Search Results (1,093)

Depression remains a leading cause of global disability, yet scalable and objective diagnostic tools are still lacking. Speech has emerged as a promising non-invasive modality for automated depression detection, due to its strong correlation with emotional state and ease of acquisition. While convolutional neural networks (CNNs) have achieved state-of-the-art performance in this domain, their high computational demands limit deployment in low-resource or real-time settings. Spiking neural networks (SNNs), by contrast, offer energy-efficient, event-driven computation inspired by biological neurons, but they are difficult to train directly and often exhibit degraded performance on complex tasks. This study investigates whether CNNs trained on audio data from the clinically annotated DAIC-WOZ dataset can be effectively converted into SNNs while preserving diagnostic accuracy. We evaluate multiple conversion thresholds using the SpikingJelly framework and find that the 99.9% mode yields an SNN that matches the original CNN in both accuracy (82.5%) and macro F1 score (0.8254). Lower threshold settings offer increased sensitivity to depressive speech at the cost of overall accuracy, while naïve conversion strategies result in significant performance loss. These findings support the feasibility of CNN-to-SNN conversion for real-world mental health applications and underscore the importance of precise calibration in achieving clinically meaningful results. Full article

The Sultanate of Oman boasts remarkable biodiversity, exemplified by such species as the Arabian leopard (Panthera pardus nimr) and the Arabian oryx (Oryx leucoryx), national symbols that highlight the extensive conservation efforts required to protect the country’s natural heritage. During decades, Omani authorities have taken significant measures to safeguard wildlife and preserve the natural environment. A sanctuary dedicated to the reintroduction of the Arabian Oryx, after extinction in nature in 1972, was established in 1980 in the Al Wusta Governorate under the patronage of the Royal Diwan and currently administrated by the recently established Environment Authority. During the almost 40 years since the reintroduction and the creation of the sanctuary, the oryx population has grown slowly but constantly. In 2021, the sanctuary hosted 738 oryx, allowing the start of the reintroduction of the species into the natural environment. Small groups of animals were released into the wild in selected areas. No animal health adverse events were recorded, and mortality was generally due to injuries received as a consequence of fighting, in particular during mating season. Standard veterinary care, including control of internal and external parasites, was regularly provided. In some occasions, immunization against certain diseases, such as clostridial infections, pasteurellosis, or mycoplasmosis, was also applied. In 2023, an FMD outbreak in cattle reported in Dhofar, about 500 km from the Al Wusta sanctuary, motivated specific prophylactic actions to prevent the risk of diffusion to oryx. From December 2023 to January 2024, an immunization program was undertaken using an FMD vaccine against serotypes A, O, and SAT 1, mostly in male oryx, while pregnant oryx were avoided for abortion risk due to handling. The following year, in January 2025, a severe outbreak occurred in oryx herds held in the sanctuary. The rapid onset and the spread of clinical symptoms among animals (100% morbidity in the second day after the first appearance of signs in some individuals) were suggestive of a highly contagious disease. The animals suffered from severe depression and inappetence, rapidly followed by abundant salivation, erosions of the oral mucosa and tongue, and diarrhea, with a short course characterized by prostration and death of the animal in the most severe cases. Therapeutical attempts (administration of antibiotics and rehydration) were mostly ineffective. Laboratory investigations (ELISA and PCR) ruled out contagious bovine pleuropneumonia (CBPP), Johne’s disease and Peste des petits ruminants (PPR). Both serology and antigen detection showed positiveness to foot-and-mouth disease (FMD). Out of a total population of 669 present in the sanctuary at the beginning of the outbreak, 226 (33.78%) oryx died. Despite the vaccinal status, the 38.49% of dead animals resulted being vaccinated against FMD. Taking into account the incalculable value of the species, the outbreak represented a very dangerous event that risked wiping out the decades of conservation efforts. Therefore, all the available means, such as accrued biosecurity and adequate prophylaxis, should be implemented to prevent the recurrence of such health risks. The delicate equilibrium of wild fauna in Oman requires study and support for an effective protection, in line with the national plan “Vision 2040”, targeting the inclusion of the Sultanate within the 20 best virtuous countries for wildlife protection. Full article

Background/Objectives: Patients with heart failure (HF) experience increased morbidity, limited daily activities, and diminished quality of life (QoL), thus relying on a family member, widely known as informal caregiver, for support. The objective of this study was to explore (a) QoL, anxiety, and depression; (b) factors associated with QoL; and (c) the impact of associated factors on QoL among HF caregivers. Materials and methods: Data collection was performed using the 36-Item Short Form Survey (SF-36), the Hospital Anxiety and Depression Scale (HADs), and the European Heart Failure Self-care Behavior Scale (EHFScBS). Also recorded were characteristics of caregivers and patients. Results: In the present study, 110 HF caregivers and the family members they provided care to were enrolled. The majority of caregivers were patients’ spouses (60%) and were female (71.8%). Within a QoL score range of 0–100, caregivers showed moderate to high levels in role-physical, role-emotional, emotional well-being, and pain (median: 75, 66.7, 64, and 67.5, respectively); moderate QoL levels in energy/fatigue, social functioning, and general health (median: 55, 56.3, and 62, respectively); and poor QoL levels in physical functioning (median: 18). Moreover, 64.5% of caregivers had anxiety and 41.8% had depression. Caregivers with HADs scores that indicate anxiety and depression had worse QoL (p = 0.001). No association was detected between caregivers’ QoL and patients’ HADs and self-care. Conclusions: QoL and anxiety/depression merit further research by clinicians, health systems, and policymakers so that evidence-based policies and interventional programs tailored to their needs can be implemented. Full article

Background and Objectives: Gestational diabetes mellitus (GDM), which is becoming increasingly common in contemporary society, is recognized for its considerable psychosocial impact on pregnant women throughout the perinatal phase. The purpose of this research was to explore the possible links between mental health status and dietary habits among pregnant women diagnosed with GDM, alongside examining how these factors correlate with clinical indicators like HbA1c measurements and the necessity for insulin therapy. Materials and Methods: The study included 82 pregnant participants, 37 with gestational diabetes mellitus and 45 without. Blood samples were collected from all participants for biochemical analysis, including fasting blood glucose, postprandial blood glucose, and HbA1c levels, which can be clinical indicators for the presence of gestational diabetes mellitus, and the need for insulin treatment was recorded. Then, participants completed a questionnaire collecting sociodemographic and clinical data as well as the Beck Anxiety Inventory (BAI), Beck Depression Inventory (BDI), Salzburg Emotional Eating Scale (SEES), and REZZY Eating Disorders Scale (REZZY). Data were statistically analyzed. Results: A previous diagnosis of gestational diabetes was more frequent in the case group (18.9%) than in the control group (2.2%) (p = 0.020). OGTT positivity was detected in 56.8% of the case group, whereas all control participants had negative results (p < 0.001). There were no statistically significant differences between the two groups in psychological symptom scores or eating behavior assessments (p > 0.05). Conclusions: Pregnant women with gestational diabetes mellitus were observed to score higher on measures of anxiety, depression, and emotional eating, particularly in response to negative emotions. These findings may indicate a potential association between gestational diabetes and psychological or behavioral factors related to metabolic regulation during pregnancy. Full article

Background: Cerebral palsy (CP) is a lifelong neurodevelopmental disorder characterised by motor impairment and commonly associated with comorbidities such as cognitive, communicative, and behavioural difficulties. While the physical and functional aspects of CP have been extensively studied, the mental health needs of this population remain largely underexplored, particularly concerning suicidal ideation and self-injurious behaviours. The purpose of this review is to synthesise the existing literature on suicidality in individuals with CP, explore theoretical and clinical risk factors, and identify key gaps in the current evidence base. Methods: A narrative literature review was conducted focusing on studies addressing suicidal ideation, self-harm, or related psychiatric outcomes in individuals with CP. Additional literature on risks and protective factors was included to support theoretical inferences and clinical interpretations. Results: Only a limited number of studies addressed suicidality directly in CP populations. However, several reports document elevated rates of depression, anxiety, and emotional distress, particularly among adults and individuals with higher levels of functioning. Communication barriers, chronic pain, social exclusion, and lack of accessible mental health services emerged as critical risk factors. Protective elements included strong family support, inclusive environments, and access to augmentative communication. Conclusions: Suicidality in individuals with CP is a neglected yet potentially serious concern. Evidence suggests underdiagnosis due to factors such as communication barriers and diagnostic overshadowing. Future research should prioritise disability-informed methodologies and validated tools for suicidal ideation, while clinicians should incorporate routine, adapted mental health screening in CP care to ensure early detection and person-centred management. Full article

Sepsis-induced myocardial injury is age-related and leads to increased mortality. Considering the importance of mitochondrial dysfunction in cardiac impairment, we aimed to investigate whether aging exacerbates the cardiac mitochondrial metabolic response to inflammation, thus leading to increased cardiac dysfunction in the elderly. Cecal ligation and puncture (CLP) was conducted in young adult (12–18 weeks) and aged (19–21 months) male C57BL/6 mice. Cardiac function was detected 20 h post-CLP. Additionally, cardiomyocytes isolated from young adult and aged male mice were used for assessments of mitochondrial respiratory function +/– TNFα or LPS. Protein levels of oxidative phosphorylation (OXPHOS), NADPH oxidase (NOX)2, NOX4, phosphor-STAT3 and STAT3 were determined in mouse hearts 24 h post-CLP and in cardiomyocytes following inflammatory stimuli. CLP significantly reduced cardiac contractility in both young and aged mice, with a higher incidence and greater severity of cardiac functional depression in the older group. Mitochondrial respiratory capacity was decreased in cardiomyocytes derived from aged mice, with increased susceptible to inflammatory toxic effects compared to those from young adult mice. The age-dependent changes were observed in myocardial OXPHOS complexes and NOX4. Importantly, CLP led to a significant increase in OXPHOS protein levels in the hearts of older mice, suggesting a possible compensatory response to decreased mitochondrial metabolic function and a greater potential for reactive oxygen species (ROS) generation. Our findings highlight that the response of aging-impaired mitochondria to inflammation may underlie the worsened cardiac functional depression in the aged group during sepsis. Full article

Depression represents one of the most prevalent mental health disorders globally, significantly impacting quality of life and posing substantial healthcare challenges. Traditional diagnostic methods rely on subjective assessments and clinical interviews, often leading to misdiagnosis, delayed treatment, and suboptimal outcomes. Recent advances in biosensing technologies offer promising avenues for objective depression assessment through detection of relevant biomarkers and physiological parameters. This review examines multi-modal biosensing approaches for depression by analyzing electrochemical biosensors for neurotransmitter monitoring alongside wearable sensors tracking autonomic, neural, and behavioral parameters. We explore sensor fusion methodologies, temporal dynamics analysis, and context-aware frameworks that enhance monitoring accuracy through complementary data streams. The review discusses clinical validation across diagnostic, screening, and treatment applications, identifying performance metrics, implementation challenges, and ethical considerations. We outline technical barriers, user acceptance factors, and data privacy concerns while presenting a development roadmap for personalized, continuous monitoring solutions. This integrative approach holds significant potential to revolutionize depression care by enabling earlier detection, precise diagnosis, tailored treatment, and sensitive monitoring guided by objective biosignatures. Successful implementation requires interdisciplinary collaboration among engineers, clinicians, data scientists, and end-users to balance technical sophistication with practical usability across diverse healthcare contexts. Full article

Background: About half of all Major Depressive Disorder (MDD) patients have anxiety disorder. There is a neurologic basis for the comorbidity of balance (vestibular) disorders and anxiety. To detect comorbid anxiety disorder in MDD patients and, importantly, to investigate its relationship with depressive severity, we use Electrovestibulography (EVestG), which is predominantly a measure of vestibular response. Methods: In a population of 42 (26 with anxiety disorder) MDD patients, EVestG signals were measured. Fourteen (eight with anxiety disorder) were not on any anti-depressants, anti-psychotics or mood stabilizers. Using standard questionnaires, participants were depression-wise labelled as reduced symptomatic (MADRS ≤ 19, R) or symptomatic (MADRS > 19, S) as well as with or without anxiety disorder. Analyses were conducted on the whole data set, matched (age/gender/MADRS) subsets and compared with medication free subsets. Low-frequency EVestG firing pattern modulation was measured. Results: The main differences between MDD populations with and without anxiety disorder populations, regardless of being medicated or not, were (1) the presence of an increased 10.8 Hz component in the dynamic movement phase recordings, (2) the presence of asymmetric right versus left 7.6–8.9 Hz and 12.1–13.8 Hz frequency bands in the no motion (static) phase recordings, and (3) these differences were dependent on depressive severity. Conclusions: The EVestG measures are capable of quantifying anxiety in MDD patients. These measures are functions of depressive severity and are hypothesized to be linked to Hippocampal Theta (~4–12 Hz). Full article

Social media platforms have emerged as valuable sources for mental health research, enabling the detection of conditions such as depression through analyses of user-generated posts. This manuscript offers practical, step-by-step guidance for applying machine learning and deep learning methods to mental health detection on social media. Key topics include strategies for handling heterogeneous and imbalanced datasets, advanced text preprocessing, robust model evaluation, and the use of appropriate metrics beyond accuracy. Real-world examples illustrate each stage of the process, and an emphasis is placed on transparency, reproducibility, and ethical best practices. While the present work focuses on text-based analysis, we discuss the limitations of this approach—including label inconsistency and a lack of clinical validation—and highlight the need for future research to integrate multimodal signals and gold-standard psychometric assessments. By sharing these frameworks and lessons, this manuscript aims to support the development of more reliable, generalizable, and ethically responsible models for mental health detection and early intervention. Full article

Psychological distress is understood to be maintained by attention. We performed two experiments examining the impact of attention training (AT) on psychological distress symptoms. Experiment one (N = 336) investigated what effects might be detected in a simple experimental design with longitudinal measurements, while experiment two (N = 214) examined whether using a different emotional stimulus could induce an immediate anxiolytic effect in response to AT. Attentional biases were operationalized as the target search latency correlated with mood and psychological distress scores. While limited evidence of attentional biases was found in participants with higher mood distress, correlations emerged in the experimental conditions at day thirty, indicating a relationship between task latency, stress, and changes in depression (experimental one). We found no immediate between–within-group differences in outcome when including different emotional stimuli (experiment two). Despite attentional biases being less apparent in community samples, attentional training for bias modification was effective in eliciting positive biases, leading to improved mood. Notably, participants in the control condition reported the greatest mood and psychological distress improvements, whereas changes in the experimental condition primarily pertained to attentional biases. Taken together, these findings suggest that AT tasks can improve distress, but not through changes in attentional biases. Full article

Preserving historical porous materials requires careful monitoring of surface humidity to mitigate deterioration processes like salt crystallization, mold growth, and material decay. While microclimate monitoring is a recognized preventive conservation tool, its role in detecting surface-specific moisture risks remains underexplored. This study evaluates the relationship between indoor microclimate fluctuations and surface moisture dynamics across 13 historical sites in Northern Italy (Lake Como, Valtellina, Valposchiavo), encompassing diverse masonry typologies and environmental conditions. High-resolution sensors recorded temperature and relative humidity for a minimum of 13 months, and eight indicators—including dew point depression, critical temperature–humidity zones, and damp effect indices—were analyzed to assess the moisture risks. The results demonstrate that multivariate microclimate data could effectively predict humidity accumulation. The key findings reveal the impact of seasonal ventilation, thermal inertia, and localized air stagnation on moisture distribution, with unheated alpine sites showing the highest condensation risk. The study highlights the need for integrated monitoring approaches, combining dew point analysis, mixing ratio stability, and buffering performance, to enable early risk detection and targeted conservation strategies. These insights bridge the gap between environmental monitoring and surface moisture diagnostics in porous heritage materials. Full article

Background: Artificial intelligence (AI) and machine learning (ML) have been reshaping maternal, fetal, neonatal, and reproductive healthcare by enhancing risk prediction, diagnostic accuracy, and operational efficiency across the perinatal continuum. However, no comprehensive synthesis has yet been published. Objective: To conduct a scoping review of reviews of AI/ML applications spanning reproductive, prenatal, postpartum, neonatal, and early child-development care. Methods: We searched PubMed, Embase, the Cochrane Library, Web of Science, and Scopus through April 2025. Two reviewers independently screened records, extracted data, and assessed methodological quality using AMSTAR 2 for systematic reviews, ROBIS for bias assessment, SANRA for narrative reviews, and JBI guidance for scoping reviews. Results: Thirty-nine reviews met our inclusion criteria. In preconception and fertility treatment, convolutional neural network-based platforms can identify viable embryos and key sperm parameters with over 90 percent accuracy, and machine-learning models can personalize follicle-stimulating hormone regimens to boost mature oocyte yield while reducing overall medication use. Digital sexual-health chatbots have enhanced patient education, pre-exposure prophylaxis adherence, and safer sexual behaviors, although data-privacy safeguards and bias mitigation remain priorities. During pregnancy, advanced deep-learning models can segment fetal anatomy on ultrasound images with more than 90 percent overlap compared to expert annotations and can detect anomalies with sensitivity exceeding 93 percent. Predictive biometric tools can estimate gestational age within one week with accuracy and fetal weight within approximately 190 g. In the postpartum period, AI-driven decision-support systems and conversational agents can facilitate early screening for depression and can guide follow-up care. Wearable sensors enable remote monitoring of maternal blood pressure and heart rate to support timely clinical intervention. Within neonatal care, the Heart Rate Observation (HeRO) system has reduced mortality among very low-birth-weight infants by roughly 20 percent, and additional AI models can predict neonatal sepsis, retinopathy of prematurity, and necrotizing enterocolitis with area-under-the-curve values above 0.80. From an operational standpoint, automated ultrasound workflows deliver biometric measurements at about 14 milliseconds per frame, and dynamic scheduling in IVF laboratories lowers staff workload and per-cycle costs. Home-monitoring platforms for pregnant women are associated with 7–11 percent reductions in maternal mortality and preeclampsia incidence. Despite these advances, most evidence derives from retrospective, single-center studies with limited external validation. Low-resource settings, especially in Sub-Saharan Africa, remain under-represented, and few AI solutions are fully embedded in electronic health records. Conclusions: AI holds transformative promise for perinatal care but will require prospective multicenter validation, equity-centered design, robust governance, transparent fairness audits, and seamless electronic health record integration to translate these innovations into routine practice and improve maternal and neonatal outcomes. Full article

This paper explores emotional analysis in Hebrew texts, focusing on improving machine learning techniques for depression detection by integrating psychological feature lexicons. Hebrew’s complex morphology makes emotional analysis challenging, and this study seeks to address that by combining traditional machine learning methods with sentiment lexicons. The dataset consists of over 350,000 posts from 25,000 users on the health-focused social network “Camoni” from 2010 to 2021. Various machine learning models—SVM, Random Forest, Logistic Regression, and Multi-Layer Perceptron—were used, alongside ensemble techniques like Bagging, Boosting, and Stacking. TF-IDF was applied for feature selection, with word and character n-grams, and pre-processing steps like punctuation removal, stop word elimination, and lemmatization were performed to handle Hebrew’s linguistic complexity. The models were enriched with sentiment lexicons curated by professional psychologists. The study demonstrates that integrating sentiment lexicons significantly improves classification accuracy. Specific lexicons—such as those for negative and positive emojis, hostile words, anxiety words, and no-trust words—were particularly effective in enhancing model performance. Our best model classified depression with an accuracy of 84.1%. These findings offer insights into depression detection, suggesting that practitioners in mental health and social work can improve their machine learning models for detecting depression in online discourse by incorporating emotion-based lexicons. The societal impact of this work lies in its potential to improve the detection of depression in online Hebrew discourse, offering more accurate and efficient methods for mental health interventions in online communities. Full article

Background: Emerging evidence indicates that some individuals recovering from COVID-19 develop persistent symptoms, including fatigue, pain, cognitive difficulties, and psychological distress, commonly known as Long COVID. These symptoms often overlap with those seen in Chronic Fatigue Syndrome/Myalgic Encephalomyelitis (CFS/ME), underscoring the need for integrative, non-pharmacological interventions. This Phase II controlled trial aimed to evaluate the feasibility and preliminary efficacy of Heart Rate Variability Biofeedback (HRV-BF) in individuals with Long COVID who meet the diagnostic criteria for CFS/ME. Specific objectives included assessing feasibility indicators (drop-out rates, side effects, participant satisfaction) and changes in fatigue, depression, anxiety, pain, and health-related quality of life. Methods: Participants were assigned alternately and consecutively to the HRV-BF intervention or Treatment-as-usual (TAU), in a predefined 1:1 sequence (quasirandom assignment). The intervention consisted of 10 HRV-BF sessions, held twice weekly over 5 weeks, with each session including a 10 min respiratory preparation and 40 min of active training. Results: The overall drop-out rate was low (5.56%), and participants reported a generally high level of satisfaction. Regarding side effects, the mean total Simulator Sickness Questionnaire score was 24.31 (SD = 35.42), decreasing to 12.82 (SD = 15.24) after excluding an outlier. A significantly greater improvement in severe fatigue was observed in the experimental group (H = 4.083, p = 0.043). When considering all outcomes collectively, a tendency toward improvement was detected in the experimental group (binomial test, p < 0.0001). Conclusions: HRV-BF appears feasible and well tolerated. Findings support the need for Phase III trials to confirm its potential in mitigating fatigue in Long COVID. Full article

Background/Objectives: Improvements in depression and anxiety, associated with moderate increases in physical activity, might induce reductions in emotional eating, especially in women with obesity, where emotion-driven eating is highly problematic. This pilot, field-based study sought to assess whether physical activity increase, itself, primarily predicts improved mood (biochemical theories) or if psychosocial factors associated with cognitive–behavioral treatment are principal correlates (behavioral theories). An aim was to inform improved treatment processes. Methods: Women with obesity participated in 6-month community-based behavioral obesity treatments emphasizing either: (a) standard education in weight-reduction methods (n = 28), (b) eating-related self-regulation methods (n = 24), or (c) self-regulation + relaxation training (n = 24). They completed a series of behavioral and psychological self-reports at baseline and Months 3 and 6. Results: Findings confirmed no significant difference in 3-month increases in physical activity, by group. There were significantly greater overall improvements in depression, emotional eating, self-regulation, and self-efficacy across the two self-regulation-focused groups (ps < 0.02), with anxiety improvement not reaching significance (p = 0.055). Separate significant paths from 3-month changes in depression and anxiety → self-efficacy change → emotional eating change were found. The same significant path was detected emanating from 6-month anxiety change; however, the hypothesized path of 6-month changes in depression → self-regulation → self-efficacy → emotional eating was, rather, significant. Weight reduction was considerably greater in the two self-regulation-based groups (~6% reduction), with simultaneously entered changes in self-regulation and self-efficacy significant predictors of those weight changes. Conclusions: Findings suggested viability in behavioral theory-driven explanations of the physical activity-mood improvement relationship. Future treatment foci on self-regulatory skills development leading to improvements in eating-related self-efficacy, emotional eating, and weight were suggested to extend the findings of this pilot study. Full article