Search Results (2,758)

Alzheimer’s disease (AD) is the most prevalent neurodegenerative disorder in the aging population. Drug repurposing provides a cost-effective strategy to identify novel therapeutics that may mitigate age-associated pathologies. Here, we report the therapeutic potential of fluoxetine, a selective serotonin reuptake inhibitor commonly used as an antidepressant, in alleviating cognitive impairment and AD-like pathology in 5xFAD mice, a transgenic model of familial AD. Chronic fluoxetine administration significantly ameliorated anxiety-like behavior and cognitive deficits in 5xFAD mice, as assessed by open field, Y-maze, and novel object recognition tests. Fluoxetine treatment was associated with reduced amyloid plaque deposition in the hippocampus and cortex, attenuation of microglial activation, and decreased expression of inflammatory cytokines. At the molecular level, fluoxetine increased phosphorylation of GSK3β at Ser9, which was associated with enhanced CREB phosphorylation and upregulation of the α-secretase ADAM10. These effects were further examined in SH-SY5Y neuronal cells, where CREB phosphorylation and ADAM10 expression were significantly modulated by GSK3β inhibition, whereas CaMKII inhibition had no detectable effect under our experimental conditions. Our findings suggest that fluoxetine modulates amyloid-associated signaling pathways in the 5xFAD model, in part through regulation of the GSK3β-CREB signaling framework. These results provide mechanistic insight into how fluoxetine may influence APP processing in an amyloid-driven pathological context, although further studies are required to clarify its translational implications in human AD. Full article

In the field of autonomous driving, camera-based perception models are mostly trained on clear weather data. Models designed to handle specific weather conditions often lack generalization to dynamically changing environments and primarily focus on weather removal rather than robust perception. This paper proposes a semantic-enabled network for object detection under diverse weather conditions. Semantic information enables the model to generate plausible content in missing regions and accurately delineate object boundaries. It also preserves visual coherence and realism across both restored and original image areas, thereby facilitating image transformation and object recognition. Specifically, our architecture consists of a Preprocessing Unit (PPU) and a Detection Unit (DTU), where the PPU utilizes a U-shaped network enriched with semantics to refine degraded images, and the DTU integrates this semantic information for object detection using a modified YOLO network. Extensive experiments demonstrate that the proposed method achieves mAP improvements ranging from 1.49% to 8.78% compared with existing approaches across multiple benchmark datasets under various weather conditions. These results demonstrate the effectiveness of semantic guidance in image enhancement and object detection, providing a comprehensive framework for improving detection performance. The source code will be made publicly available. Full article

Cybersecurity vulnerabilities are rapidly increasing, but public understanding and awareness remain limited. Since most vulnerabilities are common, they continue to exist and to be exploited. Although there are tools, including the Open Worldwide Application Security project and the common weakness enumeration method, that provide extensive information on known security problems, their information is not structured and visually shown. The tools are ineffective in speed assessment and response. We analyzed large-scale common vulnerabilities and exposures JavaScript object notation datasets to recognize key threats, to understand the underlying cause of data breaches, and to analyze vulnerability trends. Implementing keyword gate-filling techniques and better data visualization enhances the clarity and usefulness of vulnerability information. These tools enable stakeholders to make quicker and more informed decisions and implement stronger encryption and defensive measures. Finally, the results of this study lead to broad awareness, active security, and a reactive strategy to evolving cyber threats that simplifies both governmental and average-day user recognition and response to emerging attack patterns and risks across digital platforms. Full article

Background/Objectives: Pain is highly prevalent among hospitalized patients, and suboptimal pain assessment and management remain common in clinical practice. Nurses are central to timely pain recognition and intervention, yet knowledge and attitudinal gaps can hinder evidence-based pain care. Therefore, this study aimed to evaluate the impact of an evidence-based instructional program on nurses’ knowledge and attitudes toward pain management at a tertiary hospital in Saudi Arabia. Methods: A one-group pretest–posttest quasi-experimental study was conducted at King Fahad Military Medical Complex, Dhahran, Saudi Arabia (January–July 2025). Registered nurses providing direct patient care (N = 226) completed a researcher-developed questionnaire assessing pain management knowledge (30 items) and attitudes (10 items, 5-point Likert scale) immediately before and one week after a structured three-hour evidence-based educational program. Data were analyzed using descriptive statistics, paired-sample t-tests, and Pearson correlation coefficients (SPSS v30), with p < 0.05 considered statistically significant. Results: Baseline findings indicated moderate knowledge (mean of total scores = 15.54 ± 4.32) and generally positive attitudes toward pain management (mean = 3.83 ± 0.60). Knowledge scores increased significantly after the intervention to become moderate to high (pretest: 15.54 ± 4.32 vs. posttest: 18.65 ± 3.83; p < 0.001). Attitude scores also improved significantly following the program (p < 0.001). Knowledge and attitudes showed a significant positive correlation both preintervention (r = 0.241, p < 0.001) and postintervention (r = 0.435, p < 0.001). Conclusions: A brief evidence-based educational program yielded measurable improvements in nurses’ pain management knowledge and attitudes. Integrating structured pain education into continuing professional development may strengthen patient-centered pain care and support more consistent evidence-based practice in tertiary settings. Full article

Pediatric obstructive sleep apnea (OSA) is a prevalent and underdiagnosed condition associated with significant neurocognitive, behavioral, and systemic consequences. Sleep-related breathing disorders (SRBDs) in children range from primary snoring to OSA, with even mild forms increasingly linked to adverse outcomes. Given their frequent contact with pediatric patients, pediatric dentists and orthodontists are uniquely positioned to contribute to early identification and management within a multidisciplinary framework. Objectives: This narrative review aimed to summarize and critically appraise current evidence to clarify the clinical role, scope of practice, and responsibilities of pediatric dentists and orthodontists within the multidisciplinary management of pediatric obstructive sleep apnea. A comprehensive literature search was conducted in PubMed, Scopus, Web of Science, and EMBASE up to 1 November 2025. Review articles addressing the involvement of pediatric dentists and orthodontists in pediatric OSA were included. No restriction was applied to language or publication year. Two authors independently performed study selection and data extraction. The methodological quality and data extraction of the studies were structured according to the SANRA scale. Ten studies were deemed suitable for inclusion in the current review. After examination of the full texts, the available evidence was filtered into specific clinical domains aimed at clarifying the role of the pediatric dentist and orthodontist in the management of pediatric obstructive sleep apnea (OSA). Qualitative thematic analysis of the included studies identified three main areas in which pediatric dentists and orthodontists contribute to the management of pediatric OSA. The first area involves screening through recognition of clinical signs and symptoms, use of validated questionnaires, and identification of craniofacial and occlusal features associated with increased airway risk. The second area concerns participation in the diagnostic–therapeutic pathway and multidisciplinary care, including timely referral, clinical documentation, and collaboration with pediatricians, otolaryngologists, and sleep specialists. The third area relates to orthodontic treatments such as rapid maxillary expansion and mandibular advancement appliances, which may provide adjunctive benefits in selected patients, although current evidence is limited by heterogeneity and growth-related confounding factors. Pediatric dentists and orthodontists play a pivotal yet complementary role in the management of pediatric OSA. In particular, all the involved specialists are encouraged to actively participate in the screening process, interdisciplinary communication, and diagnostic and therapeutic decision-making processes. Full article

Ecological resettlement (ER), or conservation-led displacement, is widely implemented to safeguard biodiversity but often produces complex socio-ecological outcomes. This study assessed the environmental justice (both social and ecological) impacts of ER in Nepal’s Terai Arc Landscape (TAL) using an enhanced (including social, ecological, and environmental aspects) environmental justice (EJ) framework. Data were collected from 240 households across all resettled villages within the Chitwan and Parsa National Parks (NPs) of Nepal through household interviews, key informant interviews, focus groups, and field observations, supplemented by policy reviews, reports, and unpublished documents. Household demographics indicated an average family size of 5.5, gender parity (664 females, 658 males), and diverse caste/ethnic composition (ethnic: 146 households; higher caste: 64; lower caste: 6). Wealth distribution and literacy were uneven, with disparities in land ownership, assets, and social positions. Social and ecological justice outcomes were analysed using chi-square and McNemar tests. We observed a significant difference (p < 0.05) in substantive justice (food, shelter, clothing, and security) attributes before and after the resettlements. Similarly, significant improvements post-resettlement were observed in procedural and recognition justice: participation in decision-making increased from 43% to 62% (χ² = 12.34, p < 0.05). However, recognition of Indigenous knowledge and FPIC rights remained low, with 93% of households reporting inadequate acknowledgment (χ² = 198.5, p < 0.05). Distributive justice indicators, including access to compensation and forest resources, showed mixed outcomes, with 52% reporting fair compensation and 48% citing inequities (p < 0.05). Ecological outcomes also shifted significantly: forest cover decreased in 65% of surveyed areas post-resettlement, while grassland extent increased in 28% (χ² = 27.4, p < 0.05). Water source accessibility declined for 48% of households (χ² = 21.6, p < 0.05), and bushfire incidence decreased by 15% (χ² = 9.8, p < 0.05). Composite scoring revealed strong linkages between social justice deficits and ecological downturn in the resettled areas, suggesting that inadequate participation, recognition, inequitable compensation, and ecological degradation shift the issues from parks to the outside and exacerbate environmental vulnerability. These findings demonstrate that ER can achieve partial ecological objectives inside the parks but often perpetuates social inequities and ecological downturn in the resettled areas, undermining the long-term sustainability of the socio-ecological landscape. The study highlights the critical need to integrate social justice, participatory governance, and ecological monitoring into resettlement planning. Future policies should be grounded in the understanding that conservation effectiveness and social equity are mutually reinforcing, and that ignoring justice dimensions risks undermining both biodiversity outcomes and human wellbeing. Full article

Multi-sensor-based human activity recognition (HAR) models trained with deep learning often exhibit limited generalization when applied to data collected under conditions different from those seen during training. To alleviate this issue, we present an adversarial domain adaptation framework that incorporates contrastive group construction to promote class-aware feature alignment. Specifically, augmented and perturbed sample groups are generated in both source and target domains and optimized through contrastive learning objectives, allowing the feature extractor to compact semantically similar representations while separating dissimilar ones without relying on target-domain annotations. This joint design preserves semantic structure while reducing cross-domain distribution discrepancies, resulting in representations that are both domain-invariant and discriminative. Experiments conducted on the Opportunity dataset validate the effectiveness of the proposed approach, demonstrating consistent performance gains over representative unsupervised domain adaptation methods. Full article

Objectives: To strengthen the recognition of juvenile xanthogranuloma (JXG) by analyzing ultrasound findings. Methods: This study retrospectively enrolled these patients with pathologically confirmed JXG from January 2011 to March 2025. The clinical, imaging, pathological features, and prognosis of all included patients were analyzed. All the imaging features were evaluated in consensus by two radiologists. Results: Fourteen patients were included in the study. A total of 78.6% presented with solitary masses. The age of the patients ranged from 2 months to 48 years. Those aged ≤1 year accounted for 64.3% of the sample. The lesions were predominantly located on the head and face, and the skin of most patients was yellowish-orange. The ultrasound manifestations are mostly hypoechoic masses with clear boundaries and regular shapes. Contrast-enhanced ultrasound shows a slight homogeneous enhancement, and on shear wave elastography, it appears to be relatively hard. Conclusions: JXGs are more common in infants or young children and present with yellowish-orange, cutaneous lesions. Ultrasound revealed homogeneous, well-circumscribed, regular hypoechoic nodules. Multimodal imaging may be helpful for preoperative diagnosis. Full article

This paper proposes a novel salient object detection method for operational hole localization in metallurgical furnaces, addressing challenging industrial conditions including extreme illumination variations and strong electromagnetic interference to enable two-level measurement in aluminum electrolysis cells and impact position recognition of the front-of-furnace operation robot. It employs a multi-feature fusion framework combining foreground and background saliency maps with center prior maps. Foreground saliency maps are generated through spatial compactness and local contrast computations, enhancing discriminative features while suppressing shared foreground–background characteristics. Background saliency maps are constructed via sparse reconstruction to exploit redundant features. Then method integrates edge extraction and density clustering to generate center prior maps that emphasize foreground target centroids and mitigate background noise. Comprehensive evaluations on both a specialized operational hole dataset and six public datasets demonstrate superior performance compared to other methods. On the specialized dataset, it achieves a precision of 0.8954, a maximum F-measure of 0.8994, and an S-measure of 0.8662. While maintaining operational robustness, the method offers a practical solution for furnace monitoring and robotic operation guidance in metallurgical processes. Full article

To address the problems of severe fruit occlusion, large variations in target scale, and many small-scale goals being overlooked in the recognition of trellised watermelons under complex agricultural scenarios, this study proposes an improved RT-DETR-based detection model, termed RT-DETR-Watermelon. A context-guided (CG) module is embedded into the backbone network. A dedicated P2 detection layer is added to enhance the model’s sensitivity to small objects. A scale sequence feature fusion (SSFF) module and a triple feature encoder (TFE) module are introduced into the model to improve the model’s capability to detect targets at multiple scales. The original bounding box regression loss is replaced with MPDIoU (Multiple Path Distance Intersection over Union) loss, which accelerates model convergence and improves localization precision. Finally, the number of channels is adjusted to reduce parameter count, computational complexity, and storage size. The experimental results show that, compared with the original RT-DETR model, the proposed RT-DETR-Watermelon model increases precision, recall, and mean Average Precision (mAP@0.5) by 0.4, 1.8, and 1.0 percentage points, while reducing the number of parameters, computational cost, and model size by 53.5%, 23.5%, and 53.2%, respectively. Full article

Background and Objectives: Bile duct injury is a relatively rare, but critical complication of laparoscopic cholecystectomy and is most commonly attributed to misinterpretation of biliary anatomy. Intraoperative biliary imaging may enhance anatomical recognition and reduce operative uncertainty, yet the optimal imaging modality remains debated. This study aimed to compare conventional intraoperative X-ray cholangiography with two fluorescence-based techniques—intravenous and intracholecystic indocyanine green fluorescence cholangiography—with respect to biliary visualization, perioperative outcomes, and surgeon satisfaction during elective laparoscopic cholecystectomy. Materials and Methods: This prospective, single-center, single-blind randomized controlled trial included 240 adult patients scheduled for elective laparoscopic cholecystectomy between June 2021 and December 2022. Participants were randomized equally to standard intraoperative cholangiography, intravenous indocyanine green fluorescence cholangiography, or intracholecystic indocyanine green fluorescence cholangiography. The primary outcome was successful visualization of predefined extrahepatic biliary landmarks, including the critical junction. Secondary outcomes included cholangiography duration, perioperative complications, postoperative inflammatory markers, and surgeon satisfaction assessed using a five-point Likert scale. This study was registered at ClinicalTrials.gov (NCT04908826). Results: Visualization rates of the critical junction and major extrahepatic bile ducts were comparable among three groups, with no statistically significant differences observed. Both fluorescence-based techniques achieved a 100% technical success rate, whereas standard cholangiography failed in a small proportion of cases. Cholangiography duration was significantly shorter in the fluorescence groups compared with standard cholangiography (p < 0.001). Surgeon satisfaction scores were significantly higher for both fluorescence approaches, with a slight preference for intravenous administration. Perioperative complication rates and postoperative inflammatory markers were com-parable among groups. Conclusions: Intravenous and intracholecystic indocyanine green fluorescence cholangiography are non-inferior to conventional intraoperative cholangiography for biliary anatomy visualization and offer advantages in procedural efficiency and surgeon satisfaction. Fluorescence-based imaging represents a safe and effective alternative for intraoperative biliary mapping during elective laparoscopic cholecystectomy. Full article

Arabic sign language (ArASL) is the primary communication medium for Deaf and hard-of-hearing people across Arabic-speaking communities. Most current ArASL recognition systems are based solely on visual features and do not incorporate linguistic or semantic information that could improve generalization and semantic grounding. This paper introduces CLIP-ArASL, a lightweight CLIP-style multimodal approach for static ArASL letter recognition that aligns visual hand gestures with bilingual textual descriptions. The approach integrates an EfficientNet-B0 image encoder with a MiniLM text encoder to learn a shared embedding space using a hybrid objective that combines contrastive and cross-entropy losses. This design supports supervised classification on seen classes and zero-shot prediction on unseen classes using textual class representations. The proposed approach is evaluated on two public datasets, ArASL2018 and ArASL21L. Under supervised evaluation, recognition accuracies of $99.25\pm 0.14\%$ and $91.51\pm 1.29\%$ are achieved, respectively. Zero-shot performance is assessed by withholding $20\%$ of gesture classes during training and predicting them using only their textual descriptions. In this setting, accuracies of $55.2\pm 12.15\%$ on ArASL2018 and $37.6\pm 9.07\%$ on ArASL21L are obtained. These results show that multimodal vision–language alignment supports semantic transfer and enables recognition of unseen classes. Full article

Background/Objectives: To describe a previously unreported compensatory behavior used by an infant with severe bilateral congenital ptosis associated with blepharophimosis syndrome (BPES). Methods: Observational case report of a 4.5-month-old infant with severe bilateral congenital upper eyelid ptosis due to BPES. Results: The infant demonstrated classic compensatory mechanisms, including frontalis overaction and chin elevation, which were insufficient to clear the visual axis. Notably, she repeatedly used the dorsal surfaces of both fists to elevate the upper eyelids simultaneously and maintain fixation on faces. This behavior ceased following bilateral frontalis suspension surgery with silicone rods. Conclusions: In early infancy, severe bilateral ptosis may prompt the emergence of alternative, developmentally constrained compensatory behaviors. The bilateral fist lid-lift appears to represent a visually driven, sensorimotor strategy to clear the visual axis when conventional mechanisms are ineffective. Recognition of this behavior expands understanding of early compensatory responses in congenital ptosis and BPES. Full article

Background/Objectives: Behaviours that challenge (BtC) are common in people with dementia. International guidelines recommend using non-pharmacological interventions (NPIs) as first-line treatments. A promising training package that provides a framework for delivering NPIs is “Communication and Interaction Training” (CAIT); this programme has received national recognition within the UK. Our study aimed to explore staff’s perceptions of the effect of CAIT on their understanding and responses to the behaviours and emotions of people with dementia. The study also sought to further understand how CAIT worked and the conditions which help implement it. Methods: Reflexive thematic analysis was used to analyse interviews with 11 staff who had been trained in the use of CAIT and then attempted to implement the contents of the training in clinical settings. Results: Six main themes emerged regarding the impact of the training: enhancing understanding, transforming interactions, skills development, accessible and flexible, socio-cultural change enablers, and obstacles in training. CAIT was viewed positively by the participants and was perceived to improve their knowledge, attitudes and skills. Conclusions: The positive findings are consistent with previous studies on CAIT and its current use in guiding training programmes in the UK. Implications for the delivery of CAIT are discussed, as well as suggestions for further trials of the programme. Full article

Due to climate change, drought periods are becoming more frequent and more intense, posing substantial stress to Central European forest stands, especially climatically sensitive conifer forests. The early detection and accurate spatial delineation of forest damage are essential for supporting adaptive forest management decisions. This study presents a two-tier, multi-step forest damage assessment approach that combines Sentinel-2 satellite-based NDVI double-difference analysis with UAV-based high-resolution photogrammetric evaluation. In the first phase, potential damaged forest patches were identified in two sample areas of the Sopron Mountains using double-difference maps derived from monthly window NDVI maxima calculated from Sentinel-2 data. In the second phase, UAV surveys were carried out over the selected forest compartments, resulting in individual-tree-level canopy segmentation and object-based NDVI analysis. The photogrammetric point clouds were combined with ground points derived from airborne laser scanning to enable the accurate generation of canopy height models. The results confirmed that NDVI double-difference analysis is suitable for the spatial detection of both gradual drought-related damage and sudden disturbances—such as forest fire—even under sequences of drought and moderate years occurring in a sporadic pattern. The UAV-based analysis corroborated the satellite observations in detail and enabled an accurate inventory of damaged trees as well as the exploration of their spatial distribution. The proposed methodology provides an efficient, cost-effective, and operational tool for multi-scale monitoring of forest damage, contributing to the timely recognition of climate-change impacts and to the substantiation of targeted forest management interventions. Full article