Browse Articles

Instrumental strategies and symbolic legitimacy: analyzing government information disclosure security policies in China

This study explores how the Chinese government manages the trade-off between transparency and national security through the design of government information disclosure (GID) security policies. Based on a qualitative analysis of 259 policy documents issued at central, provincial, and prefectural levels between 2007 and 2024, we construct a two-dimensional framework that integrates policy instrument types (supply, environmental, demand) with policy content stages (readiness, implementation, impact). The findings reveal an overreliance on environmental-type instruments—especially during implementation—and underuse of demand-oriented tools that support public participation and accountability. Through the lens of Governmental Power Marketing (GPM), we interpret this instrument selection not only as a technical response but as a symbolic strategy to project institutional competence and legitimacy. This study contributes to digital governance literature by linking content analysis with political communication theory, offering both an analytical framework and comparative insights applicable to other regimes facing similar transparency-security dilemmas.

Association of C reactive protein triglyceride glucose index with mortality in coronary heart disease and type 2 diabetes from NHANES data

Welding 2D Semiconducting Crystals by Covalent Stitching of Grain Boundaries in WS₂

Revealing the nuances of ‘Grey Digital Divide’ in Hong Kong: A latent profile analysis

The ‘grey digital divide’ deprives older adults’ equitable access to information and support, and thereby, their well-being. Policies including subsidies for internet access and devices, digital literacy classes, and telehealth support attempted to close the divide. Yet, it remains doubtful whether the discrepancy could be narrowed, or simply transformed. The mandatory COVID track-and-trace policy, the government’s decade-long digital inclusivity initiatives and the city’s high degree of digitization makes Hong Kong an exemplar for exploring the post-pandemic digital divide. Utilizing a person-centered approach, this study elaborated the intergenerational differences in digital engagement with a random sample of 870 younger (aged 18–54 years) and older (aged 55 years or above) adults (52.1% female) via phone interviews. With 16 indicators of digital motivation, access, digital skills, and usage, latent profile analysis (LPA) yielded three profiles – Proficient, Intermediate, and Novice, with disparate patterns between the younger (90.2%, 8.8%, 0.9%) and the older (59.2%, 35.5%, 5.2%) groups, demonstrating a clear intergenerational divide. Socio-economic status influenced profile membership regardless of age, and that profile membership relates to the frequencies of various social contacts except with family/relatives. Our findings demonstrate how typology defines the needs and assists formulation of segmented interventions toward digital inclusivity. (200 words).

Telescope indexing for k-nearest neighbor search algorithms over high dimensional data & large data sets

Identifying Driving and Spectator Phonon Modes in Pentacene Exciton Transport

Correction: Exploring self-experience practices in dementia care: A scoping review

Continuous wave mud pulse data transmission method based on continuous gradation frequency keying modulation and Convolution neural network demodulation

Electrons and Their Multiple Kinetic Fates in an Ionic Liquid

Hepatotoxicity associated with statins: A retrospective pharmacovigilance study based on the FAERS database

Background Statins are commonly prescribed in clinical practice and are associated with a high risk of drug-induced liver injury (DILI). This study aims to examine the real-world data on statin-induced liver injury to assess medication safety. Methods All DILI cases reported with statins as primary suspected drugs were extracted based on the US Food and Drug Administration adverse event reporting system (FAERS) from 2004 to 2023. A disproportional analysis was conducted using reported odds ratios (ROR) and information component (IC) to assess the significant association between statins and DILI. Results A total of 7779 statin-associated DILI cases were identified. DILI patients tended to be aged >65 years (45.43%), with more females than males (48.80% vs 43.75%), and 39.95% of DILI patients required hospitalization. Statin-induced DILI cases are most commonly reported with atorvastatin (53.48%), rosuvastatin (20.44%), and simvastatin (19.46%). The DILI signals (ROR; 95% CI) for statins were ranked as follows: fluvastatin (6.90; 5.89–8.10)> atorvastatin (3.09; 2.99–3.19)> simvastatin (2.96; 2.81–3.12)> lovastatin (2.77; 2.17–3.53)> rosuvastatin (2.27; 2.16–2.39)> pravastatin (2.07; 1.81–2.37). Age-stratified analysis showed that a stronger signal was detected in patients (aged ≥65 years) than patients (aged <65 years) for atorvastatin, simvastatin, pravastatin and fluvastatin. The onset time of DILI was significantly different among the different statins (p = 0.014), and simvastatin resulted in the highest mortality rate (12.15%). Conclusion Based on FAERS database, six statins are significantly associated with liver injury, and fluvastatin, atorvastatin, and simvastatin had the greatest risk of DILI.

Adenosine improves postmenopausal obesity by regulating neutrophil extracellular traps

Introduction of Reactive Thiol Handles into Tyrosine-Tagged Proteins through Enzymatic Oxidative Coupling

Rationale and study protocol of the MAMELI Cohort study (Mapping the Methylation of repetitive elements to track the Exposome effects on health: the city of Legnano as a Living lab)

The concept of the exposome encompasses all the factors influencing human health throughout the life course. The exposome induces epigenetic changes, such as DNA methylation, which influence gene expression and impact overall health. Several recent studies have explored how repetitive elements (REs) in the genome can be activated in response to environmental stimuli. However, most of these investigations have assumed that altered RE methylation is always detrimental to individual health. The MAMELI project proposes an alternative hypothesis: that some REs are plastic entities capable of responding physiologically to environmental stimuli without compromising genome stability. This hypothesis suggests that the ability of DNA to adapt to environmental triggers could be monitored and used as an indicator of health resilience. To test this hypothesis, the MAMELI project will enroll 6,200 participants from the city of Legnano (Italy) and will be conducted in three main phases: i) A total of 200 healthy participants will undergo DNA methylation analysis through third-generation sequencing at two time points: T0 (baseline) and T1 (6 months after T0). This phase aims to identify a set of REs (“differential REs”) whose methylation changes in response to the exposome without affecting genome stability; ii) This phase will include 2,700 subjects (the original 200 participants from the discovery phase plus 2,500 additional subjects). The goal will be to develop a predictive algorithm (the MAMELI algorithm) that links the exposome to RE methylation status, creating a “RE methylation signature” reflecting the environmental impact on DNA methylation; iii) In this phase, the MAMELI algorithm will be applied to a separate cohort of 3,500 participants to compare the measured RE methylation signature with values predicted by the algorithm. Additionally, an intervention study will be embedded within the cohort to assess the reversibility of RE methylation following lifestyle changes. The MAMELI project offers a novel perspective in the field of epigenetics and environmental health, demonstrating how the epigenome can act as a sensor for environmental changes and how this interaction can be harnessed for disease prevention. If validated, the MAMELI algorithm could become a powerful tool for identifying individuals at risk and developing personalized interventions to improve global health outcomes.

cJun-N-terminal kinase activity and mitosis perturbation drive the 2-methoxyestradiol-mediated enhancement of viral oncolysis by Epizootic Hemorrhagic Disease Virus-Tel Aviv University

Synergistic Iron–Molybdenum Effects for Selective Electrocatalytic Reduction of Nitrite to Nitric Oxide

Unsupervised learning using EHR and census data to identify distinct subphenotypes of newly diagnosed hypertension patients

Background Hypertension (HTN) is a complex condition with significant heterogeneity in presentation and treatment response. Identifying distinct subphenotypes of HTN may improve our understanding of its underlying mechanisms and guide more precise treatment or public health initiatives. Methods Using EHR and Medicaid claims data from the OneFlorida+ research consortium (2012–2021), we identified a cohort of adult Floridians with newly diagnosed HTN (first diagnosis following two outpatient blood pressures ≥140/90 mmHg & no prior anti-HTN treatment). We extracted demographic and clinical data from the diagnosis visit and ≤1 year prior. We used hierarchical clustering (unsupervised machine learning) to identify distinct subphenotypes within the OneFlorida+ HTN population. Results A total of 40,686 patients were included (mean ± SD age, 60.9 ± 17.5 y; 55% women). Five subphenotypes (S1-5) were identified. S1 was characterized by older age, higher Body Mass Index (BMI), and prevalent type 2 diabetes. S2 included over 50% of Black patients who were primarily women, younger, with higher BMI, but living in communities with higher levels of socioeconomic vulnerabilities. S3 contained a higher percentage of Hispanic patients with comparatively lower BMI. S4 is characterized by higher age and co-morbidities. S5 had 94% of patients with chronic kidney disease. Distinctions in social determinants of health factors were also observed. Conclusions Unsupervised learning identified 5 HTN subphenotypes varying in demographic, socioeconomic, and risk profiles. Further investigation into the biological mechanisms of these subphenotypes and the relationships to social factors may enhance our ability to deliver targeted interventions that consider social policy implications in addition to the traditional behavioral and physiological interventions.

Redox partner exchanges between spatially confined complexes control the coupling effect of cytochrome b5 on P450 CYP3A4

Micro Crystalline Sponge Method Combined with Small-Wedge Synchrotron Crystallography for Nanogram Scale Molecular Structure Elucidation

A severity classification model of cervical spondylotic radiculopathy symptoms based on MRI radiomics: A retrospective study

Objective To develop a severity classification model for symptoms of cervical spondylotic radiculopathy (CSR) based on magnetic resonance imaging (MRI) radiomics and to evaluate the predictive value of MRI radiomics features in the classification of symptoms severity, providing an objective basis for personalized therapeutic interventions. Methods This retrospective study included 99 patients diagnosed with CSR, admitted between August 2022 and April 2023. Symptom severity was assessed using the neck disability index (NDI) scale, which facilitated the categorization of participants into mild and severe symptoms groups. A comprehensive set of 3,404 quantitative radiomics features was extracted from four predefined regions of interest (ROIs) using the 3D Slicer software. The least absolute shrinkage and selection operator (LASSO) regression analysis was used to identify the optimal subsets of radiomics features, which were subsequently used to develop a support vector machine (SVM) classification model. Model performance was evaluated using receiver operating characteristic (ROC) curve analysis with area under the curve (AUC) calculations, complemented by accuracy, precision, sensitivity and F1 score evaluations. Results Analysis of cervical T2-weighted MRI resulted in the extraction of 3,404 radiomics features from four ROIs. Using the LASSO regression for feature selection, 96 radiomics features were retained for model construction. The most discriminatory characteristics were in the intervertebral discs at levels C4/5, C5/6, and C6/7 in the mid-sagittal plane. The model demonstrated an AUC of 0.91. The accuracy of the model is 0.917, the precision is 0.979, the sensitivity is 0.833, and the F1 score is 0.890. Conclusion The severity classification model of CSR symptoms based on MRI radiomics demonstrates robust predictive performance in assessing the severity of CSR symptoms, serving as an effective decision-support tool for guiding personalized therapeutic strategies in clinical practice.

Plasma extracellular vesicle-associated miR-512-3p modulates angiogenesis in pediatric Moyamoya disease by targeting ARHGEF3.

Abstract Moyamoya disease (MMD) is a chronic cerebrovascular disorder and a leading cause of pediatric stroke. Extracellular vesicles (EVs) carrying microRNAs (miRNAs) play a pivotal role in intercellular communication within cerebrovascular diseases. This study aimed to identify specific miRNAs within plasma-derived EVs from MMD patients and investigate their functional implications. Study subjects included healthy controls (N = 13) and MMD patients (N = 23). EVs were isolated from plasma samples and characterized by transmission electron microscopy, nanoparticle tracking analysis, ExoView, RT-qPCR, and immunoblotting. miRNA profiles were assessed through NanoString analysis. Functional effects of miR-512-3p inhibition were evaluated in MMD endothelial colony-forming cells (ECFCs) by analyzing guanosine triphosphatase (GTPase) activity, tubule formation, and cell viability. MMD-derived EVs exhibited an upregulation of miR-512-3p compared to controls. Bioinformatics analysis identified RHO guanine nucleotide exchange factor 3 (ARHGEF3) as a potential target of miR-512-3p. Inhibition of miR-512-3p in MMD ECFCs resulted in increased expression of ARHGEF3 and its downstream effector RHOA, leading to enhanced GTPase activity and improved tubule formation, indicative of restored angiogenic function. Elevated levels of miR-512-3p within plasma-derived EVs may serve as a novel biomarker for MMD diagnosis. The modulation of ARHGEF3 and subsequent RHOA signaling by targeting miR-512-3p contributes to the dysregulated angiogenesis in MMD.