Search Results (179)

Mental stress is a psychological or emotional strain that typically occurs because of threatening, challenging, and overwhelming conditions and affects human behavior. Various factors, such as professional, environmental, and personal pressures, often trigger it. In recent years, various deep learning (DL)-based schemes using electroencephalograms (EEGs) have been proposed. However, the effectiveness of DL-based schemes is challenging because of the intricate DL structure, class imbalance problems, poor feature representation, low-frequency resolution problems, and complexity of multi-channel signal processing. This paper presents a novel hybrid DL framework, BDDNet, which combines a deep convolutional neural network (DCNN), bidirectional long short-term memory (BiLSTM), and deep belief network (DBN). BDDNet provides superior spectral–temporal feature depiction and better long-term dependency on the local and global features of EEGs. BDDNet accepts multiple EEG features (MEFs) that provide the spectral and time-domain features of EEGs. A novel improved crow search algorithm (ICSA) was presented for channel selection to minimize the computational complexity of multichannel stress detection. Further, the novel employee optimization algorithm (EOA) is utilized for the hyper-parameter optimization of hybrid BDDNet to enhance the training performance. The outcomes of the novel BDDNet were assessed using a public DEAP dataset. The BDDNet-ICSA offers improved recall of 97.6%, precision of 97.6%, F1-score of 97.6%, selectivity of 96.9%, negative predictive value NPV of 96.9%, and accuracy of 97.3% to traditional techniques. Full article

Background: More than merely determining our sleep pattern, our body’s internal clock also improves the quality of our sleep, alleviates the symptoms of depression, and maintains the balance of our gut flora. Methods: We carried out a 12-week randomized controlled trial with 99 adults from Kolkata, New Delhi, and Pune who reported sleep problems and symptoms of depression or anxiety. Participants received either a probiotic formulated to improve sleep quality and reduce depressive symptoms or a placebo. We tracked sleep using overnight studies and wearable devices, assessed depressive symptoms with standardized questionnaires, and analyzed stool samples to profile gut bacteria and their metabolites using gene sequencing and metabolomics. Advanced statistics and machine learning helped us pinpoint the key microbial and metabolic factors tied to sleep and mental health. Results: At the start, participants with disrupted sleep and depressive symptoms had fewer beneficial gut bacteria like Bifidobacterium and Lactobacillus, more inflammation-related microbes, and lower levels of helpful short-chain fatty acids. These imbalances were linked to poorer sleep efficiency, less REM sleep, and higher depression and anxiety scores. After 12 weeks, those taking the circadian-supporting probiotic saw a statistically significant increase in beneficial gut bacteria, improved sleep efficiency (+7.4%, p = 0.02), and greater reductions in depression and anxiety compared to the placebo. Increases in SCFA-producing bacteria most strongly predicted improvements. Conclusions: Our results show that taking a probiotic supplement can help bring your gut back into balance, support better sleep, and lift symptoms of depression and anxiety. This offers a hopeful and practical option for people looking for real relief from these deeply connected challenges. Full article

Due to the roles of oxidative stress, inflammation, and neurotransmitter imbalances in cognitive and mental dysfunction, we aimed to develop a functional drink with antioxidant and anti-inflammatory properties as well as the potential to support neurotransmitter balance for improved cognition and mental health. The Teng Mo, Fen Hong Mee, and Hong Chon Su guava varieties were screened for their polyphenol and flavonoid contents, antioxidant and anti-inflammatory effects, and suppressive effects on acetylcholinesterase (AChE), monoamine oxidase (MAO), GABA transaminase (GABA-T), and glutamate decarboxylase (GAD). Juice from the cultivar with the highest potential was selected and mixed with mint and honey syrups, pomelo-derived dietary fiber, ascorbic acid, agar, water, and fruit puree (pear/apple/orange) to create three guava jelly drink formulations. The formulation with pear puree showed the highest biological potential and was selected as the final product. It is rich in vitamin C, gallic acid, and dietary fiber, and provides approximately 37 Kcal/100 g. It also promotes the growth of lactic acid-producing bacteria in the culture. Thus, our drink shows the potential to reduce oxidative stress and inflammation, improve neurotransmitter regulation, and stimulate the gut–brain axis, thereby promoting cognition and mental wellness. However, clinical research is essential to confirm these potential benefits. Full article

Background: Occupational burnout and fatigue are critical issues affecting the health and performance of healthcare workers (HCWs) globally. These outcomes are often driven by complex and overlapping work-related stressors, which remain insufficiently understood in combination. Objective: To investigate the associations of multiple work-related stressors with occupational burnout and fatigue, and to identify distinct stress patterns and critical stressors among HCWs. Method: A cross-sectional survey was conducted using a self-administered electronic questionnaire among 2695 HCWs in Shanghai, China. Validated questionnaire scales were used to assess work-related stress (self-developed occupational stress scale for medical staff, CSSM), occupational burnout (Maslach Burnout Inventory–General Survey, MBI-GS), and fatigue (Fatigue Scale-14, FS-14). Latent profile analysis (LPA) was employed to identify distinct work-related stress patterns. Generalized linear models (GLMs) were used to explore the associations between individual stressors, stress patterns, and occupational burnout and fatigue. Additionally, weighted quantile sum (WQS) models were utilized to evaluate the combined effects of multiple stressors and identify the main contributors. Results: In this study, 77.0% and 71.2% of participants were classified as experiencing occupational burnout and fatigue, respectively. A strained doctor–patient relationship was the highest-rated work-related stressor. All work-related stressors, including career development, interpersonal relationships, work–life imbalance, physical environment, doctor–patient relationship, social environment, and workload, were significantly associated with burnout (β: 0.444~0.956, p < 0.001) and fatigue (β: 1.384~3.404, p < 0.001). WQS assigned higher weights to career development and workload for burnout, and to workload and work–life imbalance for fatigue. LPA identified two distinct occupational stress patterns. HCWs characterized by higher stress levels in physical environment, career development, workload, and interpersonal relationships exhibited significantly higher burnout scores (β = 0.325, 95% CI: 0.122, 0.528), particularly in the reduced personal accomplishment (PA) dimension (β = 1.003, 95% CI: 0.746, 1.259). Conclusions: This study highlighted the high prevalence of occupational burnout and fatigue among HCWs in Shanghai, China. Occupational stressors were associated with both burnout and fatigue, with higher workload, work–life imbalance, and poorer career development showing particularly significant contributions. These findings emphasized the urgent need for targeted interventions, including workload management, career development programs, and mental health support, to reduce occupational stress and mitigate its adverse effects on HCWs. Full article

Plateau environments present unique mental health challenges owing to stressors including hypoxia, low temperatures, and intense ultraviolet (UV) radiation. These factors induce structural and functional alterations in the gut microbiota, disrupting gut-brain axis homeostasis and contributing to the higher prevalence of depression in plateau regions relative to flatland areas. For example, studies report that 28.6% of Tibetan adults and 29.2% of children/adolescents on the Qinghai-Tibet Plateau experience depression, with increasing evidence linking this trend to alterations in the gut microbiota. Dysbiosis contributes to depression through three interconnected mechanisms: (1) Neurotransmitter imbalance: Reduced bacterial diversity impairs serotonin synthesis, disrupting emotional regulation. (2) Immune dysregulation: Compromised gut barrier function allows bacterial metabolites to trigger systemic inflammation via toll-like receptor signaling pathways. (3) Metabolic dysfunction: Decreased short-chain fatty acid levels weaken neuroprotection and exacerbate hypothalamic-pituitary-adrenal axis stress responses. Current interventions—including dietary fiber, probiotics, and fecal microbiota transplantation—aim to restore microbiota balance and increase short-chain fatty acids, alleviating depressive symptoms. However, key knowledge gaps remain in understanding the underlying mechanisms and generating population-specific data. In conclusion, existing evidence indicates an association between plateau environments, the gut microbiota, and depression, but causal relationships and underlying mechanisms require further empirical investigation. Integrating multiomics technologies to systematically explore interactions among high-altitude environments, the microbiota and the brain will facilitate the development of precision therapies such as personalized nutrition and tailored probiotics to protect mental health in high-altitude populations. Full article

Organisms respond to environmental stress primarily through the autonomic nervous system and hypothalamic–pituitary–adrenal (HPA) axis, regulating metabolism, psychological states, and immune function and modulating memory, reward processing, and immune responses. The HPA axis plays a central role in stress response, exhibiting distinct activation patterns under acute versus chronic social defeat stress. However, differences in physiological impacts and regulatory pathways between these stress conditions remain understudied. This study integrates RNA sequencing and behavioral analyses to reveal that acute social defeat stress triggers transient anxiety-like behaviors, accompanied by systemic inflammation and immediate-early gene (IEG) activation. In contrast, chronic social defeat stress induces long-term behavioral and physiological alterations, including neurotransmitter imbalance (e.g., reduced GABA and increased glutamate), sustained activation of maladaptive pathways (e.g., IL-17 signaling), and disrupted corticosterone synthesis. These findings highlight the dynamic regulatory role of the HPA axis under varying stress conditions, providing novel insights into mental health disorders such as anxiety and depression. The study identifies potential therapeutic targets to mitigate chronic social defeat stress effects and offers a theoretical foundation for personalized interventions. Full article

Health and well-being, both mental and physical, depend largely on adequate sleep. Many conditions arise from a disrupted sleep cycle, significantly deteriorating the quality of life of those affected. The analysis of the sleep cycle provide valuable information about sleep stages, which are employed in sleep medicine for the diagnosis of numerous diseases. The clinical standard for sleep data recording is polysomnography (PSG), which records electroencephalogram (EEG), electrooculogram (EOG), electromyogram (EMG), and other signals during sleep activity. Recently, machine learning approaches have exhibited high accuracy in applications such as the classification and prediction of biomedical signals. This study presents a hybrid neural network architecture composed of convolutional neural network (CNN) layers, bidirectional long short-term memory (BiLSTM) layers, and attention mechanism layers in order to process large volumes of EEG data in PSG files. The objective is to design a framework for automated feature extraction. To address class imbalance, an epoch-level random undersampling (E-LRUS) method is proposed, discarding full epochs from majority classes while preserving the temporal structure, unlike traditional methods that remove individual samples. This method has been tested on EEG recordings acquired from the public Sleep EDF Expanded database, achieving an overall accuracy rate of 78.67% along with an F1-score of 72.10%. The findings show that this method proves to be effective for sleep stage classification in patients. Full article

Male infertility is a prevalent condition affecting approximately 15% of couples worldwide. Recent evidence indicates that, beyond its immediate reproductive implications, male infertility may reflect broader health concerns. Large-scale cohort studies consistently show that men with poorer semen parameters have elevated all-cause mortality compared to fertile counterparts, with a dose-dependent pattern whereby more severe abnormalities correlate with a higher risk of early death. Proposed mechanisms linking infertility to reduced life expectancy encompass genetic, hormonal, and lifestyle factors. For instance, Klinefelter syndrome exemplifies a genetic cause of azoospermia that also predisposes to metabolic syndrome, diabetes, and certain malignancies. Low testosterone, a frequent finding in testicular dysfunction, is implicated in obesity, insulin resistance, and cardiovascular disease, all of which can shorten lifespan. Additionally, psychosocial stress and depression—commonly reported among infertile men—may contribute to health-compromising behaviors. Environmental exposures and socioeconomic factors further compound these risks. Collectively, these data underscore the importance of recognizing male infertility as an early indicator of potentially modifiable health vulnerabilities. A comprehensive evaluation of infertile men should therefore extend beyond fertility assessments to include screening for chronic diseases, hormonal imbalances, and mental health issues. Targeted surveillance for specific cancers (e.g., testicular and prostate) and early interventions—such as lifestyle modifications, appropriate hormonal therapies, and psychosocial support—can improve both reproductive outcomes and long-term well-being. Given these insights, male fertility assessment may serve as a valuable gateway to broader men’s healthcare, prompting proactive strategies that mitigate associated risks and potentially enhance longevity. Full article

Introduction: Previous neuroimaging studies have reported structural brain alterations and local network hyperexcitability in terms of increased slow-wave electroencephalography (EEG) activity in patients with borderline personality disorder (BPD). In particular, intermittent rhythmic delta and theta activity (IRDA/IRTA) has drawn attention in mental health contexts due to its links with metabolic imbalances, neuronal stress, and emotional dysregulation—processes that are highly pertinent to BPD. These functional disturbances may be reflected in corresponding structural brain changes. The current study investigated cortical thickness and subcortical volumes in BPD and examined their associations with IRDA/IRTA events per minute, symptom severity, and neuropsychological measures. Methods: Seventy female BPD patients and 36 age-matched female healthy controls (HC) were included (for clinical EEG comparisons even 72 patients were available). IRDA/IRTA rates were assessed using an automatic independent component analyses (ICA) approach. T1-weighted MRI data were obtained using a MAGNETOM Prisma 3T system and analyzed with FreeSurfer (version 7.2) for subcortical structures and CAT12 for cortical thickness and global volume measurements. Psychometric assessments included questionnaires such as Borderline Symptom List (BSL-23) and Inventory of Personality Organization (IPO). Neuropsychological performance was evaluated with the Test for Attentional Performance (TAP), Culture Fair Intelligence Test (CFT-20-R), and Verbal Learning and Memory Test (VLMT). Results: Between-group comparisons exhibited no significant increase in IRDA/IRTA rates or structural abnormalities between the BPD and HC group. However, within the BPD group, cortical thickness of the right isthmus of the cingulate gyrus negatively correlated with the IRDA/IRTA difference (after minus before hyperventilation, HV; p < 0.001). Furthermore, BPD symptom severity (BSL-23) and IPO scores positively correlated with the thickness of the right rostral anterior cingulate cortex (p < 0.001), and IPO scores were associated with the thickness of the right temporal pole (p < 0.001). Intrinsic alertness (TAP) significantly correlated with relative cerebellar volume (p = 0.01). Discussion: While no group-level structural abnormalities were observed, correlations between EEG slowing, BPD symptom severity, and alertness with cortical thickness and/or subcortical volumes suggest a potential role of the anterior cingulate cortex, temporal pole, and cerebellum in emotion regulation and cognitive functioning in BPD. Future research employing multimodal EEG-MRI approaches may provide deeper insights into the neural mechanisms underlying BPD and guide personalized therapeutic strategies. Full article

Objectives: This review explores the role of swept-source optical coherence tomography (OCT) in diagnosing and monitoring optic nerve neuropathy in Wernicke’s encephalopathy (WE) due to hyperemesis gravidarum, including a case of neuropathy from intractable vomiting in pregnancy. Methods: A literature search was conducted in the PubMed database to select high-quality reviews and original articles on the use of swept-source OCT for assessing optic nerve involvement in WE due to hyperemesis gravidarum. Results: WE is a potentially fatal neuropsychiatric syndrome caused by thiamine deficiency due to various causes, like alcoholism, malnutrition, and prolonged parenteral nutrition. This condition can cause neurological disorders such as imbalance, altered mental status, nystagmus, and ophthalmoplegia. Sometimes, there is also a deterioration of visual acuity with swelling of the optic disc. OCT is a non-invasive imaging tool that can detect optic nerve involvement in WE by assessing peripapillary retinal nerve fiber layer (pRNFL) thickness. In the acute phase, optic disc edema and increased pRNFL thickness may be observed, while chronic-phase changes include optic nerve atrophy and pRNFL thinning. WE may occur in the course of hyperemesis gravidarum in pregnant women. We present a case of a 23-year-old woman at 14 weeks of gestation with WE due to severe hyperemesis gravidarum, manifesting as visual impairment and neurological deficits. MRI confirmed the diagnosis, while OCT revealed transient pRNFL thickening followed by optic nerve atrophy. Conclusions: Early diagnosis and thiamine supplementation are crucial to preventing severe complications. OCT is a valuable tool for detecting and tracking optic nerve changes in WE. Full article

Background/Objectives: Dementia involves progressive cognitive and functional deterioration that leads to dependence and overload on family caregivers. This overload has a negative impact on the physical, mental, emotional, and occupational health of caregivers, leading to occupational imbalance and problems arising from an inadequate distribution of time devoted to caregiving. This project aims to evaluate the effects of the technology-based CAREFIT programme, structured around physical activity interventions, education, and psychoemotional and social support, on the health-related quality of life and emotional well-being of informal caregivers. Methods: The experimental group will develop the intervention programme, which will last 8 weeks and combine educational activities, physical activities, and psychoemotional and social support. Before beginning the intervention, the entire experimental group will receive a training session and educational materials on how to access and use the platform. The CAREFIT platform will consist of two educational sessions and two weekly physical sessions, combined with psychoemotional and social support activities that participants must complete. Initial, final, and follow-up evaluations will be conducted. The HRQoL and psychoemotional health (stress, anxiety, depression, and perceived social support and burden) of caregivers of people with dementia will be the main outcome measures. The effects of the intervention on the study variables will be assessed using a repeated-measures analysis of variance (ANOVA). Conclusions: The proposed protocol for the CAREFIT programme represents an innovative and multidisciplinary initiative that leverages a digital platform to promote the well-being of informal caregivers of people with dementia. This approach combines health literacy and strengthened psychoemotional and social support. Through this integration, the goal is to reduce the levels of burden, stress, anxiety, and depression among primary caregivers, while strengthening their self-care capabilities and social support networks. Full article

Thyroid disease is a health concern related to the thyroid gland, which is vital for controlling the metabolism of the human body. Predominantly affecting women in their fourth or fifth decades of life, thyroid disease can result in physical and mental issues. This research focuses on improving the diagnostic process by creating a classification model that utilises various machine learning models and a deeplearning model to categorise three types of thyroid disease conditions. This research developed an automated system capable of classifying three thyroid conditions using five machine learning models and a deep learning model. Resampling techniques, such as SMOTE oversampling and Random undersampling, are utilised to correct the issue of class imbalance in the dataset. Finally, a web-based application is developed utilising the most effective model, GBC, which facilitates easy classification of thyroid diseases. The experimental analysis showed that the Gradient Boosting Classifier (GBC), using oversampling techniques, achieved the highest level of performance in classifying thyroid diseases, obtaining an accuracy and F1-Score of 99.76%. This study demonstrated that TSH was the most indicative biomarker for thyroid disease classification. The experimental results proved that the Gradient Boosting Classifier (GBC) utilising the oversampling technique achieved a superior performance compared to other classifier models, with an accuracy and F1-Score of 99.76%. This research presented insights that can assist healthcare practitioners in promptly diagnosing thyroid diseases. Full article

Mental health is an important aspect of an individual’s overall well-being. Positive mental health is correlated with enhanced cognitive function, emotional regulation, and motivation, which, in turn, foster increased productivity and personal growth. Accurate and interpretable predictions of mental disorders are crucial for effective intervention. This study develops a hybrid deep learning model, integrating CNN and BiLSTM applied to EEG data, to address this need. To conduct a comprehensive analysis of mental disorders, we propose a two-tiered classification strategy. The first tier classifies the main disorder categories, while the second tier classifies the specific disorders within each main disorder category to provide detailed insights into classifying mental disorder. The methodology incorporates techniques to handle missing data (kNN imputation), class imbalance (SMOTE), and high dimensionality (PCA). To enhance clinical trust and understanding, the model’s predictions are explained using local interpretable model-agnostic explanations (LIME). Baseline methods and the proposed CNN–BiLSTM model were implemented and evaluated at both classification tiers using PSD and FC features. On unseen test data, our proposed model demonstrated a 3–9% improvement in prediction accuracy for main disorders and a 4–6% improvement for specific disorders, compared to existing methods. This approach offers the potential for more reliable and explainable diagnostic tools for mental disorder prediction. Full article

Depression, a highly prevalent mental disorder worldwide, arises from multifaceted interactions involving neurotransmitter imbalances, inflammatory responses, and gut–brain axis dysregulation. Emerging evidence highlights the pivotal role of bile acids (BAs) and their receptors, including farnesoid X receptor (FXR), Takeda G protein-coupled receptor 5 (TGR5), and liver X receptors (LXRs) in depression pathogenesis through modulation of neuroinflammation, gut microbiota homeostasis, and neural plasticity. Clinical investigations demonstrated altered BA profiles in depressed patients, characterized by decreased primary BAs (e.g., chenodeoxycholic acid (CDCA)) and elevated secondary BAs (e.g., lithocholic acid (LCA)), correlating with symptom severity. Preclinical studies revealed that BAs ameliorate depressive-like behaviors via dual mechanisms: direct CNS receptor activation and indirect gut–brain signaling, regulating neuroinflammation, oxidative stress, and BDNF/CREB pathways. However, clinical translation faces challenges including species-specific BA metabolism, receptor signaling complexity, and pharmacological barriers (e.g., limited blood–brain barrier permeability). While FXR/TGR5 agonists exhibit neuroprotective and anti-inflammatory potential, their adverse effects (pruritus, dyslipidemia) require thorough safety evaluation. Future research should integrate multiomics approaches and interdisciplinary strategies to develop personalized BA-targeted therapies, advancing novel treatment paradigms for depression. Full article

Burnout is a major challenge for the veterinary profession and is closely linked to negative effects on mental health, reduced job satisfaction and impaired professional sustainability. The aim of this study was to investigate the extent of burnout symptoms among Slovenian veterinarians and their association with factors such as work–life balance, ethical dilemmas and overall job satisfaction. A cross-sectional survey was conducted in 2024, to which all registered Slovenian veterinarians (N = 1250) were invited. The response rate was 38% (N = 473). Burnout was assessed using the Mayo Clinic Physicians Wellbeing Index, which captures both the traditional burnout dimensions and broader indicators of psychological distress such as anxiety and depression. Results showed that 45.5% of veterinarians reported low burnout, 26.4% reported moderate burnout, and 28.3% reported high burnout. A multivariate regression analysis revealed that work–life imbalance, ethical conflicts and long working hours were significant predictors of burnout symptoms, with younger veterinarians and women being disproportionately affected. The findings highlight the importance of addressing the systemic and individual factors that contribute to burnout in veterinary practice. Tailored interventions that focus on improving work–life balance, enhancing ethical decision-making and promoting mental health awareness are recommended to mitigate the risks of burnout. These findings contribute to the growing literature on veterinarian well-being and provide valuable insight into the development of targeted strategies to promote veterinarians’ mental health and career sustainability. Full article