Objective: New technology has enabled the increasing use of radiosurgery to ablate spinal lesions. The first generation of the CyberKnife (Accuray, Inc., Sunnyvale, CA) image-guided radiosurgery system required implanted radiopaque markers (fiducials) to localize spinal targets. A recently developed and now commercially available spine tracking technology called Xsight (Accuray, Inc.) tracks skeletal structures and eliminates the need for implanted fiducials. The Xsight system localizes spinal targets by direct reference to the adjacent vertebral elements. This study sought to measure the accuracy of Xsight spine tracking and provide a qualitative assessment of overall system performance.
Methods: Total system error, which is defined as the distance between the centroids of the planned and delivered dose distributions and represents all possible treatment planning and delivery errors, was measured using a realistic, anthropomorphic head-and-neck phantom. The Xsight tracking system error component of total system error was also computed by retrospectively analyzing image data obtained from eleven patients with a total of 44 implanted fiducials who underwent CyberKnife spinal radiosurgery.
Results: The total system error of the Xsight targeting technology was measured to be 0.61 mm. The tracking system error component was found to be 0.49 mm.
Conclusion: The Xsight spine tracking system is practically important because it is accurate and eliminates the use of implanted fiducials. Experience has shown this technology to be robust under a wide range of clinical circumstances.
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http://dx.doi.org/10.1227/01.NEU.0000249248.55923.EC | DOI Listing |
Struct Dyn
January 2025
Department of Physics, University of Wisconsin-Milwaukee, 3135 N. Maryland Ave, Milwaukee, Wisconsin 53211, USA.
There is a growing understanding of the structural dynamics of biological molecules fueled by x-ray crystallography experiments. Time-resolved serial femtosecond crystallography (TR-SFX) with x-ray Free Electron Lasers allows the measurement of ultrafast structural changes in proteins. Nevertheless, this technique comes with some limitations.
View Article and Find Full Text PDFJ Allergy Clin Immunol Glob
February 2025
Department of Molecular Medicine, Sapienza University, Rome, Italy.
Background: Many patients with X-linked agammaglobulinemia (XLA) nowadays have reached adulthood, as well as their sisters, possibly carriers of a deleterious Bruton tyrosine kinase variant. Studies on motherhood outcomes in families with XLA are lacking.
Objective: We sought to investigate adherence to carrier status screening, interest in preconception and prenatal genetic counseling, and reproductive decisions in relatives with XLA.
Water Res X
May 2025
Institute for Artificial Intelligence R&D of Serbia, Fruškogorska 1, Novi Sad 21000, Serbia.
This study evaluates three Machine Learning (ML) models-Temporal Kolmogorov-Arnold Networks (TKAN), Long Short-Term Memory (LSTM), and Temporal Convolutional Networks (TCN)-focusing on their capabilities to improve prediction accuracy and efficiency in streamflow forecasting. We adopt a data-centric approach, utilizing large, validated datasets to train the models, and apply SHapley Additive exPlanations (SHAP) to enhance the interpretability and reliability of the ML models. The results show that TKAN outperforms LSTM but slightly lags behind TCN in streamflow forecasting.
View Article and Find Full Text PDFFront Sports Act Living
January 2025
Graduate School of Health and Sports Science, Juntendo University, Inzai, Japan.
Introduction: Marathon running has become increasingly popular among amateur athletes, many of whom maintain speeds of 8-9 km/h. However, existing methods for estimating oxygen consumption (VO) during running and walking-such as the American College of Sports Medicine (ACSM) equations and commercial activity monitors-often lack accuracy and transparency. This study introduces the Hata-Yanagiya Physical Activity Calculation (HYPAC) system, a novel approach for estimating VO using Global Positioning System (GPS) and map data.
View Article and Find Full Text PDFBioinform Adv
January 2025
Digital Technologies Research Centre, National Research Council of Canada, Ottawa, ON K1K 4P7, Canada.
Motivation: Missing values are prevalent in high-throughput measurements due to various experimental or analytical reasons. Imputation, the process of replacing missing values in a dataset with estimated values, plays an important role in multivariate and machine learning analyses. The three missingness patterns, including missing completely at random, missing at random, and missing not at random, describe unique dependencies between the missing and observed data.
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