BACKGROUND: Sepsis is still the leading cause of death in the intensive care unit. Our goal was to elucidate potential early differences in serum between survivors (SURV) and non-survivors (NON-SURV) on day 28. METHODS: We applied proteomic technology to serum samples of patients with sepsis and septic shock. Serum samples from 18 patients with sepsis and septic shock were obtained during the first 12 h after diagnosis of septic shock. Patients were grouped into SURV and NON-SURV on day 28. RESULTS: Seven patients survived and 11 patients died. Using proteome analysis, two-dimensional gel electrophoresis detected more than 200 spots per gel. A differential protein expression was discovered between SURV and NON-SURV, whereby protein alterations not yet described in sepsis were revealed. CONCLUSIONS: Our results show that proteomic profiling is a useful approach for detecting protein expression dynamics in septic patients, and may bring us closer to achieving a comprehensive molecular profiling compared with genetic studies alone.
BACKGROUND:Sepsis is still the leading cause of death in the intensive care unit. Our goal was to elucidate potential early differences in serum between survivors (SURV) and non-survivors (NON-SURV) on day 28. METHODS: We applied proteomic technology to serum samples of patients with sepsis and septic shock. Serum samples from 18 patients with sepsis and septic shock were obtained during the first 12 h after diagnosis of septic shock. Patients were grouped into SURV and NON-SURV on day 28. RESULTS: Seven patients survived and 11 patients died. Using proteome analysis, two-dimensional gel electrophoresis detected more than 200 spots per gel. A differential protein expression was discovered between SURV and NON-SURV, whereby protein alterations not yet described in sepsis were revealed. CONCLUSIONS: Our results show that proteomic profiling is a useful approach for detecting protein expression dynamics in septic patients, and may bring us closer to achieving a comprehensive molecular profiling compared with genetic studies alone.
Authors: Ephraim L Tsalik; Raymond J Langley; Darrell L Dinwiddie; Neil A Miller; Byunggil Yoo; Jennifer C van Velkinburgh; Laurie D Smith; Isabella Thiffault; Anja K Jaehne; Ashlee M Valente; Ricardo Henao; Xin Yuan; Seth W Glickman; Brandon J Rice; Micah T McClain; Lawrence Carin; G Ralph Corey; Geoffrey S Ginsburg; Charles B Cairns; Ronny M Otero; Vance G Fowler; Emanuel P Rivers; Christopher W Woods; Stephen F Kingsmore Journal: Genome Med Date: 2014-11-26 Impact factor: 11.117
Authors: David R Janz; Julie A Bastarache; Gillian Sills; Nancy Wickersham; Addison K May; Gordon R Bernard; Lorraine B Ware Journal: Crit Care Date: 2013-11-14 Impact factor: 9.097