Objectives: This article compared the accuracy, reproducibility, and gap of crowns resulting from variations in print angulation of three-dimensional (3D)-printed VarseoSmile Crown (VS) and milled resin-ceramic hybrid materials (Cerasmart 270, CS, and Enamic, E).
Materials And Methods: A total of 60 specimens, consisting of VS printed at four different angulations (30, 45, 60, and 90 degrees), along with CS and E were investigated. External and internal accuracy and reproducibility were measured with the 3D deviation analysis. External and internal gaps were measured with the silicone replica technique. The results were analyzed using Welch's one-way analysis of variance with Dunnett T3 post hoc comparison at ≤ 0.05.
Results: Across all groups, external and internal accuracy were 0.55 to 20.02 μm and external and internal reproducibility were 0.05 to 0.69 μm. Overall external accuracy was not significant ( = 0.063), whereas significance was noted in overall internal accuracy and reproducibility among groups ( < 0.001). External and internal gaps were 33.76 to 93.11 μm. Statistically significant differences were found in internal and external gaps among groups ( < 0.001), with milled crowns demonstrating larger internal and smaller external gaps than 3D-printed crowns. Within the 3D-printed group, statistically, 90-degree angles exhibited the smallest external and internal gaps.
Conclusion: Both milled and 3D-printed methods achieved clinically acceptable accuracy, reproducibility, and gap dimensions, offering viable options for hybrid ceramic crown restoration. Among 3D-printed crowns, the 90-degree printing angle group exhibited satisfactory accuracy and reproducibility, alongside the best internal and external fit.
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http://dx.doi.org/10.1055/s-0044-1795116 | DOI Listing |
BMC Cardiovasc Disord
January 2025
Department of Radiology, Qujing No.1 Hospital, Kirin District Garden Road no. 1, Qujing, 655099, China.
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Institute of Biomedical Informatics, National Yang Ming Chiao Tung University, Taipei, Taiwan.
The intricate nature of microbiota sequencing data-high dimensionality and sparsity-presents a challenge in identifying informative and reproducible microbial features for both research and clinical applications. Addressing this, we introduce PreLect, an innovative feature selection framework that harnesses microbes' prevalence to facilitate consistent selection in sparse microbiota data. Upon rigorous benchmarking against established feature selection methodologies across 42 microbiome datasets, PreLect demonstrated superior classification capabilities compared to statistical methods and outperformed machine learning-based methods by selecting features with greater prevalence and abundance.
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January 2025
Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging, and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California;
Nuclear cardiology offers a diverse range of imaging tools that provide valuable insights into myocardial perfusion, inflammation, metabolism, neuroregulation, thrombosis, and microcalcification. These techniques are crucial not only for diagnosing and managing cardiovascular conditions but also for gaining pathophysiologic insights. Surrogate biomarkers in nuclear cardiology, represented by detectable imaging changes, correlate with disease processes or therapeutic responses and can serve as endpoints in clinical trials when they demonstrate a clear link with these processes.
View Article and Find Full Text PDFJMIR Cardio
December 2024
School of Biomedical Engineering, University of British Columbia, Vancouver, BC, Canada.
Background: Cardiovascular disease remains the leading cause of mortality worldwide. Cardiac fibrosis impacts the underlying pathophysiology of many cardiovascular diseases by altering structural integrity and impairing electrical conduction. Identifying cardiac fibrosis is essential for the prognosis and management of cardiovascular disease; however, current diagnostic methods face challenges due to invasiveness, cost, and inaccessibility.
View Article and Find Full Text PDFPLoS One
January 2025
Department of Anaesthesiology, Intensive Care and Pain Medicine, University Hospital Müunster, Müunster, Germany.
Objective: Acute kidney injury (AKI) is a frequent complication in critically ill patients, affecting up to 50% of patients in the intensive care units. The lack of standardized and open-source tools for applying the Kidney Disease Improving Global Outcomes (KDIGO) criteria to time series, requires researchers to implement classification algorithms of their own which is resource intensive and might impact study quality by introducing different interpretations of edge cases. This project introduces pyAKI, an open-source pipeline addressing this gap by providing a comprehensive solution for consistent KDIGO criteria implementation.
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