Browse Articles
Discover research articles across all indexed journals
Anticoagulant effects, substance basis, and quality assessment approach of Aspongopus chinensis Dallas
Background Aspongopus chinensis Dallas holds both medicinal and culinary significance in China. Notably, in regions such as Guizhou and Yunnan, it has been traditionally used as an ethnic remedy for treating various conditions, including stomach coldness, pain, kidney deficiencies, and impotence, among other ailments. This study aims to explore the chemical constituents and anticoagulant activity of A. chinensis. Methods Ultraperformance liquid chromatography-tandem quadrupole time-of-flight mass spectrometry (UPLC-Q-TOF-MS) was employed for the UPLC fingerprint analysis. The isolated compounds underwent in vitro assays to their evaluate anticoagulant properties and effects on protein fibril activity. Network pharmacology was utilized to predict potential anticoagulant targets. Furthermore, animal experiments were conducted to measur coagulation factors and assess the in vivo anticoagulant activity. Results Our findings indicate that 10 compounds were identified through UPLC-Q-TOF-MS analysis, with four compounds (uracil; 6-hydroxyquinolinic acid; 1,4-dihydro-4-oxoquinoline-2-carboxylic acid; delicatuline B) were isolated and identified from the n-butanol extract of A. chinensis. Furthermore, principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (PLS-DA) of the UPLC fingerprint data revealed significant differences between A. chinensis and its similar insects. Notably, 1,4-dihydro-4-oxoquinoline-2-carboxylic acid has been identified as a potential standard reference substance for content determination. The isolated compounds showed anticoagulant properties, and the mRNA expression levels of MMP9 and PTGS2 were significantly reduced in LPS-induced RAW264.7 cells, further supporting the network pharmacology analysis. Animal experiments confirmed the potent anticoagulant effects of A. chinensis, likely associated with the intrinsic coagulation pathway and the inhibition of platelet aggregation, Conclusion This research provides a quality assessment method for A. chinensis, and has also demonstrated its anticoagulant functions and substance basis
Discrete Brush Polymers Enhance <sup>19</sup>F MRI Performance through Architectural Precision
Application of open domain adaptive models in image annotation and classification
In the field of computer vision, the task of image annotation and classification has attracted much attention due to its wide demand in applications such as medical image analysis, intelligent surveillance, and image retrieval. However, existing methods have significant limitations in dealing with unknown target domain data, which are manifested in the problems of reduced classification accuracy and insufficient generalization ability. To this end, the study proposes an adaptive image annotation classification model for open-set domains based on dynamic threshold control and subdomain alignment strategy to address the impact of the difference between the source and target domain distributions on the classification performance. The model combines the channel attention mechanism to dynamically extract important features, optimizes the cross-domain feature alignment effect using dynamic weight adjustment and subdomain alignment strategy, and balances the classification performance of known and unknown categories by dynamic threshold control. The experiments are conducted on ImageNet and COCO datasets, and the results show that the proposed model has a classification accuracy of up to 93.5% in the unknown target domain and 89.6% in the known target domain, which is better than the best results of existing methods. Meanwhile, the model check accuracy and recall rate reach up to 89.6% and 90.7%, respectively, and the classification time is only 1.2 seconds, which significantly improves the classification accuracy and efficiency. It is shown that the method can effectively improve the robustness and generalization ability of the image annotation and classification task in open-set scenarios, and provides a new idea for solving the domain adaptation problem in real scenarios.
Cation–Anion Regulation Engineering in a Flame-Retardant Electrolyte toward Safe Na-Ion Batteries with Appealing Stability
From the jaws of the “Leviathan”: A sperm whale tooth from the Valencina Copper Age Megasite
During the excavations undertaken in 2018 at the Nueva Biblioteca sector of the Valencina Copper Age mega-site, in south-west Spain, an exceptional sperm-whale tooth was found inside a non-burial pit. This remarkable object is the first of its kind ever found for Late Prehistoric Iberia. Due to its rarity and importance, a multidisciplinary study was carried out, including photogrammetric 3D modelling, as well as taphonomic, paleontological, technological and contextual analysis. This led to a full characterisation of the artefact through the analysis of its bioerosion traces, anthropogenic marks, depositional context and socio-cultural background. The ensuing discussion covers the history and processes the tooth went through from the death of the animal and disposal on the seabed, through the disarticulation of the tooth to its collection in a coastal environment and its subsequent use and deposition in the pit.
Intrinsic Off-Centering and Light Conduction Band Structure Lead to High Thermoelectric Performance in N-Type Diamondoid AgInSe<sub>2</sub>
Association between early-pandemic food assistance use and subsequent food security trajectories among households in Washington State during the first three years of the COVID-19 pandemic
Background Research on COVID-19’s impact on food insecurity has primarily relied on cross-sectional data or long recall periods, with limited investigations into longitudinal patterns or the role of food assistance. Methods We analyzed longitudinal data from 703 respondents participating in at least three Washington State Food Security Survey waves between June 18, 2020, and January 7, 2023. We assessed food security using the United States Department of Agriculture’s six-item module, categorizing respondents’ trajectories as persistently food secure, persistently food insecure, or experiencing one or more food insecurity transitions. We categorized food assistance use as never used, used before COVID-19 but not at baseline, did not use before COVID-19 but used at baseline, or always used. We descriptively examined sociodemographic factors linked to each food security trajectory and food assistance use pattern. We assessed associations between food assistance use and food security trajectories using modified Poisson regression. Results We found that 20.2% of respondents were persistently food insecure, and 22.5% experienced one or more food insecurity transitions. Both patterns were more common among respondents who were aged 35 to 64, had a gender identity other than man or woman, were non-Hispanic Black, were single or divorced, had children, had some college education or less, reported $35,000 or less in household income, or were unemployed. In fully adjusted models, respondents who were newly using food assistance early in the COVID-19 pandemic had a higher probability of being persistently food insecure (marginal effect [ME] = 0.320, 95% CI = 0.204, 0.436) or experiencing one or more food insecurity transitions (ME = 0.216, 95% CI = 0.069, 0.363), than those who never used assistance. Conclusions Our findings highlight the importance of examining food security trajectories and food assistance use patterns and implementing policies that help households new to food assistance programs navigate these systems.
Unraveling Atomic-Level Mechanisms of Structural Transitions in Lithium Cobalt Oxide under High-Voltage Conditions
Overexpression of LINC00672 promotes autophagy in Alzheimer’s disease by upregulating GPNMB
Background Alzheimer’s disease (AD) is an irreversible neurodegenerative brain disorder, and autophagy crafts a new dawn on AD therapeutics. However, whether LINC00672 exerts its biological effects involvement in autophagy-mediated mechanisms in AD remain obscure. Methods SH-SY5Y cells were treated with Amyloid Beta 1–42 (Aβ1-42, Aβ), while an AD mouse model was established using streptozotocin (STZ). The effects of LINC00672 overexpression on cell proliferation, apoptosis, and autophagy were evaluated in Aβ-stimulated SH-SY5Y cells. Besides, the impact of LINC00672 on cognitive function and pathological changes of the hippocampal tissues were validated in AD mice. Additionally, the interaction between LINC00672 overexpression and GPNMB silencing were determined in vitro. Results Aβ stimulation diminished viability, augmented apoptosis, restricted the activation of autophagy in SH-SY5Y cells, while these alterations were partially abolished by LINC00672 overexpression. Furthermore, LINC00672 upregulation could improve cognitive impairment, and attenuate neuronal damage and even death in the STZ-treated AD mice. Additionally, GPNMB knockdown aggravated the improved neuronal injury and relatively restrained autophagy in Aβ-stimulated cells after LINC00672 overexpression. Conclusions LINC00672 exerted a protective effect in the AD progression by upregulating GPNMB to promote autophagy.
Direct Observation of C–F Bond Formation from Isolable Palladium(IV) Systems via an Outer-Sphere Pathway
Perceiving speech from a familiar speaker engages the person identity network
Numerous studies show that speaker familiarity influences speech perception. Here, we investigated the brain regions and their changes in functional connectivity involved in the use of person-specific information during speech perception. We employed functional magnetic resonance imaging to study changes in functional connectivity and Blood-Oxygenation-Level-Dependent (BOLD) responses associated with speaker familiarity in human adults while they performed a speech perception task. Twenty-seven right-handed participants performed the speech task before and after being familiarized with the voice and numerous autobiographical details of one of the speakers featured in the task. We found that speech perception from a familiar speaker was associated with BOLD activity changes in regions of the person identity network: the right temporal pole, a voice-sensitive region, and the right supramarginal gyrus, a region sensitive to speaker-specific aspects of speech sound productions. A speech-sensitive region located in the left superior temporal gyrus also exhibited sensitivity to speaker familiarity during speech perception. Lastly, speaker familiarity increased connectivity strength between the right temporal pole and the right superior frontal gyrus, a region associated with verbal working memory. Our findings unveil that speaker familiarity engages the person identity network during speech perception, extending the neural basis of speech processing beyond the canonical language network.
Quantum Interference in a Molecular Analog of the Crystalline Silicon Unit Cell
Deep VMD-attention network for arrhythmia signal classification based on Hodgkin-Huxley model and multi-objective crayfish optimization algorithm
Recent research for arrhythmia classification is increasingly based on AI-driven approaches, which are primarily grounded in ECG data, but often neglect the mathematical foundations of cardiac electrophysiology. A finite element model (FEM) of the human heart, grounded in the Hodgkin-Huxley (HH) model was established to simulate cardiac electrophysiology, and ECG signals from 200 representative points were acquired. Two types of arrhythmia characterized by significant anomalies in the variables of the HH model were simulated, and corresponding synthetic ECG signals were generated. A multi-objective optimization method based on non-dominated sorting was integrated into the crayfish optimization algorithm (MOCOA). To optimize the key parameters K and α in variational mode decomposition (VMD), a MOCOA-VMD technique specifically tailored for ECG signal processing was developed. The Pareto optimal front was generated using MOCOA with the indicators of spectral kurtosis and KL divergence, by which the optimal intrinsic mode functions were obtained. A deep VMD-attention network based on MOCOA was developed for ECG signal classification. The ablation study evaluated the effectiveness of the proposed signal decomposition method and deep attention modules. The model based on MOCOA-VMD achieves the highest accuracy of 94.46%, outperforming models constructed using EEMD, VMD, CNN and LSTM modules. Bayesian optimization was employed to fine-tune the hyperparameters and further enhance the performance of the deep model, with the best accuracy of the deep attention model after TPE optimization reaching 96.11%. Moreover, the real-world MIT-BIH arrhythmia database was utilized for further validation to prove the robustness and generalizability of the proposed model. The proposed deep VMD-attention modeling and classification strategy has shown significant promise and may offer valuable inspiration for other signal processing fields as well.
Microdroplet Cascade Catalysis for Highly Selective Production of Propylene Glycol under Ambient Conditions
Efficacy and immunogenicity of rKVAC85B in a BCG prime-boost regimen against H37Rv and HN878 Mycobacterium tuberculosis strains
Mycobacterium tuberculosis infection accounted for 1.3 million deaths worldwide in 2022. Bacillus Calmette-Guérin (BCG) is the only licensed vaccine against tuberculosis (TB); however, it has limited protective efficacy in adults. In this study, we constructed a recombinant vaccinia virus expressing Ag85B from M. tuberculosis using a novel attenuated vaccinia virus (KVAC103). We then analyzed the immunogenicity of prime-boost inoculation strategies using recombinant KVAC103 expressing Ag85B (rKVAC85B) compared to BCG. In both rKVAC85B prime-boost and BCG prime-rKVAC85B boost inoculation regimens, rKVAC85B induced the generation of specific immunoglobulin G (IgG) and secretion of interferon-γ by immune cells. In vitro analysis of Mycobacterium growth inhibition revealed a comparable immune-mediated pattern of outcomes. Furthermore, bacterial loads in the lungs were significantly lower in mice inoculated with the BCG prime-rKVAC85B boost than in the BCG-only group following a rechallenge infection with both H37Rv and HN878 strains of M. tuberculosis. These findings collectively suggest that KVAC103, incorporated into a viral vector, is a promising candidate for the development of a novel TB vaccine platform that is effective against multiple M. tuberculosis strains, including H37Rv and HN878, and that rKVAC85B effectively stimulates immune responses against M. tuberculosis infection.
Anchoring Side Chains to Carbonate Groups for Reviving Stable Polycarbonate-Based Solid-State Lithium Metal Batteries
Examining the impact of social media usage on start-ups performance: Mediating role of brand image
Social media has emerged as an assertive communication and brand-building tool in the dynamic entrepreneurship landscape. This study explores the influence of social media usage on the performance of start-ups, focusing on the mediating role of brand image. The research employs a quantitative approach, collecting data from 450 start-ups in Ghana through surveys. Data collected was processed and analyzed through PLS-SEM. The findings of the study supported all the formulated hypotheses. The outcome suggests that, social media, brand image and innovation capabilities all have direct and positive linked with startup performance. Additionally, the findings proved a mediating role of brand image between social media usage and startup performance. Understanding the dynamics between social media, brand image, and performance is vital for start-ups seeking to thrive in competitive markets. Based on the outcomes in the findings, the study recommends social media marketing tool for startup businesses in Ghana to stern competition and drive sustenance. This research contributes to academic literature and practical insights, offering nuanced perspectives on leveraging social media as a strategic tool for cultivating a brand image and influencing overall start-up performance. The implications of the study findings serve as guideline for startups and young entrepreneurs’ in developing countries as they develop their marketing strategies.
Monomeric Two-coordinate Beryllium Imido and Boryloxide Complexes Featuring Be–N and Be–O Triple Bonds
The role of AI in reducing maternal mortality: Current impacts and future potentials: Protocol for an analytical cross-sectional study
Background Maternal and newborn mortality remains a critical public health challenge, particularly in resource-limited settings. Despite global efforts, Kenya continues to report high maternal mortality rates of over 350 deaths per 100,000 live births and a neonatal mortality rate of 21 per 1,000 live births. Artificial Intelligence (AI)-enabled maternal healthcare interventions, such as Obstetric Point-of-Care Ultrasound (OPOCUS) and AI-driven SMS intervention on Promoting Mothers through Pregnancy and Postpartum (PROMPTS), offer innovative solutions to improve early detection, diagnosis, and maternal health-seeking behaviors. However, there is limited evidence on their usability, feasibility, and impact on maternal and neonatal outcomes. Objective This study aims to assess the implementation, user experiences, and impact of OPOCUS and PROMPTS on maternal and neonatal health outcomes in Kenya. Specifically, it evaluates their effectiveness in reducing maternal complications, improving antenatal and postnatal care utilization, and enhancing clinical decision-making while identifying potential barriers to adoption and scalability. Methods This mixed-methods, cross-sectional study will be conducted in ten counties in Kenya that have integrated AI-based maternal healthcare interventions. Quantitative data will be collected from health facility records, national health databases (KHIS), and structured surveys, while qualitative data will be gathered through key informant interviews (KIIs) with healthcare providers and policymakers, as well as focus group discussions (FGDs) with maternal health service users. Statistical analyses will include comparative pre- and post-AI implementation assessments, with thematic analysis for qualitative insights. Expected outcomes The study will generate empirical evidence on the feasibility, effectiveness, and barriers to AI integration in maternal health services. Findings will inform policy recommendations, enhance AI-assisted maternal healthcare design, and support the scaling of AI-driven interventions to improve maternal and neonatal health outcomes in Kenya and other low-resource settings. Conclusion AI-based maternal health interventions hold promise for reducing maternal mortality, improving diagnostic accuracy, and enhancing health-seeking behaviors. However, their success depends on user experiences, healthcare system readiness, and policy alignment. This study will provide critical insights for evidence-based scaling and policy integration of AI in maternal healthcare.