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Comprehensive scientometrics and visualization study profiles lymphoma metabolism and identifies its significant research signatures

BACKGROUND: There is a wealth of poorly utilized unstructured data on lymphoma metabolism, and scientometrics and visualization study could serve as a robust tool to address this issue. Hence, it was implemented. METHODS: After strict quality control, numerous data regarding the lymphoma metabolism...

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Autores principales: Guo, Song-Bin, Pan, Dan-Qi, Su, Ning, Huang, Man-Qian, Zhou, Zhen-Zhong, Huang, Wei-Juan, Tian, Xiao-Peng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10562636/
https://www.ncbi.nlm.nih.gov/pubmed/37822596
http://dx.doi.org/10.3389/fendo.2023.1266721
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author Guo, Song-Bin
Pan, Dan-Qi
Su, Ning
Huang, Man-Qian
Zhou, Zhen-Zhong
Huang, Wei-Juan
Tian, Xiao-Peng
author_facet Guo, Song-Bin
Pan, Dan-Qi
Su, Ning
Huang, Man-Qian
Zhou, Zhen-Zhong
Huang, Wei-Juan
Tian, Xiao-Peng
author_sort Guo, Song-Bin
collection PubMed
description BACKGROUND: There is a wealth of poorly utilized unstructured data on lymphoma metabolism, and scientometrics and visualization study could serve as a robust tool to address this issue. Hence, it was implemented. METHODS: After strict quality control, numerous data regarding the lymphoma metabolism were mined, quantified, cleaned, fused, and visualized from documents (n = 2925) limited from 2013 to 2022 using R packages, VOSviewer, and GraphPad Prism. RESULTS: The linear fitting analysis generated functions predicting the annual publication number (y = 31.685x - 63628, R² = 0.93614, Prediction in 2027: 598) and citation number (y = 1363.7x - 2746019, R² = 0.94956, Prediction in 2027: 18201). In the last decade, the most academically performing author, journal, country, and affiliation were Meignan Michel (n = 35), European Journal of Nuclear Medicine and Molecular Imaging (n = 1653), USA (n = 3114), and University of Pennsylvania (n = 86), respectively. The hierarchical clustering based on unsupervised learning further divided research signatures into five clusters, including the basic study cluster (Cluster 1, Total Link Strength [TLS] = 1670, Total Occurrence [TO] = 832) and clinical study cluster (Cluster 3, TLS = 3496, TO = 1328). The timeline distribution indicated that radiomics and artificial intelligence (Cluster 4, Average Publication Year = 2019.39 ± 0.21) is a relatively new research cluster, and more endeavors deserve. Research signature burst and linear regression analysis further confirmed the findings above and revealed additional important results, such as tumor microenvironment (a = 0.6848, R² = 0.5194, p = 0.019) and immunotherapy (a = 1.036, R² = 0.6687, p = 0.004). More interestingly, by performing a “Walktrap” algorithm, the community map indicated that the “apoptosis, metabolism, chemotherapy” (Centrality = 12, Density = 6), “lymphoma, pet/ct, prognosis” (Centrality = 11, Density = 1), and “genotoxicity, mutagenicity” (Centrality = 9, Density = 4) are crucial but still under-explored, illustrating the potentiality of these research signatures in the field of the lymphoma metabolism. CONCLUSION: This study comprehensively mines valuable information and offers significant predictions about lymphoma metabolism for its clinical and experimental practice.
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spelling pubmed-105626362023-10-11 Comprehensive scientometrics and visualization study profiles lymphoma metabolism and identifies its significant research signatures Guo, Song-Bin Pan, Dan-Qi Su, Ning Huang, Man-Qian Zhou, Zhen-Zhong Huang, Wei-Juan Tian, Xiao-Peng Front Endocrinol (Lausanne) Endocrinology BACKGROUND: There is a wealth of poorly utilized unstructured data on lymphoma metabolism, and scientometrics and visualization study could serve as a robust tool to address this issue. Hence, it was implemented. METHODS: After strict quality control, numerous data regarding the lymphoma metabolism were mined, quantified, cleaned, fused, and visualized from documents (n = 2925) limited from 2013 to 2022 using R packages, VOSviewer, and GraphPad Prism. RESULTS: The linear fitting analysis generated functions predicting the annual publication number (y = 31.685x - 63628, R² = 0.93614, Prediction in 2027: 598) and citation number (y = 1363.7x - 2746019, R² = 0.94956, Prediction in 2027: 18201). In the last decade, the most academically performing author, journal, country, and affiliation were Meignan Michel (n = 35), European Journal of Nuclear Medicine and Molecular Imaging (n = 1653), USA (n = 3114), and University of Pennsylvania (n = 86), respectively. The hierarchical clustering based on unsupervised learning further divided research signatures into five clusters, including the basic study cluster (Cluster 1, Total Link Strength [TLS] = 1670, Total Occurrence [TO] = 832) and clinical study cluster (Cluster 3, TLS = 3496, TO = 1328). The timeline distribution indicated that radiomics and artificial intelligence (Cluster 4, Average Publication Year = 2019.39 ± 0.21) is a relatively new research cluster, and more endeavors deserve. Research signature burst and linear regression analysis further confirmed the findings above and revealed additional important results, such as tumor microenvironment (a = 0.6848, R² = 0.5194, p = 0.019) and immunotherapy (a = 1.036, R² = 0.6687, p = 0.004). More interestingly, by performing a “Walktrap” algorithm, the community map indicated that the “apoptosis, metabolism, chemotherapy” (Centrality = 12, Density = 6), “lymphoma, pet/ct, prognosis” (Centrality = 11, Density = 1), and “genotoxicity, mutagenicity” (Centrality = 9, Density = 4) are crucial but still under-explored, illustrating the potentiality of these research signatures in the field of the lymphoma metabolism. CONCLUSION: This study comprehensively mines valuable information and offers significant predictions about lymphoma metabolism for its clinical and experimental practice. Frontiers Media S.A. 2023-09-26 /pmc/articles/PMC10562636/ /pubmed/37822596 http://dx.doi.org/10.3389/fendo.2023.1266721 Text en Copyright © 2023 Guo, Pan, Su, Huang, Zhou, Huang and Tian https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Endocrinology
Guo, Song-Bin
Pan, Dan-Qi
Su, Ning
Huang, Man-Qian
Zhou, Zhen-Zhong
Huang, Wei-Juan
Tian, Xiao-Peng
Comprehensive scientometrics and visualization study profiles lymphoma metabolism and identifies its significant research signatures
title Comprehensive scientometrics and visualization study profiles lymphoma metabolism and identifies its significant research signatures
title_full Comprehensive scientometrics and visualization study profiles lymphoma metabolism and identifies its significant research signatures
title_fullStr Comprehensive scientometrics and visualization study profiles lymphoma metabolism and identifies its significant research signatures
title_full_unstemmed Comprehensive scientometrics and visualization study profiles lymphoma metabolism and identifies its significant research signatures
title_short Comprehensive scientometrics and visualization study profiles lymphoma metabolism and identifies its significant research signatures
title_sort comprehensive scientometrics and visualization study profiles lymphoma metabolism and identifies its significant research signatures
topic Endocrinology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10562636/
https://www.ncbi.nlm.nih.gov/pubmed/37822596
http://dx.doi.org/10.3389/fendo.2023.1266721
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