Search Results (4,354)

Background: We (and others) have previously identified five clinically distinct diabetes subtypes. Currently, few models to identify diabetes subtypes are readily accessible. Further, while COVID-19 has been associated with increased risk of new-onset diabetes, it remains unknown whether the pandemic is also associated with changes in diabetes subtype distribution. Methods: We used the electronic health records of patients diagnosed with diabetes from 2010 to 2019 at the Kirklin Clinic of the University of Alabama at Birmingham (UAB) to train models to assign diabetes subtypes previously identified by hierarchical clustering. We then applied the trained model to conduct a retrospective cluster analysis of electronic health records of patients diagnosed with diabetes from 2020 to 2024 at UAB. We further validated our findings using data from the 2015–2023 National Health and Nutrition Examination Surveys (NHANES). Results: The trained classification model had an average specificity of 98% and an average sensitivity of 93%. Using the model, we identified a significant difference in the distribution of type 2 diabetes subtypes in patients at UAB and in participants in NHANES. In particular, the proportion of patients with severe insulin-dependent diabetes or severe insulin-resistant diabetes subtypes increased from 42% to 61% and 31% to 40% at the UAB and in NHANES, respectively. Conclusions: The model presented here can facilitate the identification of diabetes subtypes. The proportions of patients with severe subtypes of diabetes have seemed to increase in the more recent years following the pandemic. Further studies are required to determine the potential causes of this phenomenon. Full article

Height anomaly, defined as the separation between the quasi-geoid and the reference ellipsoid, is fundamental to quasi-geoid refinement. While the Goddard Mars Model-3 (GMM-3) developed by NASA’s Goddard Space Flight Center (GSFC) and the JPL Mars gravity field MRO120D (JGMRO_120D) model developed by NASA’s Jet Propulsion Laboratory (JPL) stand as two representative Martian gravity field models, the systematic differences between them and their associated physical implications remain insufficiently quantified. This study establishes a validated computational framework for Martian height anomaly determination using updated physical parameters and spherical harmonic expansions. Validation against terrestrial datasets confirms high reliability (standard deviation: 0.0695 m relative to International Centre for Global Earth Models (ICGEM)), ensuring confidence in subsequent analysis. Our analysis reveals three critical findings: (1) Systematic latitudinal biases between GMM-3 and JGMRO_120D exhibit a monotonic gradient from −1.3 m near the equator to +3.9 m at the North Pole, suggesting differential parameterization of polar mass loading or tidal models between the two centers. (2) Polar clustering of uncertainties and outliers exceeding the 95th percentile (>7 m) concentrate non-randomly at latitudes >60°, which is attributed to sparse satellite tracking and seasonal ice cap modeling limitations. (3) There is error amplification in lowland terrains, where relative errors exceed 60% in flat regions (near-zero anomalies), posing critical risks for precision landing missions. While global consistency between models is high (R² = 0.9999), the identified discrepancies provide new constraints on Mars’s geophysical models and essential guidance for future gravity field improvements and mission planning. Full article

Background and Objectives: Rapid diagnosis is fundamental to acute ischemic stroke management; however, access to neuroradiological expertise remains limited. This scoping review maps the diagnostic accuracy, workflow impact, and cost-effectiveness of leading AI platforms (Brainomix, Aidoc, RapidAI, and Viz.ai), characterizing industry and peer-reviewed metrics. Materials and Methods: Following PRISMA-ScR guidelines, we searched PubMed, Cochrane Library, and HTA repositories for studies (2019–2025). Using a PICO-based framework, 29 studies were included for thematic mapping of the technological landscape. Results: Twenty-nine studies were included. Platforms show high proximal LVO sensitivity (78–97%), while performance for distal/MVO and posterior circulation occlusions was more variable. RapidAI is frequently mapped using historical perfusion trial parameters; however, volumetric discrepancies with platforms like Viz.ai indicate outputs are not interchangeable. Brainomix shows extensive validation for automated NCCT ASPECTS in triage. Aidoc demonstrates operational advantages via worklist prioritization, while. Viz.ai is associated with door-to-puncture time reductions (11–25 min). Economically, cost-effectiveness is driven by improved functional outcomes and expanded access to thrombectomy, rather than labor substitution. Conclusions: AI platforms function as diagnostic safety nets and workflow optimizers. Reported roles, such as perfusion-centric analysis (RapidAI) or workflow coordination (Viz.ai), reflect current research trends rather than definitive technological superiority. Institutional selection should consider these evidence clusters alongside local validation and specific clinical priorities. Full article

Sport tourism has experienced significant growth within the tourism industry, driven by the increasing demand of special interest tourists to watch or participate in sports events with local sightseeing. However, the massive volume of available information related to sport events may cause challenges to existing recommendation systems, which struggle to provide tailored suggestions for these niche tourists. Therefore, this paper proposes a novel, context-aware recommender framework that utilises the ontology-driven approach with unsupervised machine learning techniques to deliver personalised event matches for running tourists. Using an ontology-driven approach, the framework establishes a knowledge base of user profiles and running events. Furthermore, K-modes clustering was also applied to categorise participants based on their event participation characteristics, while the Apriori algorithm was used to uncover hidden relationships influencing event selection. To ensure the statistical integrity of the discovered association rule, permutation testing was implemented to mitigate bias inherent in small sample sizes. By integrating refined association rules with Jena rules, the resulting prototype offers adaptive, personalised, and contextually relevant running event recommendations that evolve with shifting user preferences and trends. The effectiveness of the prototype is confirmed through rigorous validation and evaluation across various sport tourism scenarios. Full article

Background: Identifying patients most likely to benefit from immune checkpoint inhibitors (ICIs) remains a significant challenge in advanced melanoma. We evaluated the association between tumor somatic mutations and clinical outcomes, focusing on relapse-free survival (RFS) and overall survival (OS) in locoregionally advanced melanoma patients treated with neoadjuvant ipilimumab. Methods: Tumor specimens and matched peripheral blood samples from 22 patients underwent whole-exome sequencing (WES) to identify non-synonymous somatic mutations. Tumor mutational burden (TMB) was quantified, and specific mutations were analyzed for associations with survival outcomes. Results: The analysis revealed a mutational landscape dominated by single-nucleotide missense mutations with a median TMB of 11.4 mutations/MB. BRAF and NRAS mutations were detected in 73% of patients and exhibited mutual exclusivity and concurrence patterns (p < 0.05). Positional clustering identified NRAS and SLC35B4 as key contributors to melanoma (FDR p-value < 0.05). Log-rank analysis indicated that mutations in ODZ1, USP34, CEP192, EML5, KIAA1797, ATAD5, and ANKHD1–EIF4EBP were associated with shorter survival outcomes (RFS or OS). The associations remained significant in both univariate and multivariable Cox regression models adjusted for TMB. These genes can be broadly grouped into functional categories relevant to tumor progression and immune modulation. In applying multiple testing correction, none maintained statistical significance, indicating that these findings should be interpreted as exploratory and require validation in independent cohorts. Conclusions: This study identified tumor genomic alterations associated with clinical outcomes in melanoma patients treated with neoadjuvant ipilimumab, suggesting their potential role in anti-tumor immunity. These findings warrant further investigation in larger cohorts and across other ICIs in melanoma and other malignancies. Full article

Integrating Lean Construction (LC), the Circular Economy (CE), and Construction 5.0 (C5.0) remains challenging in emerging delivery contexts. This difficulty increases when procurement routines determine which practices become enforceable across tendering, contracting, and site execution. This study prioritized barriers to LCCE5.0 implementation in Morocco and translated expert judgments into actionable recommendations. A structured literature review informed the barrier inventory and conceptual framing. The study proposed a three-layer, life-cycle LCCE5.0 framework that links governance, operational routines, and digital enablers. It operationalized 40 critical barrier factors across six dimensions and five life-cycle macro-phases. A two-round Delphi study was conducted with 22 Moroccan experts using a 7-point Likert scale. Barriers were ranked using Round 2 (T2) medians with ties resolved using the interquartile range. Top-box agreement (ratings of 6–7) and consensus tiers were reported. The ranking showed strong stability across rounds, with 92.5% of barrier factors remaining stable. Kendall’s W at T2 equaled 0.817 (p < 0.001), indicating high panel consensus. Results indicated that constraints clustered in upstream governance. Three procurement-centered regulatory and contractual barriers topped the ranking (Mdn_T2 = 7). These barriers reflected missing CE procurement guidelines, limited weighting of environmental criteria, and the absence of circularity and digital requirements in tenders. Six additional barriers reinforced this procurement bottleneck. They included limited owner commitment, weak enforcement authority, limited top-management commitment, and regulatory instability. They also included low interorganizational trust, limited risk-sharing contracts, and tool-centered deployment of LCCE5.0 practices. These findings support procurement-focused recommendations to institutionalize auditable circular requirements and data-enabled verification in tendering and contracting routines. The proposed LCCE5.0 mechanism and the resulting recommendations require empirical validation beyond this Delphi-based prioritization. Full article

Background and Objectives: Diagnostics for hematologic diseases rely on integrated assessment of clinical manifestation, morphology, flow cytometry, and molecular testing. Current classification systems, including the WHO HAEM5 and the International Consensus Classification, highlight the central role of genomics in defining disease entities and risk. Simultaneously, laboratories face growing case complexity and staffing challenges. Automation, collaborative robots (cobots), digital morphology platforms, and artificial intelligence (AI) have begun to address these issues. Here we examine the application of these technologies in hematopathology and molecular diagnostics and consider their translational potential to improve diagnostic accuracy and, ultimately, patient care. Methods: A review of peer-reviewed literature and technical reports published through December 2025 was performed, focusing on digital morphology platforms, AI for peripheral blood and marrow interpretation, AI-enabled flow cytometry, automated and robotic deployments in clinical laboratories, and machine learning applications in molecular hematopathology. Results: Digital morphology analyzers show strong concordance with manual microscopy and now serve as efficient platforms for AI-assisted differentials, cell classification, and fibrosis quantification. Deep learning applied to multiparameter flow cytometry achieves performance comparable to expert review in distinguishing mature B-cell neoplasms and acute leukemias. Automated solutions, cobot systems and robotic-arm-based slide-scanning clusters have demonstrated substantial gains in throughput and pre-analytic consistency. AI models in molecular hematopathology increasingly assist with variant interpretation, genetic risk stratification, and linking morphologic and genomic findings. Conclusions: AI is beginning to change how hematopathology and molecular diagnostics are practiced. Successful translation will depend on disease-specific validation, the development of multi-modal models aligned with ICC and WHO frameworks, and laboratory governance that maintains expert oversight. Full article

Phytoplankton is a key component of marine ecosystems and a sensitive indicator of environmental change. In this study, light microscopy (LM) and DNA metabarcoding (18S-V4, 18S-V9, and rbcL) were combined to assess differences in phytoplankton diversity and community structure across three LTER sites in the northern Adriatic Sea, and to evaluate the methodological effects on community assessment. A total of 329 genera and 527 species were recorded by integrating both the approaches. Metabarcoding (MB) revealed increased taxonomic richness than LM, particularly for dinoflagellates and small phytoflagellates, while LM was better for identifying the diatoms and coccolithophores. The rbcL marker improved the taxonomic resolution for the diatoms compared to the 18S regions. The proportion of species shared among the sites increased from 13% with LM to 33–42% with MB, suggesting that MB may effectively reduce the discrepancies observed when relying solely on LM. Cluster analysis performed on species-relative abundances grouped the samples by approaches rather than sites, showing that methodological variability exceeded the ecological differences. The relative abundance patterns differed between methods but became more comparable after applying correction factors based on the 18S rRNA gene copy numbers, particularly for the dinoflagellates. Overall, MB enhances biodiversity assessment and comparability among sites, while LM remains essential for morphological validation and for abundance assessment. Full article

This study investigated the phytochemical profiles of 41 Korean wheat cultivars harvested over two consecutive years (2019 and 2020), with a focus on alkylresorcinols (ARs), phytosterols, vitamin E, and carotenoids. Validated chromatographic analyses revealed considerable variation among cultivars. AR levels, particularly heneicosylresorcinol, showed relatively consistent patterns across years, whereas the concentrations of phytosterols, vitamin E, and carotenoids varied more noticeably between years, suggesting possible associations with environmental conditions. Hierarchical clustering analysis classified the cultivars into five distinct groups according to their overall phytochemical profiles. ‘Dajoong’ and ‘Shinmichal’ exhibited the highest AR levels; ‘Hanbaek’, ‘Goso’, and ‘Joah’ were richest in β-sitosterol; ‘Eunpa’ and ‘Namhae’ showed elevated β-tocotrienol content, while ‘Uri’ and ‘Chungkye’ were notable for high lutein concentrations. ‘Saekeumkang’ displayed a balanced profile across all phytochemical classes. These findings provide baseline data on phytochemical variation among Korean wheat cultivars and offer insight into differences in phytochemical diversity. Full article

This study offers a comprehensive assessment of financial market modeling through a PRISMA-based systematic review, bibliometric analysis, and content synthesis. We examined 67 review articles (1990–2024) from Web of Science to build a conceptual framework, and 4982 articles (2000–2024) were analyzed with Biblioshiny. Five main clusters emerge: AI and deep learning for prediction; hybrid models that combine traditional and computational approaches; theoretical foundations, including the Efficient Market Hypothesis and critiques; high-frequency prediction and volatility analysis; and modeling of cryptocurrencies and digital assets. Temporal patterns show a shift from traditional econometrics to hybrid and deep learning methods, heightened attention to uncertainty and volatility during crises, rapid growth in crypto-focused modeling, and increased use of sentiment/news data after 2017. The content analysis highlights key gaps and future directions: standardized open benchmarks and reproducible frameworks; regime-sensitive validation; interpretable hybrid models that merge econometric structure with machine-learning flexibility; and wider applicability across assets, markets, and data types. The study provides a structured guide to intellectual and applied modeling, supporting future advances in forecasting, risk management, and policy design. Full article

Background: Changes in body composition, such as decreased muscle mass and increased adipose tissue, are significant in older adults, impacting health, functional capacity, and increasing the risk of metabolic diseases, functional decline, and frailty. Bioelectrical Impedance Analysis (BIA) is a non-invasive tool for assessing body composition, including fat-free mass (FFM), skeletal muscle mass (SMM), and fluid distribution (e.g., ECW/TBW ratio). Complementing BIA, the Mini Nutritional Assessment (MNA) serves as a validated tool for identifying malnutrition risk in the elderly. This study aimed to understand the correlation between BIA-derived parameters, MNA scores and clinical outcomes. Methods: This cross-sectional study involved 195 older adults (mean age 72.8 ± 5.4 years), divided into two groups based on body composition profiles determined by cluster analysis. Data collected included demographics, comprehensive BIA parameters (BMI, fat mass, FFM, SMM, ECW/TBW, phase angle), MNA scores, self-assessed health, chronic disease prevalence, frailty index (TFI), and functional limitations (EQ-5D). Statistical analyses included descriptive statistics, t-tests/ANOVA, chi-square tests, Pearson’s/Spearman’s correlations, point-biserial correlations, regression analyses, and ROC curve analysis to compare groups, explore variable relationships, and assess predictive abilities for malnutrition risk. Results: The first group had significantly higher BMI, AFM (AFM), FFM, and SMM, but a lower ECW/TBW ratio compared to Group 2 (N = 115), which was predominantly female and had higher frailty scores. MNA scores showed significant positive correlations with FFM (rho = 0.165, p = 0.021) and SMM (rho = 0.182, p = 0.011), and a negative correlation with ECW/TBW (rho = −0.188, p = 0.008). Higher adiposity (BMI, fat mass) correlated positively with arterial hypertension and obesity. Lower FFM and SMM were negatively correlated with gastroesophageal reflux disease. Skeletal muscle mass (AUC = 0.634, cut-off ≤ 17.3 kg) and ECW/TBW ratio (AUC = 0.626, cut-off ≥ 49.7%) showed modest discriminatory capacity to identify malnutrition risk. Individuals at risk of malnutrition reported greater functional limitations and lower self-assessed health. Numerous BIA parameters, including segmental muscle mass, total body water, phase angle, and impedance values, significantly correlated with MNA scores. Conclusions: The study highlights the importance of body composition analysis, particularly BIA, in correlation with MNA, for assessing nutritional status, functional limitations, and chronic disease associations in older adults. Integrating BIA and MNA into geriatric assessments provides a complementary profile of nutritional and functional vulnerability. Full article

Digital transformation has become important for SMEs that want better control, transparency, and coordinated operations. Yet, many studies treat digital tools in isolation and do not explain how AI and big data capabilities, together with process integration, drive governance outcomes. This gap limits a clear understanding of how digital transformation supports governance performance in SMEs. This study examines how digital transformation (DT) influences digital governance performance (DGP) in SMEs, with AI and big data analytical capability (AIBDAC) and process integration capability (PIC) as mediators. The research is grounded in the Resource-Based View, Dynamic Capabilities Theory, and the Technology Organization Environment framework. Data were collected from SMEs across five regions of Saudi Arabia using cluster and purposive sampling to target employees and managers involved in digital, analytical, and process integration work. A total of 396 valid responses were included in the analysis. Partial Least Squares Structural Equation Modelling (PLS SEM) was used to assess the measurement model, test the hypothesized paths, and evaluate mediation and moderation effects. The findings show that DT, AIBDAC, PIC, and top management support (TMS) have significant direct effects on DGP. AIBDAC and PIC act as key mediators, fully transmitting the effects of digital innovation capability and strategic readiness and partially mediating the effects of DT and TMS. Multi-group analysis shows that small and medium-large firms rely on different capability combinations. The study contributes by explaining how SMEs strengthen governance through capability development and offers practical guidance for improving governance through digital transformation. Full article

China is one of the world’s major lychee producers, and the fruit’s soft texture, small size, and thin peel make non-destructive robotic harvesting particularly challenging. Accurate fruit detection, branch segmentation, and precise picking-point localization are critical for enabling automated harvesting in complex natural orchard environments. This study proposes an integrated perception framework for lychee harvesting that combines object detection, density-based clustering, and semantic segmentation. An improved YOLO11s-based detection network incorporating SimAM attention, CMUNeXt feature enhancement, and MPDIoU loss is developed to enhance robustness under illumination variation, occlusion, and scale changes. The proposed detector achieves a precision of 84.3%, recall of 73.2%, and mAP of 81.6%, outperforming baseline models. Density-based clustering is employed to group individual detections into fruit clusters. Comparative experiments demonstrate that MeanShift achieves the highest clustering consistency, with an average Adjusted Rand Index (ARI) of 0.768, outperforming k-means and other baselines. An improved DeepLab v3+ semantic segmentation network with a ResDenseFocal backbone and Focal Loss is designed for accurate branch extraction under complex backgrounds. Finally, a rule-based geometric picking-point localization algorithm is formulated in the image coordinate system by integrating detection, clustering, and branch segmentation results. Experimental validation demonstrates that the proposed framework can reliably localize picking points in two-dimensional images under natural orchard conditions. The proposed method provides a practical perception solution for intelligent lychee harvesting and establishes a foundation for future 3D robotic manipulation and field deployment. Full article

Background/Objectives: Morning erections provide an intercourse-independent indicator of nocturnal erectile physiology. We aimed to examine whether body mass index (BMI) and muscle strength are associated with morning-erection frequency in apparently healthy Japanese male university students. Methods: This cross-sectional study analyzed 125 men with complete data (170 assessed; 45 excluded). Handgrip and back muscle strength were measured using dynamometry; BMI was calculated from height and weight. Morning-erection frequency was assessed using a single 6-category item and was dichotomized as low vs. high. Univariable and multivariable logistic regression models were fitted. Exploratory principal component analysis (PCA) and k-means clustering (k = 2, silhouette-supported) were performed. Results: Seventy-four participants (59.2%) were classified as low frequency. Back muscle strength was associated with high frequency (univariable odds ratio [OR] 1.61; 95% confidence interval [CI] 1.07–2.42; and p = 0.021) and remained significant after adjustment for BMI and handgrip strength (OR 1.88; 95% CI 1.02–3.47; and p = 0.045), whereas BMI and handgrip strength were not significant. Clustering identified two clusters (n = 41 and n = 84); Cluster 2 (higher BMI/strength) had a higher proportion of high morning-erection frequency (48% vs. 27%). Conclusions: In apparently healthy young men, greater back muscle strength was independently associated with higher self-reported morning-erection frequency. In this cohort, 59.2% reported infrequent morning erections, suggesting potential relevance even in early adulthood. Given the exploratory clustering, the single-item outcome, and likely residual confounding, these findings are hypothesis-generating and warrant longitudinal validation. Full article

The Distinctness, Uniformity, and Stability (DUS) testing guidelines for mung beans (Vigna radiata L.) offer a standardized framework for new variety assessment. Although these guidelines are essential for variety management, the actual efficiency and breeding value of the 31 specified DUS characteristics in improving yield potential remain largely underexplored and lack systematic validation. To address this critical research gap, 180 genetically diverse mung bean accessions were analyzed using principal component analysis (PCA) and correlation analysis. The results revealed intrinsic relationships among characteristics and identified key variation dimensions centered on “plant morphology”, “pod characteristics”, and “seed characteristics”. Cluster analysis classified the 180 accessions into four distinct clusters. Cluster 2, in particular, offers a clear selection reference for breeding materials targeting high-yield and quality. The DTOPSIS (Dynamic Technique for Order Preference by Similarity to Ideal Solution) multi-criteria decision-making model was applied, with index weights assigned using an objective weighting method. Following systematic evaluation, Yingge 2 was identified as an outstanding phenotype. Breeders may refer to its quantitative characteristics in subsequent breeding cycles. Linear regression analysis was employed to construct a yield prediction model, identifying leaf greenness, pod number per plant, and hundred-grain weight as three core DUS characteristics with statistically significant effects on final yield (p < 0.05). This study performed a systematic, multi-dimensional analysis and comprehensive evaluation of mung bean germplasm resources based on DUS characteristics, with the aim of identifying key yield-related DUS traits, screen elite germplasm for high-yield breeding, and providing a theoretical basis and practical reference for the efficient improvement and selective breeding of new mung bean varieties. Full article