Search Results (9,426)

Depression is a heterogeneous neuropsychiatric disorder with variable clinical presentation and response to treatment. This variability has motivated interest in neuroimaging biomarkers capable of disease characterization and therapeutic prediction. Positron emission tomography (PET) enables in vivo assessment of cerebral glucose utilization, neurochemical targets, inflammatory markers, and cerebral blood flow. This narrative review synthesizes PET studies conducted predominantly in adults with major depressive disorder diagnosed using DSM-based criteria, with bipolar disorder included only when imaging was performed during a depressive episode. Studies were identified through a structured, non-systematic literature search of major databases. Depression is consistently associated with regionally specific PET alterations within cortico-limbic and cortico-striatal circuits; studies most frequently report reduced glucose-derived PET measures in prefrontal and anterior cingulate regions at baseline, with treatment responders showing relative increases or redistribution of these measures following interventions. Neurochemical PET studies demonstrate altered receptor, transporter, or enzyme-related binding in serotonergic, dopaminergic, and noradrenergic systems, while neuroinflammatory and perfusion studies reveal regionally increased PET signals in subsets of patients. Overall, PET findings indicate convergent, region-specific and neurochemical alterations associated with depressive episodes and treatment response. Interpretation is constrained by methodological and clinical heterogeneity, underscoring the need for harmonized, longitudinal PET studies. Full article

This study presents the complete sequencing and comparative genomic analysis of Leimania (Viannia) naiffi and Leishmania (Viannia) shawi, species of epidemiological relevance in the Brazilian Amazon. Genome assemblies yielded sizes of 32.13 Mb and 32.51 Mb, with 8170 and 7767 annotated genes, respectively. Predicted gene functions were primarily related to catalytic, binding, and ATP-dependent activities. Pangenome analysis revealed a core genome of 6256 genes alongside notable species-specific differences, including 46 and 25 unique genes in L. naiffi and L. shawi. Functional screening identified pharmacologically promising proteins such as calpains, ABC transporters, and notably, GSK-3. Ploidy analysis indicated tetraploidy on chromosome 8 in L. naiffi and chromosome 2 in L. shawi. Genetic variability assessment detected 34,480 SNPs in L. naiffi and 26,562 in L. shawi, indicating greater genomic diversity in the former. Phylogenetic inference based on the polA1 gene confirmed the placement of both species within the Leishmania (Viannia) subgenus. These findings advance Leishmania genomics knowledge by highlighting unique genetic signatures, regions of high variability, and potential therapeutic targets. This work establishes a foundation for future research on evolution, pathogenicity, and drug development for leishmaniasis. Full article

Due to growing demands for quality, sustainability, and digitalization, data science and artificial intelligence are gaining importance across industries. The extensive product range in many sectors often poses considerable challenges. For example, machine learning (ML) models may struggle with limited data per production variant. The present paper proposes a methodology that integrates the fields of data science and physical simulations. The results from finite element method (FEM) simulations are utilized to transform the process data in such a manner that it can be compared across processes for different production variants and employed for machine learning (ML) methods and statistical analyses. The method is illustrated using an example of aluminum production. A key advantage of this approach is that it can effectively model even production variants with very low quantities. The following discussion will present how this method can be used to enhance production processes, specifically to identify parameters that directly influence product quality, which would not be evident using alternative approaches. Furthermore, the work explores the potential for precisely controlling these parameters using ML models and discusses some major challenges. Full article

Fetal hemoglobin (HbF) plays a central role in mitigating the pathophysiological effects of sickle cell disease (SCD). Understanding the genetic determinants influencing HbF expression is essential for identifying the factors contributing to its modulation. This review provides an updated synthesis of evidence on HbF modulation, focusing on β^s haplotypes and their molecular characterization through Sanger sequencing, polymorphisms consistently associated with HbF levels in genome-wide association studies (GWAS), and recent advances in gene editing targeting HbF expression. An integrative review (2016–2025) was conducted using PubMed/MEDLINE, Scopus, and Web of Science, encompassing original research, experimental studies, systematic reviews, and genomic analyses. Key regulatory loci such as BCL11A, HBS1L-MYB (HMIP), and the HBB cluster explain a significant proportion of HbF variability across populations. Furthermore, additional variants in KLF1, NFIX, BACH2, and ZBTB7A have emerged as potential modulators in specific cohorts. Regarding advances in γ-globin editing, “prime editing”, although still in the experimental phase, has recently emerged as an innovative approach capable of introducing multiple HPFH-like mutations within γ-globin promoters, expanding future therapeutic possibilities in SCD. This review also provides a comparative overview of prime editing and other gene-editing strategies for HbF modulation, such as CRISPR-Cas9 and Base editing. Collectively, this work outlines the current landscape of HbF modulation and provides an informative basis for future research aimed at advancing precision-oriented therapeutic strategies in sickle cell disease. Full article

Background/Objectives: Early turnover among new graduate nurses remains challenging in South Korea. This study examined how socialisation factors—based on Scott et al.’s transition model and Herzberg’s motivation-hygiene theory—are associated with early retention at the first hospital of employment among new graduate nurses. Methods: This retrospective cross-sectional study analysed secondary data from the Graduate Occupational Mobility Survey (GOMS), a nationally representative dataset of college and university graduates in Korea, collected using a stratified multi-stage sampling method. The study included 602 new graduate nurses from the 2017–2019 datasets who had worked as nurses at their first hospital of employment. Anticipatory socialisation factors included personal and educational characteristics. Organisational socialisation factors referred to workplace-related characteristics of the first hospital, including motivational factors and hygiene factors. The outcome variable was early retention. Multiple logistic regression analyses were performed to identify factors associated with early retention. Results: A total of 68.6% of nurses remained in their first hospital employment. Retention was more likely among nurses whose high school, nursing school, and first hospital were in the same region (p = 0.019), those employed in Seoul (p < 0.001), and those working in larger hospitals (p < 0.001). Retention was also associated with satisfaction with autonomy and authority (p = 0.013). Conversely, lower retention was observed among nurses who were dissatisfied with interpersonal relationships (p < 0.001) and those who reported satisfaction with growth opportunities (p < 0.001). Conclusions: Targeted strategies that support new graduate nurses during their transition are essential. Aligning education-to-employment regions and strengthening workplace conditions may enhance early retention. Full article

Background/Objectives: Speech difficulties are an early and disabling manifestation of Parkinson’s disease (PD), affecting communication and quality of life. This study aimed to examine demographic, clinical, dopaminergic imaging and cerebrospinal fluid (CSF) correlates of speech difficulties in early PD, comparing treatment-naïve and levodopa-treated patients. Methods: A cross-sectional analysis was conducted using data from the Parkinson’s Progression Markers Initiative (PPMI). The sample included 376 treatment-naïve and 133 levodopa-treated early PD participants. Speech difficulties were defined by Movement Disorder Society—Unified Parkinson’s Disease Rating Scale (MDS-UPDRS) Part III, with Item 3.1 ≥ 1. Group comparisons and binary logistic regression identified predictors among demographic, clinical, dopaminergic and CSF biomarker variables, including [¹²³I]FP-CIT specific binding ratios (SBRs). All analyses were cross-sectional, and findings reflect associative relationships rather than treatment effects or causal mechanisms. Results: Speech difficulties were present in 44% of treatment-naïve and 57% of levodopa-treated participants. In both cohorts, higher MDS-UPDRS Part III ON scores—reflecting greater motor severity—and lower mean putamen SBR values were significant independent predictors of speech impairment. Age was an additional predictor in the treatment-naïve group. No significant differences were found in CSF biomarkers (α-synuclein, amyloid-β, tau, phosphorylated tau). These findings indicate that striatal dopaminergic loss, particularly in the putamen, and motor dysfunction relate to early PD-related speech difficulties, whereas CSF neurodegeneration markers do not differentiate affected patients. Conclusions: Speech difficulties in early PD are primarily linked to dopaminergic and motor dysfunction rather than global neurodegenerative biomarker changes. Longitudinal and multimodal studies integrating acoustic, neuroimaging, and cognitive measures are warranted to elucidate the neural basis of speech decline and inform targeted interventions. Full article

The rapid proliferation of connected vehicles equipped with both Vehicle-to-Vehicle (V2V) sidelink and cellular interfaces creates new opportunities for real-time vehicular applications, yet achieving ultra-reliable communication without prohibitive cellular costs remains challenging. This paper addresses reliable inter-vehicle video streaming for safety-critical applications such as See-Through for Passing and Obstructed View Assist, which require stringent Service Level Objectives (SLOs) of 50 ms latency with 99% reliability. Through measurements in Seoul urban environments, we characterize the complementary nature of V2V and Vehicle-to-Network-to-Vehicle (V2N2V) paths: V2V provides ultra-low latency (mean 2.99 ms) but imperfect reliability (95.77%), while V2N2V achieves perfect reliability but exhibits high latency variability ($P_{99}$: 120.33 ms in centralized routing) that violates target SLOs. We propose a hybrid framework that exploits V2V as the primary path while selectively retransmitting only lost packets via V2N2V. The key innovation is a dual loss detection mechanism combining gap-based and timeout-based triggers leveraging Real-Time Protocol (RTP) headers for both immediate response and comprehensive coverage. Trace-driven simulation demonstrates that the proposed framework achieves a 99.96% packet reception rate and 99.71% frame playback ratio, approaching lossless transmission while maintaining cellular utilization at only 5.54%, which is merely 0.84 percentage points above the V2V loss rate. This represents a $7\times$ cost reduction versus PLR Switching (4.2 GB vs. 28 GB monthly) while reducing video stalls by $10\times$. These results demonstrate that packet-level selective redundancy enables cost-effective ultra-reliable V2X communication at scale. Full article

This paper presents a framework for inverse modeling of microwave filters based on a conditional diffusion model developed to address the intrinsic non-uniqueness of reconstructing coupling matrices from specified S-parameter responses. In the forward diffusion process, Gaussian noise is progressively added to the filter design variables, and a denoising network conditioned on the target electrical responses is trained to predict the injected noise at arbitrary diffusion steps. At inference, we initialize with Gaussian noise and execute the learned reverse denoising dynamics process; independent seeds yield diverse sets of physically feasible design-variable solutions that satisfy identical electrical-response constraints. Experiments on fourth- and sixth-order filters show that the proposed method outperforms multivalued neural networks (MVNNs) and conditional generative adversarial networks (CGANs) in prediction accuracy, solution diversity, and cumulative training cost, thereby providing a robust and efficient framework for inverse microwave-filter modeling. Full article

Spray-induced gene silencing (SIGS) represents a transformative paradigm in sustainable pest management, utilizing the exogenous application of double-stranded RNA (dsRNA) to achieve sequence-specific silencing of essential genes in arthropod pests. Unlike transgenic approaches, sprayable RNA interference (RNAi) biopesticides offer superior versatility across crop systems, flexible application timing, and a more favorable regulatory and public acceptance profile. The 2023 U.S. EPA registration of Ledprona, the first sprayable dsRNA biopesticide targeting Leptinotarsa decemlineata, marks a significant milestone toward the commercialization of non-transformative RNAi technologies. Despite the milestone, large-scale field deployment faces critical bottlenecks, primarily environmental instability, enzymatic degradation by nucleases, and variable cellular uptake across pest taxa. This review critically analyzes the mechanistic basis of spray-applied RNAi and synthesizes the recent technological breakthroughs designed to overcome physiological and environmental barriers. We highlight advanced delivery strategies, including nuclease inhibitor co-application, liposome encapsulation, and nanomaterial-based formulations that enhance persistence on plant foliage and uptake efficiency. Furthermore, we discuss how innovations in microbial fermentation have drastically reduced synthesis costs, rendering industrial-scale production economically viable. Finally, we outline the roadmap for broad adoption, addressing essential factors such as biosafety assessment, environmental fate, resistance management protocols, and the path toward cost-effective manufacturing. Full article

Background/Objectives: Multiple sclerosis (MS) is a chronic neurological disease characterized by demyelination, inflammation, and autoimmunity, leading to progressive physical and psychological impairments. Anxiety and depression are among the most prevalent neuropsychiatric comorbidities in MS, significantly impacting patients’ quality of life (QoL). This study aimed to assess the evolution of anxiety and depression in individuals with relapsing-remitting multiple sclerosis (RRMS) over a six-month follow-up period, identify associated factors, and explore potential predictors of these psychological conditions. Methods: A prospective observational study was conducted with 35 RRMS patients diagnosed at the Lucus Augusti University Hospital between January 2023 and March 2025. Psychological symptoms were assessed at baseline, after 3 months, and after 6 months using the Goldberg Anxiety and Depression Scale (GADS), the Beck Depression Inventory (BDI), and the Beck Anxiety Inventory (BAI). Data were analyzed using non-parametric tests to account for the small sample size and non-normal distribution of variables. Results: Anxiety and depression were prevalent and persistent in the study population, with no significant changes in mean scores over time (p > 0.05). However, specific symptoms, such as pessimism and loss of pleasure, showed worsening trends, while sadness and guilt remained stable. Sociodemographic factors, including lower income and employment status, were significantly associated with higher anxiety and depression scores (p < 0.05). Additionally, clinical factors such as autoimmune comorbidities and a history of mononucleosis were linked to higher depressive symptoms. Baseline anxiety and depression scores emerged as strong predictors of future levels (p < 0.01), emphasizing the importance of early assessments. Conclusions: Anxiety and depression are prevalent and persistent in RRMS patients, with specific symptoms fluctuating over time. Sociodemographic and clinical factors play a significant role in psychological outcomes, highlighting the need for integrated care models that address both physical and psychosocial aspects of MS. Early psychological assessments and targeted interventions are critical for improving QoL and mitigating the long-term burden of mental health challenges in RRMS. Full article

Ocular melanomas, comprising uveal melanoma (UM) and conjunctival melanoma (CoM), represent the most common primary intraocular and ocular surface malignancies in adults. Although rare compared with cutaneous melanoma, they exhibit unique molecular landscapes that provide critical opportunities for biomarker-driven precision medicine. In UM, recurrent mutations in GNAQ and GNA11, together with alterations in BAP1, SF3B1, and EIF1AX, have emerged as key prognostic biomarkers that stratify metastatic risk and guide surveillance strategies. Conversely, in CoM, the mutational spectrum overlaps with cutaneous melanoma, with frequent alterations in BRAF, NRAS, NF1, and KIT, offering actionable targets for personalised treatment. Beyond genomics, epigenetic signatures, microRNAs, and protein-based markers provide further insights into tumour progression, microenvironmental remodelling, and immune evasion. In parallel, liquid biopsy has emerged as a minimally invasive approach for real-time disease monitoring. Analyses of circulating tumour DNA (ctDNA), circulating tumour cells (CTCs), and exosome-derived microRNAs demonstrate increasing potential for early detection of minimal residual disease, prognostic assessment, and evaluation of treatment response. However, the clinical integration of these biomarkers remains limited by tumour heterogeneity, technical variability, and the lack of unified translational frameworks. This review synthesises current knowledge of molecular and liquid biopsy biomarkers in ocular melanoma, highlighting their relevance for diagnosis, prognosis, and treatment personalisation. The integration of established tissue-based molecular markers with novel liquid biopsy technologies will enable a unique framework for biomarker-guided precision oncology and risk-adapted surveillance in uveal and conjunctival melanoma, offering insight into strategies for early detection, therapeutic monitoring, and personalised clinical management. Full article

Background/Objectives: Research across multiple disciplines has explored how nutrition is shaped by social isolation and feelings of loneliness, especially in the elderly population. Evidence from neuroscience highlights that loneliness may alter eating patterns, encouraging emotional eating or other compensatory food behaviors. Conversely, isolation from social contexts is often linked to a reduced variety of nutrient intake. This study set out to examine how psychosocial aspects, particularly social connectedness and feeling alone, relate to adherence to the Mediterranean diet among older adults residing in Sicily, southern Italy. Methods: Dietary habits of 883 adults were collected through food frequency questionnaires and assessed for adherence to the Mediterranean diet. Loneliness was measured through a targeted question from a standardized tool designed to capture depressive symptoms. Direct questions asked whether participants were engaged in social networks, such as family, friends and neighborhoods, or religious communities, in order to assess objective aloneness. Logistic regression analyses were performed to assess associations between variables of interest. Results: After accounting for potential confounders, both loneliness and aloneness showed an association with stronger adherence to the Mediterranean diet. Specifically, individuals experiencing loneliness and aloneness were less likely to have high adherence to the Mediterranean diet (OR = 0.28, 95% CI: 0.15, 0.51, and OR = 0.26, 95% CI: 0.12, 0.54, respectively). Conclusions: These findings underscore the importance of fostering social engagement among older populations, who may particularly benefit from maintaining active social ties to support healthier eating behaviors. Full article

Computer-based engineering design tools can quicken the cadence for machine design, which enables companies to compete better in the global marketplace. The application of nonlinear optimization and tradespace analysis methods allows the exploration of design variables within dynamic mechanisms. In this paper, the design of a classical machine, the Seth Thomas pendulum street clock, which offered precision timekeeping and time display at the turn of the 20th century, will be investigated from a modern perspective. A mathematical model serves as the basis for the genetic algorithm optimization method to assess the system design in terms of accuracy, mass, quality factor, and bending stress. To validate the model, experimental data was collected on a 1906 Seth Thomas Model 04 movement. The engineering study findings indicate that the target accuracy, quality factor, and bending stress can be achieved with pendulum mass and gear thickness reductions of 1.4% and 50.3%, respectively. The tradespace exploration offers a visualization of the machine’s performance per design variable adjustments for greater insight into the original solution and subsequent recommended changes. Overall, this mechanical machine review enables an assessment of original design choices made over a century ago and provides an awareness of engineering’s progress during this period. Full article

Epilepsy remains an active and important area of research due to its complex etiology, significant global burden, and variable response to treatment. Current knowledge has provided valuable insights into the underlying molecular mechanisms of the disease and continues to guide the development of novel therapeutic strategies. This review presents a comprehensive overview of the etiologies of epilepsy, as well as traditional and modern medical and surgical treatment approaches, while highlighting future research directions. Peer-reviewed articles retrieved from PubMed and Google Scholar were analyzed and synthesized to produce this review. The etiological complexity of epilepsy arises from genetic, metabolic, structural, and inflammatory mechanisms, which often coexist rather than act independently. A wide range of anti-seizure drugs (ASDs) is currently available, with many new agents targeting novel mechanisms under development. Surgical approaches, including resection, disconnection, corpus callosotomy, and neuromodulation, are widely used for patients with drug-resistant epilepsy and result in variable seizure outcomes. In addition, minimally invasive techniques such as laser interstitial thermal therapy (LITT), stereoelectroencephalography-guided radiofrequency thermocoagulation, gamma knife radiosurgery, and high-intensity focused ultrasound have gained clinical relevance and continue to be explored. Emerging technologies, including artificial intelligence, machine learning, and precision medicine, offer promising directions for future research. Although several potential biomarkers have been identified, none are yet established for routine clinical use. Continued investigation is essential to improve understanding of epileptogenesis and to develop safer, more effective therapies. Full article

Unsafe behaviors remain a major contributor to workplace accidents within broader safety-management systems. Acknowledging the essential influence of organizational and leadership factors, this study focuses on systematically identifying and prioritizing individual-level determinants of unsafe behavior through an integrated qualitative–quantitative methodology to clarify their specific role within the wider safety framework. Grounded Theory analysis of semi-structured interviews with 40 industry professionals yielded a conceptual model encompassing demographic characteristics, general health, individual competencies, personality traits, and psychological factors. Subsequently, the Fuzzy Delphi Method, applied with 20 domain experts, validated and ranked these determinants. The analysis highlighted risk perception as the most influential factor, followed by work experience, skill level, knowledge, and risk-taking propensity, whereas variables such as family welfare, substance use, and self-display exhibited relatively minor effects. These findings reveal the multidimensional nature of unsafe behavior and underscore the importance of focusing on high-impact personal attributes to enhance workplace safety. By recognizing that many individual factors are shaped by organizational and psychosocial conditions, the study provides evidence-based insights for developing integrated safety management and targeted intervention strategies in industrial settings. Full article