ABSTRACT
Background: Sepsis is one of the most common causes of multiorgan failure. Sepsis requires the presence of infection with a resultant systemic inflammatory state. Organ dysfunction occurs from the combination of the two processes. Sepsis is the main cause of mortality at intensive care units, with 30-50% mortality rate for all septic episodes. MicroRNA (miRNA) profile data could be beneficial as a specific diagnostic biomarker for sepsis and systemic inflammatory response syndrome (SIRS). Methods: Expression of miRNAs -122, -181b, -223 and -146a levels were assayed by quantitative real time polymerase chain reaction (qRT-PCR) in a prospective case control study, where forty septic cases were compared to 40 healthy controls of matched age and gender. Results: miRNAs -122 and -181b were significantly upregulated during early septic conditions, indicating that they could be sensitive and specific biomarkers for diagnosing sepsis. miRNA-223 and miRNA-146a could also represent highly specific and sensitive diagnostic biomarkers, as they were found to be significantly down-regulated. Serum levels of miRNA-223 could be used to predict poor prognosis with 70% sensitivity and 75% specificity, whereas the other three miRNAs could not predict prognosis. Conclusion: Our study shows that all tested miRNAs can be used for early detection of sepsis, with miRNA-223 being predictive of mortality, hence preventing multi-organ failure and reducing mortality, and predicting poor outcomes, thereby assisting in early categorization of ICU patients for rapid appropriate treatment and medico legal aspects.
ABSTRACT
OBJECTIVE: To determine the predictors of growth response to growth hormone treatment in a group of isolated idiopathic growth hormone (GH). PATIENTS AND METHODS: 477 GH deficient (GHD) children with GH therapy were included in the study. Patients were followed up for a minimum of 1 year and up to 6 years. Multiple linear regressions were performed to identify predictors of growth response to rhGH in the first 4 years of treatment. RESULTS: In the first year, three significant predictors of growth were identified: GH peak [ln (ug/L)], age of onset of therapy and target height-height SDS. In the second and third years of therapy, growth velocity (GV) was both significantly and positively correlated to the GV (cm/year) of the previous year. CONCLUSION: Prediction models offer a valuable tool for individualization and assuring adherence to rhGH and thus a cost effective treatment, which is the ultimate goal of GH therapy.
