete) was performed to identify available clinical studies on the COVID-19 pandemic published between 2020 and 2024 using relevant keywords. Results: There is substantial evidence that the COVID-19 pandemic exerted negative effects on persons suffering from EDs, including those who are symptomatic, those who are in remission, and those with an EE behavior. Characteristically, people diagnosed with anorexia nervosa (AN), bulimia nervosa (BN), or binge eating disorder (BED) showed considerable symptomatic worsening after the first wave of the COVID-19 pandemic. Clinical studies investigating the effects of the COVID-19 pandemic on persons suffering from EE have also clearly demonstrated that their mood disturbances negatively affected their eating behaviors. These studies have also shown that vulnerable people were at greater risk of EE development and progression during the COVID-19 pandemic. Conclusion: The COVID-19 pandemic seems to have exerted a significant deleterious effect on people who have a history of suffering from EDs or EE. The COVID-19 pandemic has been related to the frequency of dysfunctional eating behaviors, thus decreasing therapeutic intervention efficiency in people with EDs as well as in those with disturbed eating behaviors such as EE. In this aspect, further clinical studies are strongly recommended to investigate the exact effects of the COVID-19 pandemic as well as to explore their potential long-term deleterious complications in the post-pandemic period. Public strategies and policies should be applied to provide special healthcare for this group of patients. [ABSTRACT FROM AUTHOR]

The increasing frequency and severity of drought, driven by global climate change, has emerged as a critical factor constraining the growth of landscaping trees in urban ecosystems. The local or non-local status of tree species is an important driver of plant function traits, which regulate plant performance. However, the differential impact of varying drought intensities on the functional traits of both non-local and local trees remains poorly understood. This study investigated the responses of leaf and root traits of seven typical tropical landscaping tree seedlings (three local species and four non-local species) to simulated drought conditions in a year-long greenhouse experiment. The results showed that drought significantly increased the specific leaf area, leaf thickness, and root exudate rate, while reducing root nitrogen content and leaf dry matter content, with differences observed between local and non-local species. The non-local species exhibited pronounced fluctuations in leaf and root traits between control and drought conditions. Local species tended to enhance the relationship between leaves and roots under drought, while non-local species showed a weakening of this relationship. Principal component analysis revealed that local species adopted a more conservative strategy under control conditions and a more acquisitive strategy under drought, while root strategies remained stable across conditions. The subordination function method in fuzzy mathematics identified Terminalia neotaliala (non-local) as the most drought-resistant species and Artocarpus heterophyllus (non-local) as the least drought-resistant species. Non-local species demonstrated greater drought resistance in leaf traits compared to local species, but the opposite was observed for root traits. These results underscore the importance of understanding the species-specific responses of local and non-local trees to drought stress. These findings provide a scientific basis for developing effective screening and management protocols for drought-resistant landscaping tree species. [ABSTRACT FROM AUTHOR]

In the past five years, repetitive transcranial magnetic stimulation (rTMS) has evolved significantly, driven by advancements in device design, treatment protocols, software integration, and brain-computer interfaces (BCIs). This review evaluates how these innovations enhance the safety, efficacy, and accessibility of rTMS while identifying key challenges such as protocol standardization and ethical considerations. A structured review of peer-reviewed studies from 2019 to 2024 focused on technological and clinical advancements in rTMS, including AI-driven personalized treatments, portable devices, and integrated BCIs. AI algorithms have optimized patient-specific protocols, while portable devices have expanded access. Enhanced coil designs and BCI integration offer more precise and adaptive neuromodulation. However, challenges remain in standardizing protocols, addressing device complexity, and ensuring equitable access. While recent innovations improve rTMS's clinical utility, gaps in long-term efficacy and ethical concerns persist. Future research must prioritize standardization, accessibility, and robust ethical frameworks to ensure rTMS's sustainable impact. [ABSTRACT FROM AUTHOR]

Simple Summary: This review examines the clinical and academic role of quantitative spinal cord imaging in neurodegenerative and acquired spinal cord disorders. Cervical spinal cord atrophy is readily detected in a range of motor neuron disease (MND) phenotypes (ALS, PLS, PMA, SBMA, PPS, and SMA), hereditary ataxias (SCA, FDRA), HSP, and other neurodegenerative disorders. Cord changes may be detected in the pre-symptomatic stages of the disease in association with certain genotypes. Longitudinal studies often capture progressive cord atrophy over time. Diffusion tensor imaging studies have detected cervical spinal cord diffusivity alterations in ALS, PLS, SBMA, FDRA, HSP, ALD, HAM/TSP, following spinal cord infarcts or sensory neuronopathies. The few MRS studies reveal altered metabolite ratios in the cervical cord in ALS and FDRA. These academic observations have important clinical implications, helping to define the potential diagnostic and monitoring role of these metrics and ultimately the development of clinically useful biomarkers for clinical trials, diagnostic applications, and tools to monitor disease progression. Introduction: Quantitative spinal cord imaging has facilitated the objective appraisal of spinal cord pathology in a range of neurological conditions both in the academic and clinical setting. Diverse methodological approaches have been implemented, encompassing a range of morphometric, diffusivity, susceptibility, magnetization transfer, and spectroscopy techniques. Advances have been fueled both by new MRI platforms and acquisition protocols as well as novel analysis pipelines. The quantitative evaluation of specific spinal tracts and grey matter indices has the potential to be used in diagnostic and monitoring applications. The comprehensive characterization of spinal disease burden in pre-symptomatic cohorts, in carriers of specific genetic mutations, and in conditions primarily associated with cerebral disease, has contributed important academic insights. Methods: A narrative review was conducted to examine the clinical and academic role of quantitative spinal cord imaging in a range of neurodegenerative and acquired spinal cord disorders, including hereditary spastic paraparesis, hereditary ataxias, motor neuron diseases, Huntington's disease, and post-infectious or vascular disorders. Results: The clinical utility of specific methods, sample size considerations, academic role of spinal imaging, key radiological findings, and relevant clinical correlates are presented in each disease group. Conclusions: Quantitative spinal cord imaging studies have demonstrated the feasibility to reliably appraise structural, microstructural, diffusivity, and metabolic spinal cord alterations. Despite the notable academic advances, novel acquisition protocols and analysis pipelines are yet to be implemented in the clinical setting. [ABSTRACT FROM AUTHOR]

Thalamus plays a pivotal role in the pathophysiology of neuropsychiatric conditions due to its strategic position and intricate connectivity with the cerebral cortex, limbic system, and other subcortical structures. In the present study, the potential involvement of the thalamus and subregions of the thalamus are explored in bipolar disorder (BD). In particular, functional and structural magnetic resonance imaging was performed on 73 adult patients with BD‐I and 78 healthy controls (HCs). Seed‐based thalamus and thalamic subregional functional connectivity (FC) were compared between the BD‐I patients and HCs. Compared to HCs, patients with BD‐I showed higher FC between the left thalamus and right lingual gyrus and altered FC between the dorsal thalamus and the default mode network and prefrontal regions, which may be correlated with mania symptomatology. In patients with BD‐I, the anterior subregions of the thalamus had higher FC than the posterior subregions. No significant difference in gray matter volume or local functional activity was found in the thalamus and thalamic subregions between BD‐I and HC. These findings provide evidence of disorganized thalamocortical FC in BD‐I, suggesting that the thalamus and its subregions may play important and specific roles in the neural circuitry of BD. [ABSTRACT FROM AUTHOR]

Background: Depression is a pervasive mental health condition, particularly affecting older adults, where early detection and intervention are essential to mitigate its impact. This study presents an explainable multi-layer dynamic ensemble framework designed to detect depression and assess its severity, aiming to improve diagnostic precision and provide insights into contributing health factors. Methods: Using data from the National Social Life, Health, and Aging Project (NSHAP), this framework combines classical machine learning models, static ensemble methods, and dynamic ensemble selection (DES) approaches across two stages: detection and severity prediction. The depression detection stage classifies individuals as normal or depressed, while the severity prediction stage further classifies depressed cases as mild or moderate-severe. Finally, a confirmation depression scale prediction model estimates depression severity scores to support the two stages. Explainable AI (XAI) techniques are applied to improve model interpretability, making the framework more suitable for clinical applications. Results: The framework's FIRE-KNOP DES algorithm demonstrated high efficacy, achieving 88.33% accuracy in depression detection and 83.68% in severity prediction. XAI analysis identified mental and non-mental health indicators as significant factors in the framework's performance, emphasizing the value of these features for accurate depression assessment. Conclusions: This study emphasizes the potential of dynamic ensemble learning in mental health assessments, particularly in detecting and evaluating depression severity. The findings provide a strong foundation for future use of dynamic ensemble frameworks in mental health assessments, demonstrating their potential for practical clinical applications. [ABSTRACT FROM AUTHOR]

This review explores the understudied topic of nutritional inequality among individuals with schizophrenia, highlighting the complex interplay between diet, genetics, and mental health. Unhealthy dietary patterns, socioeconomic factors, and disordered eating behaviors contribute to malnutrition, increasing the risk of physical health issues and premature mortality. Socioeconomic factors exacerbate nutritional disparities, necessitating targeted interventions. Genetic influences on nutrient metabolism remain under-researched, although nutritional genomics shows potential for personalized interventions. Current research reveals methodological gaps, urging larger sample sizes and standardized approaches. The integration of nutrigenomics, encompassing various omics disciplines, emerges as a transformative tool. The holistic life-cycle approach to schizophrenia management underscores the vital role of nutrition, calling for personalized interventions to enhance mental health outcomes. [ABSTRACT FROM AUTHOR]

Objective: Olanzapine (OLZ) is used by some patients with bipolar disorder and schizophrenia. Some of its side effects include weight gain and metabolic syndrome. Zeaxanthin (Zx), a yellow pigment found in egg yolk as well as some yellow and orange plants and fruits, is an anti-obesity factor that aids in the treatment of metabolic disorders. The effects of Zx on metabolic disorders caused by OLZ were investigated in this study. Materials and Methods: Female Wistar rats were randomly divided into seven groups (n=6): 1. control (vehicle); 2. OLZ (5 mg/kg, 14 days, intraperitoneal (i.p.)); 3-5. OLZ + Zx (12.5, 25, and 50 mg/kg, 14 days, gavage); 6. OLZ+ metformin (100 mg/kg, 14 days, i.p.); and 7. Zx (50 mg/kg, 14 days). Weight changes were checked every 3 days and food intake was monitored every day. Systolic blood pressure, insulin, blood sugar, triglyceride, cholesterol, high-density lipoprotein (HDL), low-density lipoprotein (LDL) and leptin levels were evaluated on the last day of the study. Results: OLZ caused an increase in weight (84.5% increase on day 15), food intake, blood pressure, triglyceride, LDL, insulin, blood sugar, and leptin compared to the control group (p<0.01 and p<0.001). The use of different doses of Zx (12.5, 25, and 50 mg/kg) and metformin decreased weight (the percentages of weigh reduction on day 15 were 91.92% for Zx (50 mg/kg) and 59.39% for metformin), food intake, systolic blood pressure, triglyceride, LDL, insulin, blood sugar, and leptin compared to the OLZ group. The amounts of cholesterol and HDL were not different in different groups. Conclusion: Zx alleviates metabolic abnormalities including hypertension, hyperglycemia, and dyslipidemia in rats. [ABSTRACT FROM AUTHOR]

Background: Bipolar disorder may begin as depression or mania, which can affect the treatment and prognosis. The physiological and pathological differences among pediatric bipolar disorder (PBD) patients with different onset symptoms are not clear. The aims of the present study were to investigate subcortical structural alterations in PBD patients with first-episode depressive (PBD-FED) and first-episode manic (PBD-FEM). Methods: A total of 59 individuals including 28 PBD-FED, 13 PBD-FEM, and 18 healthy controls (HCs) underwent high-resolution structural magnetic resonance scans. FreeSurfer 7.2 was used to detect changes in subcortical volumes. Simultaneously, thalamic, hippocampal, and amygdala subregion volumes were compared between the three groups. Results: Analysis of covariance controlling for age, sex, education, and estimated intracranial volume shows third and fourth ventricle enlargement in patients with PBD. Compared with the PBD-FED and HCs, the PBD-FEM group had reduced gray matter volume in the left thalamus, bilateral hippocampus, and right amygdala. Subsequent subregion analyses showed right cortico-amygdaloid transient, bilateral accessory-basal nucleus, left hippocampal tail, right hippocampal head, and body volume reduction in the PBD-FEM group. Conclusions: The present findings provided evidence of decreased subcortical structure in PBD-FEM patients, which might present its trait feature. [ABSTRACT FROM AUTHOR]

Introduction: Recent resting-state electroencephalogram (EEG) studies have consistently reported an association between aberrant functional brain networks (FBNs) and treatment-resistant traits in patients with major depressive disorder (MDD). However, little is known about the changes in FBNs in response to external stimuli in these patients. This study investigates whether changes in the salience network (SN) could predict responsiveness to pharmacological treatment in resting-state and external stimuli conditions. Methods: Thirty-one drug-naïve patients with MDD (aged 46.61 ± 10.05, female 28) and twenty-one healthy controls (aged 43.86 ± 14.14, female 19) participated in the study. After 8 weeks of pharmacological treatment, the patients were divided into non-remitted MDD (nrMDD, n = 14) and remitted-MDD (rMDD, n = 17) groups. EEG data under three conditions (resting-state, standard, and deviant) were analyzed. The SN was constructed with three cortical regions as nodes and weighted phase-lag index as edges, across alpha, low-beta, high-beta, and gamma bands. A repeated measures analysis of the variance model was used to examine the group-by-condition interaction. Machine learning-based classification analyses were also conducted between the nrMDD and rMDD groups. Results: A notable group-by-condition interaction was observed in the highbeta band between nrMDD and rMDD. Specifically, patients with nrMDD exhibited hypoconnectivity between the dorsal anterior cingulate cortex and right insula (p = 0.030). The classification analysis yielded a maximum classification accuracy of 80.65%. Conclusion: Our study suggests that abnormal condition-dependent changes in the SN could serve as potential predictors of pharmacological treatment efficacy in patients with MDD. [ABSTRACT FROM AUTHOR]

The incidence of eating disorders in children, mainly of anorexia nervosa, is dramatically increased in the last years. A timely identification of the disease is associated with higher rates of recovery. Aim of the work is to underline signs and symptoms that can be used to an early detection of anorexia nervosa in the pediatric age. A scoping review has been conducted by The Italian Pediatric Society Adolescent Study Group according to the PRISMA Extension guidelines for Scoping Reviews, using the search term "anorexia nervosa" and the following filters "review", "systematic review", "age 0–18", "last 4 years". The strategy search produced 657 studies, of which 52 were included in this revision. Screening and red flags were discussed in 23 reports, genetics in 12, neurological pathways in 11, environmental factors in 10, and gut microbiota in 7. An accurate physiological and pathological anamnesis, physical and psychological examination, including the body perception, should be taken in account as well as the presence of co-morbidities, including chronic functional abdominal pain and autoimmune/autoinflammatory diseases. Evidence suggests the role of familiar predisposition as well as of neurological morphology and pathway in anorexia nervosa development. Gut microbiota has also been included among possible risk factor for developing anorexia nervosa due to a complex direct and indirect interactions between gut and brain. The Italian Pediatric Society Adolescent Study Group suggests performing an accurate familial and personal anamnesis, including psychological evaluation as well as a physical exam including auxological parameters as a screening tool during pediatric checks to better explore the risk of developing anorexia nervosa. [ABSTRACT FROM AUTHOR]

The article "The repressed life of adult female patients with mild ADHD" published in Frontiers in Psychiatry discusses the overlooked characteristics of adult females with mild ADHD, focusing on their daily struggles and psychopathological phenotypes. It highlights the challenges faced by these individuals in the workplace, their tendency to camouflage their symptoms, and the prevalence of comorbidities such as anxiety disorders. The article also addresses the cultural differences in symptom manifestation and the impact of ADHD on social activities. The authors emphasize the need for a better understanding of these individuals to prevent underdiagnosis or misdiagnosis. [Extracted from the article]

Depression is a major cause of disability and mortality for young people worldwide and is typically first diagnosed during adolescence. In this work, we present a machine learning framework to predict adolescent depression occurring between ages 12 and 18 years using environmental, biological, and lifestyle features of the child, mother, and partner from the child's prenatal period to age 10 years using data from 8467 participants enrolled in the Avon Longitudinal Study of Parents and Children (ALSPAC). We trained and compared several cross-sectional and longitudinal machine learning techniques and found the resulting models predicted adolescent depression with recall (0.59 ± 0.20), specificity (0.61 ± 0.17), and accuracy (0.64 ± 0.13), using on average 39 out of the 885 total features (4.4%) included in the models. The leading informative features in our predictive models of adolescent depression were female sex, parental depression and anxiety, and exposure to stressful events or environments. This work demonstrates how using a broad array of evidence-driven predictors from early in life can inform the development of preventative decision support tools to assist in the early detection of risk for mental illness. [ABSTRACT FROM AUTHOR]

Depression is a major cause of disability and mortality for young people worldwide and is typically first diagnosed during adolescence. In this work, we present a machine learning framework to predict adolescent depression occurring between ages 12 and 18 years using environmental, biological, and lifestyle features of the child, mother, and partner from the child’s prenatal period to age 10 years using data from 8467 participants enrolled in the Avon Longitudinal Study of Parents and Children (ALSPAC). We trained and compared several cross-sectional and longitudinal machine learning techniques and found the resulting models predicted adolescent depression with recall (0.59 ± 0.20), specificity (0.61 ± 0.17), and accuracy (0.64 ± 0.13), using on average 39 out of the 885 total features (4.4%) included in the models. The leading informative features in our predictive models of adolescent depression were female sex, parental depression and anxiety, and exposure to stressful events or environments. This work demonstrates how using a broad array of evidence-driven predictors from early in life can inform the development of preventative decision support tools to assist in the early detection of risk for mental illness. [ABSTRACT FROM AUTHOR]

Impulsivity is elevated in psychosis and during mania in bipolar disorder. Studies in unaffected relatives may help establish whether impulsivity is a heritable, state independent endophenotype. The aim of this systematic review and meta-analysis was to examine whether impulsivity is elevated in unaffected relatives of those with bipolar disorder, schizophrenia, and schizoaffective disorder, compared to controls. Databases were systematically searched up until March 2023 for articles reporting data on a behavioral or self-report measure of impulsivity in first-degree relatives and controls. Nineteen studies were included. Behavioral (10 studies, d = 0.35, p < 0.001) and self-reported impulsivity was significantly elevated in bipolar disorder relatives compared to controls (5 studies, d = 0.46, p < 0.001), with small effect sizes. Relatives of those with schizophrenia did not show significantly elevated impulsivity compared to controls on behavioral measures (6 studies, d = 0.42, p = 0.102). There were not enough studies to conduct a meta-analysis on self-report data in schizophrenia relatives or schizoaffective disorder relatives (self-report or behavioral). Study quality was good, however there was moderate to high heterogeneity in behavioral meta-analyses. Results suggest elevated impulsivity may be an endophenotype for bipolar disorder, present in an attenuated state before and after the illness and in at-risk individuals. This trait, amongst other behavioral and psychological indices, could be used to identify those who are at risk of developing bipolar disorder. Future research should refine measurement across studies and establish which components of impulsivity are affected in those at risk of psychotic and bipolar disorders. [ABSTRACT FROM AUTHOR]

Objective: This study aims to compare the effectiveness of Intensive Short-Term Dynamic Psychotherapy (ISTDP) and Acceptance and Commitment Therapy (ACT) enriched with compassion in altering dysfunctional communication beliefs among women seeking divorce. Methods: Statistical population of this study consisted of all women seeking divorce in District 4 of Tehran city in the year 2021, who were selected from counseling centers supervised by the Organization of Psychology and Welfare through convenience sampling. Next, Eidelson and Epstein's (1982) Communication Beliefs Questionnaires were distributed for completion, and 45 individuals who scored high on these scales were selected as the sample and randomly placed into two experimental groups and one control group (each group consisting of 15 individuals). For data analysis, descriptive statistical methods (frequency indices, mean, and standard deviation) and inferential statistics (multivariate repeated measures analysis of variance) using SPSS-24 were employed. Findings: Results indicated no significant difference between the ISTDP and ACT enriched with compassion in altering dysfunctional communication beliefs. Both groups showed improvement compared to the control group, but no significant difference was noted between the two in terms of effectiveness. Conclusion: Both ISTDP and ACT enriched with compassion are effective in reducing dysfunctional communication beliefs among women seeking divorce, with no significant difference in effectiveness between the two. This suggests that both therapeutic approaches can be beneficial in this context. Future research should consider broader and more diverse samples, longer follow-up periods, and comparisons to active control groups to further understand the sustained impact and efficacy of these therapies. [ABSTRACT FROM AUTHOR]

Objective: The present study aimed to compare the effectiveness of cognitive-behavioral therapy (CBT) and schema therapy on psychological capital in women affected by marital infidelity. Methods: This research was a quasi-experimental study with a pre-test, post-test, and follow-up design with a control group. The statistical population included all women dealing with the issue of marital infidelity of their spouses who referred to counseling centers in Tehran in 2022 and expressed their readiness to participate in the treatment program in response to the researcher's call. From this population, 45 subjects were selected using purposive sampling and randomly assigned to three equal groups of 15 (two experimental groups and one control group). The Psychological Capital Questionnaire by Luthans et al. (2007) was used to collect data. Data analysis was performed using SPSS software and multivariate analysis of covariance (MANCOVA) tests. Findings: The results indicated that both schema therapy and cognitive-behavioral therapy interventions produced a significant difference in psychological capital in women affected by marital infidelity at the post-test and follow-up stages compared to the control group (p ≤ 0.001). Conclusion: The results showed that cognitive-behavioral therapy had a greater impact on psychological capital in women affected by marital infidelity compared to schema therapy. [ABSTRACT FROM AUTHOR]

Background/Objectives: Supporting the mental well-being of students through their educational journey is of vital importance. The objective of this study was to investigate the relationship between metacognition, personality traits, and various factors affecting mental health in a cohort of physical therapy (PT) and occupational therapy (OT) students. Methods: This cross-sectional observational study involved a self-administered questionnaire (distributed in October to November 2020) that gathered demographic information and included six scales measuring personality characteristics, health literacy, self-management skills, exercise habits (benefits and barriers), cognitive thinking, and cognitive deliberateness and impulsivity. Results: A cohort of 195 PT and OT students participated in the study. The findings revealed significant associations between metacognition and personality, particularly the association between low cognitive self-confidence and high emotional instability. This link suggests that students who struggle with cognitive self-confidence may also experience greater emotional instability, highlighting a need for targeted mental health support. Additionally, health literacy was negatively correlated with anxiety, while metacognition was positively correlated with perceived exercise benefits. Conclusions: This study highlights the importance of individualized metacognitive approaches to support students' mental well-being. Interventions should focus on strengthening cognitive self-confidence through methods such as resilience training, cognitive restructuring, and mindfulness practices to help manage emotional instability. Furthermore, promoting physical activity, particularly among female students, and incorporating gender-specific strategies could enhance mental health outcomes. [ABSTRACT FROM AUTHOR]

Somatoform disorders involve physical symptoms without a clear medical cause, often leading to significant distress. Effective treatment frequently includes psychotherapy, which aims to address the psychological aspects of these disorders. Cognitive-behavioral therapy (CBT) and other therapeutic methods can help patients manage symptoms, reduce distress, and improve overall functioning. This abstract highlights the role of psychotherapy in the treatment of somatoform disorders and its impact on enhancing patient outcomes. [ABSTRACT FROM AUTHOR]

Objectives: This study aimed to explore the association between bowel movement disorders and depression in adults. Method: A cross-sectional study was conducted using data from the National Health and Nutritional Examination Survey (NHANES), 2005–2010. Depression, constipation, diarrhea, and fecal incontinence were self-reported via questionnaires. Weighted logistic regression and subgroup analyses were performed to explore the association between bowel movement disorders and the risk of depression. Restricted cubic spline (RCS) was also conducted to investigate the association between bowel movements disorder and depression. Results: A total of 13,820 participants were collected. Compared to the participants with normal bowel movements, the full-adjusted depression model ORs for constipation and diarrhea were 2.28 (95%CI,1.78-2.92), 1.75 (95%CI,1.31-2.31), respectively. Any kind of bowel leakage were associated with depression. The RCS showed the possible nonlinear association between bowel movement frequency/stool shape and depression. Conclusions: Constipation, diarrhea, and bowel leakage are associated with an increased risk of depression. [ABSTRACT FROM AUTHOR]

Background/Objectives: Attention-Deficit/Hyperactivity Disorder (ADHD) is associated with an increased risk of obesity and disordered eating behaviors. This study compared weight status and eating behaviors among drug-naïve ADHD children, those on stimulant monotherapy, those on combined stimulant and antipsychotic treatment, and healthy controls. Methods: This cross-sectional study included 547 children aged 6–12 years from four Turkish provinces: 361 with ADHD (152 drug-naïve, 156 on stimulants, and 53 on combined therapy), and 186 healthy controls. Anthropometric measurements, psychiatric assessments, and eating behavior evaluations were conducted using standardized tools. Results: Drug-naïve ADHD children had the highest obesity rate (13.8%), while those on stimulant monotherapy had the lowest (4.5%) compared to controls. Combined treatment group obesity rates were similar to controls (7.5% vs. 8.6%). The drug-naïve and combined treatment groups showed increased food approach behavior and desire to drink, with the combined treatment group also showing increased emotional overeating. Conclusions: This study reveals a complex relationship between ADHD, its pharmacological management, and the risk of obesity. Stimulant monotherapy may mitigate the risk of obesity, while combined stimulant and antipsychotic treatment may lead to problematic eating behaviors. These findings emphasize the importance of monitoring weight status and eating behaviors in ADHD children, especially those receiving pharmacological interventions. [ABSTRACT FROM AUTHOR]

Background: Impulsivity assessment may serve as a valuable clinical tool in the stratification of suicide risk. Acting without forethought is a crucial feature in the psychopathology of many psychiatric disturbances and corresponds with suicidal ideations, behaviors, and attempts. Methods: We present data on biological and psychological correlates of impulsivity among young adults (n = 47). Psychological analysis included both the self-description questionnaire—Barratt Impulsiveness Scale (BIS-11)—and neuropsychological behavioral tests, including the Iowa Gambling Task (IGT), the Simple Response Time task (SRT), and the Continuous Performance Test (CPT). mRNA and micro-RNA were isolated from peripheral blood mononuclear cells (PBMC). Expression levels of Brain-Derived Neurotrophic Factor (BDNF) mRNA and its regulatory micro RNAs, mir-1-3p, mir-15a-5p, mir-26a-5p, mir-26b-5p, and mir-195-5p, were analyzed using the quantitative reverse transcription polymerase chain reaction (RT-qPCR) method. proBDNF and BDNF plasma protein levels were quantified using enzyme-linked immunosorbent assay (ELISA). Results: Significant correlations between BDNF mRNA and mir-15a-5p as well as proBDNF levels and mir-1-3p were detected. proBDNF protein levels correlated with motor and perseverance, while mir-26b correlated with cognitive complexity subdimensions of the BIS-11 scale. Correlations between BDNF, miRNAs, and the results of neuropsychological tests were also detected. Conclusions: The BDNF pathway shows a clinical potential in searching for biomarkers of impulse-control impairment. BDNF-regulatory micro-RNAs are detectable and related to clinical parameters in the studied population, which needs further research. [ABSTRACT FROM AUTHOR]