Search Results (340)

Background: Depression in men often goes unrecognized, even though it leads to high rates of suicide. Men may show symptoms that are external, behavioral, or physical, which traditional assessment tools focused on internal symptoms do not adequately reflect. Methods: A narrative review was carried out to gather evidence on depression scales tailored for men. We searched PubMed up to November 2025 for studies discussing the development, validation, and clinical use of the Gotland Male Depression Scale (GMDS), the Male Depression Risk Scale (MDRS-22 and MDRS-7), and the Gender-Sensitive Depression Screening scale (GSDS-26). We organized the findings by instrument. Results: The studies indicate that male-sensitive scales capture symptom domains such as emotional suppression, anger, risk-taking behaviors, substance misuse, and somatic complaints. The GMDS has demonstrated applicability across psychiatric, somatic, and paternal perinatal populations. The MDRS-22 and MDRS-7 were particularly sensitive to externalizing symptom patterns associated with male presentations of depression and behavioral profiles linked to elevated suicide risk. The GSDS-26 integrates both prototypical and externalizing symptoms, enabling the identification of diverse depressive profiles. However, the current evidence base remains limited due to a reliance on non-clinical samples and the scarcity of long-term and cross-cultural validation studies. Conclusions: Male-sensitive depression scales may serve as useful complementary screening tools that improve recognition of male-typical depressive presentations and behavioral patterns associated with increased suicide risk. Further clinical and longitudinal research is needed to confirm their diagnostic accuracy and clinical utility. Full article

Electroencephalography (EEG) has become an increasingly important tool in depression research due to its ability to capture objective neurophysiological abnormalities associated with depressive disorders, offering high temporal resolution, non-invasiveness, and cost-effectiveness.However, existing methods often fail to fully exploit the multi-domain information in EEG signals, resulting in limited model generalization capabilities. This paper proposes an improved bidirectional long short-term memory (BiLSTM) model that segments continuous EEG into non-overlapping 2-s epochs and learns end-to-end from multi-channel temporal sequences. After band-pass filtering and resampling, each epoch is represented as a channel–time matrix $X\in {\mathbb{R}}^{C\times T}$ (with C = 128) and processed by a BiLSTM encoder followed by a dynamic-routing encapsulated-vector classifier. On the MODMA dataset under subject-independent five-fold cross-validation, the proposed method outperforms a set of reproduced representative baselines (SVM, EEGNet, InceptionNet, Self-attention-CNN and CNN–LSTM) and achieves 84.8% accuracy with an AUC of 0.899. We further discuss recent contemporary directions (e.g., attention/Transformer-based and emotion-aware expert models) and clarify the scope of our empirical comparisons. Furthermore, experiments comparing different frequency bands and band combinations indicate that joint multi-frequency input can enhance classification performance. This study provides an effective multi-domain fusion approach for the automatic diagnosis of depression based on EEG. Full article

Automated video-based detection of cognitive disorders can enable a scalable non-invasive health monitoring. However, existing methods focus on a single disease and provide limited interpretability, whereas real-world videos often contain co-occurring conditions. We propose a novel unified multi-task method to detect depression and Parkinson’s disease (PD) from in-the-wild video data called DEPART (DEpression and PArkinson’s Recognition Technique). It performs body region extraction, Contrastive Language-Image Pre-training (CLIP)-based visual encoding, Transformer-based temporal modeling, and prototype-aware classification with a gated fusion technique. Gradient-based attention maps are used to visualize task-specific regions that drive predictions. Experiments on the In-the-Wild Speech Medical (WSM) corpus demonstrate competitive performance: the multi-task model achieves Recall of 82.39% for depression and 78.20% for PD, compared with 87.76% and 78.20%, for the best single-task models. The multi-task learning initially increases false positives for healthy persons in the PD subset, mainly due to annotation–modality mismatches, static visual content misinterpreted as motor impairments, and occasional body detection failures. After cleaning the test data, Recall for healthy individuals becomes comparable across models; the multi-task model improves Recall for both depression (from 82.39% to 87.50%) and PD (from 78.20% to 86.14%), suggesting better robustness for real-life clinical applications. Full article

Objective: Although recognition of fibromyalgia (FMS) in men is growing, the mechanisms that contribute to their psychological distress remain poorly understood. This study aims to clarify how FMS alters men’s psychological distress and to identify potential protective and risk factors involved in this process in this often-underrepresented population. Methods: This study comprised a total of 225 men aged 18–75; of these, 102 were men with FMS (based on self-report) and 123 were healthy peers (HPs), all of whom completed questionnaires on demographics, anxiety (GAD-7), depression (PHQ-9), body appreciation (BAS-2), masculine self-esteem (MSES), illness-related shame (CISS), and pain intensity (SF-MPQ). Results: Men with FMS reported significantly higher depression and anxiety, lower body appreciation, and compromised masculine identity. Between-group analysis showed body appreciation mediated the fibromyalgia–distress relationship. However, within the FMS group, compromised masculine identity and illness-related shame were the strongest pathways to distress, while body appreciation showed no effect. Moderation analysis confirmed body appreciation buffered distress in controls but not in men with FMS. Conclusion: Masculine identity threats and illness-related shame constitute central mechanisms of psychological distress in men with FMS. Body appreciation operates differently in this population than in healthy men. Findings underscore the need for gender-sensitive interventions addressing identity disruption and emphasizing functionality over appearance-based acceptance. Full article

Depression remains a major global health challenge, with a significant proportion of patients failing to respond to conventional antidepressants. This study aimed to evaluate the potential antidepressant effects and toxicological profile of a novel tetrodotoxin (TTX) oral film formulation in a mouse model of chronic unpredictable mild stress (CUMS). Male C57BL/6J mice were subjected to CUMS and treated daily with TTX oral film at doses of 10, 20, and 40 μg/kg, with fluoxetine (18 mg/kg) serving as a positive control. Behavioral assessments, including sucrose preference test, open field test, forced swimming test, elevated plus maze, and novel object recognition, demonstrated that TTX oral film administration alleviated depression- and anxiety-like behaviors and improved cognitive function. Furthermore, TTX oral film treatment restored hippocampal serotonin levels, which were depleted in CUMS mice, and showed no adverse effects on organ indexes after long-term use. Toxicological evaluation through acute toxicity testing revealed an oral LD₅₀ of 919 μg/kg, indicating a substantially improved safety profile compared to pure TTX and a wide therapeutic window. These findings suggest that the TTX oral film possesses significant antidepressant activity with favorable toxicological properties, supporting its potential as a novel and safe treatment for depression. Full article

In the post-Moore’s Law era, conventional Von Neumann architectures face critical limitations, such as the “memory wall” and excessive power consumption, particularly when processing unstructured data. Neuromorphic computing, inspired by the human brain, offers a promising solution through parallel processing and adaptive learning. Among the candidates for artificial synapses, memristors based on two-dimensional MXenes (specifically Ti₃C₂T_x) have attracted significant attention due to their unique layered structure, high metallic conductivity, and tunable physicochemical properties. This review provides a comprehensive analysis of MXene-based memristors, from material synthesis to system-level applications. We examine how different synthesis strategies, including etching methods, directly influence device performance and elucidate the underlying resistive switching mechanisms driven by ion migration, valence change, and interfacial processes. Furthermore, the review demonstrates the efficacy of MXenes in emulating biological synaptic functions—such as spike-timing-dependent plasticity (STDP) and long-term potentiation/depression (LTP/LTD)—and their application in tasks like handwritten digit recognition. Finally, we highlight emerging frontiers in flexible electronics and in-sensor computing, offering insights into the future trajectory of integrated sensing, memory, and computation. Full article

Background/Objectives: Baichuan Baile (BCBL), a novel functional dietary formula, has been shown to exert antidepressant-like effects through modulation of the 5-HT system in our prior studies. Given the close neurobiological connections between depression and insomnia, along with its pharmacodynamic profile guided by TCM theory and nutritional assessments, BCBL is likely to possess beneficial effects against insomnia. However, this hypothesis and its underlying mechanisms require further validation. Methods: The chemical constituents of BCBL were analyzed by UPLC-Q-TOF-MS, and network pharmacology was applied to predict potential sleep-relevant targets and pathways. Subsequently, BCBL was evaluated for sedative-hypnotic effects using pentobarbital-induced hypnosis, locomotor activity, and polysomnography (EEG/EMG). Its therapeutic efficacy was further assessed in insomnia models induced by environmental stress, serotonin depletion, and rotarod-based sleep deprivation. The rotarod-induced chronic model was selected for mechanistic studies due to its sustained insomnia-like phenotype. Finally, key network-predicted targets were validated in this model through histopathology, Western blotting, and ELISA. Results: Pharmacological evaluation confirmed that BCBL significantly promoted sleep at both behavioral and EEG levels, confirming its sedative-hypnotic properties. BCBL mitigated environmental stress-triggered impairments in NREM sleep continuity and duration, and exerted protective effects against body weight loss and sleep disturbances in a serotonin depletion-induced insomnia model. In the rotarod sleep deprivation model, BCBL treatment increased spontaneous alternation rates and recognition indices, ameliorated hippocampal pathological alterations, and reduced hippocampal levels of HIF-1α, TNF-α, and IL-1β. Furthermore, BCBL elevated the p-GSK3β/GSK3β ratio and enhanced SIRT₁ expression in the hypothalamus. It also modulated the activity of key sleep–wake neurotransmitters/neuromodulators (serotonin, dopamine, adenosine, and glutamate) and key circadian rhythm regulators (BMAL₁, PER₂, and CLOCK) in this region. Conclusions: BCBL exhibits significant therapeutic efficacy against insomnia, indicating its potential as a dietary supplement for managing insomnia. Its mechanisms appear to involve anti-inflammatory effects, rebalancing of neurotransmitters/neuromodulators, and stabilization of circadian rhythm gene expression. Full article

Background and objectives: Antiseizure medications are essential for epilepsy management but often cause adverse effects that impact treatment adherence and quality of life. This study investigates the incidence rate and determinants of high-burden adverse effects of antiseizure medications. Materials and Methods: This study was a cross-sectional study including data extraction by a medical record review and administration of a standardized scale. It was conducted at an epilepsy outpatient clinic in Saudi Arabia and included adult patients on antiseizure medications. The validated Arabic version of the Liverpool Adverse Events Profile (LAEP) was used. The total LAEP scores ranged from 19 to 76. In this study, LAEP scores ≥ 45 were classified as high-burden adverse effects. Results: Of 153 included patients, 84 (54.9%) had high-burden adverse effects. The overall mean (SD) LAEP score was 45.63 (21.04). The most frequently rated adverse effects were difficulty in concentrating, with a mean score of 2.71 out of 4, followed closely by disturbed sleep (2.69), sleepiness (2.63), and memory problems (2.56). Of examined variables, generalized seizure and polytherapy were significantly associated with increased adverse effects. Likewise, uncontrolled seizure and presence of depression comorbidity were also associated with increased risk of adverse effects, but not statistically significant. Conclusion: The study reported a high rate of adverse effects of antiseizure medications and identified patients at high risk of adverse effects. Early recognition of these patients is important to provide appropriate care, including counselling, regular monitoring, and management of psychiatric comorbidities. Central nervous system symptoms were the most frequently reported adverse effects. Initiation of antiseizure medications with low doses and gradual titration may improve tolerability. Future research should focus on prediction adverse effects using pharmacogenomic AI-based decision-making tools. Full article

Parkinson disease (PD) and mood disorders represent two substantial global health burdens that increasingly co-occur as both conditions rise in prevalence worldwide. Diagnosing Parkinson disease in patients with pre-existing mood disorders is clinically challenging due to overlapping symptoms, medication effects, and shared neurobiological mechanisms. Apathy, psychomotor slowing, and fatigue may mimic depressive symptoms, leading to delayed recognition of early parkinsonism. Development of an underlying neurodegenerative disorder could account for some treatment-resistant symptoms or treatment failures if not recognized. Therefore, the identification of PD will change the treatment and management plan significantly. Accurate diagnosis of PD requires a detailed neurologic examination focusing on bradykinesia, rigidity, and resting tremor, supported when appropriate by dopamine transporter imaging (DaT scan) or other emerging biomarkers. Understanding the temporal relationship between psychiatric and motor features helps differentiate prodromal PD from primary mood disorders. Management of patients with both mood disorders and PD integrates dopaminergic replacement therapy for motor symptoms with individualized treatment of psychiatric comorbidities. Levodopa remains the cornerstone for motor control, while dopamine agonists, MAO-B inhibitors, and COMT inhibitors can be added as needed. For depression and anxiety, SSRIs and SNRIs are first-line choices; quetiapine or clozapine are preferred when treatment for psychosis is necessary. Intentional, thoughtful polypharmacy is frequently required. Non-pharmacologic interventions—including cognitive behavioral therapy, structured exercise, and patient–caregiver education—enhance mood, function, and quality of life. Multidisciplinary collaboration between neurology, psychiatry, and allied health professionals is essential for optimal outcomes. This review offers guidance to healthcare providers as well as other interested parties involved in patients with mood disorders who may also be developing or have PD, especially to those who may have limited access to neurologic resources. Full article

Perinatal loss affects 23 million pregnancies worldwide each year, representing a painful experience that disrupts expectations and impacts emotional, physical, social, and spiritual well-being. This cross-sectional observational study assessed symptoms of anxiety, depression, self-judgment (self-criticism, isolation, over-identification), and shame in women who experienced perinatal loss, as well as their predictive value for psychopathology. Participants were 501 women, divided into five groups according to time since loss: 0–6 months, 7–18 months, 19–30 months, 31–42 months, and more than 43 months. Findings showed that women 7–18 months post-loss reported the highest psychopathology levels, with significant differences in anxiety. Isolation and shame were the strongest predictors of depressive and anxiety symptoms. Although symptoms decreased over time, they remained elevated years after the loss. These results underscore the lasting psychological impact of perinatal loss and the importance of sustained recognition, assessment, and intervention to support women’s mental health. Full article

The early diagnosis of depression is often impeded by the subjectivity inherent in traditional clinical assessments. To advance objective screening, this study proposes a lightweight neural network framework designed to discriminate between pathological depressive states and non-pathological transient negative emotions using EEG signals. Diverging from conventional methods that rely on single-domain features, we construct a comprehensive multi-domain feature space via Wavelet Packet Decomposition. Specifically, the framework integrates frequency (α/β power spectral density ratio), spatial (normalized α-asymmetry), and non-linear (Sample Entropy) attributes to capture the heterogeneous neurophysiological dynamics of depression. To effectively synthesize these diverse features, a multi-head additive attention mechanism is introduced. This mechanism empowers the model to adaptively recalibrate feature weights, thereby prioritizing the most discriminative patterns associated with the disorder. Experimental validation on the DEAP (negative emotion) and HUSM (major depressive disorder) datasets demonstrates that the proposed method achieves a classification accuracy of 92.2% and an F1-score of 93%. Comparative results indicate that our model significantly outperforms baseline SVM and standard deep learning approaches. Furthermore, the architecture exhibits high computational efficiency and rapid convergence, highlighting its potential as a deployable engine for real-time mental health monitoring in clinical scenarios. Full article

Background: Mental health literacy remains low in South Africa, particularly in nonurban settings. This study aims to determine the sociodemographic and geographic influences of mental health literacy among community health clinic attendees. Methods: This study used secondary data which adopted a cross-sectional study design and was conducted between November 2019 and January 2020. A total of 385 participants were recruited through convenience sampling, with approximately 77 individuals per clinic across five sites. A two-part questionnaire was used, where part A included demographic information and part B consisted of the three fictive clinical case studies which measured the participants’ mental health literacy. The participants’ responses regarding disorder recognition and perceived causes were analyzed via Pearson’s chi-square tests. Using three fictive cases with clinical pictures indicative of mental depressive disorder, schizophrenia and general anxiety disorder, the following were assessed: (1) what type of illness do you think the person is suffering from, and (2) what do you think causes the persons’ suffering? To identify predictors of recognition and perceived causes, hierarchical logistic regression was performed. Statistical significance was set at p < 0.05. All analyses were conducted via STATA version 18.1 (StataCorp, College Station, TX, USA). Results: The mean age of the study participants was 37.39 ± 11.14 years (range: 13–80). Factors such as geographic location, gender and level of education were significant predictors of recognition. Participants attending urban clinics were more likely to correctly identify correct mental disorders than those attending township clinics were [OR = 0.32; 95% CI: (0.11, 0.93); Wald ${\chi }^2$(1): 4.3681; p value = 0.036]. For correct causes, significant predictors included gender, education level, and geographic location. Urban clinic attendees were significantly more accurate at identifying the correct cause of mental disorders than township attendees [OR = 0.42; 95% CI: (0.21, 0.83); Wald ${\chi }^2$(1): 6.1504; p value = 0.013]. Conclusions: Mental health literacy in Tshwane community healthcare clinics reflects deep-rooted sociodemographic and geographic inequalities. Strengthening township clinic capacity, integrating culturally relevant health education, and prioritizing gender-sensitive outreach are essential to improve the recognition and understanding of mental disorders in underserved communities. Full article

Emotional stress plays a critical role in mental health conditions such as anxiety, depression, and cognitive decline, yet its assessment remains challenging due to the subjective and episodic nature of conventional self-report methods. Multimodal physiological approaches, integrating signals such as electrocardiogram (ECG), electrodermal activity (EDA), and electromyography (EMG), offer a promising alternative by enabling objective, continuous, and complementary characterization of autonomic stress responses. Recent advances in machine learning and artificial intelligence (ML/AI) have become central to this paradigm, as they provide the capacity to model nonlinear dynamics, inter-modality dependencies, and individual variability that cannot be effectively captured by rule-based or single-modality methods. This paper reviews multimodal physiological stress recognition with an emphasis on ECG-centered systems and their integration with EDA and EMG. We summarize stress-related physiological mechanisms, catalog public and self-collected databases, and analyze their ecological validity, synchronization, and annotation practices. We then examine preprocessing pipelines, feature extraction methods, and multimodal fusion strategies across different stages of model design, highlighting how ML/AI techniques address modality heterogeneity and temporal misalignment. Comparative analysis shows that while deep learning models often improve within-dataset performance, their generalization across subjects and datasets remains limited. Finally, we discuss open challenges and future directions, including self-supervised learning, domain adaptation, and standardized evaluation protocols. This review provides practical insights for developing robust, generalizable, and scalable multimodal stress recognition systems for mental health monitoring. Full article

Background/Objectives: Reduced serotonin transporter (SERT) function is associated with increased vulnerability to emotional and metabolic dysregulation, particularly in elderly women. Most preclinical studies relied on young male rodents with complete Sert deficiency; the Western diet (WD) acerbates these abnormalities. However, complete Sert loss does not fully reflect the human condition of partial SERT dysfunction. Here, we examined the effects of WD in aged female Sert^+/− mice on metabolic, biochemical, molecular, and behavioral outcomes. Methods: Wild-type (WT) and Sert^+/− mice were fed WD or a control diet. Emotionality, cognition, glucose tolerance (GT), plasma ¹HNMR spectroscopy metabolome and biochemical parameters were studied. Gene expression analyses of nitric oxide (NO)-related markers were performed in the hypothalamus, dorsal raphe, and liver. Results: WD-exposed WT mice showed impaired GT and reduced plasma lactate and branched-chain amino acid levels; metabolome changes were more pronounced in mutants, while GT was unchanged. Naïve Sert^+/− mice exhibited lower lactate and alanine levels compared with WT controls. WD increased leptin and cholesterol levels in both genotypes, whereas triglyceride concentrations were reduced in Sert^+/− mice. Both WD and Sert deficiency increased Nos expression, while arginase expression was differentially regulated by genotype and diet. Malondialdehyde levels were elevated in the prefrontal cortex of Sert^+/− mice regardless diet. WD also impaired object recognition memory and induced anxiety- and depression-like behaviors, with more pronounced effects in Sert^+/− mice, except marble test behavior. Conclusions: Partial Sert deficiency aggravates some but not all WD-induced metabolic alterations, enhances oxidative stress, dysregulates arginine–NO signaling, and modifies behavior, highlighting the translational relevance of Sert^+/− mice for modeling SERT dysfunction. Full article

Introduction: Mental health literacy is an emerging topic that has implications for individuals’ health and well-being. Objective: To assess Mental Health Literacy (MHL) regarding depression among Portuguese public security police officers. Methods: Quantitative, descriptive, cross-sectional study using the QualisMental Questionnaire, which includes a vignette describing a case of depression, and the Personal Stigma Scale. Results: The sample comprises 253 professionals. Only 36.36% of respondents correctly identified the case as depression (95% CI: 30.40; 42.33). The distress was predominantly classified as “stress” (34.78%) or “anxiety” (32.81%), suggesting a defense mechanism that opts for socially less stigmatizing labels. Although the majority reject the belief that depression is a “personal weakness,” revealing low explicit stigma, the perceived usefulness of hierarchical figures in help-seeking is low (38.7% useful). High confidence is observed in informal networks (friends: 95.7% useful) and in mental health professionals, but there is marked distrust of psychopharmacology (antidepressants: 40.7% harmful). Conclusions: A paradox is observed between low personal stigma and low recognition of depression. MHL interventions should focus on neutralizing organizational stigma and increasing competencies for managing mental health crises arising from first aid, namely direct approaches to topics such as suicide. Full article