RationaleSepsis is a common cause of death in the intensive care unit with mortality up to 70% when accompanied by multiple organ dysfunction. Rapid diagnosis and the institution of appropriate antibiotic therapy and pressor support are therefore critical for survival. MicroRNAs are small non-coding RNAs that play an important role in the regulation of numerous cellular processes, including inflammation and immunity.ObjectivesWe hypothesized changes in expression of microRNAs during sepsis may be of diagnostic value in the intensive care unit (ICU).MethodsMassively parallel sequencing of microRNAs was utilised for screening microRNA candidates. Putative microRNAs were validated using quantitative real-time PCR (qRT-PCR). This study includes data from both a training cohort (UK) and an independent validation cohort (Sweden). A linear discriminant statistical model was employed to construct a diagnostic microRNA signature.ResultsA panel of known and novel microRNAs were detectable in the blood of patients with sepsis. After qRT-PCR validation, microRNA miR-150 and miR-4772-5p-iso were able to discriminate between patients who have systemic inflammatory response syndrome and patients with sepsis. This finding was also validated in independent cohort with an average diagnostic accuracy of 86%. Fractionating the cellular components of blood reveals miR-4772-5p-iso is expressed differentially in monocytes. Functional experiments using primary human monocytes demonstrate that it expressed in response to TLR ligation.ConclusionsTaken together, these data provide a novel microRNA signature of sepsis that should allow rapid point-of-care diagnostic assessment of patients on ICU and also provide greater insight into the pathobiology of this severe disease.
The term IORT (intraoperative radiotherapy) is currently used for various techniques that show huge differences in dose delivery and coverage of the tissue at risk. The largest evidence for boost IORT preceding whole breast irradiation (WBI) originates from intraoperative electron treatments (IOERT) with single doses around 10 Gy. At median follow-up periods at 6 years, outstandingly low local recurrence rates of less than 1% are observed. Higher local relapse rates were described for G3 tumors and triple negative breast cancers as well as for IORT following primary systemic treatment for locally advanced tumors. Even there, long term (>5y) local tumor control rates mostly beyond 95% were maintained. Compared to other boost methods, an intraoperative treatment has evident advantages in terms of precision (by avoiding a “spatial and/or temporal miss”), cosmetic outcome and patient comfort. Direct visualisation of a tumor bed during surgery guarantees for an accurate dose delivery, which has additionally gained importance in times of primary reconstruction techniques after lumpectomy, since IORT is performed before breast tissue including parts of the tumor bed is mobilized for plastic purposes. As a consequence of direct tissue exposure without distension by hematoma/seroma, IORT allows for small treatment volumes and complete skin sparing, both having a positive effect on late tissue tolerance and, hence, cosmetic appearance. Boost IORT marginally prolongs the surgical procedure, while significantly shortening postoperative radiotherapy. Its combination with external beam radiotherapy to the whole breast (WBI) is currently tested in two multicentric prospective trials: as kV-IORT in the multicentric TARGIT-B (oost) study, and as IOERT in the HIOB trial (3 weeks hypofractionated WBI preceded by IORT electron boost).
Only 4 out of the 36 evaluated formulas gave mean differences between measured and calculated osmolality of less than 1 mosmol/kg H(2)O. Zander's novel formula showed excellent concordance with measured osmolality and facilitates a more precise diagnosis based on blood gas analysers. The new equation has the potential to replace separate measurements of osmolality in many cases.
A metabolomics approach for prediction of bacteremic sepsis in patients in the emergency room (ER) was investigated. In a prospective study, whole blood samples from 65 patients with bacteremic sepsis and 49 ER controls were compared. The blood samples were analyzed using gas chromatography coupled to time-of-flight mass spectrometry. Multivariate and logistic regression modeling using metabolites identified by chromatography or using conventional laboratory parameters and clinical scores of infection were employed. A predictive model of bacteremic sepsis with 107 metabolites was developed and validated. The number of metabolites was reduced stepwise until identifying a set of 6 predictive metabolites. A 6-metabolite predictive logistic regression model showed a sensitivity of 0.91(95% CI 0.69–0.99) and a specificity 0.84 (95% CI 0.58–0.94) with an AUC of 0.93 (95% CI 0.89–1.01). Myristic acid was the single most predictive metabolite, with a sensitivity of 1.00 (95% CI 0.85–1.00) and specificity of 0.95 (95% CI 0.74–0.99), and performed better than various combinations of conventional laboratory and clinical parameters. We found that a metabolomics approach for analysis of acute blood samples was useful for identification of patients with bacteremic sepsis. Metabolomics should be further evaluated as a new tool for infection diagnostics.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.