AKIRIN1: A Potential New Reference Gene in Human Natural Killer Cells and Granulocytes in Sepsis.

Timely and reliable distinction of sepsis from non-infectious systemic inflammatory response syndrome (SIRS) supports adequate antimicrobial therapy and saves lives but is clinically challenging. Blood transcriptional profiling promises to deliver insights into the pathomechanisms of SIRS and sepsis and to accelerate the discovery of urgently sought sepsis biomarkers. However, suitable reference genes for normalizing gene expression in these disease conditions are lacking. In addition, variability in blood leukocyte subtype composition complicates gene profile interpretation. Here, we aimed to identify potential reference genes in natural killer (NK) cells and granulocytes from patients with SIRS and sepsis on intensive care unit (ICU) admission. Discovery by a two-step probabilistic selection from microarray data followed by validation through branched DNA assays in independent patients revealed several candidate reference genes in NK cells including AKIRIN1, PPP6R3, TAX1BP1, and ADRBK1. Initially, no candidate genes could be validated in patient granulocytes. However, we determined highly similar AKIRIN1 expression also in SIRS and sepsis granulocytes and no change by in vitro LPS challenge in granulocytes from healthy donors. Inspection of external neutrophil transcriptome datasets further support unchanged AKIRIN1 expression in human systemic inflammation. As a potential new reference gene in NK cells and granulocytes in infectious and inflammatory diseases, AKIRIN1 may improve our pathomechanistic understanding of SIRS and sepsis and help identifying new sepsis biomarkers.

Association of Microcirculation, Macrocirculation, and Severity of Illness in Septic Shock: A Prospective Observational Study to Identify Microcirculatory Targets Potentially Suitable for Guidance of Hemodynamic Therapy.

PURPOSE: Clinically unapparent microcirculatory impairment is common and has a negative impact on septic shock, but specific therapy is not established so far. This prospective observational study aimed at identifying candidate parameters for microcirculatory-guided hemodynamic therapy. ClinicalTrials.gov : NCT01530932. MATERIALS AND METHODS: Microcirculatory flow and postcapillary venous oxygen saturation were detected during vaso-occlusive testing (VOT) on days 1 (T0), 2 (T24), and 4 (T72) in 20 patients with septic shock at a surgical intensive care unit using a laser Doppler spectrophotometry system (O2C). RESULTS: Reperfusional maximal venous capillary oxygen saturation (SvcO2max) showed negative correlations with Simplified Acute Physiology Score II (SAPSII)/Sequential Organ Failure Assessment (SOFA) score, norepinephrine dosage, and lactate concentration and showed positive correlations with cardiac index (CI). At T24 and T72, SvcO2max was also inversely linked to fluid balance. With respect to any predictive value, SvcO2max and CI determined on day 1 (T0) were negatively correlated with SAPS II/SOFA on day 4 (T72). Moreover, SvcO2max measured on day 1 or day 2 was negatively correlated with cumulated fluid balance on day 4 ( r= -.472, P < .05 and r = -.829, P < .001). By contrast, CI neither on day 1 nor on day 2 was correlated with cumulated fluid balance on day 4 ( r = -.343, P = .17 and r = -.365, P = .15). CONCLUSION: In patients with septic shock, microcirculatory reserve as assessed by SvcO2max following VOT was impaired and negatively correlated with severity of illness and fluid balance. In contrast to CI, SvcO2max determined on day 1 or day 2 was significantly negatively correlated with cumulative fluid balance on day 4. Therefore, early microcirculatory measurement of SvcO2max might be superior to CI in guidance of sepsis therapy to avoid fluid overload. This has to be addressed in future clinical studies.

An Algorithm for Systemic Inflammatory Response Syndrome Criteria-Based Prediction of Sepsis in a Polytrauma Cohort.

OBJECTIVES: Lifesaving early distinction of infectious systemic inflammatory response syndrome, known as "sepsis," from noninfectious systemic inflammatory response syndrome is challenging in the ICU because of high systemic inflammatory response syndrome prevalence and lack of specific biomarkers. The purpose of this study was to use an automatic algorithm to detect systemic inflammatory response syndrome criteria (tachycardia, tachypnea, leukocytosis, and fever) in surgical ICU patients for ICU-wide systemic inflammatory response syndrome prevalence determination and evaluation of algorithm-derived systemic inflammatory response syndrome descriptors for sepsis prediction and diagnosis in a polytrauma cohort. DESIGN: Cross-sectional descriptive study and retrospective cohort study. SETTING: Electronic medical records of a tertiary care center's surgical ICU, 2006-2011. PATIENTS: All ICU admissions and consecutive polytrauma admissions. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Average prevalence of conventional systemic inflammatory response syndrome (≥ 2 criteria met concomitantly) from cross-sectional application of the algorithm to all ICU patients and each minute of the study period was 43.3%. Of 256 validated polytrauma patients, 85 developed sepsis (33.2%). Three systemic inflammatory response syndrome descriptors summarized the 24 hours after admission and before therapy initiation: 1) systemic inflammatory response syndrome criteria average for systemic inflammatory response syndrome quantification over time, 2) first-to-last minute difference for trend detection, and 3) change count reflecting systemic inflammatory response syndrome criteria fluctuation. Conventional systemic inflammatory response syndrome for greater than or equal to 1 minute had 91% sensitivity and 19% specificity, whereas a systemic inflammatory response syndrome criteria average cutoff value of 1.72 had 51% sensitivity and 77% specificity for sepsis prediction. For sepsis diagnosis, systemic inflammatory response syndrome criteria average and first-to-last minute difference combined yielded 82% sensitivity and 71% specificity compared with 99% sensitivity and only 31% specificity of conventional systemic inflammatory response syndrome from a nested case-control analysis. CONCLUSIONS: Dynamic systemic inflammatory response syndrome descriptors improved specificity of sepsis prediction and particularly diagnosis, rivaling established biomarkers, in a polytrauma cohort. They may enhance electronic sepsis surveillance once evaluated in other patient populations.

Pneumonia in the first week after polytrauma is associated with reduced blood levels of soluble herpes virus entry mediator.

Background: Pneumonia develops frequently after major surgery and polytrauma and thus in the presence of systemic inflammatory response syndrome (SIRS) and organ dysfunction. Immune checkpoints balance self-tolerance and immune activation. Altered checkpoint blood levels were reported for sepsis. We analyzed associations of pneumonia incidence in the presence of SIRS during the first week of critical illness and trends in checkpoint blood levels. Materials and methods: Patients were studied from day two to six after admission to a surgical intensive care unit (ICU). Blood was sampled and physician experts retrospectively adjudicated upon the presence of SIRS and Sepsis-1/2 every eight hours. We measured the daily levels of immune checkpoints and inflammatory markers by bead arrays for polytrauma patients developing pneumonia. Immune checkpoint time series were additionally determined for clinically highly similar polytrauma controls remaining infection-free during follow-up. We performed cluster analyses. Immune checkpoint time trends in cases and controls were compared with hierarchical linear models. For patients with surgical trauma and with and without sepsis, selected immune checkpoints were determined in study baseline samples. Results: In polytrauma patients with post-injury pneumonia, eleven immune checkpoints dominated subcluster 3 that separated subclusters 1 and 2 of myeloid markers from subcluster 4 of endothelial activation, tissue inflammation, and adaptive immunity markers. Immune checkpoint blood levels were more stable in polytrauma cases than controls, where they trended towards an increase in subcluster A and a decrease in subcluster B. Herpes virus entry mediator (HVEM) levels (subcluster A) were lower in cases throughout. In unselected surgical patients, sepsis was not associated with altered HVEM levels at the study baseline. Conclusion: Pneumonia development after polytrauma until ICU-day six was associated with decreased blood levels of HVEM. HVEM signaling may reduce pneumonia risk by strengthening myeloid antimicrobial defense and dampening lymphoid-mediated tissue damage. Future investigations into the role of HVEM in pneumonia and sepsis development and as a predictive biomarker should consider the etiology of critical illness and the site of infection.

Key Signature Genes of Early Terminal Granulocytic Differentiation Distinguish Sepsis From Systemic Inflammatory Response Syndrome on Intensive Care Unit Admission.

Infection can induce granulopoiesis. This process potentially contributes to blood gene classifiers of sepsis in systemic inflammatory response syndrome (SIRS) patients. This study aimed to identify signature genes of blood granulocytes from patients with sepsis and SIRS on intensive care unit (ICU) admission. CD15+ cells encompassing all stages of terminal granulocytic differentiation were analyzed. CD15 transcriptomes from patients with sepsis and SIRS on ICU admission and presurgical controls (discovery cohort) were subjected to differential gene expression and pathway enrichment analyses. Differential gene expression was validated by bead array in independent sepsis and SIRS patients (validation cohort). Blood counts of granulocyte precursors were determined by flow cytometry in an extension of the validation cohort. Despite similar transcriptional CD15 responses in sepsis and SIRS, enrichment of canonical pathways known to decline at the metamyelocyte stage (mitochondrial, lysosome, cell cycle, and proteasome) was associated with sepsis but not SIRS. Twelve of 30 validated genes, from 100 selected for changes in response to sepsis rather than SIRS, were endo-lysosomal. Revisiting the discovery transcriptomes revealed an elevated expression of promyelocyte-restricted azurophilic granule genes in sepsis and myelocyte-restricted specific granule genes in sepsis followed by SIRS. Blood counts of promyelocytes and myelocytes were higher in sepsis than in SIRS. Sepsis-induced granulopoiesis and signature genes of early terminal granulocytic differentiation thus provide a rationale for classifiers of sepsis in patients with SIRS on ICU admission. Yet, the distinction of this process from noninfectious tissue injury-induced granulopoiesis remains to be investigated.

Bedside determination of microcirculatory oxygen delivery and uptake: a prospective observational clinical study for proof of principle.

Assessment of microcirculatory functional capacity is considered to be of prime importance for therapy guidance and outcome prediction in critically ill intensive care patients. Here, we show determination of skin microcirculatory oxygen delivery and consumption rates to be a feasible approach at the patient's bedside. Real time laser-doppler flowmetry (LDF) and white light spectrophotometry (WLS) were used for assessment of thenar skin microperfusion, regional Hb and postcapillary venous oxygen saturation before and after forearm ischemia. Adapted Fick's principle equations allowed for calculation of microcirculatory oxygen delivery and uptake. Patient groups with expected different microcirculatory status were compared [control (n = 20), sepsis-1/2 definition criteria identified SIRS (n = 10) and septic shock patients (n = 20), and the latter group further classified according to sepsis-3 definition criteria in sepsis (n = 10) and septic shock (n = 10)], respectively. In otherwise healthy controls, microcirculatory oxygen delivery and uptake approximately doubled after ischemia with maximum values (mDO2max and mVO2max) significantly lower in SIRS or septic patient groups, respectively. Scatter plots of mVO2max and mDO2max values defined a region of unphysiological low values not observed in control but in critically ill patients with the percentage of dots within this region being highest in septic shock patients. LDF and WLS combined with vasoocclusive testing reveals significant differences in microcirculatory oxygen delivery and uptake capacity between control and critically ill patients. As a clinically feasible technique for bedside determination of microcirculatory oxygen delivery and uptake, LDF and WLS combined with vasoocclusive testing holds promise for monitoring of disease progression and/or guidance of therapy at the microcirculatory level to be tested in further clinical trials.ClinicalTrials.gov: NCT01530932.

Predicting Pulmonary Function Testing from Quantified Computed Tomography Using Machine Learning Algorithms in Patients with COPD.

INTRODUCTION: Quantitative computed tomography (qCT) is an emergent technique for diagnostics and research in patients with chronic obstructive pulmonary disease (COPD). qCT parameters demonstrate a correlation with pulmonary function tests and symptoms. However, qCT only provides anatomical, not functional, information. We evaluated five distinct, partial-machine learning-based mathematical models to predict lung function parameters from qCT values in comparison with pulmonary function tests. METHODS: 75 patients with diagnosed COPD underwent body plethysmography and a dose-optimized qCT examination on a third-generation, dual-source CT with inspiration and expiration. Delta values (inspiration-expiration) were calculated afterwards. Four parameters were quantified: mean lung density, lung volume low-attenuated volume, and full width at half maximum. Five models were evaluated for best prediction: average prediction, median prediction, k-nearest neighbours (kNN), gradient boosting, and multilayer perceptron. RESULTS: The lowest mean relative error (MRE) was calculated for the kNN model with 16%. Similar low MREs were found for polynomial regression as well as gradient boosting-based prediction. Other models led to higher MREs and thereby worse predictive performance. Beyond the sole MRE, distinct differences in prediction performance, dependent on the initial dataset (expiration, inspiration, delta), were found. CONCLUSION: Different, partially machine learning-based models allow the prediction of lung function values from static qCT parameters within a reasonable margin of error. Therefore, qCT parameters may contain more information than we currently utilize and can potentially augment standard functional lung testing.

Fast, progressive loading of binary-encoded declarative-3D Web content.

Until recently, a major drawback of declarative-3D approaches for the Web was the encoding of scene-graph-related structured data along with a text-based description of unstructured vertex data. Loading times were long, and 3D Web content wasn't available until the full page had completely loaded. To overcome this limitation requires external mesh data containers that are referenced in the scene description. In particular, sequential image geometry containers and explicit binary containers align well with GPU buffer structures, thus enabling fast decoding and GPU uploads. Furthermore, progressive binary geometry enables simple, yet highly progressive transmission of arbitrary mesh data on the Web.