Search Results (3,747)

A comprehensive bibliometric analysis of literature is imperative to elucidate current research landscapes and hotspots in the interplay between gut microbiota and hypertension, identify knowledge gaps, and establish theoretical foundations for the future. We used publications retrieved from the Web of Science Core Collection (WoSCC) and SCOPUS databases (January 2001–December 2024) to analyze the annual publication trends with GraphPad Prism 9.5.1, to evaluate co-authorship, keywords clusters, and co-citation patterns with VOSviewer 1.6.20, and conducted keyword burst detection and keyword co-occurrence utilizing CiteSpace v6.4.1. We have retrieved 2485 relevant publications published over the past 24 years. A 481-fold increase in global annual publications in this field was observed. China was identified as the most productive country, while the United States demonstrated the highest research impact. For the contributor, Yang Tao (University of Toledo, USA) and the University of Florida (USA) have emerged as the most influential contributors. Among journals, the highest number of articles was published in Nutrients (n = 135), which also achieved the highest citation count (n = 5397). The emergence of novel research hotspots was indicated by high-frequency keywords, mainly “hypertensive disorders of pregnancy”, “mendelian randomization”, “gut-heart axis”, and “hepatitis B virus”. “Trimethylamine N-oxide (TMAO)” and “receptor” may represent promising new research frontiers in the gut microbiota–hypertension nexus. The current research trends are shifting from exploring the factors influencing gut microbiota and hypertension to understanding the underlying mechanisms of these factors and the potential therapeutic applications of microbial modulation for hypertension management. Full article

Background and Objectives: The Hippo pathway, via Yes-associated protein 1 (YAP1), regulates cell proliferation, apoptosis, and tissue regeneration. Aberrant YAP1 activation is linked to tumor progression and immune evasion in various cancers, including breast carcinoma, despite conflicting evidence on its prognostic value. Preclinical studies have explored drugs targeting YAP1–TEAD interactions, but therapeutic application is limited. Materials and Methods: This study included 50 patients with locally advanced breast cancer, who were assessed by a multidisciplinary tumor board and underwent neoadjuvant treatment per tumor subtype and clinical guidelines. Eligibility required both pre-treatment core biopsy and post-treatment surgical resection samples. Due to the absence of residual tumor in some patients achieving complete pathological response, post-treatment tissue was available and analyzable in 30 patients. YAP1 expression was evaluated immunohistochemically for nuclear and cytoplasmic staining patterns. ROC analysis identified a cutoff for YAP1 expression, defining tumors with ≥70% nuclear and ≥80% cytoplasmic staining. Results: YAP1 expression had a significant relationship with tumor subtype (p = 0.001), being most frequent in HER-2-positive tumors (55.6%) and least frequent in luminal tumors (11.1%). YAP1 positivity significantly predicted axillary pathological complete response (pCR) (p = 0.01). In YAP1-positive patients, 77.8% achieved axillary pCR compared to 31.7% in YAP1-negative patients, though the YAP1 status and breast pCR association were insignificant (p = 0.07). The Mann–Whitney U test indicated that higher Ki-67 values were significantly associated with positive YAP1 expression (p = 0.028). In contrast, there was no association between ER, PR status, age, and tumor size. Following treatment, there was a statistically significant change in YAP1 expression, with nuclear staining decreasing (p = 0.004) while cytoplasmic staining increased (p = 0.002). YAP1 was significantly linked to axillary pCR, HER-2 status, and Ki-67. Conclusions: Post treatment, nuclear YAP1 decreased, whereas cytoplasmic expression increased, showing a localization shift. These results suggest that YAP1 may predict treatment response and become a future therapeutic target. Full article

The human exposome, including dietary exposures such as pesticides, additives, and environmental contaminants, plays a critical role in shaping the gut microbiota (GM) and long-term health outcomes. While the Mediterranean Diet is known for its health-promoting effects, the role of food quality, specifically organic vs. conventional products, in modulating GM within this dietary pattern remains underexplored. The aim of this study was to evaluate (1) whether an Italian Mediterranean Organic Diet (IMOD) confers additional benefits compared to an Italian Mediterranean non-Organic Diet (IMnOD), and (2) the impact of IMOD and IMnOD versus a free diet (No Diet) on GM and anthropometric parameters. A randomized, controlled trial was conducted on 39 healthy subjects. Eligible subjects were divided into the following groups: (1) 4 weeks No Diet, (2) 4 weeks IMOD, and (3) 4 weeks IMnOD. Microbiota profiling (16S rRNA sequencing), body composition (BIA), and dietary adherence (MEDAS, FFQ) were evaluated. Distinct microbial shifts following both IMOD and IMnOD compared to No Diet were revealed. Several taxa previously associated with short-chain fatty acid (SCFA) biosynthesis (i.e., Anaerobutyricum hallii, Anaerostipes hadrus, and Dorea longicatena) were increased after both Mediterranean Diet interventions, while Parabacteroides distasonis showed a specific increase in the IMOD group. No significant changes in body weight or composition were observed. These findings suggest that adherence to a Mediterranean Diet, regardless of food source, reshapes the gut microbiota, while organic food intake may influence specific microbial trajectories. Our results support the relevance of food quality in dietary interventions. Full article

Over a three-year R&D project, the perception of mobile phone use in Spanish secondary schools shifted from initial tolerance to increasingly prohibitive policies. Drawing on the Actor–Network Theory, this study examines how mobile phones—alongside institutional discourses and school and family concerns—acted as dynamic actants, shaping public and political responses. The research adopted a qualitative design combining policy and media document analysis, nine semi-structured interviews with key stakeholders, ten regional case studies, and twelve focus groups. The study concluded with a public multiplier event that engaged the broader educational community. The Madrid region, among the first to adopt a restrictive stance, contributed two school-based case studies and three focus groups with teachers, students, and families. Findings suggest that the turn toward prohibition was motivated less by pedagogical evidence than by cultural anxieties, consistent with what it conceptualizes as a techno-pessimistic shock. This shift mirrors the historical patterns of societal reaction to disruption and technological saturation. Rather than reinforcing binary framings of promotion versus prohibition, such moments invite critical reflection. The study argues for nuanced, evidence-based, and multilevel governance strategies to address the complex role of mobile technologies in education. Full article

Modern financial practices introduce complex risks, which in turn force financial institutions to rely increasingly on computational risk analytics (CRA). The purpose of our research is to attempt to systematically explore the evolution and intellectual structure of CRA in banking using a detailed bibliometric analysis of the literature sourced from Web of Science from 2000 to 2025. A comprehensive search in the Web of Science (WoS) Core Collection yielded 1083 peer-reviewed publications, which we analyzed using analytical tools like VOSviewer 1.6.20 and Bibliometrix (Biblioshiny 5.0) so as to examine the dataset and uncover bibliometric characteristics like citation patterns, keyword occurrences, and thematic clustering. Our initial analysis results uncover the presence of key research clusters focusing on bankruptcy prediction, AI integration in financial services, and advanced deep learning applications. Furthermore, our findings note a transition of CRA from an emerging to an expanding domain, especially after 2019, with terms like machine learning (ML), artificial intelligence (AI), and deep learning (DL) being identified as prominent keywords and a recent shift towards blockchain, explainability, and financial stability being present. We believe that this study tries to address the need for an updated mapping of CRA, providing valuable insights for future academic inquiry and practical financial risk management applications. Full article

With the rise in short-term intercultural mobility programs, enhancing students’ adaptive capacity through structured experiential learning has become a key concern. Using constructivist grounded theory, this study draws on extending the situated learning and embodied cognition theories as analytical frameworks to explore international students’ cross-cultural adaptation within the context of study tours. It develops a three-phase framework (pre-departure, on-site immersion, and post-tour reflection) to trace their transition from cultural distance to adaptation. The findings reveal that the process through which international students shift from cross-cultural distance to multidimensional adaptation can be further accelerated by environmental settings, situational behaviors, and short-term emotional responses within study tour contexts. Moreover, culture-led and nature-led environments evoke distinct patterns of participation and emotional responses, facilitating varying degrees and dimensions of adaptation across psychological, social, and cultural domains. The study extends situated and embodied learning perspectives by conceptualizing study tours as dynamic, context-sensitive learning sites. By considering adaptation processes context-dependent, this study deepens the understanding of how learning, emotion, and environment interact to shape intercultural development and offers practical insights for designing responsive, stage-sensitive study tour programs. Full article

Evaluating the economic value of carbon sinks is fundamental to advancing carbon market mechanisms and supporting sustainable regional development. This study focuses on Fujian Province in China, aiming to assess the spatiotemporal evolution of carbon sink value and analyze the influence of socio-economic drivers. Carbon sink values from 2000 to 2020 were estimated using Net Ecosystem Productivity (NEP) simulation combined with the carbon market valuation method. Eleven socio-economic variables were selected through correlation and multicollinearity testing, and their impacts were examined using Geographically and Temporally Weighted Regression (GTWR) at the county level. The results indicate that the total carbon sink value in Fujian declined from CNY 3.212 billion in 2000 to CNY 2.837 billion in 2020, showing a spatial pattern of higher values in the southern region and lower values in the north. GTWR analysis reveals spatiotemporal heterogeneity in the effects of socio-economic factors. For example, the influence of urbanization and retail sales of consumer goods shifts direction over time, while the effects of industrial structure, population, road, and fixed asset investment vary across space. This study emphasizes the necessity of incorporating spatial and temporal dynamics into carbon sink valuation. The findings suggest that northern areas of Fujian should prioritize ecological restoration, rapidly urbanizing regions should adopt green development strategies, and counties guided by investment and consumption should focus on sustainable development pathways to maintain and enhance carbon sink capacity. Full article

Grassland degradation influences arthropod community structure and abundance, which, in turn, modulate element cycling in grassland ecosystems through predation and soil structure modification. In order to explore the influence of degradation on arthropods in Bayinbuluk alpine grassland, we selected four degraded transects (i.e., non-degraded: ND, lightly degraded: LD, moderately degraded: MD, and heavily degraded: HD) to collect soil samples and determine their composition, spatial distribution, and diversity patterns, in addition to the factors driving community change. Following identification and analysis, the following results were obtained: (1) A total of 342 soil arthropods were captured in this study, belonging to 4 classes, 11 orders, and 24 families. (2) With the intensification of degradation, the dominant groups exhibited significant alteration: the initial dominant groups were Pygmephoridae and Microdispidae; however, as the level of degradation became more severe, the dominant groups gradually shifted to Campodeidae and Formicidae, as these groups are more adaptable to environmental changes. (3) Common groups included six families, including Parasitoididae and Onychiuridae, and rare groups included 16 families, such as Macrochelidae. (4) As degradation intensified, both the species diversity and population size of the arthropod community increased. Our Redundancy Analysis (RDA) results demonstrated that the key driving factors affecting the arthropod community were soil organic carbon (SOC), electrical conductivity (EC), soil total nitrogen (TN), and available nitrogen (AN). The above results indicate that grassland degradation, by altering soil properties, increases arthropod diversity, induces alterations in the dominant species, and reduces mite abundance, with these changes being closely related to soil carbon and nitrogen contents. The results of this study provide basic data for understanding the changes in soil arthropod communities during the degradation of alpine grasslands and also offer support for the sustainable development of soil organisms in grassland ecosystems. Full article

Background: Development Assistance for Health (DAH) plays a vital role in health financing across Sub-Saharan Africa, particularly in tackling communicable diseases such as HIV/AIDS, malaria, and tuberculosis. Despite its importance, the efficiency and equity of DAH allocation remain contested. Objectives: The study aims to evaluate the cost-effectiveness of DAH in Sub-Saharan Africa from 1995 to 2018, as well as to explore differences in efficiency across diseases and country contexts. Methods: Data were drawn from the Institute for Health Metrics and Evaluation and applied Generalized Cost-Effectiveness Analysis in conjunction with the Gross Domestic Product-based thresholds. Averted Disability-Adjusted Life Years were analyzed across countries and diseases, and countries were categorized by the Human Development Index (HDI) level to assess differential DAH performance. Results: DAH cost-effectiveness showed similar patterns across HDI groups, with roughly equal proportions of cost-effective and dominated outcomes in both low- and middle-HDI countries. Thirteen countries were identified as very cost-effective, nine as cost-effective, and two as non-cost-effective. Twenty-one countries were dominated, reflecting persistent inefficiencies in aid impact that transcends the various levels of development. Conclusions: Tailoring DAH allocation to specific disease burdens and development levels enhances its impact. The study underscores the need for targeted investment and a strategic shift toward integrated health system strengthening. Additionally, microinsurance is highlighted as a key mechanism for improving healthcare access and financial protection in low-income settings. Full article

Amyotrophic lateral sclerosis (ALS) is characterized by upper and lower motor neuron failure and poor prognosis. This study performed a meta-analysis of gene expression datasets that compared bulk-processed post-mortem spinal cord from ALS and control (CTL) patients. The analysis included 569 samples (454 ALS, 115 CTL) from 348 individuals (262 ALS, 86 CTL). Patterns of differential expression bias, related to mRNA abundance, gene length and GC content, were discernable from individual studies but attenuated by meta-analysis. A total of 213 differentially expressed genes (DEGs) were identified (144 ALS-increased, 69 ALS-decreased). ALS-increased DEGs were most highly expressed by microglia and associated with MHC class II, immune response and leukocyte activation. ALS-decreased DEGs were abundantly expressed by mature oligodendrocytes (e.g., the MOL5 phenotype) and associated with myelin production, plasma membrane and sterol metabolism. Comparison to spatial transcriptomics data showed that DEGs were prominently expressed in white matter, with increased DEG expression strongest in the ventral/lateral white matter. These results highlight white matter as the spinal cord region most strongly associated with the shifts in mRNA abundance observed in bulk-processed tissues. These shifts can be explained by attrition of mature oligodendrocytes and an ALS-emergent microglia phenotype that is partly shared among neurodegenerative conditions. Full article

China’s rapid urbanization has given rise to the phenomenon of “urban villages”, which are often regarded as chaotic fringe areas in traditional studies. With the rise of the concept of resilient cities, the value of urban villages as potential carriers of sustainable development has been re-examined. This study adopted research methods such as field investigations, in-depth interviews, and conceptual sampling. By analyzing the interlinked governance relationship between Xiamen City and the urban villages in the Bay Area, aspects such as rural housing improvement, environmental governance, residents’ feedback, geographical pattern, and spatial production were evaluated. A field investigation was conducted in six urban villages within the four bays of Xiamen. A total of 45 people in the urban villages were interviewed, and the spatial status of the urban villages was recorded. This research found that following: (1) Different types of urban villages have formed significantly differentiated role positionings under the framework of regional governance. Residential community types XA and WL provide long-term and stable living spaces for migrant workers in Xiamen; tourism development types DS, HX, BZ, and HT allow the undertaking of short-term stay tourists and provide tourism services. (2) These urban villages achieve the construction of their resilience through resisting risks, absorbing policy resources, catering to the expansion of urban needs, and co-construction in coordination with planning. The multi-cultural inclusiveness of urban villages and their transformation led by cultural shifts have become the driving force for their sustainable development. Through the above mechanisms, urban villages have become the source of resilience and sustainability of healthy cities and provide a model reference for high-density urban construction. Full article

Temporal domain generalization is crucial for the temporal forecasting of sensor data due to the non-stationary and evolving nature of most sensor-generated time series. However, temporal dynamics vary in scale, semantics, and structure, leading to distribution shifts that a single model cannot easily generalize over. Additionally, conflicts between temporal domain-specific patterns and limited model capacity make it difficult to learn shared parameters that work universally. To address this challenge, we propose an ensemble learning framework that leverages multiple domain-specific models to improve temporal domain generalization for sensor data forecasting. We first segment the original sensor time series into distinct temporal tasks to better handle the distribution shifts inherent in sensor measurements. A meta-learning strategy is then applied to extract shared representations across these tasks. Specifically, during meta-training, a recurrent encoder combined with variational inference captures contextual information for each task, which is used to generate task-specific model parameters. Relationships among tasks are modeled via a self-attention mechanism. For each query, the prediction results are adaptively reweighted based on all previously learned models. At inference, predictions are directly generated through the learned ensemble mechanism without additional tuning. Extensive experiments on public sensor datasets demonstrate that our method significantly enhances the generalization performance in forecasting across unseen sensor segments. Full article

Residential segregation remains a persistent challenge in European urban environments and is an increasing focal point in urban policy debates. This study investigates the changing geographies of ethnic diversity and residential segregation in Riga, the capital city of Latvia. The research addresses the complex dynamics of ethnic residential patterns within the distinctive context of post-socialist urban transformation, examining how historical legacies of ethnic diversity interact with contemporary migration flows to reshape neighborhood ethnic composition. Using geo-referenced data from 2000, 2011, and 2021 census rounds, we examined changes in the spatial distribution of five major ethnic groups. Our analysis employs the Dissimilarity Index to measure ethnic residential segregation and the Location Quotient to identify the residential concentration of ethnic groups across the city. The findings reveal that Riga’s ethnic landscape is undergoing a gradual yet impactful transformation. The spatial distribution of ethnic groups is shifting, with the increasing segregation of certain groups, particularly traditional ethnic minorities, coupled with a growing concentration of Europeans and non-Europeans in the inner city. The findings reveal distinctive patterns of ethnic diversification and demographic change, wherein long-term trends intersect with contemporary migration dynamics to produce unique trajectories of ethnic residential segregation, which differ from those observed in Western European contexts. However, the specific dynamics in Riga, particularly the persistence of traditional ethnic minority communities and the emergence of new ethnic groups, highlight the unique context of post-socialist urban landscapes. Full article

Agriculture accounts for 29% of the Gross Domestic Product of the Nusa Tenggara Islands (NTIs). However, recurring agricultural droughts pose a major threat to the sustainability of agriculture in this region. The interplay between precipitation, solar radiation, and surface temperature as meteorological factors plays a key role in affecting vegetation (Soil-Adjusted Vegetation Index) and agricultural drought (Temperature Vegetation Dryness Index) in the NTIs. Based on the analyses of interplay with temporal lag, this study investigates the effect of each factor on agricultural drought and attempts to provide early warnings regarding drought in the NTIs. We collected surface information data from Moderate-Resolution Imaging Spectroradiometer (MODIS). Meanwhile, rainfall was estimated from Himawari-8 based on the INSAT Multi-Spectral Rainfall Algorithm (IMSRA). The results showed reliable performance for 8-day and monthly scales against gauges. The drought analysis results reveal that the NTIs suffer from mild-to-moderate droughts, where cropland is the most vulnerable, causing shifts in the rice cropping season. The driving factors could also explain >60% of the vegetation and surface-dryness conditions. Furthermore, our monthly and 8-day TVDI estimation models could capture spatial drought patterns consistent with MODIS, with coefficient of determination (R²) values of more than 0.64. The low error rates and the ability to capture the spatial distribution of droughts, especially in open-land vegetation, highlight the potential of these models to provide an estimation of agricultural drought. Full article

This study examines a Mei-Yu rainfall event using rain gauges (RG) and OTT Parsivel disdrometers to observe precipitation characteristics and raindrop size distributions (RSD), with comparisons made against Weather Research and Forecasting (WRF) model simulations. Results show that Parsivel-derived rain rates (RR) are slightly underestimated relative to RG measurements. Both observations and simulations identify 1–3 mm raindrops as the dominant precipitation contributors, though the model overestimates small and large drop contributions. At low RR, decreased small-drop and increased large-drop concentrations cause corresponding leftward and rightward RSD shifts with decreasing altitude—a pattern well captured by simulations. However, at elevated rainfall rates, the simulated concentration of large raindrops shows no significant increase, resulting in negligible rightward shifting of RSD in the model outputs. Autoconversion from cloud droplets to raindrops (ATcr), collision and breakup between raindrops (AGrr), ice melting (MLir), and evaporation of raindrops (VDrv) contribute more to the number density of raindrops. At 0.1 < RR < 1 mm·h⁻¹, ATcr dominates, while VDrv peaks in this intensity range before decreasing. At higher intensities (RR > 20 mm·h⁻¹), AGrr contributes most, followed by MLir. When the RR is high enough, the breakup of raindrops plays a more important role than collision, leading to a decrease in the number density of raindrops. The overestimation of raindrop breakup from the numerical parameterization may be one of the reasons why the RSD does not shift significantly to the right toward the surface under the heavy RR grade. The RSD near the surface varies with the RR and characterizes surface precipitation well. Toward the surface, ATcr and VDrv, but not AGrr, become similar when precipitation approaches. Full article