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Current use of specific wearables and factors that would motivate future use of wearables: Results based on the general German adult population
Background A conscious use of wearables may be beneficial for health in general. Therefore, we aimed to investigate the current use of specific wearables and factors that would motivate future use of wearables in the German general adult population. Methodology/Principal findings Data were taken from a quota-based online survey which took place in January 2026 (German general adult population, mean age of 47.3 years, 18–74 years, n = 2,591). Individuals were asked about their current use of specific wearables, and non-users were also asked about the factors that would motivate future use of wearables. Slightly more than one in three (34.8%) used one or more wearables. Based on the users, 80.8% used a smartwatch, and 24.3% used a fitness tracker, whereas only a small minority used other wearables (smart ring: 5.4%; smart clothing: 4.1%; smart glasses: 2.0%; hearables: 2.9%). Key factors that would motivate future use of wearables were (in descending order of frequency): need for medical monitoring (e.g., due to future chronic illnesses) (42.0%), lower prices (33.3%), interest in monitoring health (32.9%), health data as an incentive to lead a healthy lifestyle (27.2%), experience technological advances (18.2%), and greater user-friendliness (13.5%). Regressions showed that higher odds of currently using at least one wearable were associated with, among others, being married, living with a dog, leading a healthier lifestyle, and having more chronic conditions. Conclusions/Significance Apart from smartwatches, there is still a lot of potential for growth in user numbers for other wearables. We recommend research in other countries and based on longitudinal data.
Key requirements for developing a self-care mobile application for tuberculosis: A mixed-method approach based on systematic review and needs assessment
Correction: Oropouche infection in Peruvian patients: A systematic review and meta-analysis
The ROBOKOP v1.0 knowledge graph system for exploring relationships between biomedical entities
Metabolomics-constrained modelling reveals dominant oxidative metabolism in the Egyptian fruit bat myocardium
Aim The present study aimed to elucidate which pathways contribute to cardiometabolic adaptation in Egyptian fruit bats. Methods Utilising cardiac tissues from Egyptian fruit bats ( Rousettus aegyptiacus ) and C57BL/6J mice, we combined liquid chromatography-mass spectrometry metabolic profiling, non-targeted ¹H NMR spectroscopy, and in silico computational modelling using the genome-scale mammalian network CardioNet. By integrating complementary untargeted and targeted metabolomics with genome-scale flux balance analysis, this approach enables systems-level inference of pathway activity beyond static metabolite abundance measurements. Main findings Our analyses revealed that bat hearts exhibit a distinct metabolic profile characterised by depleted glycogen reserves and increased reliance on lipid oxidation to meet energy demands. Notably, bat hearts displayed elevated fluxes in oxidative phosphorylation, β-oxidation of long-chain fatty acids, and the Krebs cycle, alongside reduced amino acid catabolism. These findings suggest that bats have evolved unique metabolic strategies to support the high-energy demands of flight, maintaining cardiac function without succumbing to pathological remodelling. Conclusions This study provides the first comprehensive insight into the metabolic adaptations in the cardiac tissue of a bat species, contributing to our understanding of how these mammals endure extreme physiological stresses.
Inhibition of runt related transcription factor 1 with Ro24-7429 improves lung function in experimental models of idiopathic pulmonary fibrosis
Parenteral nutrition in advanced cancer: A qualitative study on decision-making and information needs of patients and carers
Objectives To identify the information needs of people with advanced cancer, and their carers, to make an informed decision to commence or discontinue parenteral nutrition (PN). Methods Semi-structured interviews with people who had advanced cancer and were receiving PN, and their informal carers were audio-recorded with consent and transcribed verbatim. Analysis was conducted using a framework analysis approach. Patients were recruited via four hospitals including a cancer centre and intestinal failure units. Carers were recruited via recruiting hospitals, advertisement on social media and support group forums. Results Interviews were conducted with five patients and six carers. Five overarching themes were identified: factors affecting the decision: lack of choice and the importance of hope and advocacy, communication and information: whose role is it?, tackling discussions around benefits, risks and challenges of PN, the reality of living with home PN and neglected conversations: stopping PN and advance care planning. Patients and carers suggested essential information provided should include how to recognise complications, what to expect with home PN, and the risks and benefits of PN. They also recommended ways to improve service delivery including identification of the professional responsible for PN, improving communication through multiprofessional meetings and establishing a clear home PN pathway and service specification. Conclusions This study has identified information that patients with advanced cancer and their carers need to make decisions around commencing and discontinuing parenteral nutrition. This knowledge can contribute to the development of decision tools to support shared decision-making among patients, carers and healthcare professionals.
Mechanism elucidation of Polygonatum sibiricum-Prepared Cervus elaphus velvet antler in intervening non-alcoholic fatty liver disease based on network analysis combined with proteomics
Prerequisites and challenges of the role played by death Doula for supporting patients with cancer in end-of-life (EOL) stages: Qualitative study
End-of-life (EOL) doulas support dying people and their families. They are a response to the expectations of healthcare professionals (including palliative care providers) in end-of-life situations.The present study aimed to explore the perspective of palliative care team members about the prerequisites and challenges of the role played by death doulas to support patients with cancer in EOL stages. This qualitative study was conducted in 2023–2024 through in-depth semi-structured interviews with 14 healthcare staff. Participants were selected using purposive sampling considering maximum diversity and the study’s inclusion criteria. Conventional content analysis using Lundman and Graneheim’s approach were applied for data analysis. Data analysis revealed two main themes: the prerequisite for establishing the doula role (categories:drivers for shaping the doula role, recruitment and training of doulas, infrastructural preparation) and challenges ahead (categories:patient/family unawareness and lack of formal recognition). Based on the findings, policymakers and planners can develop operational programs to establish the role of death doulas and, by taking anticipated potential challenges into account, formulate preventive strategies. Furthermore, in light of the identified educational gaps, comprehensive educational content can be designed to adequately prepare death doulas for their roles.
Inference limits in partially observable Ethereum blockchains
Advanced persistent threat detection through multi-modal behavioral analysis
Advanced Persistent Threats (APTs) represent sophisticated cyberattacks characterized by stealth, persistence, and evasion of traditional detection mechanisms. We observed that APT behaviors during lateral movement and data exfiltration share notable similarities with insider threat activities, leading us to explore cross-domain learning opportunities. This paper introduces a novel machine learning approach leveraging the CERT Insider Threat Dataset to simulate and detect APT behaviors through AI-augmented analytics. Our methodology integrates multi-modal data analysis, language model-driven behavioral understanding, and advanced machine learning to create realistic APT simulations from insider threat data. We developed three key technical components: a multi-agent language model architecture for log analysis, temporal sequence modeling for behavioral pattern recognition, and deep evidential clustering for uncertainty-aware threat detection that reduces false positives. Our research contributes four advances: a novel methodology for simulating APT patterns using insider threat data, an AI-enhanced multi-modal approach processing structured logs and communications, superior performance compared to existing methods, and practical deployment guidelines for enterprise environments. Experimental results achieved 96.3% detection accuracy while reducing false positives by 42% compared to state-of-the-art methods. Our system successfully simulates realistic APT scenarios across attack stages while providing interpretable explanations through natural language generation. The integration of large language models enables sophisticated analysis of unstructured data sources, offering contextual understanding beyond traditional approaches. This research addresses a critical gap for organizations seeking enhanced APT detection without extensive APT-specific training data. Our approach’s ability to learn from insider threat patterns while maintaining high accuracy makes it valuable for enterprise security operations and threat hunting teams facing resource constraints.
An automated cloud-based system for in-situ geotechnical site characterization using cone penetration test (CPT/CPTu)
Modeling of solid oxide fuel cells and optimal parameter extraction at various operating data using an optimization method
One promising technology for a clean and effective energy conversion option is the solid oxide fuel cell (SOFC) being developed for a broad, widespread role in mobile equipment power supply, and stationary power generation. In this endeavor, an optimal design model based on extracted unknown parameters of the SOFC stack, a dimensional nonlinear optimization problem, is developed using the Puma optimization algorithm (POA). The idea of predator-prey relationships in the natural world forms the basis of POA. By implementing innovative and powerful techniques at every stage of exploration and exploitation, this algorithm has enhanced its performance against a broad variety of optimization tasks. Additionally, a new class of intelligent mechanisms, which is a type of phase change hyper-heuristic, is proposed. There are four operating circumstances in which the stack model is tested: four temperatures in the range 923–1073 K and 3 bar, with two conditions for validation and the others for testing the model. The proposed POA is compared with several well-known algorithms. The findings of the simulation are contrasted with those from published works using the Marine Predator Algorithm (MPA), Moth Flame Algorithm (MFA), Sine Cosine Algorithm (SCA), and Grey Wolf Optimizer (GWO), demonstrating the superior performance of POA in comparison to these competitive algorithms. Under different operating conditions, the computed polarization curves, V-I and P-I, closely resemble the measured datasets. Statistical indices and the ANOVA test confirm that there are differences in the mean values among the optimizer groups, demonstrating the viability and robustness of the proposed optimizer in comparison to other recent complex optimizers. Finally, the proposed POA yields significantly improved parameters with good convergence rates across various SOFC operating conditions.
Evaluating LED parameters in a growth chamber to maintain potato yield and enhance minitubers production in greenhouse with molecular insights
Transcriptome sequencing combined with experimental verification to explore potential key genes related to uric acid in diabetic retinopathy
Purpose Diabetic retinopathy (DR), a major microvascular complication of diabetes and leading global blindness cause, involves uric acid (UA) in its onset and progression. This study aimed to identify UA-related genes (UARGs) in DR and clarify their molecular mechanisms for improved diagnosis and treatment. Methods Using public database transcriptome data, key UARGs were screened via differential expression analysis, machine learning, receiver operating characteristic (ROC) analysis, and expression profiling, followed by gene set enrichment analysis (GSEA), immune infiltration analysis, molecular regulatory network construction, and clinical validation with reverse transcription quantitative polymerase chain reaction (RT-qPCR). Results MMP15 and FOXK1 were identified as potential key genes, with significantly elevated expression in DR patient blood samples. GSEA showed MMP15 enriched in Apc targets requiring Myc and Smarca2 targets up, and FOXK1 in phosphatidylinositol signaling system and proteasome. Immune infiltration analysis revealed differences in 7 immune cell types, with central memory CD8 T cells and natural killer cells showing the strongest positive correlation; MMP15 was negatively correlated with NK cells, and FOXK1 with central memory CD4 T cells and effector memory CD8 T cells. Twelve transcription factors (e.g., HOXB7, CATA6) jointly targeted both genes. Conclusion MMP15 and FOXK1 were identified as potential key genes associated with uric acid in DR, which may provide a reference for further exploration of the pathogenesis and targeted therapy of DR.
Fault tolerance in para-line network topologies: theory and applications in smart systems
Identification and characterization of two new markers for differentiating fall armyworm strains across the Western Hemisphere
The fall armyworm (FAW) ( Spodoptera frugiperda (J.E. Smith)) is a major pest of corn and several other crops. Although native to the Western Hemisphere, established populations were detected in western Africa in 2016 and have since been found in most corn producing areas in the Eastern Hemisphere. FAW consists of two populations historically called “host strains” that are believed to be undergoing sympatric speciation with reproductive isolation being driven primarily by host plant use. The C- and R-strains are morphologically indistinguishable and so can only be identified by a small set of molecular markers with polymorphisms in a single locus, the Triosephosphate isomerase gene ( Tpi ), currently the most commonly used for population studies. This reliance on a single marker limits confidence in the accuracy of strain identification and the consequent extrapolations of strain characteristics, indicating a need for additional markers to test these findings. This paper describes two new strain markers that provide new genetic tools for strain analysis. The data provide support for earlier conclusions about the primacy of the Z -chromosome in strain identity, the strain divergence of the Z -chromosome as a single genetic unit and expand upon previous observations to quantify differences in how each strain is evolving. These markers will facilitate further studies on the population behavior of FAW in the Western Hemisphere and should advance our understanding of the global movements of this important agricultural pest.
Large-scale multimodal pre-trained model driven ceramic design knowledge graph construction and cross-domain innovative design reasoning mechanism
A large language model framework for sample-free population synthesis
Synthetic populations provide the demographic foundations for agent-based models in transport, public health, disaster management and other sectors, enabling credible representations of individual characteristics and behaviours. Many established synthesis methods rely on census microdata; however, such data are infrequently collected, privacy-restricted, and usually available only as small public-use samples at coarse geographic scales. This paper introduces a sample-free framework that uses a large language model (LLM) to generate complete, household-structured populations directly from aggregate demographic data. The framework is LLM agnostic and follows a multi-step process: objective definition, input preparation, LLM selection, and synthetic household generation. No model fine-tuning is required, meaning that data requirements are low and the framework is easily accessible. Population synthesis is formulated as an iterative prompting process in which an LLM generates households guided by the discrepancies between synthetic and target distributions. The model draws on prior knowledge encoded during pre-training to propose plausible attribute combinations, resulting in both statistical alignment and structural feasibility. In a global evaluation covering 109 countries, the framework achieved very close alignment on simpler marginals such as gender (SRMSE: 0.003) and household size (SRMSE: 0.026), while more structurally complex attributes such as household composition (SRMSE: 0.062) and age (SRMSE: 0.128) were also reproduced with good accuracy. These results were supported by detailed case studies in Newcastle upon Tyne (UK) and Dar es Salaam (Tanzania). The principal contribution of the framework is to enable the construction of coherent household-structured populations when detailed microdata are unavailable, expanding the applicability of agent-based modelling in data-constrained settings.