Cannabis Sativa Revisited-Crosstalk between microRNA Expression, Inflammation, Oxidative Stress, and Endocannabinoid Response System in Critically Ill Patients with Sepsis.
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ABSTRACT:
SUBMITTER: Dinu AR
PROVIDER: S-EPMC7072707 | biostudies-literature | 2020 Jan
REPOSITORIES: biostudies-literature
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<i>Cannabis Sativa</i> Revisited-Crosstalk between microRNA Expression, Inflammation, Oxidative Stress, and Endocannabinoid Response System in Critically Ill Patients with Sepsis.
Dinu Anca Raluca AR Rogobete Alexandru Florin AF Bratu Tiberiu T Popovici Sonia Elena SE Bedreag Ovidiu Horea OH Papurica Marius M Bratu Lavinia Melania LM Sandesc Dorel D
Cells 20200128 2
Critically ill patients with sepsis require a multidisciplinary approach, as this situation implies multiorgan distress, with most of the bodily biochemical and cellular systems being affected by the condition. Moreover, sepsis is characterized by a multitude of biochemical interactions and by dynamic changes of the immune system. At the moment, there is a gap in our understanding of the cellular, genetic, and molecular mechanisms involved in sepsis. One of the systems intensely studied in recen ...[more]
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Project description:Background: Systemic inflammation is a whole body reaction that can have an infection-positive (i.e. sepsis) or infection-negative origin. It is important to distinguish between septic and non-septic presentations early and reliably, because this has significant therapeutic implications for critically ill patients. We hypothesized that a molecular classifier based on a small number of RNAs expressed in peripheral blood could be discovered that would: 1) determine which patients with systemic inflammation had sepsis; 2) be robust across independent patient cohorts; 3) be insensitive to disease severity; and 4) provide diagnostic utility. The overall goal of this study was to identify and validate such a molecular classifier. Methods and Findings: We conducted an observational, non-interventional study of adult patients recruited from tertiary intensive care units (ICU). Biomarker discovery was conducted with an Australian cohort (n = 105) consisting of sepsis patients and post -surgical patients with infection-negative systemic inflammation. Using this cohort, a four-gene classifier consisting of a combination of CEACAM4, LAMP1, PLA2G7 and PLAC8 RNA biomarkers was identified. This classifier, designated SeptiCyte® Lab, was externally validated using RT-qPCR and receiver operating characteristic (ROC) curve analysis in five cohorts (n = 345) from the Netherlands. Cohort 1 (n=59) consisted of unambiguous septic cases and infection-negative systemic inflammation controls; SeptiCyte® Lab gave an area under curve (AUC) of 0.96 (95% CI: 0.91-1.00). ROC analysis of a more heterogeneous group of patients (Cohorts 2-5; 249 patients after excluding 37 patients with infection likelihood possible) gave an AUC of 0.89 (95% CI: 0.85-0.93). Disease severity, as measured by Sequential Organ Failure Assessment (SOFA) score or the Acute Physiology and Chronic Health Evaluation (APACHE) IV score, was not a significant confounding variable. The diagnostic utility o f SeptiCyte® Lab was evaluated by comparison to various clinical and laboratory parameters that would be available to a clinician within 24 hours of ICU admission. SeptiCyte® Lab was significantly better at differentiating sepsis from infection-negative systemic inflammation than all tested parameters, both singly and in various logistic combinations. SeptiCyte® Lab more than halved the diagnostic error rate compared to PCT in all tested cohorts or cohort combinations. Conclusions: SeptiCyte® Lab is a rapid molecular assay that may be clinically useful in the management of ICU patients with systemic inflammation. SIRS and Sepsis ICU patients, admission samples Retrospective, mutli-site sutdy using retrospective physician adjudication as a comparator
2015-10-24 | E-GEOD-74224 | biostudies-arrayexpress
Project description:Objective: It is unclear whether the host response of gram-positive sepsis differs from gram-negative sepsis at a transcriptome level. Using microarray technology, we compared the gene-expression profiles of gram-positive sepsis and gram-negative sepsis in critically ill patients. Design: A prospective cross-sectional study. Setting: A 20-bed general intensive care unit of a tertiary referral hospital. Patients: Seventy-two patients admitted to the intensive care unit. Interventions: Intravenous blood was collected for leukocyte separation and RNA extraction. Microarray experiements were then performed examing the expression level of 19,232 genes in each sample. Measurements and Main Results: There was no difference in the expression profile between gram-positive and gram-negative sepsis. The finding remained unchanged even when genes with lower expression level were included or after statistical stringency was lowered. There were, however, ninety-four genes differentially expressed between sepsis and control patients. These genes included those involved in immune regulation, inflammation and mitochondrial function. Hierarchical cluster analysis confirmed that the difference in gene expression profile existed between sepsis and control patients, but not between gram-positive and gram-negative patients. Conclusion: Gram-positive and gram-negative sepsis share a common host response at a transcriptome level. These findings support the hypothesis that the septic response is non-specific and is designed to provide a more general response that can be elicited by a wide range of different micro-organisms. The study included seventy-two critically ill patients admitted to the intensive care unit (ICU) of Nepean Hospital, Sydney, Australia. Of these, fifty-five patients were diagnosed to have sepsis, as confirmed by microbiological culture. The remaining seventeen patients did not have sepsis and were therefore used as controls. The study was approved by the hospital ethics committee and informed consent was obtained from all patients or their relatives. Patient Samples. Whole blood was taken from each patient on admission to ICU. Neutrophils were separated from whole blood using density-gradient separation with Ficoll-PaqueP P(Amersham). Subsequent neutrophil RNA extraction was performed using guanidinium thiocyanate (Ambion). Microarray Experiment. The neutrophil RNA was converted to cDNA, fluorescently labeled and hybridized to its complimentary sequences on the microarray (Invitrogen). The fluorescent signals on each micrroarray were captured using the GenePix 4000B laser scanner (Axon Instruments). Expression level of each gene was represented by the intensity of its fluorescent signal. Data Extraction. All signal intensity values were processed using background-subtraction method. Prior to analysis, all values were log-transformed and normalized by fitting a print-tip group Lowess curve. Normalization minimizes bias due to dye chemistry, signal intensity or location of a gene on the array. It ensures the detection of genes that are truly differentially expressed, instead of those caused by experimental artifacts or variation in the hybridization process. After normalization, genes that had more than 50% of data missing were removed. We then selected genes that had at least 80% of the data showing two-fold changes from the geneâs median values. After filtering, 1617 genes were available for further analysis.
2010-05-25 | E-GEOD-6535 | biostudies-arrayexpress
Project description:Background: Systemic inflammation is a whole body reaction that can have an infection-positive (i.e. sepsis) or infection-negative origin. It is important to distinguish between septic and non-septic presentations early and reliably, because this has significant therapeutic implications for critically ill patients. We hypothesized that a molecular classifier based on a small number of RNAs expressed in peripheral blood could be discovered that would: 1) determine which patients with systemic inflammation had sepsis; 2) be robust across independent patient cohorts; 3) be insensitive to disease severity; and 4) provide diagnostic utility. The overall goal of this study was to identify and validate such a molecular classifier. Methods and Findings: We conducted an observational, non-interventional study of adult patients recruited from tertiary intensive care units (ICU). Biomarker discovery was conducted with an Australian cohort (n = 105) consisting of sepsis patients and post -surgical patients with infection-negative systemic inflammation. Using this cohort, a four-gene classifier consisting of a combination of CEACAM4, LAMP1, PLA2G7 and PLAC8 RNA biomarkers was identified. This classifier, designated SeptiCyte® Lab, was externally validated using RT-qPCR and receiver operating characteristic (ROC) curve analysis in five cohorts (n = 345) from the Netherlands. Cohort 1 (n=59) consisted of unambiguous septic cases and infection-negative systemic inflammation controls; SeptiCyte® Lab gave an area under curve (AUC) of 0.96 (95% CI: 0.91-1.00). ROC analysis of a more heterogeneous group of patients (Cohorts 2-5; 249 patients after excluding 37 patients with infection likelihood possible) gave an AUC of 0.89 (95% CI: 0.85-0.93). Disease severity, as measured by Sequential Organ Failure Assessment (SOFA) score or the Acute Physiology and Chronic Health Evaluation (APACHE) IV score, was not a significant confounding variable. The diagnostic utility o f SeptiCyte® Lab was evaluated by comparison to various clinical and laboratory parameters that would be available to a clinician within 24 hours of ICU admission. SeptiCyte® Lab was significantly better at differentiating sepsis from infection-negative systemic inflammation than all tested parameters, both singly and in various logistic combinations. SeptiCyte® Lab more than halved the diagnostic error rate compared to PCT in all tested cohorts or cohort combinations. Conclusions: SeptiCyte® Lab is a rapid molecular assay that may be clinically useful in the management of ICU patients with systemic inflammation. SIRS and Sepsis ICU patients, admission samples
2015-10-24 | GSE74224 | GEO
Project description:Objective: It is unclear whether the host response of gram-positive sepsis differs from gram-negative sepsis at a transcriptome level. Using microarray technology, we compared the gene-expression profiles of gram-positive sepsis and gram-negative sepsis in critically ill patients. Design: A prospective cross-sectional study. Setting: A 20-bed general intensive care unit of a tertiary referral hospital. Patients: Seventy-two patients admitted to the intensive care unit. Interventions: Intravenous blood was collected for leukocyte separation and RNA extraction. Microarray experiements were then performed examing the expression level of 19,232 genes in each sample. Measurements and Main Results: There was no difference in the expression profile between gram-positive and gram-negative sepsis. The finding remained unchanged even when genes with lower expression level were included or after statistical stringency was lowered. There were, however, ninety-four genes differentially expressed between sepsis and control patients. These genes included those involved in immune regulation, inflammation and mitochondrial function. Hierarchical cluster analysis confirmed that the difference in gene expression profile existed between sepsis and control patients, but not between gram-positive and gram-negative patients. Conclusion: Gram-positive and gram-negative sepsis share a common host response at a transcriptome level. These findings support the hypothesis that the septic response is non-specific and is designed to provide a more general response that can be elicited by a wide range of different micro-organisms. Keywords: disease state analysis, gram-positive sepsis, gram-negative sepsis
2007-12-01 | GSE6535 | GEO
Project description:Sepsis is a frequent complication in critically ill patients and highly heterogeneous that is associated with high morbidity and mortality rates, especially in the elderly population. Utilizing RNA-Sequencing (RNA-Seq) to analysis biological pathways is widely used in clinical and molecular genetics studies, but in elderly patients with sepsis are still lacking. Hence, we aim to investigate the mortality-relevant biological features and transcriptomic features in elderly patients who were admitted to intensive care unit (ICU) for sepsis.
2022-11-03 | GSE216902 | GEO
Project description:Sepsis is defined by life-threatening organ dysfunction during infection and is the leading cause of death in hospitals. During sepsis, there is a high risk that new onset of atrial fibrillation (AF) can occur, which is associated with significant morbidity and mortality. Consequently, early prediction of AF during sepsis would allow testing of interventions in the intensive care unit (ICU) to prevent AF and its severe complications. In this paper, we present a novel automated AF prediction algorithm for critically ill sepsis patients using electrocardiogram (ECG) signals. From the heart rate signal collected from 5-min ECG, feature extraction is performed using the traditional time, frequency, and nonlinear domain methods. Moreover, variable frequency complex demodulation and tunable Q-factor wavelet-transform-based time-frequency methods are applied to extract novel features from the heart rate signal. Using a selected feature subset, several machine learning classifiers, including support vector machine (SVM) and random forest (RF), were trained using only the 2001 Computers in Cardiology data set. For testing the proposed method, 50 critically ill ICU subjects from the Medical Information Mart for Intensive Care (MIMIC) III database were used in this study. Using distinct and independent testing data from MIMIC III, the SVM achieved 80% sensitivity, 100% specificity, 90% accuracy, 100% positive predictive value, and 83.33% negative predictive value for predicting AF immediately prior to the onset of AF, while the RF achieved 88% AF prediction accuracy. When we analyzed how much in advance we can predict AF events in critically ill sepsis patients, the algorithm achieved 80% accuracy for predicting AF events 10 min early. Our algorithm outperformed a state-of-the-art method for predicting AF in ICU patients, further demonstrating the efficacy of our proposed method. The annotations of patients' AF transition information will be made publicly available for other investigators. Our algorithm to predict AF onset is applicable for any ECG modality including patch electrodes and wearables, including Holter, loop recorder, and implantable devices.
| S-EPMC8391773 | biostudies-literature
Project description:This SuperSeries is composed of the SubSeries listed below.
2021-01-11 | GSE134364 | GEO
Project description:BackgroundEarly recognition of sepsis is challenging, and diagnostic criteria have changed repeatedly. We assessed the robustness of sepsis-3 criteria in intensive care unit (ICU) patients.MethodsWe studied the apparent incidence and associated mortality of sepsis-3 among patients who were prospectively enrolled in the Molecular Diagnosis and Risk Stratification of Sepsis (MARS) cohort in the Netherlands, and explored the effects of minor variations in the precise definition and timing of diagnostic criteria for organ failure.ResultsAmong 1081 patients with suspected infection upon ICU admission, 648 (60%) were considered to have sepsis according to prospective adjudication in the MARS study, whereas 976 (90%) met sepsis-3 criteria, yielding only 64% agreement at the individual patient level. Among 501 subjects developing ICU-acquired infection, these rates were 270 (54%) and 260 (52%), respectively (yielding 58% agreement). Hospital mortality was 234 (36%) vs 277 (28%) for those meeting MARS-sepsis or sepsis-3 criteria upon presentation (p < 0.001), and 121 (45%) vs 103 (40%) for those having sepsis onset in the ICU (p < 0.001). Minor variations in timing and interpretation of organ failure criteria had a considerable effect on the apparent prevalence of sepsis-3, which ranged from 68 to 96% among those with infection at admission, and from 22 to 99% among ICU-acquired cases.ConclusionThe sepsis-3 definition lacks robustness as well as discriminatory ability, since nearly all patients presenting to ICU with suspected infection fulfill its criteria. These should therefore be specified in greater detail, and applied more consistently, during future sepsis studies.Trial registrationThe MARS study is registered at ClinicalTrials.gov (identifier NCT01905033).
| S-EPMC6716896 | biostudies-literature
Project description:BackgroundData that indicate vitamin A status in critically ill children with sepsis are sparse. The association between serum vitamin A levels and the clinical outcomes of sepsis has not been well assessed. The aim of this study was to assess the prevalence of vitamin A deficiency in critically ill children with sepsis and its association with clinical outcomes.MethodsCritically ill children with sepsis admitted to the pediatric intensive care unit were engaged in this prospective study. Sex- and age-matched approximate-health children from the Department of Pediatric Surgery were enrolled as the control group. Blood samples were collected from all patients in the first 24 h of admission for the measurement of serum vitamin A status. We compared vitamin A status between the sepsis group and the control group. In addition, we compared the clinical characteristics of the two subgroups of septic patients with vitamin A deficiency and those without vitamin A deficiency. Univariate and multivariable methods were used to evaluate the association between vitamin A deficiency and septic shock.ResultsOne hundred sixty septic children and 49 approximate-health children were enrolled in this study. Vitamin A deficiency was found in 94 (58.8%) subjects in the study group and 6 (12.2%) subjects in the control group (P < 0.001). In septic patients, 28-day mortality and hospital mortality in patients with vitamin A deficiency were not significantly higher than that in patients without vitamin A deficiency (P > 0.05). However, vitamin A levels were inversely associated with higher PRISM scores in septic children with VAD (r = - 0.260, P = 0.012). Vitamin A deficiency was associated with septic shock with an unadjusted odds ratio (OR) of 3.297 (95% confidence interval (CI), 1.169 to 9.300; P = 0.024). In a logistic model, vitamin A deficiency (OR, 4.630; 95% CI, 1.027-20.866; P = 0.046), procalcitonin (OR, 1.029; 95% CI, 1.009-1.048; P = 0.003), and the Pediatric Risk of Mortality scores (OR, 1.132; 95% CI, 1.009-1.228; P = 0.003) were independently associated with septic shock.ConclusionThe prevalence of vitamin A deficiency was high in children with sepsis. Vitamin A deficiency may be a marker of mortality in critically ill children with sepsis.Trial registrationClinicaltrials.gov , NCT03598127.
| S-EPMC6670191 | biostudies-literature
Project description:BackgroundSuboptimal triage of critically ill patients with surgical sepsis may contribute to adverse outcomes. Patients transferred to a tertiary care center after spending 24 hours or longer at an outside facility were compared with patients who had early triage to a tertiary care center with the null hypothesis that management parameters and outcomes would be similar between groups.MethodsThis prospective observational cohort study included 308 patients treated for surgical sepsis in a surgical intensive care unit at a tertiary care center. Patients transferred after spending more than 24 hours at an outside facility (n = 69) were compared with patients who were directly admitted or transferred within 24 hours (n = 239). Patient characteristics, management parameters, and outcomes were compared between groups. This study was registered at ClinicalTrials.gov (NCT02276066).ResultsAverage outside facility length of stay in the delayed transfer group was 43 hours. Delayed transfer patients had higher sequential organ failure assessment (7 vs. 5, p = 0.003) and APACHE II scores (19 vs. 16, p = 0.007) on admission. The interval between admission and source control was significantly longer in the delayed transfer group (12.1 hours vs. 1.0 hours, p = 0.009). The incidence of nosocomial infection was significantly higher in the delayed transfer group (41% vs. 23%, p = 0.005). Delayed transfer was independently associated with a 10-day increase in hospital length of stay. Delayed transfer patients were less likely to be discharged home (22% vs. 59%, p < 0.001) and suffered twofold higher in-hospital mortality (14.5% vs. 7.1%, p = 0.056).ConclusionPatients with surgical sepsis who spent more than 24 hours at an outside facility prior to transfer had greater initial illness severity, longer intervals between admission and source control, and more nosocomial infections compared with patients who had early triage to a tertiary care center.Level of evidenceCare management/therapeutic, Level IV; Epidemiologic/prognostic, Level III.
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