The accurate determination of an individual's unique human leukocyte antigen (HLA) allele holds important significance in evaluating the risk associated with autoimmune and infectious diseases, such as human immunodeficiency virus (HIV) infection. Several allelic variants within the HLA system have been linked to either increased protection or susceptibility in the context of infectious and autoimmune diseases. This study aimed to determine the frequency and association of HLA alleles between people living with HIV (PLHIV) as the case group and Peruvian individuals without HIV with high-risk behaviors of sexually transmitted diseases as the control group.
View Article and Find Full Text PDFPurpose: The purpose of the study was to evaluate the dosimetric impact of sexual-sparing radiotherapy for prostate cancer, with magnetic resonance-only treatment planning.
Material And Methods: Fifteen consecutive patients receiving prostate cancer radiotherapy were selected. A synthetic CT was generated with a deep learning method from each T2-weighted MRI performed at the time of treatment planning.
Background: Tuberculosis (TB) is a highly prevalent chronic infectious disease in developing countries, with Peru being one of the most affected countries in the world. The variants of the -acetyltransferase 2 () gene are related to xenobiotic metabolism and have potential usefulness in TB studies.
Aim: To determine whether gene variants and acetylator phenotypes are associated with active TB in Peruvian patients.
This paper proposes a model based on machine learning for the prediction of road traffic noise for the city of Bogota-Colombia. The input variables of the model were: vehicle capacity, speed, type of flow and number of lanes. The input data were obtained through measurement campaigns in which audio and video recordings were made.
View Article and Find Full Text PDFBackground And Purpose: Magnetic resonance imaging (MRI)-to-computed tomography (CT) synthesis is essential in MRI-only radiotherapy workflows, particularly through deep learning techniques known for their accuracy. However, current supervised methods are limited to specific center's learnings and depend on registration precision. The aim of this study was to evaluate the accuracy of unsupervised and supervised approaches in the context of prostate MRI-to-CT generation for radiotherapy dose calculation.
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