The condensed-history electron transport algorithms in the Monte Carlo code MCNP4C are derived from ITS 3.0, which is a well-validated code for coupled electron-photon simulations. This, combined with its user-friendliness and versatility, makes MCNP4C a promising code for medical physics applications. Such applications, however, require a high degree of accuracy. In this work, MCNP4C electron depth-dose distributions in water are compared with published ITS 3.0 results. The influences of voxel size, substeps and choice of electron energy indexing algorithm are investigated at incident energies between 100 keV and 20 MeV. Furthermore, previously published dose measurements for seven beta emitters are simulated. Since MCNP4C does not allow tally segmentation with the *F8 energy deposition tally, even a homogeneous phantom must be subdivided in cells to calculate the distribution of dose. The repeated interruption of the electron tracks at the cell boundaries significantly affects the electron transport. An electron track length estimator of absorbed dose is described which allows tally segmentation. In combination with the ITS electron energy indexing algorithm, this estimator appears to reproduce ITS 3.0 and experimental results well. If, however, cell boundaries are used instead of segments, or if the MCNP indexing algorithm is applied, the agreement is considerably worse.
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http://dx.doi.org/10.1088/0031-9155/47/9/303 | DOI Listing |
Introduction: Systemic lupus erythematosus (SLE) causes widespread inflammation and damage in affected organs. Severity is determined by the type of organ systems affected and the extent of involvement. SLE occurs in childhood or adulthood and disease severity varies according to age of onset.
View Article and Find Full Text PDFJAMA Cardiol
January 2025
Department of Emergency Medicine, Rush University Medical Center, Chicago, Illinois.
Importance: Lung ultrasound (LUS) aids in the diagnosis of patients with dyspnea, including those with cardiogenic pulmonary edema, but requires technical proficiency for image acquisition. Previous research has demonstrated the effectiveness of artificial intelligence (AI) in guiding novice users to acquire high-quality cardiac ultrasound images, suggesting its potential for broader use in LUS.
Objective: To evaluate the ability of AI to guide acquisition of diagnostic-quality LUS images by trained health care professionals (THCPs).
Updates Surg
January 2025
Department of Radiation Oncology, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, 330006, People's Republic of China.
Whether primary lesion surgery improves survival in patients with de novo metastatic breast cancer (dnMBC) is inconclusive. We aimed to establish a prognostic prediction model for patients with de novo metastatic breast invasive ductal carcinoma (dnMBIDC) based on machine learning algorithms and to investigate the value of primary site surgery. The data used in our study were obtained from the Surveillance, Epidemiology, and End Results database (SEER, 2010-2021) and the First Affiliated Hospital of Nanchang University (1st-NCUH, June 2013-June 2023).
View Article and Find Full Text PDFAnn Surg
January 2025
Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden.
Objective: To assess performance of an algorithm for automated grading of surgery-related adverse events (AEs) according to Clavien-Dindo (C-D) classification.
Summary Background Data: Surgery-related AEs are common, lead to increased morbidity for patients, and raise healthcare costs. Resource-intensive manual chart review is still standard and to our knowledge algorithms using electronic health record (EHR) data to grade AEs according to C-D classification have not been explored.
MethodsX
June 2025
Department of Biological and Pharmaceutical Environmental Sciences and Technologies, University of Campania "L. Vanvitelli", Via Antonio Vivaldi, 43, Caserta 81100, CE, Italy.
This study explores the application of fuzzy soft classification techniques combined with vegetation indices to address spectral overlap and heterogeneity in agricultural image processing. The methodology focuses on the integration of three key vegetation indices: Soil-Adjusted Vegetation Index (SAVI), Modified Soil-Adjusted Vegetation Index (MSAVI), and Modified Chlorophyll Absorption in Reflectance Index (MCARI), with Modified Possibilistic C-Means (MPCM) clustering. The analysis involves preprocessing the image data, calculating the vegetation indices, and applying the MPCM algorithm to perform soft classification, allowing pixels to belong to multiple classes with varying degrees of membership.
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