Search Results (182)

Background: Post-concussion syndrome (PCS) and Functional Neurological Disorder (FND), including Functional Cognitive Disorder (FCD), are two frequently encountered but diagnostically complex conditions. While PCS is conceptualized as a sequela of mild traumatic brain injury (mTBI), FND/FCD encompasses symptoms incompatible with recognized neurological disease, often arising in the absence of structural brain damage. Yet, both conditions exhibit considerable clinical overlap—particularly in the domains of cognitive dysfunction, emotional dysregulation, and symptom persistence despite negative investigations. Objective: This review critically examines the shared and divergent features of PCS and FND/FCD. We explore their respective epidemiology, diagnostic criteria, and risk factors—including personality traits and trauma exposure—as well as emerging insights from neuroimaging and biomarkers. We propose the “Functional Overlay Model” as a clinical tool for navigating diagnostic ambiguity in patients with persistent post-injury symptoms. Results: PCS and FND/FCD frequently share features such as subjective cognitive complaints, fatigue, anxiety, and heightened somatic vigilance. High neuroticism, maladaptive coping, prior psychiatric history, and trauma exposure emerge as common risk factors. Neuroimaging studies show persistent network dysfunction in both PCS and FND, with overlapping disruption in fronto-limbic and default mode systems. The Functional Overlay Model helps to identify cases where functional symptomatology coexists with or replaces an initial organic insult—particularly in patients with incongruent symptoms and normal objective testing. Conclusions: PCS and FND/FCD should be conceptualized along a continuum of brain dysfunction, shaped by injury, psychology, and contextual factors. Early recognition of functional overlays and stratified psychological interventions may improve outcomes for patients with persistent, medically unexplained symptoms after head trauma. This review introduces the Functional Overlay Model as a novel framework to enhance diagnostic clarity and therapeutic planning in patients presenting with persistent post-injury symptoms. Full article

Background: Self-harm in young people is influenced by multiple factors, with media playing a significant role. While research has examined its harmful and protective effects, little attention has been paid to how healthcare professionals interpret and respond to media’s role in shaping young people’s experiences of self-harm. To our knowledge, no research has examined adolescent mental health professionals’ perspectives and, crucially, how these are constructed and understood. The study aimed to examine the following: (1) how mental health practitioners construct and use discourses to interpret the role of media in young people’s self-harm; and (2) how these discourses shape clinical understanding and practice. Methods: This qualitative study employed semi-structured interviews with ten clinicians from child and adolescent mental health services across England working with young people who self-harm. Data were analysed using critical discourse analysis to uncover how broader societal and institutional narratives shape clinicians’ perspectives. Results: Two dominant discourses were identified: “Media as Disruptor” and “The Hidden World of Youth”. These discourses framed media as both a risk factor and a potential intervention tool, positioning media as a powerful yet morally ambiguous force in young people’s lives. Clinicians largely framed media’s influence as negative but acknowledged its capacity for education and intervention. Conclusions: This research offers new insights into how media-related self-harm risks and benefits are framed and managed in mental health care settings. The study underscores the need for systemic changes in clinical practice, enhanced training, updated guidelines and a shift towards broader sociocultural perspectives in understanding self-harm and suicidal behaviour. Full article

Housing informality in Morocco has taken root within Rabat’s formal neighborhoods, quietly reshaping façades, extending plot lines, and redrawing the texture of entire blocks. This ongoing transformation runs up against the rigidity of official planning frameworks, producing tension between state enforcement and tacit tolerance, as residents navigate persistent legal and economic ambiguities. Prior Moroccan studies are neighborhood-specific or socio-economic; the field lacks a city-wide, multi-class analysis linking everyday tactics to long-term governance dilemmas and policy design. The paper, therefore, asks how and why residents and architects across affordable, middle-class, and affluent districts craft unapproved modifications, and what urban order emerges from their cumulative effects. A mixed qualitative design triangulates (i) five resident focus groups and two architect focus groups, (ii) 50 short, structured interviews, and (iii) 500 geo-referenced façade photographs and observational field notes, thematically coded and compared across housing types. In addition to deciphering informality methods and impacts, the results reveal that informal modifications are shaped by both reactive needs—such as accommodating family growth and enhancing security—and proactive drivers, including esthetic expression and real estate value. Despite their legal ambiguity, these modifications are socially normalized and often viewed by residents as value-adding improvements rather than infractions. Full article

Background and Objectives: The accurate segmentation of pulmonary nodules in computed tomography (CT) remains a critical yet challenging task due to variations in nodule size, shape, and boundary ambiguity. This study proposes CAAF-ResUNet (Context-Aware Adaptive Attention Fusion ResUNet), a novel deep learning model designed to address these challenges through adaptive feature fusion and edge-sensitive learning. Materials and Methods: Central to our approach is the Adaptive Attention Controller (AAC), which dynamically adjusts the contribution of channel and position attention based on contextual features in each input. To further enhance boundary localization, we incorporate three complementary boundary-aware loss functions: Sobel, Laplacian, and Hausdorff. Results: An extensive evaluation of two benchmark datasets demonstrates the superiority of the proposed model, achieving Dice scores of 90.88% on LUNA16 and 85.92% on LIDC-IDRI, both exceeding prior state-of-the-art methods. A clinical validation of a dataset comprising 804 CT slices from 35 patients at the University Medical Center of Ho Chi Minh City confirmed the model’s practical reliability, yielding a Dice score of 95.34% and a notably low Miss Rate of 4.60% under the Hausdorff loss configuration. Conclusions: These results establish CAAF-ResUNet as a robust and clinically viable solution for pulmonary nodule segmentation, offering enhanced boundary precision and minimized false negatives, two critical properties in early-stage lung cancer diagnosis and radiological decision support. Full article

This paper presents a new methodology for analyzing lip articulation during fingerspelling aimed at extracting robust visual patterns that can overcome the inherent ambiguity and variability of lip shape. The proposed approach is based on unsupervised clustering of lip movement trajectories to identify consistent articulatory patterns across different time profiles. The methodology is not limited to using a single model. Still, it includes the exploration of varying cluster configurations and an assessment of their robustness, as well as a detailed analysis of the correspondence between individual alphabet letters and specific clusters. In contrast to direct classification based on raw visual features, this approach pre-tests clustered representations using a model-based assessment of their discriminative potential. This structured approach enhances the interpretability and robustness of the extracted features, highlighting the importance of lip dynamics as an auxiliary modality in multimodal sign language recognition. The obtained results demonstrate that trajectory clustering can serve as a practical method for generating features, providing more accurate and context-sensitive gesture interpretation. Full article

Rock imagery in the Puebloan region of the American Southwest often combines elements from different animal, human, plant, and natural sources. Blended elements may depict or refer to other-wordly states of existence or to creation narratives. Beings with combined elements can shift from shapes familiar in the present world and transport the viewer’s frame of reference to the spirit world. Puebloan belief in layering worlds below and above the present world is an important underlying social construct. Other worlds, especially those below, refer to past mythical times when animals and humans existed in primordial forms or were not fully formed, or may refer to the land of the dead or the underworld. Certain animal forms may have been selected because they are spirit guides, have specific powers, or were guardian-gods of cardinal directions. Some animals, such as birds, were chosen as messengers of prayers or offerings, while others (such as bears) had healing powers. The placement of images on the landscape or in relation to natural features imparts added power to the imagery. Ambiguity and multiple meanings also enhance these powers and incorporate concepts of emergence and transformation. Some images refer to the transformation that occurs when dancers wear kachina masks and then assume the attributes of those kachinas. Examples will be presented from images dating to the pre-European contact period (1300 to 1540 AD) found at Petroglyph National Monument, in the central Rio Grande valley of New Mexico. Comparisons to painted wall murals in kivas (ceremonial rooms) made during the same time period are presented. Full article

Terrain conductivity meters (TCMs) are efficient devices for different sorts of subsurface investigations, including detecting and tracing buried utilities, such as metallic pipes and cables. However, data collected using TCMs are usually ambiguous and hard to interpret. This ambiguity originates from the complex shape of apparent conductivity anomalies, the influence of irrelevant conductive bodies, and the interference of random noise with the collected data. To overcome this ambiguity and produce more interpretable apparent conductivity maps, a three-step filtering routine is proposed and tested using different real datasets. The filtering routine begins with applying a Savitzky–Golay (SG) filter to reduce the effect of random noise. This is followed by a modified rolling ball (MRB) filter to convert the complex M-shape of the anomaly into a single trough pointing to the underground utility. Finally, a virtual resolution enhancement (VRE) filter is applied to enhance the pinpointing apex of the trough. The application of the proposed filtering routine to apparent conductivity data collected using different terrain conductivity meters over different utilities in different urban environments shows a significant improvement of the data and an effective ability to reveal masked underground utilities. The proposed triple filtering routine can be a starting point for a new generation of TCMs with a built-in operation mode for instantaneous delineation and characterization of underground utilities in real time. Full article

Work is a key social determinant of mental health, and adverse organizational conditions in healthcare settings increase psychosocial risks. Leadership influences workplace well-being, yet its impact on mental health and gender inequalities remains underexplored. Despite the feminization of the health sector, disparities persist in leadership access, role expectations, and work–family reconciliation, exacerbating occupational stress. Aims: This study examines leadership practices in public hospitals, focusing on their relationship with mental health, organizational dimensions (recognition and role stress), and gender disparities. It explores the perspectives of both workers and managers to understand how leadership shapes workplace conditions and well-being. Methods: A qualitative, cross-sectional study was conducted as part of the FONDECYT project 1220547. Semi-structured interviews were conducted with 64 workers from public hospitals in Santiago, Chile, including clinical and administrative staff. The analysis supported by Grounded Theory identified key categories: constructive and destructive leadership, recognition, role stress, and gender disparities in leadership. Results: Constructive leadership—characterized by communication, fairness, and recognition—was linked to a healthier work environment and improved well-being. In contrast, destructive leadership (characterized by abuse of power and imposition, or inaction, lack of support, and absence of effective direction) contributed to role stress, workplace mistreatment, and job dissatisfaction. Recognition was a crucial but insufficient motivator, as the lack of formal mechanisms led to frustration. Role stress emerged as a significant risk for well-being, with subordinates experiencing overload, ambiguity, and conflicting expectations. Gender inequalities persisted as women faced more tremendous barriers to leadership and difficulties balancing work and family responsibilities. Workers and managers had differing perspectives, with subordinates prioritizing fairness and recognition while managers emphasized operational constraints. Conclusions: Leadership training should emphasize trust, equity, and recognition to enhance workplace well-being. Institutional policies must address role stress, strengthen formal recognition systems, and promote gender equity in leadership. Future research should integrate quantitative methods to explore leadership’s impact on organizational conditions and mental health outcomes. Full article

Financial technology (FinTech) rapidly transforms financial landscapes across ASEAN-4 countries by enhancing financial inclusion and digital service accessibility. However, the key factors driving FinTech development in these economies remain ambiguous. While existing studies highlight the economic and technological aspects of FinTech adoption, limited research distinguishes the unique conditions shaping FinTech’s evolution in developing ASEAN markets. This study bridges this gap by identifying economic and non-economic determinants and exploring their mediating effects. This research aims to investigate the primary drivers of FinTech development in ASEAN-4, emphasizing the roles of financial access and technological readiness as mediators in fostering a sustainable FinTech ecosystem. Utilizing structural equation modeling (SEM) with SmartPLS3, this study analyzes secondary data from 2008 to 2018, evaluating macroeconomic indicators, banking conditions, internet penetration, innovation levels, population dynamics, and human development factors. General banking conditions, access to finance, and technological readiness significantly impact FinTech development. Additionally, financial accessibility and technological infrastructure mediate the influence of economic stability, innovation, and digital penetration on FinTech growth. This study underscores policymakers’ and stakeholders’ need to enhance digital infrastructure and financial accessibility to accelerate FinTech growth. Strengthening financial ecosystems will drive digital transformation and economic resilience in emerging ASEAN economies. Full article

Classic Hodgkin lymphoma (cHL) in patients with immune deficiency/dysregulation represents a critical unmet need in hematology, demanding the appropriate revision of classification and therapeutic paradigms. Epstein–Barr virus (EBV) is a pivotal driver of lymphomagenesis in this high-risk subset, where viral oncoproteins (e.g., LMP1/2A) exploit immune vulnerabilities to activate NF-κB, rewire tumor microenvironments (TME), and evade immune surveillance. EBV-positive cHL, prevalent in immunosuppressed populations, exhibits distinct molecular hallmarks, including reduced somatic mutations, unique HLA associations, and profound PD-L1-mediated immune suppression, that diverge from EBV-negative cases reliant on genetic aberrations. Despite advances in combined antiretroviral therapy, HIV co-infection exacerbates pathogenesis, M2 macrophage dominance, and T-cell exhaustion, while links to other viruses remain ambiguous. Current therapies fail to adequately target these viral and immune complexities, leaving patients with poorer outcomes. This review synthesizes insights into EBV’s etiological role, immune contexture disparities, and the genetic–environmental interplay shaping cHL heterogeneity. The WHO classification highlights the need to reclassify EBV-associated cHL as a distinct subset, integrating viral status and immune biomarkers into diagnostic frameworks. Urgent priorities include global epidemiological studies to clarify causal mechanisms, development of virus-targeted therapies (e.g., EBV-specific T-cell strategies, PD-1/CTLA-4 blockade), and personalized regimens for immune-dysregulated cohorts. Full article

The interplay between land availability and the housing market highlights the importance of government intervention through land policies. Effective land policies ensure in-time land availability and facilitate private sector involvement in housing development. This study examines how public land policies influence formal housing development by the private sector, with a particular focus on land banking strategies—mechanisms involving the acquisition and holding of land for future use to ensure availability and capture value increases. While land banking policy aims to serve public benefits, private-sector land banking often prioritizes profit, creating governance challenges that shape housing development outcomes. This paper analyzes this phenomenon in the context of Indonesia by developing analytical framework of legitimacy, effectiveness, efficiency, and fairness. As a rapidly growing country with significant housing backlogs and a private-sector-dominated market, Indonesia’s land regulations present a critical case for examining these dynamics. Our analysis shows that while regulatory framework regulations emphasize land consolidation, acquisition, and development as instruments to facilitate private sector involvement in housing development, weak enforcement and regulatory ambiguities often undermine their effectiveness. The findings indicate that private-sector land banking is largely speculative, driven by profit-maximization strategies rather than housing provision, and is reinforced by inconsistent policy enforcement at the municipal level. A municipal case study further illustrates how governance challenges and discretionary compliance allow private developers to prioritize profitability over the affordable housing needs outlined in public policies. While land policies in Indonesia are framed as comprehensive planning tools, their implementation often favors specific beneficiaries, limiting their broader social impact. Full article

Human gesture image recognition is the process of identifying, deciphering, and classifying human gestures in images or video frames using computer vision algorithms. These gestures can vary from the simplest hand motions, body positions, and facial emotions to complicated gestures. Two significant problems affecting the performance of human gesture picture recognition methods are ambiguity and invariance. Ambiguity occurs when gestures have the same shape but different orientations, while invariance guarantees that gestures are correctly classified even when scale, lighting, or orientation varies. To overcome this issue, hand-crafted features can be combined with deep learning to greatly improve the performance of hand gesture image recognition models. This combination improves the model’s overall accuracy and dependability in identifying a variety of hand movements by enhancing its capacity to record both shape and texture properties. Thus, in this study, we propose a hand gesture recognition method that combines Reset50 model feature extraction with the Tamura texture descriptor and uses the adaptability of GAM to represent intricate interactions between the features. Experiments were carried out on publicly available datasets containing images of American Sign Language (ASL) gestures. As Tamura-ResNet50-OptimizedGAM achieved the highest accuracy rate in the ASL datasets, it is believed to be the best option for human gesture image recognition. According to the experimental results, the accuracy rate was 96%, which is higher than the total accuracy of the state-of-the-art techniques currently in use. Full article

In many extended object tracking applications (e.g., tracking vehicles using a millimeter-wave radar), the shape of an extended object (EO) remains unchanged while the orientation angle varies over time. Thus, tracking the shape and the orientation angle as individual parameters is reasonable. Moreover, the tight coupling between the orientation angle and the heading angle contains information on improving estimation performance. Hence, this paper proposes a constrained filtering approach utilizing this information. First, an EO model is built using an orientation vector with a heading constraint. This constraint is formulated using the relation between the orientation vector and the velocity vector. Second, based on the proposed model, a variational Bayesian (VB) approach is proposed to estimate the kinematic, shape, and orientation vector states. A pseudo-measurement is constructed from the heading constraint and is incorporated into the VB framework. The proposed approach can also address the ambiguous issue in orientation angle estimation. Simulation and real-data results are presented to illustrate the effectiveness of the proposed model and estimation approach. Full article

Rheumatoid arthritis (RA) is a chronic autoimmune disease that can cause severe joint damage and functional impairment. Ultrasound imaging has shown promise in providing real-time assessment of synovium inflammation associated with the early stages of RA. Accurate segmentation of the synovium region and quantification of inflammation-specific imaging biomarkers are crucial for assessing and grading RA. However, automatic segmentation of the synovium in 3D ultrasound is challenging due to ambiguous boundaries, variability in synovium shape, and inhomogeneous intensity distribution. In this work, we introduce a novel network architecture, Swin Transformers with Deep Attentive Features for 3D segmentation (SwinDAF3D), which integrates Swin Transformers into a Deep Attentive Features framework. The developed architecture leverages the hierarchical structure and shifted windows of Swin Transformers to capture rich, multi-scale and attentive contextual information, improving the modeling of long-range dependencies and spatial hierarchies in 3D ultrasound images. In a six-fold cross-validation study with 3D ultrasound images of RA patients’ finger joints (n = 72), our SwinDAF3D model achieved the highest performance with a Dice Score (DSC) of 0.838 ± 0.013, an Intersection over Union (IoU) of 0.719 ± 0.019, and Surface Dice Score (SDSC) of 0.852 ± 0.020, compared to 3D UNet (DSC: 0.742 ± 0.025; IoU: 0.589 ± 0.031; SDSC: 0.661 ± 0.029), DAF3D (DSC: 0.813 ± 0.017; IoU: 0.689 ± 0.022; SDSC: 0.817 ± 0.013), Swin UNETR (DSC: 0.808 ± 0.025; IoU: 0.678 ± 0.032; SDSC: 0.822 ± 0.039), UNETR++ (DSC: 0.810 ± 0.014; IoU: 0.684 ± 0.018; SDSC: 0.829 ± 0.027) and TransUNet (DSC: 0.818 ± 0.013; IoU: 0.692 ± 0.017; SDSC: 0.815 ± 0.016) models. This ablation study demonstrates the effectiveness of combining a Swin Transformers feature pyramid with a deep attention mechanism, improving the segmentation accuracy of the synovium in 3D ultrasound. This advancement shows great promise in enabling more efficient and standardized RA screening using ultrasound imaging. Full article

Background/Objectives: This study analyzed data on the actual timing of the first and second doses of the Measles, Mumps, and Rubella (MMR) vaccination in Slovakia according to the vaccination schedule. Methods: Histograms were constructed using immunological survey data on MMR vaccination conducted in Slovakia in 2018. Results: For the first dose (2560 individuals), 83.4% of them were vaccinated timely (15th–18th month, mostly in the 16th month), while 13.8% of them were delayed. For the second dose (1061 individuals), 72.7% of vaccinations were timely (11th year), and 23.2% were delayed. There was a bimodal distribution of the timing of the administration of the second dose, with peaks at the beginning of the 11th year and at the turn of the 11th and 12th year. Conclusions: The unexpected shape of the histograms suggests that ambiguous interpretations of the vaccination schedule may be one of the causes of vaccination delays. Full article