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Constructing a digital twin maturity assessment framework for the building construction phase based on an improved matter-element model: A case study of a construction project in Xinyang, China
Digital twin technology has the potential to enhance construction efficiency, reduce costs, and minimize errors. However, its application during the construction phase remains at an early stage, largely constrained by the absence of standardized guidelines and principles. To address this challenge, it is essential to establish a comprehensive and universal maturity assessment framework to facilitate the effective implementation of this technology in the construction phase of building projects. This study focuses on two critical aspects: the development of the maturity assessment framework and its empirical validation. The proposed framework encompasses a maturity assessment indicator system covering five dimensions: acquisition layer, data layer, modeling layer, analysis layer, and application layer. For the first time, an optimized matter-element model based on dynamic thresholds and nonlinear correlation is introduced to improve the accuracy of maturity assessments. Furthermore, a feedback mechanism based on Importance-Performance Analysis (IPA) is utilized to clarify the formulation of optimization strategies. Finally, the framework is applied to the CAZ Innovation Industrial Park construction phase in Xinyang, Henan Province. The assessment results demonstrate that the system precisely measures the project’s maturity level and provides effective improvement recommendations. This study not only offers technological support for assessing and optimizing the digital twin maturity during the construction phase of building projects but also provides methodological insights into global digital twin maturity assessments.
Elucidating the role of group A Streptococcus genomics and pharyngeal microbiota in acute paediatric pharyngitis
Spatial multiple distortion as a facile strategy boosting the efficacy of photothermal and photodynamic therapy
Oral bowel cleansers and ischemic colitis risk: A real-world disproportionality analysis
Background Ischemic colitis (IC) is a serious but underrecognized complication potentially associated with bowel preparation. While previous studies have reported sporadic cases, the true frequency and drug-specific associations remain unclear. This study evaluates the association between oral bowel cleansers and IC using real-world pharmacovigilance data. Methods We conducted a disproportionality analysis using 20 years of data (2004–2024) from the FDA Adverse Event Reporting System (FAERS). IC cases linked to bisacodyl, polyethylene glycol (PEG), and oral sulfate solution (OSS) were identified. Multivariate logistic regression was applied to explore factors associated with IC and serious clinical outcomes. Results Among 43,958 adverse event reports related to bowel cleansers, 75 cases of IC were identified. Bisacodyl showed the strongest disproportionality signal for IC (reporting odds ratio (ROR) = 237.25), with a reporting proportion of 7.9%, followed by PEG (ROR = 2.18) and OSS (ROR = 3.64). Older age (≥70 years) and cardiovascular comorbidities were associated with more severe outcomes, such as hospitalization and death. Notably, reports of IC associated with PEG included six fatal and three life-threatening events. Conclusions To our knowledge, this is one of the largest pharmacovigilance analyses exploring ischemic colitis associated with bowel preparation agents. The findings raise concerns about the presumed safety of PEG and reveal a strong disproportionality signal for bisacodyl. These results highlight the need for individualized bowel preparation strategies, especially in elderly patients with comorbidities.
Identification of biomarkers for renal cell carcinoma in plasma samples measured using liquid chromatography-mass spectrometry and gas chromatography-mass spectrometry
Effects of Light’s criteria on the diagnostic accuracy of pleural fluid carcinoembryonic antigen concentrations for malignant pleural effusion
Correction: Blockchain-based zero trust networks with federated transfer learning for IoT security in industry 5.0
Error-driven intralimb and interlimb adaptations under asymmetric treadmill and cueing conditions
Investigation of manufacturing bilayer gears using flow forming process to enhance the strength of gears
Machine learning-based transcriptomic analysis identifies NAMPT and SAT1 as potential biomarkers and therapeutic targets in ferroptosis-associated rheumatoid arthritis
Background Rheumatoid arthritis (RA) is an autoimmune disease with chronic presentation, involving symmetric joints and systemic involvement. Ferroptosis is iron-dependent programmed cell death through lipid peroxide accumulation, implicated in inflammatory diseases, including RA. However, its underlying mechanisms and gene-level contributions to RA pathogenesis remain largely unexplored. Therefore, this study emphasizes identifying ferroptosis-related genes associated with RA, evaluating their diagnostic, prognostic, and therapeutic potential, and exploring their role in immune modulation. Methods The transcriptomic dataset (GSE89408) from the peripheral blood gene expression was downloaded from the Gene Expression Omnibus (GEO) database. We extracted the differentially expressed genes (DEGs) using R software and the most relevant modules relevant to RA were identified through weighted gene coexpression network analysis (WGCNA). We also identified the differentially expressed ferroptosis genes. The gene ontology and pathways involving the common genes were identified and the protein-protein interaction network was constructed. The hub genes were identified using three machine learning algorithms, least absolute shrinkage and selection operator (LASSO), random forest (RF), and support vector machine (SVM), after which the diagnostic efficiency of the hub genes and the correlation with immune infiltrating cells were predicted. Results A total of 9176 DEGs and a module of 314 genes were obtained which has a significant correlation with RA and 17 genes were selected after the intersection. Using the three machine-learning algorithms, we retrieved 8 hub genes (CISD2, LACTB, PRNP, SAT1, NAMPT, MITD1, SOD2, and FASN) between RA and ferroptosis which showed good diagnostic performance using the ROC curve and nomogram plots. Functional annotation analysis was utilized to inspect the biological functions of the hub genes and the genes showed a substantial association with the immune infiltrating cells. Conclusion NAMPT, CISD2, LACTB, PRNP, SAT1, SOD2, MITD1, and FASN may modulate ferroptosis and RA by influencing immunity, and NAMPT and SAT1 contribute significantly to the diagnosis and treatment of the disease. Future studies focusing on validating these genes in larger cohorts and exploring their therapeutic potential will provide deeper insights.
Factors influencing inappropriate antibiotic prescription in respiratory tract infections in general practice
Seasonal variation and structural influences on indoor radon exposure in residential buildings of Khuzestan Province
The Founders’ 400 and Chicago Perinatal Origins of Disease study protocol: Following a prospective, longitudinal cohort from early pregnancy through two years of postnatal life
Introduction The primary aim of the Chicago Perinatal Origins of Disease (CPOD) study is to characterize social, environmental, and biological exposures from early pregnancy through two years of postnatal life among a diverse cohort of mother-fetus/child dyads in the Chicago metropolitan community and to examine associations with pregnancy and early childhood health outcomes. This study is committed to ensuring the inclusion of participants historically underrepresented in perinatal research and most impacted by perinatal health inequities. CPOD is designed to align with key stakeholder and community input. Methods Approximately 400 pregnant people 8–28 weeks gestation and their neonates will be recruited into a longitudinal, prospective observational study enriched for participants who self-identify as Black and/or Latinx. Pregnant participants are followed at three time points antenatally and during their delivery hospitalization; mother-child dyads are followed at five time points in the first two years of life. Semi-structured interviews, patient-reported quantitative surveys, electronic health record abstraction, biological specimens, and environmental sampling from participant homes comprise data collection methods. Biospecimens (including placental biopsies) from mothers, infants, and other household members are collected, processed, and stored in a biorepository. Translational approaches, including a variety of biospecimen analyses (e.g., epigenetics, metabolomics, placental histopathology, microbiome analyses), will be employed to evaluate psychosocial and environmental exposures associated with biologic changes, and how dysregulation of one’s underlying biology during pregnancy and early childhood are associated with adverse health outcomes. Discussion CPOD is a unique, prospective, observational study that includes a large, ethnically diverse cohort; rich, multifactorial phenotypic characterization of maternal health and pregnancy outcomes, neonatal health, and early childhood neurobehavior; and development of a biorepository of social, environmental, and clinical data and biospecimens from early pregnancy to two years of postnatal life. Using translational science approaches, data from this cohort will provide clinical and mechanistic insights into how environmental and psychosocial exposures, both during pregnancy and transgenerationally, influence changes in the underlying biology of maternal-child dyads, and how these changes are associated with the risk of adverse health outcomes that contribute to future disease.
H-fusion SEG: dual-branch hyper-attention fusion network with SAM integration for robust skin disease segmentation
Machine learning based prediction of carbon concentration in carburized steel
Design and evaluation of a blended basketball training program using the ADDIE model
This study aimed to develop and evaluate a blended learning basketball training program for university students based on the ADDIE instructional design model. A quasi-experimental one-group pre-test–post-test design was conducted with 30 first-year undergraduates in Wuhu, China. Over five days (30 hours), participants engaged in online theoretical learning and offline practical sessions covering five basketball skill areas, followed by a satisfaction survey. All measured skills improved significantly (p < .001), with large effect sizes in dribbling (d = 2.14) and passing (d = 3.34), while improvements in shooting and three-step layups were relatively smaller but still significant. Mean satisfaction was 4.07/5, with high ratings for instructional support and learning resources. These findings support the effectiveness of the ADDIE-based blended learning model in enhancing basketball skills and student engagement. This structured approach offers practical value for improving skill acquisition in sports education.
Characteristics of quinolone resistance in Escherichia coli isolated from wildlife in Poland
Investigating the cytotoxicity and genotoxicity of Vortioxetine with in vivo and in silico methods
Inequities in breast cancer outcomes in Chile: An analysis of case fatality ratios and survival rates (2007–2018)
Breast cancer is a leading cause of illness and death among women in Chile, yet national data on health outcomes remain limited in the absence of a cancer registry. This observational study examines disparities in breast cancer case fatality ratios and survival rates by health insurance provider and geographic region using national hospital discharge and mortality databases from 2007 to 2018. We analyzed 58,254 hospital discharges and 16,615 deaths related to breast cancer. Case fatality and survival estimates were computed using crude ratios, Kaplan-Meier methods, and Cox proportional hazards models. Nationally, the average case fatality ratio was 26.8 percent. Patients in the public health insurance system had significantly higher fatality ratio (27.5 percent) than those in the private system (15.7 percent). One- and five-year survival rates were lower for publicly insured patients (93.4 percent and 80.8 percent) than for privately insured patients (97.3 percent and 90.2 percent). Within the public system, survival varied by income-based segment, with the lowest rates among the most socioeconomically disadvantaged group. Patients in the Metropolitan Region showed better survival compared to those living in other regions. Cox regression analysis confirmed that health insurance type, age, year of diagnosis, and region of residence were significant predictors of survival. These findings suggest that, despite universal health guarantees in Chile, meaningful inequities in breast cancer outcomes persist. The methodology used in this study relies on administrative data and can be applied in other countries or regions with access to comparable hospital discharge and mortality records, supporting broader efforts to monitor and reduce healthcare disparities.