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Quantitative assessment of TLR4 gene polymorphisms and T2DM risk: A meta-analysis.

Molecular genetics & genomic medicine

Fan J, Liang R.
PMID: 32822111
Mol Genet Genomic Med. 2020 Oct;8(10):e1466. doi: 10.1002/mgg3.1466. Epub 2020 Aug 21.

BACKGROUND: Numerous studies have evaluated the association between TLR4 gene polymorphisms and T2DM risk. However, the findings were inconsistent and controversial.METHODS: In order to drive a more precise estimation, we carried out a meta-analysis based on 41 studies involving...

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Fan J, Liang R. Quantitative assessment of TLR4 gene polymorphisms and T2DM risk: A meta-analysis. Mol Genet Genomic Med. 2020;8(10):e1466doi: 10.1002/mgg3.1466.
Fan, J., & Liang, R. (2020). Quantitative assessment of TLR4 gene polymorphisms and T2DM risk: A meta-analysis. Molecular genetics & genomic medicine, 8(10), e1466. https://doi.org/10.1002/mgg3.1466
Fan, Jinzhuo, and Liang, Renxian. "Quantitative assessment of TLR4 gene polymorphisms and T2DM risk: A meta-analysis." Molecular genetics & genomic medicine vol. 8,10 (2020): e1466. doi: https://doi.org/10.1002/mgg3.1466
Fan J, Liang R. Quantitative assessment of TLR4 gene polymorphisms and T2DM risk: A meta-analysis. Mol Genet Genomic Med. 2020 Oct;8(10):e1466. doi: 10.1002/mgg3.1466. Epub 2020 Aug 21. PMID: 32822111; PMCID: PMC7549608.
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Human Genetics of Obesity and Type 2 Diabetes Mellitus: Past, Present, and Future.

Circulation. Genomic and precision medicine

Ingelsson E, McCarthy MI.
PMID: 29899044
Circ Genom Precis Med. 2018 Jun;11(6):e002090. doi: 10.1161/CIRCGEN.118.002090.

Type 2 diabetes mellitus (T2D) and obesity already represent 2 of the most prominent risk factors for cardiovascular disease, and are destined to increase in importance given the global changes in lifestyle. Ten years have passed since the first...

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Ingelsson E, McCarthy MI. Human Genetics of Obesity and Type 2 Diabetes Mellitus: Past, Present, and Future. Circ Genom Precis Med. 2018;11(6):e002090doi: 10.1161/CIRCGEN.118.002090.
Ingelsson, E., & McCarthy, M. I. (2018). Human Genetics of Obesity and Type 2 Diabetes Mellitus: Past, Present, and Future. Circulation. Genomic and precision medicine, 11(6), e002090. https://doi.org/10.1161/CIRCGEN.118.002090
Ingelsson, Erik, and McCarthy, Mark I. "Human Genetics of Obesity and Type 2 Diabetes Mellitus: Past, Present, and Future." Circulation. Genomic and precision medicine vol. 11,6 (2018): e002090. doi: https://doi.org/10.1161/CIRCGEN.118.002090
Ingelsson E, McCarthy MI. Human Genetics of Obesity and Type 2 Diabetes Mellitus: Past, Present, and Future. Circ Genom Precis Med. 2018 Jun;11(6):e002090. doi: 10.1161/CIRCGEN.118.002090. PMID: 29899044; PMCID: PMC6405228.
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Gene Expression Mining in Type 2 Diabetes Research.

Methods in molecular biology (Clifton, N.J.)

Dunbar DR.
PMID: 31586324
Methods Mol Biol. 2020;2076:109-117. doi: 10.1007/978-1-4939-9882-1_6.

Gene expression analysis by microarray and more recently by next-generation sequencing has become a core part of biomedical research and its value can be seen in thousands of research papers. A successful gene expression experiment needs to be augmented...

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Dunbar DR. Gene Expression Mining in Type 2 Diabetes Research. Methods Mol Biol. 2020;2076:109-117doi: 10.1007/978-1-4939-9882-1_6.
Dunbar, D. R. (2020). Gene Expression Mining in Type 2 Diabetes Research. Methods in molecular biology (Clifton, N.J.), 2076109-117. https://doi.org/10.1007/978-1-4939-9882-1_6
Dunbar, Donald R. "Gene Expression Mining in Type 2 Diabetes Research." Methods in molecular biology (Clifton, N.J.) vol. 2076 (2020): 109-117. doi: https://doi.org/10.1007/978-1-4939-9882-1_6
Dunbar DR. Gene Expression Mining in Type 2 Diabetes Research. Methods Mol Biol. 2020;2076:109-117. doi: 10.1007/978-1-4939-9882-1_6. PMID: 31586324.
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[Why is there a type-2 diabetes?].

Deutsche medizinische Wochenschrift (1946)

Ritz E.
PMID: 8482235
Dtsch Med Wochenschr. 1993 Apr 23;118(16):602-3. doi: 10.1055/s-2008-1059368.

No abstract available.

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Ritz E. Warum gibt es den Typ-II-Diabetes? [Why is there a type-2 diabetes?]. Dtsch Med Wochenschr. 1993;118(16):602-3doi: 10.1055/s-2008-1059368.
Ritz, E. (1993). Warum gibt es den Typ-II-Diabetes? [Why is there a type-2 diabetes?]. Deutsche medizinische Wochenschrift (1946), 118(16), 602-3. https://doi.org/10.1055/s-2008-1059368
Ritz, E. "Warum gibt es den Typ-II-Diabetes?" [Why is there a type-2 diabetes?]. Deutsche medizinische Wochenschrift (1946) vol. 118,16 (1993): 602-3. doi: https://doi.org/10.1055/s-2008-1059368
Ritz E. Warum gibt es den Typ-II-Diabetes? [Why is there a type-2 diabetes?]. Dtsch Med Wochenschr. 1993 Apr 23;118(16):602-3. doi: 10.1055/s-2008-1059368. German. PMID: 8482235.
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Prioritizing Type 2 Diabetes Genes by Weighted PageRank on Bilayer Heterogeneous Networks.

IEEE/ACM transactions on computational biology and bioinformatics

Shang H, Liu ZP.
PMID: 31095494
IEEE/ACM Trans Comput Biol Bioinform. 2021 Jan-Feb;18(1):336-346. doi: 10.1109/TCBB.2019.2917190. Epub 2021 Feb 03.

The prevalence of diabetes mellitus has been increasing rapidly in recent years. Type 2 diabetes makes up about 90 percent cases of diabetes. The interacting mixed effects of genetics and environments build possible interpretable pathogenesis. Thus, finding the causal...

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Shang H, Liu ZP. Prioritizing Type 2 Diabetes Genes by Weighted PageRank on Bilayer Heterogeneous Networks. IEEE/ACM Trans Comput Biol Bioinform. 2021;18(1):336-346doi: 10.1109/TCBB.2019.2917190.
Shang, H., & Liu, Z. P. (2021). Prioritizing Type 2 Diabetes Genes by Weighted PageRank on Bilayer Heterogeneous Networks. IEEE/ACM transactions on computational biology and bioinformatics, 18(1), 336-346. https://doi.org/10.1109/TCBB.2019.2917190
Shang, Haixia, and Liu, Zhi-Ping. "Prioritizing Type 2 Diabetes Genes by Weighted PageRank on Bilayer Heterogeneous Networks." IEEE/ACM transactions on computational biology and bioinformatics vol. 18,1 (2021): 336-346. doi: https://doi.org/10.1109/TCBB.2019.2917190
Shang H, Liu ZP. Prioritizing Type 2 Diabetes Genes by Weighted PageRank on Bilayer Heterogeneous Networks. IEEE/ACM Trans Comput Biol Bioinform. 2021 Jan-Feb;18(1):336-346. doi: 10.1109/TCBB.2019.2917190. Epub 2021 Feb 03. PMID: 31095494.
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Diabetes in China: Epidemiology and Genetic Risk Factors and Their Clinical Utility in Personalized Medication.

Diabetes

Hu C, Jia W.
PMID: 29263166
Diabetes. 2018 Jan;67(1):3-11. doi: 10.2337/dbi17-0013.

The incidence of type 2 diabetes (T2D) has rapidly increased over recent decades, and T2D has become a leading public health challenge in China. Compared with European descents, Chinese patients with T2D are diagnosed at a relatively young age...

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Hu C, Jia W. Diabetes in China: Epidemiology and Genetic Risk Factors and Their Clinical Utility in Personalized Medication. Diabetes. 2018;67(1):3-11doi: 10.2337/dbi17-0013.
Hu, C., & Jia, W. (2018). Diabetes in China: Epidemiology and Genetic Risk Factors and Their Clinical Utility in Personalized Medication. Diabetes, 67(1), 3-11. https://doi.org/10.2337/dbi17-0013
Hu, Cheng, and Jia, Weiping. "Diabetes in China: Epidemiology and Genetic Risk Factors and Their Clinical Utility in Personalized Medication." Diabetes vol. 67,1 (2018): 3-11. doi: https://doi.org/10.2337/dbi17-0013
Hu C, Jia W. Diabetes in China: Epidemiology and Genetic Risk Factors and Their Clinical Utility in Personalized Medication. Diabetes. 2018 Jan;67(1):3-11. doi: 10.2337/dbi17-0013. PMID: 29263166.
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Systematically analyses of the common dysregulated networks to understand the common pathologies between T2D and atherosclerosis.

Gene

Lin Z, Yang F, Sun L, Gao J, Cao Y, Qiu H, Zhan X.
PMID: 29705125
Gene. 2018 Sep 10;671:110-116. doi: 10.1016/j.gene.2018.04.078. Epub 2018 Apr 26.

BACKGROUND: Diabetic macroangiopathy, atherosclerosis secondary to diabetes mellitus, causes cerebro-cardiovascular diseases (CVD), which increase the risk of death in patients with DM and significantly reduce their quality of life. Therefore, mechanisms underlying the common shared pathologies between type 2...

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Lin Z, Yang F, Sun L, et al. Systematically analyses of the common dysregulated networks to understand the common pathologies between T2D and atherosclerosis. Gene. 2018;671:110-116doi: 10.1016/j.gene.2018.04.078.
Lin, Z., Yang, F., Sun, L., Gao, J., Cao, Y., Qiu, H., & Zhan, X. (2018). Systematically analyses of the common dysregulated networks to understand the common pathologies between T2D and atherosclerosis. Gene, 671110-116. https://doi.org/10.1016/j.gene.2018.04.078
Lin, Zijing, et al. "Systematically analyses of the common dysregulated networks to understand the common pathologies between T2D and atherosclerosis." Gene vol. 671 (2018): 110-116. doi: https://doi.org/10.1016/j.gene.2018.04.078
Lin Z, Yang F, Sun L, Gao J, Cao Y, Qiu H, Zhan X. Systematically analyses of the common dysregulated networks to understand the common pathologies between T2D and atherosclerosis. Gene. 2018 Sep 10;671:110-116. doi: 10.1016/j.gene.2018.04.078. Epub 2018 Apr 26. PMID: 29705125.
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Cross-population analysis for functional characterization of type II diabetes variants.

BMC bioinformatics

Elmansy D, Koyutürk M.
PMID: 31216985
BMC Bioinformatics. 2019 Jun 20;20:320. doi: 10.1186/s12859-019-2835-0.

BACKGROUND: As Genome-Wide Association Studies (GWAS) have been increasingly used with data from various populations, it has been observed that data from different populations reveal different sets of Single Nucleotide Polymorphisms (SNPs) that are associated with the same disease....

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Elmansy D, Koyutürk M. Cross-population analysis for functional characterization of type II diabetes variants. BMC Bioinformatics. 2019;20:320doi: 10.1186/s12859-019-2835-0.
Elmansy, D., & Koyutürk, M. (2019). Cross-population analysis for functional characterization of type II diabetes variants. BMC bioinformatics, 20320. https://doi.org/10.1186/s12859-019-2835-0
Elmansy, Dalia, and Koyutürk, Mehmet. "Cross-population analysis for functional characterization of type II diabetes variants." BMC bioinformatics vol. 20 (2019): 320. doi: https://doi.org/10.1186/s12859-019-2835-0
Elmansy D, Koyutürk M. Cross-population analysis for functional characterization of type II diabetes variants. BMC Bioinformatics. 2019 Jun 20;20:320. doi: 10.1186/s12859-019-2835-0. PMID: 31216985; PMCID: PMC6584529.
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Identifying molecular insight of synergistic complexities for SARS-CoV-2 infection with pre-existing type 2 diabetes.

Computers in biology and medicine

Islam MB, Chowdhury UN, Nain Z, Uddin S, Ahmed MB, Moni MA.
PMID: 34340124
Comput Biol Med. 2021 Sep;136:104668. doi: 10.1016/j.compbiomed.2021.104668. Epub 2021 Jul 23.

The ongoing COVID-19 outbreak, caused by SARS-CoV-2, has posed a massive threat to global public health, especially to people with underlying health conditions. Type 2 diabetes (T2D) is lethal comorbidity of COVID-19. However, its pathogenetic link remains unclear. This...

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Islam MB, Chowdhury UN, Nain Z, et al. Identifying molecular insight of synergistic complexities for SARS-CoV-2 infection with pre-existing type 2 diabetes. Comput Biol Med. 2021;136:104668doi: 10.1016/j.compbiomed.2021.104668.
Islam, M. B., Chowdhury, U. N., Nain, Z., Uddin, S., Ahmed, M. B., & Moni, M. A. (2021). Identifying molecular insight of synergistic complexities for SARS-CoV-2 infection with pre-existing type 2 diabetes. Computers in biology and medicine, 136104668. https://doi.org/10.1016/j.compbiomed.2021.104668
Islam, M Babul, et al. "Identifying molecular insight of synergistic complexities for SARS-CoV-2 infection with pre-existing type 2 diabetes." Computers in biology and medicine vol. 136 (2021): 104668. doi: https://doi.org/10.1016/j.compbiomed.2021.104668
Islam MB, Chowdhury UN, Nain Z, Uddin S, Ahmed MB, Moni MA. Identifying molecular insight of synergistic complexities for SARS-CoV-2 infection with pre-existing type 2 diabetes. Comput Biol Med. 2021 Sep;136:104668. doi: 10.1016/j.compbiomed.2021.104668. Epub 2021 Jul 23. PMID: 34340124; PMCID: PMC8299293.
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Identifying molecular insight of synergistic complexities for SARS-CoV-2 infection with pre-existing type 2 diabetes.

Computers in biology and medicine

Islam MB, Chowdhury UN, Nain Z, Uddin S, Ahmed MB, Moni MA.
PMID: 34340124
Comput Biol Med. 2021 Sep;136:104668. doi: 10.1016/j.compbiomed.2021.104668. Epub 2021 Jul 23.

The ongoing COVID-19 outbreak, caused by SARS-CoV-2, has posed a massive threat to global public health, especially to people with underlying health conditions. Type 2 diabetes (T2D) is lethal comorbidity of COVID-19. However, its pathogenetic link remains unclear. This...

Cite
Islam MB, Chowdhury UN, Nain Z, et al. Identifying molecular insight of synergistic complexities for SARS-CoV-2 infection with pre-existing type 2 diabetes. Comput Biol Med. 2021;136:104668doi: 10.1016/j.compbiomed.2021.104668.
Islam, M. B., Chowdhury, U. N., Nain, Z., Uddin, S., Ahmed, M. B., & Moni, M. A. (2021). Identifying molecular insight of synergistic complexities for SARS-CoV-2 infection with pre-existing type 2 diabetes. Computers in biology and medicine, 136104668. https://doi.org/10.1016/j.compbiomed.2021.104668
Islam, M Babul, et al. "Identifying molecular insight of synergistic complexities for SARS-CoV-2 infection with pre-existing type 2 diabetes." Computers in biology and medicine vol. 136 (2021): 104668. doi: https://doi.org/10.1016/j.compbiomed.2021.104668
Islam MB, Chowdhury UN, Nain Z, Uddin S, Ahmed MB, Moni MA. Identifying molecular insight of synergistic complexities for SARS-CoV-2 infection with pre-existing type 2 diabetes. Comput Biol Med. 2021 Sep;136:104668. doi: 10.1016/j.compbiomed.2021.104668. Epub 2021 Jul 23. PMID: 34340124; PMCID: PMC8299293.
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Progress in Defining the Genetic Contribution to Type 2 Diabetes in Individuals of East Asian Ancestry.

Current diabetes reports

Spracklen CN, Sim X.
PMID: 33846905
Curr Diab Rep. 2021 Apr 13;21(6):17. doi: 10.1007/s11892-021-01388-2.

PURPOSE OF REVIEW: Prevalence of type 2 diabetes (T2D) and progression of complications differ between worldwide populations. While obesity is a major contributing risk factor, variations in physiological manifestations, e.g., developing T2D at lower body mass index in some...

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Spracklen CN, Sim X. Progress in Defining the Genetic Contribution to Type 2 Diabetes in Individuals of East Asian Ancestry. Curr Diab Rep. 2021;21(6):17doi: 10.1007/s11892-021-01388-2.
Spracklen, C. N., & Sim, X. (2021). Progress in Defining the Genetic Contribution to Type 2 Diabetes in Individuals of East Asian Ancestry. Current diabetes reports, 21(6), 17. https://doi.org/10.1007/s11892-021-01388-2
Spracklen, Cassandra N, and Sim, Xueling. "Progress in Defining the Genetic Contribution to Type 2 Diabetes in Individuals of East Asian Ancestry." Current diabetes reports vol. 21,6 (2021): 17. doi: https://doi.org/10.1007/s11892-021-01388-2
Spracklen CN, Sim X. Progress in Defining the Genetic Contribution to Type 2 Diabetes in Individuals of East Asian Ancestry. Curr Diab Rep. 2021 Apr 13;21(6):17. doi: 10.1007/s11892-021-01388-2. PMID: 33846905.
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Showing 73 to 83 of 83 entries