The OLINDA/EXM version 2.0 personal computer code was created as an upgrade to the widely used OLINDA/EXM 1.0 and 1.1 codes. This paper documents the upgrades that were implemented. New decay data and anthropomorphic and biokinetic models were implemented in the software, and the software alpha and beta tested. Agreement of doses between the OLINDA/EXM codes 1 and 2 was very good. Use of the new anthropomorphic and biokinetic models results in understandable differences between the codes. Previous models were retained in the new code, and those results were identical to those in the previous code. OLINDA/EXM 2.0 represents an upgrade from version 1, with new modeling data recommended by the international community. It standardizes internal dose calculations for dose assessments in clinical trials with radiopharmaceuticals, theoretical calculations for existing pharmaceuticals, teaching, and other purposes.
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http://dx.doi.org/10.1097/HP.0000000000001682 | DOI Listing |
Oral Maxillofac Surg
January 2025
Department of Oral and Maxillofacial Surgery, University Hospital Regensburg, Franz-Josef-Strauß-Allee 11, 93053, Regensburg, Germany.
Purpose: This study aimed to clarify the applicability of smartphone-based three-dimensional (3D) surface imaging for clinical use in oral and maxillofacial surgery, comparing two smartphone-based approaches to the gold standard.
Methods: Facial surface models (SMs) were generated for 30 volunteers (15 men, 15 women) using the Vectra M5 (Canfield Scientific, USA), the TrueDepth camera of the iPhone 14 Pro (Apple Inc., USA), and the iPhone 14 Pro with photogrammetry.
JMIR Form Res
January 2025
Limburg Clinical Research Center/Mobile Health Unit, Faculty of Medicine and Life Sciences, Hasselt University, Hasselt, Belgium.
Background: Consumer-oriented wearable devices (CWDs) such as smartphones and smartwatches have gained prominence for their ability to detect atrial fibrillation (AF) through proprietary algorithms using electrocardiography or photoplethysmography (PPG)-based digital recordings. Despite numerous individual validation studies, a direct comparison of interdevice performance is lacking.
Objective: This study aimed to evaluate and compare the ability of CWDs to distinguish between sinus rhythm and AF.
J Family Med Prim Care
December 2024
Department of Psychiatry and Behavioral Health, Jersey Shore University Medical Center, Neptune, NJ, USA.
Objective: Selecting the right medication for major depressive disorder (MDD) is challenging, and patients are often on several medications before an effective one is found. Using patient EEG patterns with computer models to select medications is a potential solution, however, it is not widely performed. Therefore, we evaluated a commercially available EEG data analysis system to help guide medication selection in a clinical setting.
View Article and Find Full Text PDFJ Family Med Prim Care
December 2024
Indian Council of Medical Research, Ansari Nagar, New Delhi, India.
Background: Cause-of-death (CoD) information is crucial for health policy formulation, planning, and program implementation. Verbal Autopsy (VA) is an approach employed for the collection and analysis of CoD estimates at the population level where medical certification of cause of death is low and, secondly, for integrating it with the existing public health system by utilizing the grassroots level workforce.
Objective: The study aims to understand the field perspectives on implementing the 2022 WHO VA instrument in rural India through the existing public health system.
Int J Cardiovasc Imaging
January 2025
Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.
The initial evaluation of stenosis during coronary angiography is typically performed by visual assessment. Visual assessment has limited accuracy compared to fractional flow reserve and quantitative coronary angiography, which are more time-consuming and costly. Applying deep learning might yield a faster and more accurate stenosis assessment.
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