Search Results (4,254)

Conventional image encryption schemes struggle to meet the high security demands of medical images due to their large data volume, strong pixel correlation, and structural redundancy. To address these challenges, we propose a grayscale medical image encryption algorithm based on a novel 5-D memristor chaotic system. The algorithm integrates a Symmetric L-type Josephus Spiral Scrambling (SLJSS) module and a Dynamic Codon-based Multi-RNA Diffusion (DCMRD) module to enhance spatial decorrelation and diffusion complexity. Simulation results demonstrate that the proposed method achieves near-ideal entropy (e.g., 7.9992), low correlation (e.g., 0.0043), and high robustness (e.g., NPCR: 99.62%, UACI: 33.45%) with time complexity of $\mathcal{O}\left(11MN\right)$, confirming its effectiveness and efficiency for medical image protection. Full article

Background/Objectives: The TOPAZ-1 phase III trial reported a survival benefit of using durvalumab, an anti-programmed death ligand 1 (anti-PD-L1) antibody, in combination with gemcitabine and cisplatin (GCD) treatment in patients with advanced biliary tract cancer. This retrospective study investigated the efficacy and safety of GCD treatment for advanced biliary tract cancer in real-world conditions. Methods: The study subjects were 52 patients with biliary tract cancer who received GCD therapy between January 2023 and May 2024. The observation parameters included the modified Glasgow Prognostic Score (mGPS), neutrophil–lymphocyte ratio (NLR), platelet–lymphocyte ratio (PLR), tumor markers (CEA, CA19-9), overall response rate (ORR), disease control rate (DCR), progression-free survival (PFS), overall survival (OS), and adverse events. Results: The cohort included 36 men and 16 women, with a median age of 73.0 years. There were 36 cases of cholangiocarcinoma (distal: 10, perihilar: 19, intrahepatic: 7), 13 cases of gallbladder cancer, and 3 cases of ampullary carcinoma. The stages were locally advanced in 30 cases and metastatic in 22 cases. Biliary drainage was performed in 30 cases. There were 38 cases receiving first-line therapy and 14 cases receiving second-line or later treatments. The median values at the start of GCD therapy were ALB 3.7 g/dL, CRP 0.39 mg/dL, NLR 2.4, PLR 162.5, CEA 4.8 ng/mL, and CA19-9 255.9 U/mL. The mGPS distribution was 0:23 cases, 1:18 cases, and 2:11 cases. The treatment outcomes were ORR 25.0% (CR 2 cases, PR 11 cases), DCR 78.8% (SD 28 cases, PD 10 cases, NE 1 case), median PFS 8.6 months, and median OS 13.9 months. The PLR was suggested to be useful for predicting PFS. A decrease in CEA at six weeks after the start of treatment was a significant predictor of PFS and OS. Gallbladder cancer had a significantly poorer prognosis compared to other cancers. The immune-related adverse events included hypothyroidism in two cases, cholangitis in one case, and colitis in one case. Conclusions: The ORR, DCR, and PFS were comparable to those in the TOPAZ-1 trial. Although limited by its retrospective design and small sample size, this study suggests that GCD therapy is an effective treatment regimen for unresectable biliary tract cancer in real-world clinical practice. Full article

Building change detection and building damage assessment are two essential tasks in post-disaster analysis. Building change detection focuses on identifying changed building areas between bi-temporal images, while building damage assessment involves segmenting all buildings and classifying their damage severity. These tasks play a critical role in disaster response and urban development monitoring. Although supervised learning has significantly advanced building change detection and damage assessment, its reliance on large labeled datasets remains a major limitation. In contrast, self-supervised learning enables the extraction of meaningful data representations without explicit training labels. To address this challenge, we propose a self-supervised learning approach that unifies denoising autoencoders and contrastive learning, enabling effective data representation for building change detection and damage assessment. The proposed architecture integrates a dual denoising autoencoder with a Vision Transformer backbone and contrastive learning strategy, complemented by a Feature Pyramid Network-ResNet dual decoder and an Edge Guidance Module. This design enhances multi-scale feature extraction and enables edge-aware segmentation for accurate predictions. Extensive experiments were conducted on five public datasets, including xBD, LEVIR, LEVIR+, SYSU, and WHU, to evaluate the performance and generalization capabilities of the model. The results demonstrate that the proposed Denoising AutoEncoder-enhanced Dual-Fusion Network (DAEDFN) approach achieves competitive performance compared with fully supervised methods. On the xBD dataset, the largest dataset for building damage assessment, our proposed method achieves an F1 score of 0.892 for building segmentation, outperforming state-of-the-art methods. For building damage severity classification, the model achieves an F1 score of 0.632. On the building change detection datasets, the proposed method achieves F1 scores of 0.837 (LEVIR), 0.817 (LEVIR+), 0.768 (SYSU), and 0.876 (WHU), demonstrating model generalization across diverse scenarios. Despite these promising results, challenges remain in complex urban environments, small-scale changes, and fine-grained boundary detection. These findings highlight the potential of self-supervised learning in building change detection and damage assessment tasks. Full article

Most public seismic recordings, sampled at hundreds of Hz, tend to be unlabelled, i.e., not catalogued, mainly because of the sheer volume of samples and the amount of time needed by experts to confidently label detected events. This is especially challenging for very low signal-to-noise ratio microseismic events that characterise landslides during rock and soil mass displacement. Whilst numerous supervised machine learning models have been proposed to classify landslide events, they rely on a large amount of labelled datasets. Therefore, there is an urgent need to develop tools to effectively automate the data-labelling process from a small set of labelled samples. In this paper, we propose a semi-supervised method for labelling of signals recorded by seismometers that can reduce the time and expertise needed to create fully annotated datasets. The proposed Siamese network approach learns best class-exemplar anchors, leveraging learned similarity between these anchor embeddings and unlabelled signals. Classification is performed via soft-labelling and thresholding instead of hard class boundaries. Furthermore, network output explainability is used to explain misclassifications and we demonstrate the effect of anchors on performance, via ablation studies. The proposed approach classifies four landslide classes, namely earthquakes, micro-quakes, rockfall and anthropogenic noise, demonstrating good agreement with manually detected events while requiring few training data to be effective, hence reducing the time needed for labelling and updating models. Full article

Hyperloop ultra-high-speed transport presents a transformative opportunity for future mobility systems in smart cities. However, assessing its socio-economic impact remains challenging due to Hyperloop’s unique technological, modal, and operational characteristics. As a novel, fifth mode of transportation—distinct from both aviation and rail—Hyperloop requires tailored evaluation tools for policymakers. This study proposes a custom-designed framework to quantify its macroeconomic effects through changes in gross domestic product (GDP) at the city level. Unlike traditional economic models, the proposed approach is specifically adapted to Hyperloop’s multimodality, infrastructure, speed profile, and digital-green footprint. A Poisson pseudo-maximum likelihood (PPML) model is developed and applied at two technology readiness levels (TRL-6 and TRL-9). Case studies of Glasgow, Berlin, and Busan are used to simulate impacts based on geo-spatial features and city-specific trade and accessibility indicators. Results indicate substantial GDP increases driven by factors such as expanded 60 min commute catchment zones, improved trade flows, and connectivity node density. For instance, under TRL-9 conditions, GDP uplift reaches over 260% in certain scenarios. The framework offers a scalable, reproducible tool for policymakers and urban planners to evaluate the economic potential of Hyperloop within the context of sustainable smart city development. Full article

This study investigates the effect of Hull Vane^® on the total resistance and propulsion performance of a patrol vessel using computational fluid dynamics (CFD). Utilizing SHIPFLOW software, multiple simulations were conducted to evaluate how Hull Vane^® position and angle of attack influence hydrodynamic performance. A patrol vessel hull form the MAXSURF’s library was selected to investigate resistance and propulsive performance. Nine (9) configurations (named Cases A to I) of the Hull Vane^® were examined based on variations in longitudinal position and angle of attack. A grid independence study was conducted to determine the optimal mesh configuration. Validation was performed using the Holtrop–Mennen power prediction method and MAXSURF. According to this study, results indicate that Hull Vane^® configurations significantly reduce total resistance and delivered power at higher vessel speeds, with the best improvement in resistance occurring in Case C and in propulsion power in Case B. Propulsive efficiency was maximized in Case E. Furthermore, the study also demonstrates the potential of Hull Vane^® as a practical retrofit for enhancing naval vessel performance and reducing energy consumption. Full article

Although PD-1/PD-L1 inhibitors have transformed cancer immunotherapy, a substantial proportion of patients derive no clinical benefit due to resistance driven by the tumour microenvironment (TME). Transforming growth factor-β (TGF-β) is a key immunosuppressive cytokine implicated in this resistance. Several bifunctional antibodies that co-target PD-1 and TGF-β signalling have entered clinical trials and shown encouraging efficacy, but the mechanistic basis of their synergy is not fully understood. Here, we engineered 015s, a bifunctional fusion antibody that simultaneously targets murine PD-1 and TGF-β and evaluated its antitumour efficacy and mechanistic impact in pre-clinical models. Antibody 015s exhibited high affinity, dual target binding, and the effective inhibition of PD-1 and TGF-β signalling. In vivo, 015s significantly suppressed tumour growth compared with anti-mPD-1 or TGF-β receptor II (TGF-βRII) monotherapy. When combined with the CD24-targeted ADC, 015s produced even greater antitumour activity and achieved complete tumour regression. Mechanistic studies demonstrated that 015s significantly reduced tumour cell migration and invasion, reversed epithelial–mesenchymal transition (EMT), decreased microvascular density, and attenuated collagen deposition within the TME. Antibody 015s also decreased bioactive TGF-β1 and increased intratumoural IFN-γ, creating a more immunostimulatory milieu. These findings support further development of PD-1/TGF-β bifunctional antibodies for cancers with high TGF-β activity or limited response to immune checkpoint blockade. Full article

Teachers often raise their vocal volume to improve intelligibility or capture students’ attention. While this practice is common in second language (L2) teaching, its effects on tone perception remain understudied. To fill this gap, this study explores the effects of loud speech on Mandarin tone perception for L2 learners. Twenty-two native Mandarin speakers and twenty-two Thai L2 learners were tested on their perceptual accuracy and reaction time in identifying Mandarin tones in loud and normal modes. Results revealed a significant between-group difference: native speakers consistently demonstrated a ceiling effect across all tones, while L2 learners exhibited lower accuracy, particularly for Tone 3, the falling-rising tone. The loud speech had different impacts on the two groups. For native speakers, tone perception accuracy remained stable across different speech modes. In contrast, for L2 learners, loud speech significantly reduced the accuracy of Tone 3 identification and increased confusion between Tones 2 and 3. Reaction times in milliseconds were prolonged for all tones in loud speech for both groups. When subtracting the length of the tones, the delay of RT was evident only for Tones 3 and 4. Therefore, raising the speaking volume negatively affects the Mandarin tone perception of L2 learners, especially in distinguishing Tone 2 and Tone 3. Our findings have implications for both theories of L2 tone perception and pedagogical practices. Full article

Task switching is an important cognitive function required for daily life, and task reconfiguration is one of the main explanations for the origins of switching costs. Studies have demonstrated that sleep significantly affects task switching abilities. However, there remains insufficient evidence on how poor sleep quality impacts task switching abilities among university students. A total of 85 university students were included in this study and classified into a poor sleep quality group (PSQ group, n = 47) and normal control group (NC group, n = 38) based on their Pittsburgh Sleep Quality Index scores. A task-cueing paradigm with different cue-to-target intervals (CTIs) was used to evaluate the participants’ task switching abilities and explore the process of task reconfiguration. An ANCOVA and subsequent simple effect analysis showed that the RT switching costs of the NC group decreased significantly as the CTI increased. However, there was no significant decrease in the PSQ group. Additionally, a significant difference was observed between different CTI conditions in repeat trials for the PSQ group, while no significant difference was observed for the NC group. The results showed that students with poor sleep quality exhibited slower task reconfiguration processes compared to the normal controls. Additionally, their capacity to resist interference and maintain task rules was found to be impaired. Full article

Emotion regulation is essential for mental health. However, many people ignore their own emotional regulation or are deterred by the high cost of psychological counseling, which poses significant challenges to making effective support widely available. This review systematically examines the convergence of multimodal sensing technologies and large language models (LLMs) for the development of Automated Emotional Regulation (AER) systems. The review draws upon a comprehensive analysis of the existing literature, encompassing research papers, technical reports, and relevant theoretical frameworks. Key findings indicate that multimodal sensing offers the potential for rich, contextualized data pertaining to emotional states, while LLMs provide improved capabilities for interpreting these inputs and generating nuanced, empathetic, and actionable regulatory responses. The integration of these technologies, including physiological sensors, behavioral tracking, and advanced LLM architectures, presents the improvement of application, moving AER beyond simpler, rule-based systems towards more adaptive, context-aware, and human-like interventions. Opportunities for personalized interventions, real-time support, and novel applications in mental healthcare and other domains are considerable. However, these prospects are counterbalanced by significant challenges and limitations. In summary, this review synthesizes current technological advancements, identifies substantial opportunities for innovation and application, and critically analyzes the multifaceted technical, ethical, and practical challenges inherent in this domain. It also concludes that while the integration of multimodal sensing and LLMs holds significant potential for AER, the field is nascent and requires concerted research efforts to realize its full capacity to enhance human well-being. Full article

Background/Objectives: Antibiotic use is common among hospitalized pediatric patients. However, inappropriate use, including excessive use of Watch antibiotics, can contribute to antimicrobial resistance, adverse events, and increased healthcare costs. Consequently, there is a need to continually assess their usage among this vulnerable population. This was the objective behind this study. Methods: The medical records of all pediatric patients (under 12 years) admitted and treated with antibiotics at a Ghanaian Teaching Hospital between January 2022 and March 2022 were extracted from the hospital’s electronic database. The prevalence and appropriateness of antibiotic use were based on antibiotic choices compared with current guidelines. Influencing factors were also assessed. Results: Of the 410 admitted patients, 319 (77.80%) received at least one antibiotic. The majority (68.65%; n = 219/319) were between 0 and 2 years, and males (54.55%; n = 174/319). Ceftriaxone was the most commonly prescribed antibiotic (20.69%; n = 66/319), and most of the systemic antibiotics used belonged to the WHO Access and Watch groups, including a combination of Access and Watch groups (42.90%; n = 136/319). Neonatal sepsis (24.14%; n = 77/319) and pneumonia (14.42%; n = 46/319) were the most common diagnoses treated with antibiotics. Antibiotic appropriateness was 42.32% (n = 135/319). Multivariate analysis revealed ceftriaxone prescriptions (aOR = 0.12; CI = 0.02–0.95; p-value = 0.044) and surgical prophylaxis (aOR = 0.07; CI = 0.01–0.42; p-value = 0.004) were associated with reduced antibiotic appropriateness, while a pneumonia diagnosis appreciably increased this (aOR = 15.38; CI = 3.30–71.62; p-value < 0.001). Conclusions: There was high and suboptimal usage of antibiotics among hospitalized pediatric patients in this leading hospital. Antibiotic appropriateness was influenced by antibiotic type, diagnosis, and surgical prophylaxis. Targeted interventions, including education, are needed to improve antibiotic utilization in this setting in Ghana and, subsequently, in ambulatory care. Full article

Background: Malnutrition and unintended weight loss are frequent in cancer patients and linked to poorer outcomes. However, data on long-term weight trajectories, particularly comparing different cancer entities, remain limited. Methods: In this retrospective, multicenter study, we analyzed 145 patients diagnosed with either head and neck cancer (HNC; n = 48) or high-grade B-cell lymphoma (HGBCL; n = 97). Body weight, C-reactive protein (CrP), albumin, and modified Glasgow Prognostic Score (mGPS) were assessed at diagnosis and at 3, 6, 9, and 12 months. Clinically relevant weight loss was defined as >5% from baseline. Survival analyses were performed for HGBCL patients. Results: Weight loss was common in both cohorts, affecting 32.2% at 3 months and persisting in 42.3% at 12 months. Nearly half of HNC patients had sustained >5% weight loss at one year, whereas HGBCL patients were more likely to regain weight, with significantly higher rates of weight gain at 6 and 12 months (p = 0.04 and p = 0.02). At baseline, HGBCL patients showed elevated CrP and lower albumin compared to HNC (both p < 0.001). Weight loss at 6 months was significantly associated with reduced overall survival in HGBCL (p < 0.01). Both Δweight at 6 months and mGPS emerged as useful prognostic indicators. Conclusions: This study reveals distinct patterns of weight change and systemic inflammation between HNC and HGBCL patients during the first year after diagnosis. These findings highlight the need for entity-specific nutritional monitoring and tailored supportive care strategies extending into survivorship. Prospective studies integrating body composition analyses are warranted to better guide long-term management. Full article

Cancer disparities in low- and middle-income countries (LMICs) arise from multifaceted interactions between environmental exposures, infectious agents, and systemic inequities, such as limited access to care. The exposome, a framework encompassing the totality of non-genetic exposures throughout life, offers a powerful lens for understanding these disparities. In LMICs, populations are disproportionately affected by air and water pollution, occupational hazards, and oncogenic infections, including human papillomavirus (HPV), hepatitis B virus (HBV), Helicobacter pylori (H. pylori), human immunodeficiency virus (HIV), and neglected tropical diseases, such as schistosomiasis. These infectious agents contribute to increased cancer susceptibility and poor outcomes, particularly in immunocompromised individuals. Moreover, climate change, food insecurity, and barriers to healthcare access exacerbate these risks. This review adopts a population-level exposome approach to explore how environmental and infectious exposures intersect with genetic, epigenetic, and immune mechanisms to influence cancer incidence and progression in LMICs. We highlight the critical pathways linking chronic exposure and inflammation to tumor development and evaluate strategies such as HPV and HBV vaccination, antiretroviral therapy, and environmental regulation. Special attention is given to tools such as exposome-wide association studies (ExWASs), which offer promise for exposure surveillance, early detection, and public health policy. By integrating exposomic insights into national health systems, especially in regions such as sub-Saharan Africa (SSA) and South Asia, LMICs can advance equitable cancer prevention and control strategies. A holistic, exposome-informed strategy is essential for reducing global cancer disparities and improving outcomes in vulnerable populations. Full article

Background: Lysosomal storage diseases (LSDs) are a genetically and clinically heterogeneous group of inborn errors of metabolism caused by variants in genes encoding lysosomal hydrolases, membrane proteins, activator proteins, or transporters. These disease-causing variants lead to enzymatic deficiencies and the progressive accumulation of undegraded substrates within lysosomes, disrupting cellular function across multiple organ systems. While classical phenotypes typically manifest in infancy or early childhood with severe multisystem involvement, a combination of advances in molecular diagnostics [particularly next-generation sequencing (NGS)] and improved understanding of disease heterogeneity have enabled the identification of attenuated forms characterised by residual enzyme activity and later-onset presentations. These milder phenotypes often evade early recognition due to nonspecific or isolated symptoms, resulting in significant diagnostic delays and missed therapeutic opportunities. Objectives/Methods: This study characterises the clinical, biochemical, and molecular profiles of 10 adult patients diagnosed with LSDs, all representing attenuated forms, and discusses them alongside a narrative review. Results: Enzyme activity, molecular data, and phenotypic assessments are described to explore genotype–phenotype correlations and identify diagnostic challenges. Conclusions: These findings highlight the variable expressivity and organ involvement of attenuated LSDs and reinforce the importance of maintaining clinical suspicion in adults presenting with unexplained cardiovascular, neurological, ophthalmological, or musculoskeletal findings. Enhanced recognition of atypical presentations is critical to facilitate earlier diagnosis, guide management, and enable cascade testing for at-risk family members. Full article

Background: Intracerebral hemorrhage (ICH) is a severe form of stroke associated with high morbidity and mortality. While neurosurgical evacuation may offer theoretical benefits, its impact on survival and hospital course remains debated. We aimed to compare the outcomes of surgical versus conservative management in patients with lobar, capsulo-lenticular, and thalamic ICH and to identify factors influencing mortality and the surgical decision. Methods: This single-center, retrospective cohort study included adult patients admitted to the County Clinical Emergency Hospital of Sibiu (2017–2023) with spontaneous supratentorial ICH confirmed via CT (deepest affected structure determining lobar, capsulo-lenticular, or thalamic location). We collected data on demographics, clinical presentation (Glasgow Coma Scale [GCS], anticoagulant use), hematoma characteristics (volume, extension), treatment modality (surgical vs. conservative), and in-hospital outcomes (mortality, length of stay). Statistical analyses included t-tests, χ², correlation tests, and logistic regression to identify independent predictors of mortality and surgery. Results: A total of 445 patients were analyzed: 144 lobar, 150 capsulo-lenticular, and 151 thalamic. Surgical intervention was more common in patients with larger volumes and lower GCS. Overall, in-hospital mortality varied by location, reaching 13% in the lobar group, 20.7% in the capsulo-lenticular group, and 35.1% in the thalamic group. Within each location, surgical intervention did not significantly reduce overall in-hospital mortality despite the more severe baseline presentation in surgical patients. In lobar ICH specifically, no clear survival advantage emerged, although surgery may still benefit those most severely compromised. For capsulo-lenticular hematomas > 30 mL, surgery was associated with lower mortality (39.4% vs. 61.5%). In patients with large lobar ICH, surgical intervention was associated with mortality rates similar to those seen in less severe, conservatively managed cohorts. Multivariable adjustment confirmed GCS and hematoma volume as independent mortality predictors; age and volume predicted the likelihood of surgical intervention. Conclusions: Despite targeting more severe cases, neurosurgical evacuation did not uniformly lower in-hospital mortality. In lobar ICH, surgical patients with larger hematomas (~48 mL) and lower GCS (~11.6) had mortality rates (~13%) comparable to less severe, conservative cohorts, indicating that surgical intervention was associated with similar mortality rates despite higher baseline risk. However, these findings do not establish a causal survival benefit and should be interpreted in the context of non-randomized patient selection. For capsulo-lenticular hematomas > 30 mL, surgery was associated with lower observed mortality (39.4% vs. 61.5%). Thalamic ICH remained most lethal, highlighting the difficulty of deep-brain bleeds and frequent ventricular extension. Across locations, hematoma volume and GCS were the primary outcome predictors, indicating the need for timely intervention, better patient selection, and possibly minimally invasive approaches. Future prospective multicenter research is necessary to refine surgical indications and validate these findings. To our knowledge, this investigation represents the largest and most contemporary single-center cohort study of supratentorial intracerebral hemorrhage conducted in Romania. Full article