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      Phenomic Imaging

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          Abstract

          Human phenomics is defined as the comprehensive collection of observable phenotypes and characteristics influenced by a complex interplay among factors at multiple scales. These factors include genes, epigenetics at the microscopic level, organs, microbiome at the mesoscopic level, and diet and environmental exposures at the macroscopic level. “Phenomic imaging” utilizes various imaging techniques to visualize and measure anatomical structures, biological functions, metabolic processes, and biochemical activities across different scales, both in vivo and ex vivo. Unlike conventional medical imaging focused on disease diagnosis, phenomic imaging captures both normal and abnormal traits, facilitating detailed correlations between macro- and micro-phenotypes. This approach plays a crucial role in deciphering phenomes. This review provides an overview of different phenomic imaging modalities and their applications in human phenomics. Additionally, it explores the associations between phenomic imaging and other omics disciplines, including genomics, transcriptomics, proteomics, immunomics, and metabolomics. By integrating phenomic imaging with other omics data, such as genomics and metabolomics, a comprehensive understanding of biological systems can be achieved. This integration paves the way for the development of new therapeutic approaches and diagnostic tools.

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          RNA-Seq: a revolutionary tool for transcriptomics.

          RNA-Seq is a recently developed approach to transcriptome profiling that uses deep-sequencing technologies. Studies using this method have already altered our view of the extent and complexity of eukaryotic transcriptomes. RNA-Seq also provides a far more precise measurement of levels of transcripts and their isoforms than other methods. This article describes the RNA-Seq approach, the challenges associated with its application, and the advances made so far in characterizing several eukaryote transcriptomes.
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            A radiomics approach to assess tumour-infiltrating CD8 cells and response to anti-PD-1 or anti-PD-L1 immunotherapy: an imaging biomarker, retrospective multicohort study

            Because responses of patients with cancer to immunotherapy can vary in success, innovative predictors of response to treatment are urgently needed to improve treatment outcomes. We aimed to develop and independently validate a radiomics-based biomarker of tumour-infiltrating CD8 cells in patients included in phase 1 trials of anti-programmed cell death protein (PD)-1 or anti-programmed cell death ligand 1 (PD-L1) monotherapy. We also aimed to evaluate the association between the biomarker, and tumour immune phenotype and clinical outcomes of these patients.
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              Dual- and Multi-Energy CT: Principles, Technical Approaches, and Clinical Applications.

              In x-ray computed tomography (CT), materials having different elemental compositions can be represented by identical pixel values on a CT image (ie, CT numbers), depending on the mass density of the material. Thus, the differentiation and classification of different tissue types and contrast agents can be extremely challenging. In dual-energy CT, an additional attenuation measurement is obtained with a second x-ray spectrum (ie, a second "energy"), allowing the differentiation of multiple materials. Alternatively, this allows quantification of the mass density of two or three materials in a mixture with known elemental composition. Recent advances in the use of energy-resolving, photon-counting detectors for CT imaging suggest the ability to acquire data in multiple energy bins, which is expected to further improve the signal-to-noise ratio for material-specific imaging. In this review, the underlying motivation and physical principles of dual- or multi-energy CT are reviewed and each of the current technical approaches is described. In addition, current and evolving clinical applications are introduced.
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                Author and article information

                Contributors
                tianmei@fudan.edu.cn
                Journal
                Phenomics
                Phenomics
                Phenomics
                Springer Nature Singapore (Singapore )
                2730-583X
                2730-5848
                3 November 2023
                3 November 2023
                December 2023
                : 3
                : 6
                : 597-612
                Affiliations
                [1 ]Human Phenome Institute, Fudan University, ( https://ror.org/013q1eq08) 825 Zhangheng Road, Pudong New District, Shanghai, 201203 China
                [2 ]Department of Nuclear Medicine and PET Center, The Second Affiliated Hospital of Zhejiang University School of Medicine, ( https://ror.org/059cjpv64) Hangzhou, 310009 Zhejiang China
                [3 ]GRID grid.8547.e, ISNI 0000 0001 0125 2443, Department of Radiology, Huashan Hospital, State Key Laboratory of Medical Neurobiology, National Center for Neurological Disorders, , Fudan University, ; Shanghai, 200040 China
                Author information
                http://orcid.org/0000-0001-6422-6963
                http://orcid.org/0000-0002-1468-1834
                http://orcid.org/0000-0003-1777-5999
                http://orcid.org/0009-0001-8602-2467
                http://orcid.org/0009-0004-3869-5546
                http://orcid.org/0009-0007-9206-9062
                http://orcid.org/0000-0002-9627-0763
                http://orcid.org/0000-0002-7519-7229
                http://orcid.org/0000-0003-1129-3147
                http://orcid.org/0000-0002-0212-6331
                http://orcid.org/0000-0002-0991-9529
                http://orcid.org/0000-0003-1849-9199
                http://orcid.org/0000-0002-8890-4973
                http://orcid.org/0000-0002-1587-2114
                Article
                128
                10.1007/s43657-023-00128-8
                10781914
                38223684
                cf729001-2686-4198-8870-efc8579cee53
                © The Author(s) 2023

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 23 December 2021
                : 13 August 2023
                : 17 August 2023
                Funding
                Funded by: Shanghai Municipal Science and Technology Major Project
                Award ID: 2017SHZDZX01
                Categories
                Review
                Custom metadata
                © International Human Phenome Institutes (Shanghai) 2023

                phenomics,imaging,genomics,transcriptomics,proteomics,immunomics,metabolomics

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