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Degradable Diblock Copolymer Vesicles via Radical Ring-Opening Polymerization-Induced Self-Assembly in Aqueous Media

Health service utilization and associated factors among fee waiver beneficiaries in Ethiopia: Systematic review and meta-analysis

Background Health service utilization serves as a vital indicator of healthcare access and equity. In Ethiopia, the fee waiver system is a key component of healthcare financing reforms designed to improve access to essential health services for economically disadvantaged populations. However, the evidence regarding health service utilization among fee waiver beneficiaries remains inconsistent. This systematic review and meta-analysis synthesize existing studies to provide comprehensive insight on health service utilization and associated factors among fee waiver beneficiaries in Ethiopia. Methods A systematic search of peer-reviewed articles and gray literature was conducted up to February 2024, in databases such as PubMed/MEDLINE, African Journals Online (AJOL), Cumulative Index to Nursing & Allied Health Literature (CINAHL), Science Direct, Research4life, and Google Scholar. A systematic review and meta-analysis were conducted in accordance with the PRISMA guidelines. Data were extracted using Microsoft Excel and analyzed with STATA 17 software. The quality of studies was assessed using Joanna Briggs Institute (JBI) checklists. The pooled prevalence of health service utilization among fee waiver beneficiaries was estimated using random-effects meta-analysis. Subgroup analyses were performed based on study regions. Publication bias was evaluated with a DOI plot, the Luis Furuya Kanamori (LFK) index, and Egger’s test, while heterogeneity was assessed using the I² statistic. Results The study analyzed seven primary studies comprising a total of 11,488 participants. All the included studies demonstrated a low risk of bias, and no significant evidence of publication bias was detected among them. The pooled prevalence of health service utilization was found to be 60.57% (95% CI: 58.11–63.04; I² = 54.2%, p = 0.041). A family size of fewer than five was negatively and significantly associated with health service utilization (OR = 0.69, 95% CI: 0.51–0.95; I² = 0.0%, p = 0.47). On the other hand, having chronic diseases was positively and significantly associated with health service utilization among fee waiver beneficiaries (OR = 4.85, 95% CI: 1.34–17.56; I² = 93.5%, p < 0.001). Residence showed no significant association (OR = 1.58; 95% CI: 0.03–71.49), with wide confidence intervals reflecting considerable uncertainty. Conclusion The findings suggest that a significant number of beneficiaries accessed health services, indicating that the system is likely contributing to enhanced healthcare access for the target population. However, this also highlights the need for further efforts to ensure broader and more equitable utilization. The analysis reveals that health service utilization is negatively associated with a family size of fewer than five and positively associated with having chronic diseases. To improve the utilization rate among poor populations, policymakers in Ethiopia should implement integrated strategies that address these key factors and target barriers to healthcare access.

Manipulating Hydrocarbon Chain-Melting Transitions in Dialkylammonium Halide Barocaloric Materials through Desymmetrization

Optimizing document management and retrieval with multimodal transformers and knowledge graphs

In the digital age, multimodal archival data is experiencing explosive growth, and how to efficiently and accurately retrieve information from it has become a key challenge. Traditional retrieval methods struggle to effectively handle multi-source heterogeneous multimodal data, leading to poor retrieval accuracy and efficiency. To address this issue, this paper proposes the MDKG-RL model, which organically integrates knowledge graph reasoning, deep reinforcement learning dynamic optimization, and multimodal Transformer architecture to achieve deep semantic understanding of multimodal data and intelligent optimization of retrieval strategies. The experiments, based on the ICDAR 2023 and AIDA Corpus datasets, show that MDKG-RL achieves a mean reciprocal rank (MRR) of 0.85, a normalized discounted cumulative gain (NDCG) of 0.88, and an entity linking accuracy of 92.4%. Compared to the baseline model, MRR improves by 13.3%, NDCG increases by 12.8%, and response time is reduced by 38.2%, significantly outperforming other comparison models. Ablation experiments also confirm the indispensability of each module. Visual analysis further demonstrates the model’s clear advantages in retrieval accuracy and efficiency, though error analysis reveals its shortcomings in handling long-tail entities and cross-modal ambiguity. The MDKG-RL model provides an innovative and effective solution for multimodal archival retrieval, not only improving retrieval performance but also laying the foundation for future research. In the future, model performance and generalization capabilities can be further enhanced by expanding data, optimizing strategies, and extending application scenarios, thereby promoting the development and application of multimodal retrieval technology in the fields of information management and knowledge discovery.

Oxaborole-Functionalized Sialylated Glycan Probe for High-Fidelity Fluorescence Imaging of Cancer Tissue

Prevalence and determinants of prehypertension (elevated blood pressure or high normal BP) according to different classifications in India during 2015–2021: Evidence from the large national surveys

Background Since the advent of American Joint National Commission (JNC-7) guidelines, epidemiological studies have reported that prehypertension is a common presentation in the general population, with a prevalence of 25% to 55% globally. The present study aimed to estimate the prevalence of prehypertension (elevated blood pressure or high normal BP) and its determinants based on different standard classifications using the large population-based data from the fourth and fifth rounds of National Family Health Surveys (NHFS), India. We also intended to identify the trends of prehypertension between NFHS-4 and NFHS-5 at national, state and district levels. Methods We analyzed the data from the National Family Health Surveys (NFHS) 4 and 5 conducted in 2015−16 and 2019−20, respectively. Prevalence of pre-hypertension and its equivalent terms, elevated blood pressure and high normal BP was reported as per the Joint National Committee (JNC 7), 2017 American College of Cardiology/American Heart Association (ACC/AHA), and Indian Guidelines for Hypertension (IGH –IV) respectively. GeoDa (spatial and cluster maps) was used to compute Local Indicators of Spatial Association (LISA). We also calculated Moran’s Index to explain the data’s overall clustering and project the strength and patterns of spatial autocorrelation to represent district-level results. Results Prevalence of prehypertension (elevated blood pressure or high normal BP) showed an increasing trend across all three classifications from NFHS-4 to NFHS-5 in India (35.8% vs. 48.8% as per JNC 7, 6.1% vs 8.8% as per ACC/AHA and 12.5% vs 20.8% according to IGH-IV). Age > 29 years was significant risk factors for pre-hypertension in both the surveys as per JNC 7 and IGH -IV guidelines. Women had higher odds of having prehypertension according to all three guidelines in both surveys. Education had a protective effect across classifications as evident from NFHS-5 data, which was variable in the previous NFHS-4 survey. The prevalence of prehypertension (JNC 7/8) has increased above 50% in NFHS-5 survey in most states of India, namely, Delhi, most districts of Punjab, Himachal Pradesh, Haryana, Rajasthan, Uttarakhand, Uttar Pradesh, Chhattisgarh, Madhya Pradesh, Jharkhand, Odisha, Manipur, Mizoram, Arunachal Pradesh, Tamil Nadu, Lakshadweep and Andaman and Nicobar Islands. However, Goa, Sikkim, Assam, Nagaland and West Bengal demonstrated a declining trend in prevalence of prehypertension. In NFHS-5, 117 districts were observed as hotspots (“high-high” clustering) clustered zones, mostly in Arunachal Pradesh, Rajasthan, Madhya Pradesh, Uttar Pradesh, and Punjab. Conclusion We found a high prevalence of prehypertension in large population based survey in Indian population. The findings also highlighted marked differences in estimates of prehypertension (elevated blood pressure or high normal BP) based on different classifications. These results will help guide researchers, public health policymakers and clinicians to uniformly define prehypertension for its effective management. These trends should be considered as an interim warning signal to formulate guidelines with strong implementation of interventions to prevent and control prehypertension and hypertension.

Synthesis of a Stable Tricobalt Carbide Cluster

How to influence the continuous usage intention of game-based Internet public welfare users? An empirical analysis based on SEM and fsQCA

This study focuses on UTAUT and integrates the D&M, SDT, and SET models to explore the factors influencing continuous usage intention in the context of gamified online public welfare. By applying structural equation modeling and fuzzy-set qualitative comparative analysis to 389 valid survey samples, this study identifies that public value, leisure and entertainment, and self-payment significantly influence users’ continuous usage intention. Furthermore, game design and tool convenience, along with subjective norms, are key drivers of these three factors. In contrast, gaming convenience and social factors have limited impact on user engagement. The findings reveal three enhancing configurations—functionality-driven, comprehensive support, and pure public value paths—and four inhibiting configurations—complete disengagement, social impact without incentives, convenience without enjoyment, and engagement without enjoyment paths. The crucial role of subjective norms is found to be consistently significant in both the SEM and fsQCA results, making it a key factor in promoting user engagement in gamified public welfare activities. This research contributes to the theoretical understanding of user participation in game-based charity activities and provides insights for improving user retention. Its originality lies in the integration of SDT and SET models alongside D&M and UTAUT, as well as the use of mixed methodologies to examine the multifaceted drivers of continuous usage intention.

Visualization and Quantification of Base-Level SO₂ in Live Cells without Intracellular Background Interference Using Sensitive ¹⁹F-NMR

Estimating nesting habitat characteristics for the Kentish plover (Anarhynchus alexandrinus) with the effect of substrate and vegetation using a Bayesian network approach

Coastal habitats play an important role in the nesting ecology of shorebirds; however, these habitats are increasingly threatened by human activity and ongoing habitat loss. The conservation of shorebird populations thus necessitates understanding the utilization pattern of artificial coastal habitats by these birds. Substrate particle size and vegetation cover are key environmental factors influencing the nest site selection and nest success in ground-nesting shorebirds such as plovers. This study aimed to investigate the impact of substrate particle size and vegetation cover for the Kentish plover (Anarhynchus alexandrinus) nesting sites within an artificial coastal environment, the Saemangeum reclaimed land. Geological criteria and 1-m2 quadrat photos were used to develop Bayesian network (BN) models to analyze the impact of these variables on nest site selection and nest success in 2020. The BN models predicted the impact of substrate particle size and vegetation cover on the likelihood of nest presence and nest success. The results indicated that Kentish plovers prefer sandy sites with moderate vegetation cover and achieve higher nest success in habitats with mixed soil types, including medium (0.25–0.5 mm), fine (0.125–0.25 mm), and very fine (0.063–0.125 mm) sand, along with small proportions of mud (<0.063 mm). These findings highlight the importance of evaluating the complex interactions between plover nests and substrate characteristics, including soil porosity and permeability. Vegetation cover must also be managed with attention to the trade-offs involved, such as predation risk, nest camouflage, crypsis, and thermoregulation which influence plover nesting preference and success. This study provides valuable quantitative insights and emphasizes the need for incorporating multi-layered ecological factors along with inherent uncertainties in coastal environments to restore appropriate artificial coastal habitats for shorebird conservation.

Domino Effect of Catalysis: Coherence between Reaction Network and Catalyst Restructuring Accelerating Surface Carburization for CO₂ Hydrogenation

Analysis of variant interactions in families with autism points to genes involved in the development of the central nervous system

Whole-genome sequencing data of simplex families with autism spectrum disorder (ASD) were analyzed by searching for statistical interactions between loci. The resulting variant pairs mapped to 411 genes, of which 368 had not been associated with ASD before. The variants were used to build an ASD predictor based on an open-source machine learning library. The predictor correctly classifies over 78% of samples from a test set with an average significance level of 8.9· 10-158. Gene Ontology (GO) enrichment analysis of the identified risk genes points to functions related to the development of the Central Nervous System (CNS). Clustering cases on the basis of risk variants improves predictor accuracy and reveals additional overrepresented GO terms. Some of the detected statistical interactions can be linked to known biological interactions between genes involved in the development of the CNS. Analysis of the statistical interactions also points to genes whose biological functions are not yet known.

Study protocol for developing an urban deprivation index in Nepal: Data review, measurement, visualization and real-world application in urban poverty alleviation

Background Over the last two decades, Nepal has experienced substantial urbanization, with an increasing number of people choosing to move to cities. Although cities offer a wealth of opportunities, it also provides significant challenges. Many of those migrating to and living in cities contend with poor conditions and live in poverty. Defining and measuring urban deprivation is challenging due to its multi-dimensional nature, encompassing various dimensions such as housing, employment, living expenses, education, healthcare, and other unique challenges associated with city life. This study draws on the ‘Domains of Urban Deprivation Framework’ and evaluates the availability, commonness, and applicability of these domains. It measures multiple urban deprivation indices relevant to the context of Nepal. Method The research will commence with a review of the availability of data covering the urban domains listed in the Urban Deprivation Framework within routine data collected in Nepal at the province, district, and municipal levels. This will involve examining existing datasets and identifying any gaps or limitations in the data that could impact the construction of the local urban deprivation indices. To understand the commonness of different urban deprivation within different urban contexts in Nepal, a Delphi survey will be conducted in two municipalities and nationally with government policymakers, community representatives, data experts/researchers, and civil society actors. In the three urban contexts, stakeholders will prioritize and weigh the indicators according to their respective urban contexts and rank domains that reflects the priorities across different geographical areas and stakeholder communities. We will compare responses across these groups of stakeholders and explore contextual differences. The composite score for each domain will be calculated by summing the weighted scores of all indices and normalizing the results to ensure that they fall within a defined range. We will then plot the deprivation indices in urban areas at the provincial, district, and municipal levels. The urban deprivation index in Nepal will provide granular data that will enable policymakers and stakeholders to explore the urban deprivation index visually and access key insights for informed decision-making and resource allocation. Ethics and dissemination Ethical approval was obtained from the Ethical Review Board of Nepal Health Research Council (Reference number: 213/2024) and the School of Medicine Research Ethics Committee at the University of Leeds, UK. The findings will be disseminated in a peer-reviewed journal and presented at conferences.

Unveiling the Solvation Chemistry and Surface Effects on CO₂ Reduction Reaction Pathways in Nonaqueous Li–CO₂ Batteries

Simultaneous EEG-fNIRS study of visual cognitive processing: ERP analysis and decision-related hemodynamic responses in healthy adults

This study explores the neural and hemodynamic underpinnings of intentional memory processing through a multimodal approach combining electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS). Data were drawn from a publicly available dataset in which participants viewed visual scenes and decided whether to remember them, enabling classification into four experimental conditions based on motivation and subsequent memory performance: Want to Remember and Remembered (RR), Want to Remember but Forgot (RF), Did Not Want to Remember but Remembered (FR), and Did Not Want to Remember and Forgot (FF). EEG analyses focused on event-related potentials (ERPs) during the first second following stimulus presentation. The RR and RF conditions showed enhanced ERP amplitudes, particularly in parietal and occipital channels, peaking around 300 ms post-stimulus. Time-frequency analysis using wavelet transform further revealed greater theta and low alpha power in the RR and RF conditions, again especially in parietal and occipital regions. fNIRS analysis examined hemodynamic responses during the subsequent 9-second decision period. While visual inspection revealed variability in oxygenated hemoglobin (HbO) levels across channels and conditions, statistical analyses using Cohen’s D and one-way ANOVA did not identify any significant differences between the conditions (p > 0.05). These findings suggest that while EEG metrics capture early, intention-driven neural dynamics, fNIRS may reflect more distributed and variable patterns of cognitive engagement. The integration of EEG and fNIRS provides a comprehensive framework for investigating cognitive motivation and memory, highlighting the temporal and spatial signatures of intentional memory processing.

Mechanism-Guided Development of Directed C–H Functionalization of Bicyclo[1.1.1]pentanes

Assessment of heavy metals in soil and water from Bahi district, Tanzania

This study presents analysis of heavy metals concentrations in soil and water samples collected from villages near Bahi Swamp found in Bahi district, Tanzania. The research involved quantitative analyses methods of heavy metals in the laboratory and the use of descriptive statistical methods to draw conclusion. A total of 45 soil samples and 21 water samples were collected from five locations near Bahi Swamp which was pre-selected from northern and the eastern part. Heavy metals from soil and water samples were analyzed by using energy dispersive X-ray fluorescence (ED-XRF) spectrometry and atomic absorption spectrometry (AAS) technique, respectively. The concentrations of nine heavy metals (Pb, Cd, Mn, Zr, Cu, As, Zn, Sr, Cs) in soil samples and three in water samples (Mn, Zn, Cu) were determined. The mean concentrations of heavy metal from soil samples were Pb (29.60 ± 7.50 mg kg-1), Cd (403.20 ± 507.44 mg kg-1), Mn (478.27 ± 245.86 mg kg-1), Zr (206.00 ± 79.47 mg kg-1), Cu (52.00 ± 5.24 mg kg-1), As (5.27 ± 1.66 mg kg-1), Zn (48.47 ± 31.18 mg kg-1), Sr (21.93 ± 36.99 mg kg-1), and Cs (34.00 ± 10.95 mg kg-1). Soil samples exhibited diverse trace element patterns, with concentrations following the order As <Pb < Zn < Cu < Sr < Cs < Zr < Cd < Mn. Notably, Cd concentrations in Bahi Sokoni (BSS) and Bahi Matajila (BTS) exceeded other locations by over 20 times of the concentrations ranged from 38 to1157 mg kg-1. Manganese (Mn) concentrations varied significantly, but were still within the permissible limits set by regulatory authorities such as, FAO/WHO, US EPA and TBS. Also, the study of metal oxides found that the mean concentration of tellurium oxide (TeO2) in soil samples was significantly higher than typical natural abundance levels. Water samples displayed a wide range of Mn concentrations, while Zn was detected in specific sites. Thus, the findings pose a potential health and environmental concerns, serving as contribution for further research and highlighting environmental regulatory compliance with an emphasis that Bahi is known for its uranium deposits.

Stereoselectivity of Aminoacyl-RNA Loop-Closing Ligation

Coli bond: A dual-function encryption system for secure information storage and transmission by microorganisms

With global data expected to reach 175 zettabytes by 2025, traditional storage methods face unprecedented challenges, including security risks, limited durability, and high maintenance costs associated with centralized infrastructure. While DNA-based storage systems have demonstrated high density and chemical stability, most existing methods focus primarily on static storage, lacking effective strategies for secure and controllable information transmission. Coli Bond offers a revolutionary approach by combining the molecular precision of DNA storage with the controllable dynamics of synthetic biology, providing an innovative platform for data encryption and storage. In this system, controllable dynamics refer to information transfer regulated by caffeine concentration and temperature. The system leverages synthetic biology to engineer an auxotrophic Escherichia coli strain with a caffeine degradation pathway, enabling precise control of information transfer through conditional growth. A temperature-sensitive self-destruction mechanism ensures irreversible destruction of stored information under specific conditions, preventing unauthorized access and enhancing data security. Experimental validation demonstrated the system’s stability and reliability under various real-world conditions, including survival and function in commercial beverages, during transmission cycles, and under temperature variation. The results confirmed high transmission efficiency during initial contact and a rapid decline in strain viability after multiple transfers, providing an inherent layer of security. By integrating the high density of DNA storage with the dynamic control capabilities of synthetic biology, “Coli Bond” offers a secure and adaptable platform for the storage and transmission of DNA-encoded information, paving the way for future advancements in information storage and transmission technologies.

Revisiting the Hunter-Sanders Model for π–π Interactions