Identification of the sturdy predictor for sepsis primarily based on clustering evaluation
Singer, M. et al. The third worldwide consensus definitions for sepsis and septic shock (Sepsis-3). JAMA 315, 801–810. https://doi.org/10.1001/jama.2016.0287 (2016).
Google Scholar
Rudd, Ok. E. et al. International, regional, and nationwide sepsis incidence and mortality, 1990–2017: Evaluation for the International Burden of Illness Research. Lancet 395, 200–211. https://doi.org/10.1016/s0140-6736(19)32989-7 (2020).
Google Scholar
Kim, J., Kim, Ok., Lee, H. & Ahn, S. Epidemiology of sepsis in Korea: a population-based examine of incidence, mortality, price and threat elements for demise in sepsis. Clin. Exp. Emerg. Med. 6, 49–63. https://doi.org/10.15441/ceem.18.007 (2019).
Google Scholar
Bone, R. C. et al. Definitions for sepsis and organ failure and tips for the usage of modern therapies in sepsis. The ACCP/SCCM Consensus Convention Committee. American Faculty of Chest Physicians/Society of Crucial Care Medication. Chest 101, 1644–1655. https://doi.org/10.1378/chest.101.6.1644 (1992).
Google Scholar
Seigel, T. A. et al. Inadequacy of temperature and white blood cell rely in predicting bacteremia in sufferers with suspected an infection. J. Emerg. Med. 42, 254–259. https://doi.org/10.1016/j.jemermed.2010.05.038 (2012).
Google Scholar
Brunkhorst, F. M., Al-Nawas, B., Krummenauer, F., Forycki, Z. F. & Shah, P. M. Procalcitonin, C-reactive protein and APACHE II rating for threat analysis in sufferers with extreme pneumonia. Clin. Microbiol. Infect. 8, 93–100. https://doi.org/10.1046/j.1469-0691.2002.00349.x (2002).
Google Scholar
Simon, L., Gauvin, F., Amre, D. Ok., Saint-Louis, P. & Lacroix, J. Serum procalcitonin and C-reactive protein ranges as markers of bacterial an infection: A scientific assessment and meta-analysis. Clin. Infect. Dis. 39, 206–217. https://doi.org/10.1086/421997 (2004).
Google Scholar
Gabay, C. & Kushner, I. Acute-phase proteins and different systemic responses to irritation. N. Engl. J. Med. 340, 448–454. https://doi.org/10.1056/nejm199902113400607 (1999).
Google Scholar
Póvoa, P. et al. C-reactive protein as a marker of an infection in critically ailing sufferers. Clin. Microbiol. Infect. 11, 101–108. https://doi.org/10.1111/j.1469-0691.2004.01044.x (2005).
Google Scholar
Tang, B. M., Eslick, G. D., Craig, J. C. & McLean, A. S. Accuracy of procalcitonin for sepsis analysis in critically ailing sufferers: Systematic assessment and meta-analysis. Lancet Infect. Dis. 7, 210–217. https://doi.org/10.1016/s1473-3099(07)70052-x (2007).
Google Scholar
Nakamura, A. et al. Efficacy of procalcitonin within the early analysis of bacterial infections in a essential care unit. Shock 31, 586–591. https://doi.org/10.1097/SHK.0b013e31819716fa (2009).
Google Scholar
Assicot, M. et al. Excessive serum procalcitonin concentrations in sufferers with sepsis and an infection. Lancet 341, 515–518. https://doi.org/10.1016/0140-6736(93)90277-n (1993).
Google Scholar
Pettilä, V., Hynninen, M., Takkunen, O., Kuusela, P. & Valtonen, M. Predictive worth of procalcitonin and interleukin 6 in critically ailing sufferers with suspected sepsis. Intensive Care Med. 28, 1220–1225. https://doi.org/10.1007/s00134-002-1416-1 (2002).
Google Scholar
Karzai, W., Oberhoffer, M., Meier-Hellmann, A. & Reinhart, Ok. Procalcitonin—A brand new indicator of the systemic response to extreme infections. An infection 25, 329–334. https://doi.org/10.1007/bf01740811 (1997).
Google Scholar
Sfeir, T., Saha, D. C., Astiz, M. & Rackow, E. C. Function of interleukin-10 in monocyte hyporesponsiveness related to septic shock. Crit. Care Med. 29, 129–133. https://doi.org/10.1097/00003246-200101000-00026 (2001).
Google Scholar
Cannon, J. G. et al. Circulating interleukin-1 and tumor necrosis think about septic shock and experimental endotoxin fever. J. Infect. Dis. 161, 79–84. https://doi.org/10.1093/infdis/161.1.79 (1990).
Google Scholar
Brodin, P. et al. Variation within the human immune system is basically pushed by non-heritable influences. Cell 160, 37–47. https://doi.org/10.1016/j.cell.2014.12.020 (2015).
Google Scholar
Brodin, P. & Davis, M. M. Human immune system variation. Nat. Rev. Immunol. 17, 21–29. https://doi.org/10.1038/nri.2016.125 (2017).
Google Scholar
Kurts, C., Panzer, U., Anders, H. J. & Rees, A. J. The immune system and kidney illness: Primary ideas and scientific implications. Nat. Rev. Immunol. 13, 738–753. https://doi.org/10.1038/nri3523 (2013).
Google Scholar
Dahmer, M. Ok., Cornell, T. & Quasney, M. W. Genetic and epigenetic elements within the regulation of the immune response. Curr. Opin. Pediatr. 28, 281–286. https://doi.org/10.1097/mop.0000000000000356 (2016).
Google Scholar
Zimmermann, P. & Curtis, N. Elements that affect the immune response to vaccination. Clin. Microbiol. Rev. https://doi.org/10.1128/cmr.00084-18 (2019).
Google Scholar
Klein, S. L. & Flanagan, Ok. L. Intercourse variations in immune responses. Nat. Rev. Immunol. 16, 626–638. https://doi.org/10.1038/nri.2016.90 (2016).
Google Scholar
Gavazzi, G. & Krause, Ok. H. Ageing and an infection. Lancet Infect. Dis. 2, 659–666. https://doi.org/10.1016/s1473-3099(02)00437-1 (2002).
Google Scholar
Ongrádi, J. & Kövesdi, V. Elements which will affect on immunosenescence: an appraisal. Immun. Ageing 7, 7. https://doi.org/10.1186/1742-4933-7-7 (2010).
Google Scholar
Knox, D. B., Lanspa, M. J., Kuttler, Ok. G., Brewer, S. C. & Brown, S. M. Phenotypic clusters inside sepsis-associated a number of organ dysfunction syndrome. Intensive Care Med. 41, 814–822. https://doi.org/10.1007/s00134-015-3764-7 (2015).
Google Scholar
Horan, T. C., Andrus, M. & Dudeck, M. A. CDC/NHSN surveillance definition of well being care-associated an infection and standards for particular varieties of infections within the acute care setting. Am. J. Infect. Management. 36, 309–332. https://doi.org/10.1016/j.ajic.2008.03.002 (2008).
Google Scholar
Nahm, C. H., Choi, J. W. & Lee, J. Delta neutrophil index in automated immature granulocyte counts for assessing illness severity of sufferers with sepsis. Ann. Clin. Lab. Sci. 38, 241–246 (2008).
Google Scholar
Yonezawa, Ok. et al. Affiliation between the neutrophil myeloperoxidase index and subsets of bacterial infections. Int. J. Lab. Hematol. 32, 598–605. https://doi.org/10.1111/j.1751-553X.2010.01227.x (2010).
Google Scholar
Ahlqvist, E. et al. Novel subgroups of adult-onset diabetes and their affiliation with outcomes: A knowledge-driven cluster evaluation of six variables. Lancet Diabetes Endocrinol. 6, 361–369. https://doi.org/10.1016/s2213-8587(18)30051-2 (2018).
Google Scholar
Baek, B. & Lee, H. Prediction of survival and recurrence in sufferers with pancreatic most cancers by integrating multi-omics knowledge. Sci. Rep. 10, 18951. https://doi.org/10.1038/s41598-020-76025-1 (2020).
Google Scholar
Kwon, Y. J., Kim, H. S., Jung, D. H. & Kim, J. Ok. Cluster evaluation of dietary elements related to low muscle mass index in middle-aged and older adults. Clin. Nutr. 39, 3369–3376. https://doi.org/10.1016/j.clnu.2020.02.024 (2020).
Google Scholar
Johnson, W. E., Li, C. & Rabinovic, A. Adjusting batch results in microarray expression knowledge utilizing empirical Bayes strategies. Biostatistics 8, 118–127 (2007).
Google Scholar
Leek, J. T., Johnson, W. E., Parker, H. S., Jaffe, A. E. & Storey, J. D. The sva bundle for eradicating batch results and different undesirable variation in high-throughput experiments. Bioinformatics 28, 882–883 (2012).
Google Scholar
Aminzadeh, Z. & Parsa, E. Relationship between age and peripheral white blood cell rely in sufferers with sepsis. Int. J. Prev. Med. 2, 238–242 (2011).
Google Scholar
Caterino, J. M., Scheatzle, M. D., Forbes, M. L. & D’Antonio, J. A. Bacteremic elder emergency division sufferers: Procalcitonin and white rely. Acad. Emerg. Med. 11, 393–396. https://doi.org/10.1197/j.aem.2003.10.027 (2004).
Google Scholar
Butcher, S., Chahel, H. & Lord, J. M. Evaluation article: Ageing and the neutrophil: no urge for food for killing?. Immunology 100, 411–416. https://doi.org/10.1046/j.1365-2567.2000.00079.x (2000).
Google Scholar
Weiskopf, D., Weinberger, B. & Grubeck-Loebenstein, B. The getting older of the immune system. Transpl. Int. 22, 1041–1050. https://doi.org/10.1111/j.1432-2277.2009.00927.x (2009).
Google Scholar
Wenisch, C., Patruta, S., Daxböck, F., Krause, R. & Hörl, W. Impact of age on human neutrophil perform. J. Leukoc. Biol. 67, 40–45. https://doi.org/10.1002/jlb.67.1.40 (2000).
Google Scholar
Ahn, C. et al. The delta neutrophil index (DNI) as a prognostic marker for mortality in adults with sepsis: A scientific assessment and meta-analysis. Sci. Rep. 8, 6621. https://doi.org/10.1038/s41598-018-24211-7 (2018).
Google Scholar
Park, S. Y., Lee, J. S., Oh, J. & Park, J. Y. Delta neutrophil index as a predictive and prognostic issue for Candidemia sufferers: A matched case-control examine. BMC Infect. Dis. 20, 396. https://doi.org/10.1186/s12879-020-05117-0 (2020).
Google Scholar
Park, B. H. et al. Delta neutrophil index as an early marker of illness severity in critically ailing sufferers with sepsis. BMC Infect. Dis. 11, 299. https://doi.org/10.1186/1471-2334-11-299 (2011).
Google Scholar
Seok, Y. et al. Delta neutrophil index: A promising diagnostic and prognostic marker for sepsis. Shock 37, 242–246. https://doi.org/10.1097/SHK.0b013e3182454acf (2012).
Google Scholar
Kim, H. et al. Usefulness of the delta neutrophil index as a promising prognostic marker of acute cholangitis in emergency departments. Shock 47, 303–312. https://doi.org/10.1097/shk.0000000000000722 (2017).
Google Scholar
Celik, I. H. et al. The worth of delta neutrophil index in neonatal sepsis analysis, follow-up and mortality prediction. Early Hum. Dev. 131, 6–9. https://doi.org/10.1016/j.earlhumdev.2019.02.003 (2019).
Google Scholar
Azab, B., Chainani, V., Shah, N. & McGinn, J. T. Neutrophil-lymphocyte ratio as a predictor of main adversarial cardiac occasions amongst diabetic inhabitants: A 4-year follow-up examine. Angiology 64, 456–465. https://doi.org/10.1177/0003319712455216 (2013).
Google Scholar
Hung, H. Y. et al. Impact of preoperative neutrophil-lymphocyte ratio on the surgical outcomes of stage II colon most cancers sufferers who don’t obtain adjuvant chemotherapy. Int. J. Colorectal Dis. 26, 1059–1065. https://doi.org/10.1007/s00384-011-1192-x (2011).
Google Scholar
Tomita, M., Shimizu, T., Ayabe, T., Yonei, A. & Onitsuka, T. Preoperative neutrophil to lymphocyte ratio as a prognostic predictor after healing resection for non-small cell lung most cancers. Anticancer Res. 31, 2995–2998 (2011).
Google Scholar
Sharaiha, R. Z. et al. Elevated preoperative neutrophil:lymphocyte ratio as a predictor of postoperative illness recurrence in esophageal most cancers. Ann. Surg. Oncol. 18, 3362–3369. https://doi.org/10.1245/s10434-011-1754-8 (2011).
Google Scholar
Kahramanca, S. et al. Neutrophil-to-lymphocyte ratio as a predictor of acute appendicitis. Ulus Travma Acil. Cerrahi Derg. 20, 19–22. https://doi.org/10.5505/tjtes.2014.20688 (2014).
Google Scholar
Ishizuka, M., Shimizu, T. & Kubota, Ok. Neutrophil-to-lymphocyte ratio has an in depth affiliation with gangrenous appendicitis in sufferers present process appendectomy. Int. Surg. 97, 299–304. https://doi.org/10.9738/cc161.1 (2012).
Google Scholar
de Jager, C. P. et al. Lymphocytopenia and neutrophil-lymphocyte rely ratio predict bacteremia higher than standard an infection markers in an emergency care unit. Crit Care 14, R192. https://doi.org/10.1186/cc9309 (2010).
Google Scholar
Liu, X. et al. Prognostic significance of neutrophil-to-lymphocyte ratio in sufferers with sepsis: A potential observational examine. Med. Inflamm. 2016, 8191254. https://doi.org/10.1155/2016/8191254 (2016).
Google Scholar
Huang, Z., Fu, Z., Huang, W. & Huang, Ok. Prognostic worth of neutrophil-to-lymphocyte ratio in sepsis: A meta-analysis. Am. J. Emerg. Med. 38, 641–647. https://doi.org/10.1016/j.ajem.2019.10.023 (2020).
Google Scholar
Moon, S. & Lee, H. JDSNMF: Joint deep semi-non-negative matrix factorization for studying integrative illustration of molecular alerts in Alzheimer’s illness. J. Pers. Med. 11, 686. https://doi.org/10.3390/jpm11080686 (2021).
Google Scholar
Lee, T., Kim, J., Uh, Y. & Lee, H. Deep neural community for estimating low density lipoprotein ldl cholesterol. Clin. Chim. Acta 489, 35–40. https://doi.org/10.1016/j.cca.2018.11.022 (2019).
Google Scholar
Hwang, S. et al. A deep neural community for estimating low-density lipoprotein ldl cholesterol from digital well being information: Actual-time routine scientific utility. JMIR Med. Inform. 9, e29331. https://doi.org/10.2196/29331 (2021).
Google Scholar
