Purpose: Ultrahigh-dose-rate (FLASH) irradiation has been reported to reduce normal tissue damage compared with conventional dose rate (CONV) irradiation without compromising tumor control. This proof-of-concept study aims to develop a deep learning (DL) approach to quantify the FLASH isoeffective dose (dose of CONV that would be required to produce the same effect as the given physical FLASH dose) with postirradiation mouse intestinal histology images.
Methods And Materials: Eighty-four healthy C57BL/6J female mice underwent 16 MeV electron CONV (0.12 Gy/s; n = 41) or FLASH (200 Gy/s; n = 43) single fraction whole abdominal irradiation. Physical dose ranged from 12 to 16 Gy for FLASH and 11 to 15 Gy for CONV in 1 Gy increments. Four days after irradiation, 9 jejunum cross-sections from each mouse were hematoxylin and eosin stained and digitized for histological analysis. CONV data set was randomly split into training (n = 33) and testing (n = 8) data sets. ResNet101-based DL models were retrained using the CONV training data set to estimate the dose based on histological features. The classical manual crypt counting (CC) approach was implemented for model comparison. Cross-section-wise mean squared error was computed to evaluate the dose estimation accuracy of both approaches. The validated DL model was applied to the FLASH data set to map the physical FLASH dose into the isoeffective dose.
Results: The DL model achieved a cross-section-wise mean squared error of 0.20 Gy on the CONV testing data set compared with 0.40 Gy of the CC approach. Isoeffective doses estimated by the DL model for FLASH doses of 12, 13, 14, 15, and 16 Gy were 12.19 ± 0.46, 12.54 ± 0.37, 12.69 ± 0.26, 12.84 ± 0.26, and 13.03 ± 0.28 Gy, respectively.
Conclusions: Our proposed DL model achieved accurate CONV dose estimation. The DL model results indicate that in the physical dose range of 13 to 16 Gy, the biologic dose response of small intestinal tissue to FLASH irradiation is represented by a lower isoeffective dose compared with the physical dose. Our DL approach can be a tool for studying isoeffective doses of other radiation dose modifying interventions.
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http://dx.doi.org/10.1016/j.ijrobp.2023.12.032 | DOI Listing |
ACS Sens
December 2024
Electronics and Telecommunications Research Institute (ETRI), Daejeon 34129, Republic of Korea.
The increasing prevalence of obesity and metabolic disorders has created a significant demand for personalized devices that can effectively monitor fat metabolism. In this study, we developed an advanced breath analyzer system designed to provide real-time monitoring of exercise-induced fat burning by analyzing volatile organic compounds (VOCs) present in both oral and alveolar breath. Acetone in exhaled breath and β-hydroxybutyric acid (BOHB) in the blood are both biomarkers closely linked to the metabolic fat burning process occurring in the liver, particularly after exercise.
View Article and Find Full Text PDFJ Phys Chem B
December 2024
Centro de Química Estrutural, Institute of Molecular Sciences, Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais, 1049-001 Lisboa, Portugal.
This study addresses a critical gap in the existing literature on carbon dioxide and ionic liquid (IL) mixtures, where fragmented and incomplete data, particularly for flow properties, hinder practical applications. Therefore, this work aimed to establish a robust and efficient method for predicting the density of the CO-IL mixtures across diverse operating conditions and IL families using novel validation techniques. Both linear and symbolic regression models provided relevant insights but failed to accurately capture the IL-CO interactions in a mixture that determine the molar volume of CO at infinite dilution when solubilized by a given IL.
View Article and Find Full Text PDFPhys Ther
December 2024
Leni and Peter W. May Department of Orthopaedics and Institute for Healthcare Delivery Science, Department of Population Health Science & Policy, Icahn School of Medicine at Mount Sinai, New York, NY 10029, United States.
Objective: Prehabilitation may have benefits for total hip arthroplasty (THA) and total knee arthroplasty (TKA), given an aging population with multimorbidity and the growth of value-based programs that focus on reducing postoperative costs. We aimed to describe prehabilitation use and examine predictors of utilization in fee-for-service Medicare beneficiaries.
Methods: This retrospective cohort study using the Medicare Limited Data Set included fee-for-service Medicare beneficiaries who were ≥ 66 years old and who underwent inpatient elective THA or TKA between January 1, 2016, and September 30, 2021.
Anal Chem
December 2024
MOBILion Systems, Chadds Ford, Pennsylvania 19317, United States.
Rapid identification of asparagine (Asn) deamidation and isoaspartate (Asp) in proteins remains a challenging analytical task during the development of biological therapeutics. For this study, 46 therapeutically relevant peptides corresponding to 13 peptide families (13 unmodified peptides and 33 modified peptides) were obtained; modified peptides included Asn deamidation and isoAsp. The peptide families were characterized by three methods: reversed-phase ultrahigh performance liquid chromatography-mass spectrometry (RP-UHPLC-MS); flow injection analysis high-resolution ion mobility-mass spectrometry (FIA-HRIM-MS); and shortened gradient RP-UHPLC-HRIM-MS.
View Article and Find Full Text PDFAlzheimers Dement
December 2024
Department of Neurology, University of Kansas School of Medicine, Kansas City, Kansas, USA.
Introduction: TOMM40 and APOC1 variants can modulate the APOE-ε4-related Alzheimer's disease (AD) risk by up to fourfold. We aim to investigate whether the genetic modulation of ε4-related AD risk is reflected in brain morphology.
Methods: We tested whether 27 magnetic resonance imaging-derived neuroimaging markers of neurodegeneration (volume and thickness in temporo-limbic regions) are associated with APOE-TOMM40-APOC1 polygenic profiles using the National Alzheimer's Coordinating Center Uniform Data Set linked to the AD Genetic Consortium data.
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