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Zapaishchykova A, Liu KX, Saraf A, Ye Z, Catalano PJ, Benitez V, Ravipati Y, Jain A, Huang J, Hayat H, Likitlersuang J, Vajapeyam S, Chopra RB, Familiar AM, Nabavidazeh A, Mak RH, Resnick AC, Mueller S, Cooney TM, Haas-Kogan DA, Poussaint TY, Aerts HJWL, Kann BH. Automated temporalis muscle quantification and growth charts for children through adulthood. Nat Commun 2023; 14:6863. [PMID: 37945573 PMCID: PMC10636102 DOI: 10.1038/s41467-023-42501-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2023] [Accepted: 10/12/2023] [Indexed: 11/12/2023] Open
Abstract
Lean muscle mass (LMM) is an important aspect of human health. Temporalis muscle thickness is a promising LMM marker but has had limited utility due to its unknown normal growth trajectory and reference ranges and lack of standardized measurement. Here, we develop an automated deep learning pipeline to accurately measure temporalis muscle thickness (iTMT) from routine brain magnetic resonance imaging (MRI). We apply iTMT to 23,876 MRIs of healthy subjects, ages 4 through 35, and generate sex-specific iTMT normal growth charts with percentiles. We find that iTMT was associated with specific physiologic traits, including caloric intake, physical activity, sex hormone levels, and presence of malignancy. We validate iTMT across multiple demographic groups and in children with brain tumors and demonstrate feasibility for individualized longitudinal monitoring. The iTMT pipeline provides unprecedented insights into temporalis muscle growth during human development and enables the use of LMM tracking to inform clinical decision-making.
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Affiliation(s)
- Anna Zapaishchykova
- Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Boston, MA, USA
- Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Kevin X Liu
- Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Anurag Saraf
- Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Boston, MA, USA
- Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Zezhong Ye
- Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Boston, MA, USA
- Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Paul J Catalano
- Department of Data Science, Dana-Farber Cancer Institute, Boston, MA, USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Viviana Benitez
- Dana-Farber/Boston Children's Cancer and Blood Disorders Center, Harvard Medical School, Boston, MA, USA
| | - Yashwanth Ravipati
- Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Boston, MA, USA
- Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Arnav Jain
- Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Boston, MA, USA
- Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Julia Huang
- Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Boston, MA, USA
- Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Hasaan Hayat
- Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
- Michigan State University, East Lansing, MI, USA
| | - Jirapat Likitlersuang
- Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Boston, MA, USA
- Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Sridhar Vajapeyam
- Dana-Farber/Boston Children's Cancer and Blood Disorders Center, Harvard Medical School, Boston, MA, USA
- Department of Radiology, Boston Children's Hospital, Boston, MA, USA
| | - Rishi B Chopra
- Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Ariana M Familiar
- Children's Hospital of Philadelphia, Philadelphia, USA
- University of Pennsylvania, Pennsylvania, USA
| | - Ali Nabavidazeh
- Children's Hospital of Philadelphia, Philadelphia, USA
- University of Pennsylvania, Pennsylvania, USA
| | - Raymond H Mak
- Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Boston, MA, USA
- Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Adam C Resnick
- Children's Hospital of Philadelphia, Philadelphia, USA
- University of Pennsylvania, Pennsylvania, USA
| | - Sabine Mueller
- Department of Neurology, Neurosurgery and Pediatrics, University of California, San Francisco, USA
| | - Tabitha M Cooney
- Dana-Farber/Boston Children's Cancer and Blood Disorders Center, Harvard Medical School, Boston, MA, USA
| | - Daphne A Haas-Kogan
- Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Tina Y Poussaint
- Dana-Farber/Boston Children's Cancer and Blood Disorders Center, Harvard Medical School, Boston, MA, USA
- Department of Radiology, Boston Children's Hospital, Boston, MA, USA
| | - Hugo J W L Aerts
- Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Boston, MA, USA
- Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Radiology and Nuclear Medicine, CARIM & GROW, Maastricht University, Maastricht, the Netherlands
| | - Benjamin H Kann
- Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Boston, MA, USA.
- Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA.
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Kurogi K, Rasool MI, Alherz FA, El Daibani AA, Bairam AF, Abunnaja MS, Yasuda S, Wilson LJ, Hui Y, Liu MC. SULT genetic polymorphisms: physiological, pharmacological and clinical implications. Expert Opin Drug Metab Toxicol 2021; 17:767-784. [PMID: 34107842 DOI: 10.1080/17425255.2021.1940952] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
Abstract
INTRODUCTION Cytosolic sulfotransferases (SULTs)-mediated sulfation is critically involved in the metabolism of key endogenous compounds, such as catecholamines and thyroid/steroid hormones, as well as a variety of drugs and other xenobiotics. Studies performed in the past three decades have yielded a good understanding about the enzymology of the SULTs and their structural biology, phylogenetic relationships, tissue/organ-specific/developmental expression, as well as the regulation of the SULT gene expression. An emerging area is related to the functional impact of the SULT genetic polymorphisms. AREAS COVERED The current review aims to summarize our current knowledge about the above-mentioned aspects of the SULT research. An emphasis is on the information concerning the effects of the polymorphisms of the SULT genes on the functional activity of the SULT allozymes and the associated physiological, pharmacological, and clinical implications. EXPERT OPINION Elucidation of how SULT SNPs may influence the drug-sulfating activity of SULT allozymes will help understand the differential drug metabolism and eventually aid in formulating personalized drug regimens. Moreover, the information concerning the differential sulfating activities of SULT allozymes toward endogenous compounds may allow for the development of strategies for mitigating anomalies in the metabolism of these endogenous compounds in individuals with certain SULT genotypes.
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Affiliation(s)
- Katsuhisa Kurogi
- Department of Pharmacology, College of Pharmacy and Pharmaceutical Sciences, University of Toledo Health Science Campus, Toledo, OH 43614 USA.,Department of Biochemistry and Applied Biosciences, University of Miyazaki, Miyazaki, 889-2192, Japan
| | - Mohammed I Rasool
- Department of Pharmacology, College of Pharmacy and Pharmaceutical Sciences, University of Toledo Health Science Campus, Toledo, OH 43614 USA.,Department of Pharmacology, College of Pharmacy, University of Karbala, Karbala, Iraq
| | - Fatemah A Alherz
- Department of Pharmacology, College of Pharmacy and Pharmaceutical Sciences, University of Toledo Health Science Campus, Toledo, OH 43614 USA.,Department of Pharmaceutical Sciences, College of Pharmacy, Princess Nourah Bint Abdulrahman University, Riyadh, Saudi Arabia
| | - Amal A El Daibani
- Department of Pharmacology, College of Pharmacy and Pharmaceutical Sciences, University of Toledo Health Science Campus, Toledo, OH 43614 USA
| | - Ahsan F Bairam
- Department of Pharmacology, College of Pharmacy and Pharmaceutical Sciences, University of Toledo Health Science Campus, Toledo, OH 43614 USA.,Department of Pharmacology, College of Pharmacy, University of Kufa, Najaf, Iraq
| | - Maryam S Abunnaja
- Department of Pharmacology, College of Pharmacy and Pharmaceutical Sciences, University of Toledo Health Science Campus, Toledo, OH 43614 USA
| | - Shin Yasuda
- Department of Pharmacology, College of Pharmacy and Pharmaceutical Sciences, University of Toledo Health Science Campus, Toledo, OH 43614 USA.,Department of Bioscience, School of Agriculture, Tokai University, Kumamoto City, Kumamoto 862-8652, Japan
| | - Lauren J Wilson
- Department of Pharmacology, College of Pharmacy and Pharmaceutical Sciences, University of Toledo Health Science Campus, Toledo, OH 43614 USA
| | - Ying Hui
- Department of Pharmacology, College of Pharmacy and Pharmaceutical Sciences, University of Toledo Health Science Campus, Toledo, OH 43614 USA.,Department of Obstetrics and Gynecology, Beijing Hospital, Beijing, China
| | - Ming-Cheh Liu
- Department of Pharmacology, College of Pharmacy and Pharmaceutical Sciences, University of Toledo Health Science Campus, Toledo, OH 43614 USA
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Lee SH, Kim SH, Lee WY, Chung BC, Park MJ, Choi MH. Metabolite profiling of sex developmental steroid conjugates reveals an association between decreased levels of steroid sulfates and adiposity in obese girls. J Steroid Biochem Mol Biol 2016; 162:100-9. [PMID: 27154415 DOI: 10.1016/j.jsbmb.2016.04.020] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/30/2015] [Revised: 04/14/2016] [Accepted: 04/27/2016] [Indexed: 12/12/2022]
Abstract
Free and conjugated steroids coexist in a dynamic equilibrium due to complex biosynthetic and metabolic processes. This may have clinical significance related to various physiological conditions, including sex development involving the reproductive system. Therefore, we performed quantitative profiling of 16 serum steroids conjugated with glucuronic and sulfuric acids using liquid chromatography-mass spectrometry (LC-MS). All steroid conjugates were purified by solid-phase extraction and then separated through a 3-μm particle size C18 column (150mm×2.1mm) at a flow rate of 0.3 mL/min in the negative ionization mode. The LC-MS-based analysis was found to be linear (r(2)>0.99), and all steroid conjugates had a limit-of-quantification (LOQ) of 10ng/mL, except for cholesterol sulfate and 17β-estradiol-3,17-disulfate (20ng/mL). The extraction recoveries of all steroid conjugates ranged from 97.9% to 110.7%, while the overall precision (% CV) and accuracy (% bias) ranged from 4.8% to 10.9% and from 94.4% to 112.9% at four different concentrations, respectively. Profiling of steroid conjugates corrected by adiposity revealed decreased levels of steroid sulfates (P<0.01) in overweight and obese girls compared to normal girls. The suggested technique can be used for evaluating metabolic changes in steroid conjugates and for understanding the pathophysiology and relative contributions of adiposity in childhood obesity.
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Affiliation(s)
- Su Hyeon Lee
- Molecular Recognition Research Center, Korea Institute of Science and Technology, Seoul 02792, South Korea; Department of Chemistry, Yonsei University, Seoul 03722, South Korea
| | - Shin Hye Kim
- Department of Pediatrics, Inje University Sanggye Paik Hospital, Seoul 01757, South Korea
| | - Won-Yong Lee
- Department of Chemistry, Yonsei University, Seoul 03722, South Korea
| | - Bong Chul Chung
- Molecular Recognition Research Center, Korea Institute of Science and Technology, Seoul 02792, South Korea
| | - Mi Jung Park
- Department of Pediatrics, Inje University Sanggye Paik Hospital, Seoul 01757, South Korea
| | - Man Ho Choi
- Molecular Recognition Research Center, Korea Institute of Science and Technology, Seoul 02792, South Korea.
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