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Graphene oxide decoration with ZnAl LDH and further functionalization with APTES for enhancing the toughness of polyurethane coatings

Global patterns and trends of carbon monoxide poisoning: A comprehensive spatiotemporal analysis using joinpoint regression and ARIMA modeling, 1990–2021

Background Carbon monoxide (CO) poisoning causes approximately 41,000 deaths annually worldwide despite being preventable. Previous studies focused primarily on mortality alone, lacked systematic socio-demographic analysis, and provided no predictive models. This study comprehensively analyzes global CO poisoning patterns using spatiotemporal methods to inform evidence-based prevention strategies. Methods We analyzed Global Burden of Disease Study 2021 data from 204 countries (1990–2021) for age-standardized incidence, mortality, and disability-adjusted life years (DALYs). Joinpoint regression identified temporal trends with statistical precision, spatial statistics quantified geographic clustering, and ARIMA modeling projected trends through 2050. We examined associations with socio-demographic index (SDI) across regions and countries. Results Global age-standardized incidence rates decreased significantly by 35.1% from 12.13 (95% UI: 8.30–17.00) to 7.87 (95% UI: 5.54–10.81) per 100,000 population (annual percentage change: −1.16%, 95% UI: −1.35% to −0.96%, p < 0.001). Mortality rates declined more dramatically by 53.9% from 0.76 (95% UI: 0.66–0.91) to 0.35 (95% UI: 0.24–0.40) per 100,000 (annual change: −2.79%, 95% UI: −3.14% to −2.44%, p < 0.001). DALY rates showed the steepest reduction of 59.5% from 37.59 (95% UI: 31.75–44.76) to 15.22 (95% UI: 10.67–17.57) per 100,000 (annual change: −3.18%, 95% UI: −3.51% to −2.84%, p < 0.001). Eastern Europe demonstrated the highest burden (37.98 per 100,000 in 2021). Males experienced significantly higher mortality than females (0.50 vs 0.20 per 100,000, p < 0.001). SDI analysis revealed an inverted U-shaped relationship (Spearman’s r = 0.76, p < 0.001), with peak burden at moderate development levels (SDI: 0.6–0.7). Conclusions These findings directly address previous research gaps by demonstrating: (1) faster mortality decline than incidence decline indicates improved global treatment capabilities; (2) the SDI-burden relationship identifies moderate-development countries as priority intervention targets; (3) significant male predominance (2.5-fold higher mortality) supports gender-specific prevention programs; and (4) persistent Eastern European hotspots require targeted infrastructure improvements. Predictive models forecast continued decline through 2050 and enable evidence-based healthcare planning. This comprehensive analysis provides the first multi-dimensional global assessment, offering crucial evidence for differentiated prevention strategies worldwide.

Multimodal Siamese networks for dementia detection from speech in women

Analyzing the impact of spatial centrality and courtyard diversity on tourist attractions in the walled city of Lefkoşa

The Walled City of Lefkoşa faces the dual challenge of preserving its historic urban fabric while addressing the evolving demands of modern tourism. A key problem lies in understanding the interplay between spatial centrality within the urban street network and the diversity of courtyard activities influencing tourist attraction and engagement. This study hypothesizes that while spatial centrality plays a significant role in guiding tourist movement and enhancing visibility, the diversity and adaptability of courtyard spaces have a greater impact on enriching visitor experiences. To test this hypothesis, the research employs a mixed-methods approach, combining quantitative and qualitative analyses. PageRank and Straightness centrality analysis are used to model the street network and assess the prominence and connectivity of key streets. Simultaneously, field observations and visitor data are integrated into GIS-based mapping tools to visualize spatial patterns of movement and activity. The findings reveal that courtyards hosting diverse and adaptable activities significantly enhance tourist engagement, even in less central locations. In contrast, areas with high centrality but limited courtyard adaptability struggle to sustain prolonged visitor interactions. These results underscore the importance of integrating spatial centrality with thoughtfully designed, multifunctional courtyard spaces in urban planning strategies. This research offers actionable insights for urban planners, architects, and heritage managers, advocating for a balanced approach that preserves the historic character of urban areas while meeting the dynamic needs of contemporary tourism.

Optimizing plane detection in point clouds through line sampling

Effectiveness and challenges of digital tools implementation for enhancing infectious disease surveillance data quality in low- and middle-income countries: A systematic review protocol

Prospero ID-1023840 Background Monitoring infectious diseases is essential for preventing and controlling outbreaks, especially in low- and middle-income countries (LMICs), where issues like poor infrastructure, lack of trained staff, and limited resources can make data collection challenging. Digital tools such as mobile health apps and electronic reporting systems show promise in addressing these problems. However, it’s still unclear how well these tools actually improve the quality of data, like how quickly information is reported, how accurate it is, whether all necessary data is captured, and if the data can be trusted. Objectives This review aims to explore three main points: (1) how digital tools influence the quality of infectious disease data in LMICs; (2) what factors help or hinder their successful use; and (3) what recommendations can be made for policymakers and health workers based on the evidence. Methods We will search several databases, including PubMed/MEDLINE, EMBASE, Scopus, CINAHL, and Google Scholar, for studies published from January 2000 to July 2025. To further reduce publication bias, we will search the following institutional repositories (African Health Observatory and Indian Council of Medical Research). The types of studies are randomised trials, quasi-experimental studies, and mixed-methods evaluations that compare digital solutions with traditional methods in LMIC settings. Data extracted will include outcomes such as delays in reporting, error rates, and completeness, and factors like infrastructure and workforce readiness. The quality of each study will be assessed using ROBINS-I for non-randomized studies and ROB2 for randomized controlled trials. Where possible, we will combine data statistically using meta-analysis and analyse qualitative findings for deeper insights. Expected Outcomes This review will offer a clear picture of how effective digital tools are in improving disease surveillance. It will identify common challenges, such as poor connectivity and issues with system integration, and emphasize factors that lead to success, like proper training and government support. Overall, the findings will help shape better strategies to strengthen digital disease monitoring, finally contributing to stronger global health security.

A comprehensive survey of deep face verification systems adversarial attacks and defense strategies

Abstract Face Verification (FV) systems have exhibited remarkable performance in verification tasks and have consequently garnered extensive adoption across various applications, from identity duplication to authentication in mobile payments. However, the surge in popularity of face verification has raised concerns about potential vulnerabilities in the face of adversarial attacks. These concerns originate from the fact that advanced FV systems, which rely on deep neural networks, have recently demonstrated susceptibility to crafted input samples known as adversarial examples. Although imperceptible to human observers, adversarial examples can deceive deep neural networks during the testing and deployment phases. These vulnerabilities raised significant concerns about the deployment of deep neural networks in safety-critical contexts, prompting extensive investigations into adversarial attacks and corresponding defense strategies. This comprehensive survey provides a comprehensive overview of recent advances in deep face verification, encompassing a broad spectrum of topics such as algorithmic designs, database utilization, protocols, and application scenarios. Furthermore, we conduct an in-depth examination of state-of-the-art algorithms to generate adversarial examples and the defense mechanisms devised to mitigate such adversarial threats.

Transcranial focused ultrasound to nucleus accumbens reverses fentanyl-induced conditioned place preference in rats

Fentanyl-related opioid use disorder (OUD) continues to be a substantial public health concern. Current pharmacological treatments, using methadone and buprenorphine, can cause adverse side effects, and patients often relapse, necessitating alternative treatment strategies. Transcranial focused ultrasound (tFUS), a noninvasive neuromodulation technique with deep brain penetrability and high spatial selectivity, is emerging as a potential intervention for treatment of OUD. To evaluate utility of tFUS in modulating the function of reward circuitry, we applied functionally suppressive tFUS to the nucleus accumbens (nAcc) of male rats to determine if it attenuated fentanyl-induced conditioned place preference (CPP). Fentanyl CPP scores were significantly attenuated following tFUS administration to the nAcc, suggesting tFUS application weakens the rewarding effects of fentanyl in rats. Physiological, behavioral, and neuropathological endpoints were unaffected by tFUS, confirming that other factors did not contribute to the observed effects. Taken together, these results support the use of tFUS as a potential treatment strategy of OUD by non-invasively modulating activity in key reward-related brain regions implicated in OUD.

Younger Dryas glacier advances in the tropical Andes driven by increased precipitation

Abstract There is currently a debate about the timing and drivers of former glacier behaviour and climate change in the tropical Andes. Using 10Be dating we determined the ages of 21 boulders on moraines in the Santa Cruz Valley, Peru (∼10°S, altitudes ~ 4100 to ~ 4300 m a.s.l.). Former glacier extent is marked by a suite of nested outer lateral and terminal moraines. These moraines are dated to 11.1 ka, 11.6 ka, 11.8 ka and 12.0 ka, falling within the Younger Dryas Chronozone (YDC; ∼12.9–11.6 ka). Nine 10Be samples from the Lake Arhuaycocha catchment document a period of glacier thinning and lateral contraction between 12.0 ka and 11.8 ka. Reconstructed glacier Equilibrium Line Altitudes (ELA) at 11.0 to 12.0 ka with an area–altitude balance ratio (AABR) of 1.00-2.50 are between 4675 and 4835 m a.s.l. for the Arhuaycocha glacier, between 4692 and 4832 m a.s.l. for the Taullicocha glacier and between 4800 and 4940 m a.s.l. for the Artizon glacier. These values represent a depression of 300–400 m in elevation compared to contemporary values for the ELA. We infer that the glacier advances at this time were driven by increased precipitation and that these changes were most likely a response to seasonal changes in the position of the ITCZ.

Identifying clusters of multimorbid disease and differences by age, sex, and socioeconomic status: A systematic review

Background The prevalence of multimorbidity has been growing due to the ageing population and increasingly unhealthy lifestyles. There is interest in identifying clusters of disease and how they are influenced. Aims This systematic review aims to (i) investigate the most common clusters in the adult population with multimorbidity (ii) identify methods used to define clusters (iii) examine if clusters differ based on age, sex and socioeconomic status. Methods We searched Medline, Embase, SCOPUS, Web of Science Core Collection, and CINAHL using concepts of multimorbidity and clustering techniques to identify relevant papers. Secondary data, including commonly reported clustering techniques, identified clusters, and other characteristics were extracted. All studies were quality assessed using the Newcastle-Ottawa Bias scale. Results From a total of 24,231 papers, 125 were included in the review. There was a total of 918 different clusters identified, which were categorized into 59 broad groups. A cardiometabolic cluster appeared most frequently within the identified studies and across age strata. The most common clustering technique was Latent Class Analysis (n = 51). Disease cluster prevalence appeared to differ based on age, whereas no differences could be identified by sex. Conclusion Across the 125 papers identified, irrespective of clustering method, a relatively common set of clusters of disease were found. The Cardiometabolic cluster was the most frequently identified cluster across all age groups. Studies that stratified participants by age or sex identified distinct clusters within each subgroup, which differed from those observed in clusters formed from the general adult population (18+).Latent class analysis was the most common clustering technique within this review, but it was not explored if different clustering methods led to different clusters. Further work is needed to distinguish the most prevalent clusters within specific stratified cohorts of different ages, sex, and socioeconomic status; nonetheless, data strongly suggests that there are different clusters that arise dependent on stratifications. With the expected increasing burden of multimorbidity, healthcare services may need to think about the most prevalent disease combinations within certain strata and how joint-specialist services can be tailored to treat those common conditions.

Associations between dietary inflammatory index (DII) scores and attention deficit hyperactivity disorder (ADHD) in children

Simultaneous interpreting with auto-subtitling: Investigating viewer cognitive effort, stress, and comprehension

Simultaneous interpreting (SI) enables real-time cross-language communication without significant delays and is vital for fast-paced environments such as multilingual conferences. Automatic subtitles, powered by artificial intelligence (AI), is an important mode of audiovisual translation and has been widely deployed by virtual conferencing platforms to help users overcome language barriers. While the cognitive and emotional impacts of SI have been explored in prior studies, research directly comparing SI, auto-subtitling, and their combined use remains limited. This study investigates and compares the effectiveness of three interlingual translation modes, auto-subtitling, SI, and a combined dual-modality approach, on comprehension, cognitive load, and stress levels. Mandarin Chinese-speaking participants viewed a video presentation delivered in Arabic, a language they did not understand. Participants were divided into three groups: Group A relied on automatic subtitles in Simplified Chinese characters, Group B relied on SI in Mandarin, and Group C used a combination of both methods. Analysis of electroencephalographic data and comprehension test results revealed no statistically significant differences in content comprehension across the groups. However, Group A reported the poorest viewing experience, with the highest stress levels, while Group B expended the greatest cognitive effort. Group C exhibited the lowest levels of cognitive effort and stress, underscoring the advantages of dual-modality systems. These findings suggest that combining accurate automatic subtitles with professional interpreting may enhance accessibility, reduce cognitive demands, and improve the viewing experience, offering valuable insights into the integration of AI-driven technologies in SI.

Identification of a magnetohydrodynamic trigger for plasma explosions in magnetic fusion beyond existing paradigms

Principal component conditional generative adversarial networks for imbalanced ECG classification enhancement

With over a century of development, electrocardiogram (ECG) diagnostics has become the preferred tool for healthcare professionals in cardiovascular disease diagnosis and monitoring. As wearable devices and mobile monitoring technologies become widespread, ECG data are trending toward diversity and long-term collection, making traditional manual annotation methods inadequate for massive data analysis demands. This research addresses core challenges in ECG signal classification—extremely imbalanced data, significant individual physiological differences, and difficulties in long sequence fitting—by proposing a Principal Component Analysis-based Conditional Generative Adversarial Network (PCA-CGAN). Through in-depth analysis of ECG signal principal component distribution characteristics, we discovered that just a few principal components can explain over 90% of signal variance, revealing the inherent inefficiency and limitations of traditional complete waveform generation methods. Based on this theoretical foundation, we shift the data augmentation paradigm from generating surface waveforms to generating high information density principal component features, resolving waveform jitter and heterogeneity issues present in traditional methods. Simultaneously, we designed a two-stage conditional encoding-decoding architecture that builds category-independent feature spaces from early training stages, fundamentally breaking the feature space bias caused by the “Matthew effect” and effectively preventing majority classes from compressing minority class features during generation. Using the Transformer’s global attention mechanism, the model precisely captures key diagnostic features of various arrhythmias, maximizing inter-class differences while maintaining intra-class consistency. Experiments demonstrate that PCA-CGAN not only achieves stable convergence on a large-scale heterogeneous dataset comprising 43 patients for the first time but also resolves the “dilution effect” problem in data augmentation, avoiding the asymmetric phenomenon where Precision increases while Recall decreases. After data augmentation, the ResNet model’s average F1 score improved significantly, with particularly outstanding performance on rare categories such as atrial premature beats, far surpassing traditional methods like SigCWGAN and TD-GAN. This research redefines the objectives and methods of ECG signal generation from the theoretical perspectives of information entropy and feature manifolds, providing a systematic solution to data imbalance problems in the medical field while establishing a theoretical foundation for the application of ECG-assisted diagnostic systems in real clinical environments.

Longitudinal assessment of pragmatic and cognitive decay in healthy aging, and interplay with subjective cognitive decline and cognitive reserve

Mental health related stigma in a primary care setting in Karnataka, rural India: Service user and carer perspectives

Mental health related stigma and discrimination are significant issues requiring urgent global action. There is a particular lack of research on stigma in low-and middle-income countries. This study explores the experiences of service users and their carers who access primary care for treatment of mental health conditions in rural Karnataka, India. Purposive sampling was used to conduct qualitative interviews of service users and their carers. This study highlights several barriers to access mental healthcare that individuals face on a system level. The results also bring to light pervasive experiences of stigmatising attitudes and discriminatory behaviour that participants with mental health conditions face from within their communities. Cohesion within both the family and the community was highlighted as a priority for participants, using a “what matters most” approach. This study brings insights to contribute to the development of locally relevant anti-stigma interventions, promoting access to care, and the human rights and quality of life for individuals with mental health conditions in a rural community in Karnataka, India.

Cardiac function and extracellular matrix morphology are altered by chronic high fat diet in Drosophila larvae

Cardiovascular disease is characterized by aberrant and excessive extracellular matrix (ECM) remodelling, termed fibrosis. Fibrotic remodelling is typically triggered by inflammation, which occurs systemically in obesity. Despite the contribution of fibrosis to adverse clinical outcomes and disease progression, there are no available treatments for this condition. Developing therapeutics for chronic conditions requires an understanding of in vivo ECM regulation, and how the ECM responds to a systemic challenge. We have therefore developed a Drosophila model for obesity via chronic high fat diet feeding of larvae and evaluated the response of the cardiac ECM to this metabolic challenge. We found that this model displays a striking reorganisation of the cardiac ECM, with fibres oriented anterior to posterior, rather than in a complex network, suggesting tension modulation is altered. We also observe corresponding deficits in heart function, with high fat diet treatments resulting in an inability to contract the heart effectively at systole. Our study reveals that different genotypes tolerate different levels of dietary fat, and that some genotypes may require a different dietary supplementation regime to generate a cardiac phenotype. In summary, the Drosophila model for chronic high fat diet recapitulates many of the defects observed in human cardiovascular disease, allowing further evaluation of genetic and environmental influences on cardiac structure and physiology in disease states.

Antibiotic use in companion animals in veterinary teaching hospitals in Thailand

The high volume of antibiotics used for companion animals, off-label use of human-registered antibiotics for veterinary purposes, and close human-animal interactions raises policy concerns related to antimicrobial resistance in companion animals. This study aimed to assess the volume and type of antibiotic usage in companion animals at veterinary teaching hospitals in Thailand. In 2018, we obtained a dataset of visits that occurred between 2015 and 2017 from eight selected veterinary teaching hospitals with a high caseload of companion animals. In total, we included 938,522 dogs and 242,342 cats in our study. Total antibiotic consumption was estimated, and antibiotic usage was standardized by calculating the amount of antibiotic consumed by weight per year (mg/kg/year). The top five antibiotics used include amoxicillin–clavulanic acid, enrofloxacin, cephalexin, doxycycline, and metronidazole, all of which are commonly used to treat bacterial and parasitic infections. Critically important antimicrobials for human medicine, such as amoxicillin–clavulanic acid and enrofloxacin, were among the most used. From 2015 to 2017, total antibiotic usage (kg) markedly decreased (i.e., by 57.0%), with a particularly notable reduction of 78.2% observed for amoxicillin–clavulanic acid. Moreover, veterinarians’ overall prescriptions of antibiotics registered for human use decreased by 16% over the same period. However, there was a notable increase in the use of injectable antibiotics registered for human use relative to oral forms. Furthermore, we observed a relatively high usage of third-generation cephalosporins, which may contribute to the emergence of antimicrobial resistance in companion animals. This study emphasizes the need to educate veterinarians and veterinary students on the rational use of antibiotics and highlights the importance of regular monitoring and surveillance of antibiotic use in companion animals.

BTFormer: Blast transformer for human blastocyst components segmentation

Assessing embryo quality through segmentation of blastocyst components is crucial, as embryo morphology directly correlates with its potential for implantation. However, automatic blastocyst segmentation remains a challenging task due to factors such as poor contrast, noise, and ambiguous boundaries between different tissue structures. In this study, we introduce a novel transformer-based architecture, termed BTFormer (Blastocyst Transformer), designed to effectively segment blastocyst components. Firstly, we use an axial-free attention mechanism with lower computational resources, which catches non-local feature maps with long-range cues to alleviate the mistake of local structure. Secondly, to enjoy the rotation consistency of the embryo images, we propose an axial-free attention block with a soft aggregation operation to embed features extracted by axial-free attention with different angles, which collect global cues and broadcast a diversified receptive field. We validated our method on a typical public dataset and achieved the state-of-the-art segmentation performance with accuracy, precision, recall, Dice coefficient, and Jaccard index of 93.86%, 91.81%, 92.25%, 92.02% and 85.45%. Extensive qualitative experimental results demonstrate the effectiveness of our proposed method.

Rectal swabs vs bulk faeces PCR testing for the diagnosis of enteric conditions (RecSwabFaeces): Protocol for a single group diagnostic accuracy comparative trial

Obtaining a stool specimen for diagnostic testing of enteric conditions (e.g., gastroenteritis) can be a challenging and unpleasant experience. A person is required to obtain a sample pot from a healthcare location, return home and wait until they have a bowel motion, and then deal with the challenges of returning the sample to the clinic or pathology centre. This trial aims to identify whether the simpler approach of obtaining a rectal swab is effective for diagnosing enteric conditions. Recruitment will take place in a variety of settings, including suspected norovirus clusters, Hepatitis A clusters, in both hospital, and community settings. We will compare paired stool and rectal swab sample polymerase chain reaction (PCR) testing to determine whether rectal swabs are a reliable proxy for faecal sampling. Persons who would normally be provided with a faecal specimen container, will also be provided with a rectal swab for self-collection or clinician-collection (within 24 hours of the bulk faeces collection). We will assess the sensitivity, specificity, positive predictive value, negative predictive value using standard confusion matrix. The gold standard reference will be considered the bulk faeces PCR tests. We hypothesise that rectal swab PCR testing will be equally as effective as bulk faeces PCR testing. If successful, rectal swab PCR testing could be implemented as routine practice with several key benefits. Firstly, it would improve the patient experience by conveniently enabling stool collection at the point of care, which is rarely possible currently. Secondly, it would reduce healthcare costs, and streamline collection processes by eliminating the need for persons to return to a clinic to deliver specimen. Thirdly, the ease of testing will likely increase testing rates and compliance, leading to better diagnoses and more accurate clinical care. The protocol described has the potential to revolutionise daily clinical practice. Trial registration: Australian New Zealand Clinical Trials Registry (ANZCTR): ACTRN12624000095561.