Search Results (562)

Coronary artery disease (CAD) is the most prevalent form of cardiovascular disease. A growing body of research shows that interleukins (ILs), such as IL-8, IL-18 and IL-16, elicit pro-inflammatory responses and may play critical roles in the pathologic process of CAD. Single nucleotide polymorphisms (SNPs), capable of generating functional modifications in IL genes, appear to be associated with CAD risk. This study aims to evaluate the associations of ten previously identified SNPs of the three cytokines with susceptibility to or protection of CAD. A systematic review and meta-analysis were conducted using Pubmed, EMBASE, WOS, CENTRAL, CNKI, CBM, Weipu, WANFANG Data and Google Scholar databases for relevant literature published up to September 2024. Odds ratios (ORs) with 95% confidence intervals (CIs) were calculated for the four genetic models of the investigated SNPs in overall and subgroups analyses. Thirty-eight articles from 16 countries involving 14574 cases and 13001 controls were included. The present meta-analysis revealed no significant association between CAD and IL-8-rs2227306 or five IL-16 SNPs (rs8034928, rs3848180, rs1131445, rs4778889 and rs11556218). However, IL-8-rs4073 was significantly associated with an increased risk of CAD across all genetic models. In contrast, three IL-18 (rs187238, rs1946518 and rs1946519) variants containing minor alleles were associated with decreased risks of CAD under all models. Subgroups analyses by ethnicity indicated that IL-8-rs4073 conferred a significantly higher risk of CAD among Asians, including East, South and West Asians (allelic OR = 1.46, homozygous OR = 1.96, heterozygous OR = 1.47, dominant OR = 1.65), while it showed an inversely significant association with CAD risk in Caucasians (homozygous OR = 0.82, dominant OR = 0.85). Additionally, IL-18-rs187238 and IL-18-rs1946518 were significantly associated with reduced CAD risks in East Asians (for rs187238: allelic OR = 0.72, homozygous OR = 0.33, heterozygous OR = 0.73, dominant OR = 0.71; for rs1946518: allelic OR = 0.62, homozygous OR = 0.38, heterozygous OR = 0.49, dominant OR = 0.45). IL-18-rs187238 also demonstrated protective effects in Middle Eastern populations (allelic OR = 0.76, homozygous OR = 0.63, heterozygous OR = 0.72, dominant OR = 0.71). No significant associations were observed in South Asians or Caucasians for these IL-18 SNPs. Consistent with the overall analysis results, subgroups analyses further highlighted a significant association between IL-8-rs4073 and increased risk of acute coronary syndrome (heterozygous OR = 0.72). IL-18-rs187238 was significantly associated with decreased risks of myocardial infarction (MI) (allelic OR = 0.81, homozygous OR = 0.55, dominant OR = 0.80) and multiple vessel stenosis (allelic OR = 0.54, heterozygous OR = 0.45, dominant OR = 0.45). Similarly, IL-18-rs1946518 was significantly associated with reduced MI risk (allelic OR = 0.75, heterozygous OR = 0.68). These findings support the role of cytokine gene IL-8 and IL-18 variants as predisposing factors for the development and progression of CAD. Full article

Soil moisture is a key variable that affects ecosystem carbon and water cycles and that can directly affect climate change. Remote sensing is the best way to obtain global soil moisture data. Currently, soil moisture remote sensing products have coarse spatial resolution, which limits their application in agriculture, the ecological environment, and urban planning. Soil moisture downscaling methods rely mainly on optical data. Affected by weather, the spatial discontinuity of optical data has a greater impact on the downscaling results. The synthetic aperture radar (SAR) backscatter coefficient is strongly correlated with soil moisture. This study was based on the Google Earth Engine (GEE) platform, which integrated Moderate-Resolution Imaging Spectroradiometer (MODIS) optical and SAR backscattering coefficients and used machine learning methods to downscale the soil moisture product, reducing the original soil moisture with a resolution of 10 km to 1 km and 100 m. The downscaling results were verified using in situ observation data from the Shandian River and Wudaoliang. The results show that in the two study areas, the downscaling results after adding SAR backscattering coefficients are better than before. In the Shandian River, the R increases from 0.28 to 0.42. In Wudaoliang, the R value increases from 0.54 to 0.70. The RMSE value is 0.03 (cm³/cm³). The downscaled soil moisture products play an important role in water resource management, natural disaster monitoring, ecological and environmental protection, and other fields. In the monitoring and management of natural disasters, such as droughts and floods, it can provide key information support for decision-makers and help formulate more effective emergency response plans. During droughts, affected areas can be identified in a timely manner, and the allocation and scheduling of water resources can be optimized, thereby reducing agricultural losses. Full article

An organization’s success depends on its ability to attract and retain skilled personnel. Superintendents play a critical role in overseeing project sites in the construction industry and can adapt to the increasingly complicated requirements of modern construction projects. This study examines traditional and modern personnel selection methods to determine effective tactics, essential competencies, and emerging trends regarding supervisory personnel. The research methodology follows the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) framework. First, this study examines traditional and modern selection methods used by organizations and engineering firms to provide a comprehensive overview of the topic and assist in selecting appropriate staff recruitment procedures. Second, the Web of Science, Scopus, and Google Scholar databases were reviewed to identify superintendent selection approaches and competencies, over the period January 2000 to September 2024. A total of 22 relevant papers were analyzed. Superintendent selection processes included questionnaires (57%), interviews (26%), literature reviews (14%), and data-driven AI tools (3%). Forty competency criteria were identified, with the top five being knowledge, communication skills, leadership, health and safety expertise, and commitment. As a result, novel approaches employing Industry 4.0 technologies, including virtual reality (VR), wearable sensing devices (WSDs), natural language processing (NLP), blockchain, and computer vision, are recommended. These findings support a better understanding of how best to identify the most qualified supervisory personnel and provides enhanced methods for evaluating job applicants. Full article

In landscape planning and design, geospatial technologies (GSTs) are used to aid in visualizing and interpreting geographic environments, identifying geospatial patterns, and making decisions around information based on maps and geospatial information. GSTs are related to the different tools and technologies used to represent the earth’s surface and have transformed the practice of landscape design and geospatial education. These technologies play an important role in promoting the development and application of STEM-relevant geospatial thinking. Curricula that incorporate GSTs have been used across educational levels, from elementary school through college, and have been shown to support the development of geospatial learning and understanding. The present work discusses the use of one type of GST, virtual globes, as a tool for developing geospatial thinking, with a specific focus on Google Earth. This review highlights outcomes of several studies using Google Earth in the context of disciplines related to landscape design, such as geography and earth science. Furthermore, the potential mechanisms underlying the effectiveness of this technology for supporting the development of geospatial knowledge, such as its role in facilitating data visualization and supporting student’s ability to think flexibly about spatial patterns and relations, are discussed. Finally, the limitations of the current research on Google Earth as a tool for supporting geospatial learning are discussed, and suggestions for future research are provided. Full article

(1) Background: Inflammatory bowel diseases (IBDs) are characterized by chronic and complex inflammatory processes of the digestive tract that evolve with frequent relapses and manifest at any age; they predominantly affect young individuals. Diet plays a direct role in maintaining the gut mucosal integrity and immune function. Regarding the diet, the administration of probiotics stands out. The use of probiotics for IBD treatment has shown promising effects on consumers’ quality of life. (2) Methods: This study aimed to conduct a literature review on the effects of probiotic and smart probiotic ingestion on IBD and analyze the available literature based on the searched keywords using boxplot diagrams to search for scientific data in the online literature published up to October 2024. (3) Results: Google Scholar (containing ~6 × 10⁶ articles) and Science Direct (containing ~5 × 10⁶ articles) were the databases with the highest number of articles for the keywords used in the study. When analyzing the content of the articles, although probiotic microorganisms are currently not part of the standard treatment protocol for IBD, these live biotherapeutics have proven to be an effective treatment option, considering the adverse effects of conventional therapies. Furthermore, the development of genetically engineered probiotics or smart probiotics is a promising treatment for IBD. (4) Conclusions: Probiotics and smart probiotics could represent the future of nutritional medicine in IBD care, allowing patients to be treated in a more natural, safe, effective, and nutritious way. However, although many studies have demonstrated the potential of this biotherapy, clinical trials standardizing dosage and strains are still necessary. Full article

This study aims to analyse consumer experiences, purchase behaviours, and emotional responses through Booking and Expedia’s mobile applications. The 2000 user reviews collected from Google Play were subjected to a comprehensive sentiment analysis, text mining, and topic modelling process to identify the key elements that shape consumers’ emotional experiences and purchase decisions. According to the results of text mining and sentiment analysis performed with Python’s WordNet library, 81.9% of Booking.com reviews are positive, 8.4% are negative, and 11.3% are neutral, whereas 55.8% of Expedia reviews are positive, 37.8% are negative, and 8.0% are neutral. In the topic modelling analysis, Booking.com emphasised ease of booking, while Expedia emphasised difficulties in cancellation and refund processes. These findings provide valuable insights into how consumers’ emotional states and purchasing behaviours are reflected in their experiences with mobile applications. The study enables the development of strategic recommendations for marketing management to better analyse consumers’ expectations and experiences. Full article

The eastern segment of the Sunan-Qilian Fault (ES-SQF) is located within the seismic gap between the 1927 M8.0 Gulang earthquake and the 1932 M7.6 Changma earthquake in China. It also aligns with the extension direction of the largest surface rupture zone associated with the 2022 Mw6.7 Menyuan earthquake. Understanding the activity parameters of this fault is essential for interpreting strain distribution patterns in the central–western segment of the Qilian–Haiyuan fault zone, located along the northeastern margin of the Tibetan Plateau, and for evaluating the seismic hazards in the region. High-resolution Google Earth satellite imagery and UAV (Unmanned Aerial Vehicle)-based photogrammetry provide favorable conditions for detailed mapping and the study of typical landforms along the ES-SQF. Combined with field geological surveys, the ES-SQF is identified as a continuous, singular-fault structure extending approximately 68 km in length. The fault trends in the WNW direction and along its trace, distinctive features, such as ridges, gullies, and terraces, show clear evidence of synchronous left lateral displacement. This study investigates the Qingsha River and the Dongzhong River. High-resolution digital elevation models (DEMs) derived from UAV imagery were used to conduct a detailed mapping of faulted landforms. An analysis of stripping trench profiles and radiocarbon dating of collected samples indicates that the most recent surface-rupturing seismic event in the area occurred between 3500 and 2328 y BP, pointing to the existence of an active fault from the Holocene epoch. Using the LaDiCaoz program to restore and measure displaced terraces at the study site, combined with geomorphological sample collection and testing, we estimated the fault’s slip rate since the Holocene to be approximately 2.0 ± 0.3 mm/y. Therefore, the ES-SQF plays a critical role in strain distribution across the central–western segment of the Qilian–Haiyuan fault zone. Together with the Tuolaishan fault, it accommodates and dissipates the left lateral shear deformation in this region. Based on the slip rate and the elapsed time since the last event, it is estimated that a seismic moment equivalent to Mw 7.5 has been accumulated on the ES-SQF. Additionally, with the significant Coulomb stress loading on the ES-SQF caused by the 2016 Mw 5.9 and 2022 Mw 6.7 Menyuan earthquakes, there is a potential for large earthquakes to occur in the future. Our results also indicate that high-resolution remote sensing imagery can facilitate detailed studies of active tectonics. Full article

Obesity is a complex health risk influenced by genetic, environmental, and lifestyle factors. This review systematically assessed the association between interleukin gene polymorphisms (rs16944, rs17561, rs1143623, rs1143633, rs1143634, rs1800587, rs2234677, and rs4848306), IL-4 (rs180275, rs1805010, IL-6 rs13306435, rs1800795, rs1800796, rs1800797, rs2228145, rs2228145, rs2229238, and rs4845623), IL-10 (rs1518110, rs1518111, rs1800871, rs1800872, rs1800896, rs1878672, rs2834167, rs3024491, rs3024496, rs3024498, and rs3024505), IL-15 (rs3136617, rs3136618, and rs2296135), and IL-18 (rs187238, rs1946518, rs2272127, rs2293225, and rs7559479) and the risk of overweight and obesity in adults, focusing on IL-6 rs1800795 through a meta-analysis. The focus on IL-6 in this review arises from its pleiotropic nature and unclear effect on obesity risk. The review included studies published from 1998 to 2023, sourced from Science Direct, EBSCOhost, Web of Science, and Google Scholar. Bias was assessed with the Cochrane Collaboration tool, and funnel plots were used for publication bias. Results were synthesized into pooled odds ratios (ORs) and confidence intervals (CIs). Thirty studies comprising approximately 29,998 participants were included. The selection criteria required that the articles include participants who were overweight or obese, and this condition needed to be linked to IL polymorphisms. In a meta-analysis, in the dominant model, the pooled OR was 1.26 (95% CI 1.08 to 1.47), indicating those with the GC/CC genotype for IL-6 rs1800795 are 1.26 times more likely to be overweight/obese than GG genotype carriers. For the recessive model, the OR was 1.25 (95% CI 1.04 to 1.51). The overdominant model showed no significant association (OR 1.08, 95% CI 0.94 to 1.25). Interleukin gene variation, particularly the IL-6 rs1800795 variant, is modestly associated with obesity risk. This suggests that other factors, such as the environment, also play a role in obesity. Thus, individuals with this particular IL-6 variant may have a slightly higher likelihood of being overweight or obese compared to those without it, but this is just one of many factors influencing obesity risk. Full article

Coastal zones, where land meets ocean, are home to a large portion of the global population and play a crucial role in human survival and development. These regions are shaped by complex geological processes and influenced by both natural and anthropogenic factors, making effective management essential for addressing population growth, environmental degradation, and resource sustainability. However, the inherent complexity of coastal zones complicates their study, and traditional in situ methods are often inefficient. Remote sensing technologies have significantly advanced coastal zone research, with different sensors providing diverse perspectives. These sensors are typically used for classification tasks (e.g., coastline extraction, coastal classification) and retrieval tasks (e.g., aquatic color, wetland monitoring). Recent improvements in resolution and the advent of deep learning have led to notable progress in classification, while platforms like Google Earth Engine (GEE) have enabled the development of high-quality, global-scale products. This paper provides a comprehensive overview of coastal zone interpretation, discussing platforms, sensors, spectral characteristics, and key challenges while proposing potential solutions for future research and management. Full article

An increasing number of studies highlight the critical role of both maternal and paternal nutrition and body weight before conception in shaping offspring health. Traditionally, research has focused on maternal factors, particularly in utero exposures, as key determinants of chronic disease development. However, emerging evidence underscores the significant influence of paternal preconception health on offspring metabolic outcomes. While maternal health remains vital, with preconception nutrition playing a pivotal role in fetal development, paternal obesity and poor nutrition are linked to increased risks of metabolic disorders, including type 2 diabetes and cardiovascular disease in children. This narrative review aims to synthesize recent findings on the effects of both maternal and paternal preconception health, emphasizing the need for integrated early interventions. The literature search utilized PubMed, UNF One Search, and Google Scholar, focusing on RCTs; cohort, retrospective, and animal studies; and systematic reviews, excluding non-English and non-peer-reviewed articles. The findings of this review indicate that paternal effects are mediated by epigenetic changes in sperm, such as DNA methylation and non-coding RNA, which influence gene expression in offspring. Nutrient imbalances during preconception in both parents can lead to low birth weight and increased metabolic disease risk, while deficiencies in folic acid, iron, iodine, and vitamin D are linked to developmental disorders. Additionally, maternal obesity elevates the risk of chronic diseases in children. Future research should prioritize human studies to explore the influence of parental nutrition, body weight, and lifestyle on offspring health, ensuring findings are applicable across diverse populations. By addressing both maternal and paternal factors, healthcare providers can better reduce the prevalence of metabolic syndrome and its associated risks in future generations. Full article

Background: Biofilm-associated infections frequently span multiple body sites and represent a significant clinical challenge, often requiring a multidisciplinary approach involving surgery and antimicrobial therapy. These infections are commonly healthcare-associated and frequently related to internal or external medical devices. The formation of biofilms complicates treatment, as they create environments that are difficult for most antimicrobial agents to penetrate. Fluoroquinolones play a critical role in the eradication of biofilm-related infections. Numerous studies have investigated the synergistic potential of combining fluoroquinolones with other chemical agents to augment their efficacy while minimizing potential toxicity. Comparative research suggests that the antibiofilm activity of fluoroquinolones is superior to that of beta-lactams and glycopeptides. However, their activity remains less effective than that of minocycline and fosfomycin. Noteworthy combinations include fluoroquinolones with fosfomycin and aminoglycosides for enhanced activity against Gram-negative organisms and fluoroquinolones with minocycline and rifampin for more effective treatment of Gram-positive infections. Despite the limitations of fluoroquinolones due to the intrinsic characteristics of this antibiotic, they remain fundamental in this setting thanks to their bioavailability and synergisms with other drugs. Methods: A comprehensive literature search was conducted using online databases (PubMed/MEDLINE/Google Scholar) and books written by experts in microbiology and infectious diseases to identify relevant studies on fluoroquinolones and biofilm. Results: This review critically assesses the role of fluoroquinolones in managing biofilm-associated infections in various clinical settings while also exploring the potential benefits of combination therapy with these antibiotics. Conclusions: The literature predominantly consists of in vitro studies, with limited in vivo investigations. Although real world data are scarce, they are in accordance with fluoroquinolones’ effectiveness in managing early biofilm-associated infections. Also, future perspectives of newer treatment options to be placed alongside fluoroquinolones are discussed. This review underscores the role of fluoroquinolones in the setting of biofilm-associated infections, providing a comprehensive guide for physicians regarding the best use of this class of antibiotics while highlighting the existing critical issues. Full article

The scientific literature on residential segregation in large metropolitan areas highlights various explanatory factors, including economic, social, political, landscape, and cultural elements related to both migrant and local populations. This paper contrasts the impact of these factors individually, such as the immigrant rate and neighborhood segregation. To achieve this, a machine learning analysis was conducted on a sample of neighborhoods in the main Spanish metropolitan areas (Madrid and Barcelona), using a database created from a combination of official statistical sources and textual sources, such as Wikipedia. These texts were transformed into indexes using Natural Language Processing (NLP) and other artificial intelligence algorithms capable of interpreting images and converting them into indexes. The results indicate that the factors influencing immigrant concentration and segregation differ significantly, with crucial roles played by the urban landscape, population size, and geographic origin. While land prices showed a relationship with immigrant concentration, their effect on segregation was mediated by factors such as overcrowding, social support networks, and landscape degradation. The novel application of AI and big data, particularly through ChatGPT and Google Street View, has enhanced model predictability, contributing to the scientific literature on segregated spaces. Full article

Background/Objectives: The growing demand for artificial intelligence (AI) in healthcare is driven by the need for more robust and automated diagnostic systems. These methods not only provide accurate diagnoses but also promise to enhance operational efficiency and optimize resource utilization in clinical workflows. In the field of dental lesion detection, the application of deep learning models to various imaging techniques has gained significant prominence. This study presents a comprehensive systematic review of the utilization of deep learning methods for detecting dental lesions across different imaging modalities, including panoramic imaging, periapical radiographs, and cone-beam computed tomography (CBCT). A systematic search was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines to ensure a structured and transparent review process. Methods: This study addresses four key research questions related to the types of objects used for AI in dental images, state-of-the-art approaches for detecting lesions in dental images, data augmentation methods, and challenges and possible solutions to the existing AI-based dental lesion detection. Furthermore, this systematic review was performed on 29 primary studies identified from multiple electronic databases. This review focused on studies published between 2019 and 2024, sourced from IEEE, Web of Knowledge, Springer, ScienceDirect, PubMed, and Google Scholar. Results: We identified five types of lesions in dental images as periapical lesions, cyst lesions, jawbone lesions, dental caries, and apical lesions. Among the fourteen state-of-the-art deep learning approaches, the results demonstrate that deep learning models, such as U-Net, AlexNet, and You Only Look Once (YOLO) version 8 (YOLOv8) are commonly employed for dental lesion detection. These deep learning models have the potential to serve as integral components of decision-making processes by improving detection accuracy and supporting clinical workflows. Furthermore, we found that among twelve types of data augmentation techniques, flipping, rotation, and reflection methods played an important role in increasing the diversity of the datasets. We also identified six challenges for dental lesion detection, and the main issues were identified as data integration, poor data quality, limited model generalization, and overfitting. Proposed solutions against the aforementioned challenges include the integration of larger datasets, model optimization, and diversification of data sources. Conclusions: This study provides a comprehensive overview of current methodologies and potential advancements in dental lesion detection using deep learning. The findings indicate that possible solutions against the challenges of AI-based diagnostic methods in dental lesion detection need to be more generalizable regardless of image type, the number of data, and data quality. Full article

High-accuracy gully erosion susceptibility maps play a crucial role in erosion vulnerability assessment and risk management. The principal purpose of the present research is to evaluate the predictive power of individual machine learning models such as random forest (RF), decision tree (DT), and support vector machine (SVM), and ensemble machine learning approaches such as stacking, voting, bagging, and boosting with k-fold cross validation resampling techniques for modeling gully erosion susceptibility in the Oued El Abid watershed in the Moroccan High Atlas. A dataset comprising 200 gully points, identified through field observations and high-resolution Google Earth imagery, was used, alongside 21 gully erosion conditioning factors selected based on their importance, information gain, and multi-collinearity analysis. The exploratory results indicate that all derived gully erosion susceptibility maps had a good accuracy for both individual and ensemble models. Based on the receiver operating characteristic (ROC), the RF and the SVM models had better predictive performances, with AUC = 0.82, than the DT model. However, ensemble models significantly outperformed individual models. Among the ensembles, the RF-DT-SVM stacking model achieved the highest predictive accuracy, with an AUC value of 0.86, highlighting its robustness and superior predictive capability. The prioritization results also confirmed the RF-DT-SVM ensemble model as the best. These findings highlight the superiority of ensemble learning models over individual ones and underscore their potential for application in similar geo-environmental contexts. Full article

Offshore mariculture is critical for global food security and economic development. Advances in deep learning and data-driven approaches, enable the rapid and effective monitoring of offshore mariculture distribution and changes. However, detector performance depends heavily on training data quality. The lack of standardized classifications and public datasets for offshore mariculture facilities currently hampers effective monitoring. Here, we propose to categorize offshore mariculture facilities into six types: TCC, DWCC, FRC, LC, RC, and BC. Based on these categories, we introduce a benchmark dataset called OMAD-6. This dataset includes over 130,000 instances and more than 16,000 high-resolution remote sensing images. The images with a spatial resolution of 0.6 m were sourced from key regions in China, Chile, Norway, and Egypt, from the Google Earth platform. All instances in OMAD-6 were meticulously annotated manually with horizontal bounding boxes and polygons. Compared to existing remote sensing datasets, OMAD-6 has three notable characteristics: (1) it is comparable to large, published datasets in instances per category, image quantity, and sample coverage; (2) it exhibits high inter-class similarity; (3) it shows significant intra-class diversity in facility sizes and arrangements. Based on the OMAD-6 dataset, we evaluated eight state-of-the-art methods to establish baselines for future research. The experimental results demonstrate that the OMAD-6 dataset effectively represents various real-world scenarios, which have posed considerable challenges for current instance segmentation algorithms. Our evaluation confirms that the OMAD-6 dataset has the potential to improve offshore mariculture identification. Notably, the QueryInst and PointRend algorithms have distinguished themselves as top performers on the OMAD-6 dataset, robustly identifying offshore mariculture facilities even with complex environmental backgrounds. Its ongoing development and application will play a pivotal role in future offshore mariculture identification and management. Full article