|
[1]
|
Rudd, K.E., Johnson, S.C., Agesa, K.M., Shackelford, K.A., Tsoi, D., Kievlan, D.R., et al. (2020) Global, Regional, and National Sepsis Incidence and Mortality, 1990-2017: Analysis for the Global Burden of Disease Study. The Lancet, 395, 200-211. [Google Scholar] [CrossRef] [PubMed]
|
|
[2]
|
Weng, L., Xu, Y., Yin, P., Wang, Y., Chen, Y., Liu, W., et al. (2023) National Incidence and Mortality of Hospitalized Sepsis in China. Critical Care, 27, Article No. 84. [Google Scholar] [CrossRef] [PubMed]
|
|
[3]
|
Singer, M., Deutschman, C.S., Seymour, C.W., Shankar-Hari, M., Annane, D., Bauer, M., et al. (2016) The Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3). JAMA, 315, 801-810. [Google Scholar] [CrossRef] [PubMed]
|
|
[4]
|
Hotchkiss, R.S. and Karl, I.E. (2003) The Pathophysiology and Treatment of Sepsis. New England Journal of Medicine, 348, 138-150. [Google Scholar] [CrossRef] [PubMed]
|
|
[5]
|
Gotts, J.E. and Matthay, M.A. (2016) Sepsis: Pathophysiology and Clinical Management. BMJ, 353, i1585. [Google Scholar] [CrossRef] [PubMed]
|
|
[6]
|
Hotchkiss, R.S., Monneret, G. and Payen, D. (2013) Sepsis-Induced Immunosuppression: From Cellular Dysfunctions to Immunotherapy. Nature Reviews Immunology, 13, 862-874. [Google Scholar] [CrossRef] [PubMed]
|
|
[7]
|
Hotchkiss, R.S. and Opal, S. (2010) Immunotherapy for Sepsis—A New Approach against an Ancient Foe. New England Journal of Medicine, 363, 87-89. [Google Scholar] [CrossRef] [PubMed]
|
|
[8]
|
Seymour, C.W., Yende, S., Scott, M.J., Pribis, J., Mohney, R.P., Bell, L.N., et al. (2013) Metabolomics in Pneumonia and Sepsis: An Analysis of the GenIMS Cohort Study. Intensive Care Medicine, 39, 1423-1434. [Google Scholar] [CrossRef] [PubMed]
|
|
[9]
|
Haak, B.W., Prescott, H.C. and Wiersinga, W.J. (2018) Therapeutic Potential of the Gut Microbiota in the Prevention and Treatment of Sepsis. Frontiers in Immunology, 9, Article 2042. [Google Scholar] [CrossRef] [PubMed]
|
|
[10]
|
Ahlstedt, C., Sivapalan, P., Kriz, M., Jacobson, G., Sylvest Meyhoff, T., Skov Kaas-Hansen, B., et al. (2024) Effects of Restrictive Fluid Therapy on the Time to Resolution of Hyperlactatemia in ICU Patients with Septic Shock. A Secondary Post Hoc Analysis of the CLASSIC Randomized Trial. Intensive Care Medicine, 50, 678-686. [Google Scholar] [CrossRef] [PubMed]
|
|
[11]
|
Kopterides, P., Theodorakopoulou, M., Nikitas, N., Ilias, I., Vassiliadi, D.A., Orfanos, S.E., et al. (2012) Red Blood Cell Transfusion Affects Microdialysis-Assessed Interstitial Lactate/Pyruvate Ratio in Critically Ill Patients with Late Sepsis. Intensive Care Medicine, 38, 1843-1850. [Google Scholar] [CrossRef] [PubMed]
|
|
[12]
|
Sun, S., Wang, D., Dong, D., Xu, L., Xie, M., Wang, Y., et al. (2023) Altered Intestinal Microbiome and Metabolome Correspond to the Clinical Outcome of Sepsis. Critical Care, 27, Article No. 127. [Google Scholar] [CrossRef] [PubMed]
|
|
[13]
|
Nüse, B., Holland, T., Rauh, M., Gerlach, R.G. and Mattner, J. (2023) L-Arginine Metabolism as Pivotal Interface of Mutual Host-Microbe Interactions in the Gut. Gut Microbes, 15, Article 2222961. [Google Scholar] [CrossRef] [PubMed]
|
|
[14]
|
Wang, J., Sun, Y., Teng, S. and Li, K. (2020) Prediction of Sepsis Mortality Using Metabolite Biomarkers in the Blood: A Meta-Analysis of Death-Related Pathways and Prospective Validation. BMC Medicine, 18, Article No. 83. [Google Scholar] [CrossRef] [PubMed]
|
|
[15]
|
Lawlor, D.A., Harbord, R.M., Sterne, J.A.C., Timpson, N. and Davey Smith, G. (2008) Mendelian Randomization: Using Genes as Instruments for Making Causal Inferences in Epidemiology. Statistics in Medicine, 27, 1133-1163. [Google Scholar] [CrossRef] [PubMed]
|
|
[16]
|
Davey Smith, G. and Ebrahim, S. (2003) ‘Mendelian Randomization’: Can Genetic Epidemiology Contribute to Understanding Environmental Determinants of Disease? International Journal of Epidemiology, 32, 1-22. [Google Scholar] [CrossRef] [PubMed]
|
|
[17]
|
Porcu, E., Rüeger, S., Lepik, K., Agbessi, M., Ahsan, H., Alves, I., et al. (2019) Mendelian Randomization Integrating GWAS and eQTL Data Reveals Genetic Determinants of Complex and Clinical Traits. Nature Communications, 10, Article No. 3300. [Google Scholar] [CrossRef] [PubMed]
|
|
[18]
|
Burgess, S. and Thompson, S.G. (2017) Interpreting Findings from Mendelian Randomization Using the MR-Egger Method. European Journal of Epidemiology, 32, 377-389. [Google Scholar] [CrossRef] [PubMed]
|
|
[19]
|
Dalla-Riva, J., Stenkula, K.G., Petrlova, J. and Lagerstedt, J.O. (2013) Discoidal HDL and apoA-I-Derived Peptides Improve Glucose Uptake in Skeletal Muscle. Journal of Lipid Research, 54, 1275-1282. [Google Scholar] [CrossRef] [PubMed]
|
|
[20]
|
Liu, L., Wen, Y., Zhang, L., Xu, P., Liang, X., Du, Y., et al. (2018) Assessing the Associations of Blood Metabolites with Osteoporosis: A Mendelian Randomization Study. The Journal of Clinical Endocrinology & Metabolism, 103, 1850-1855. [Google Scholar] [CrossRef] [PubMed]
|