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Background Reliable cardiac output monitoring is particularly useful in the cirrhotic patient undergoing liver transplant surgery, because cirrhosis of the liver is associated with a vasodilated and high output state, known as cirrhotic cardiomyopathy, that challenges the reliability of pulse contour cardiac output technology. The contractility of the ventricle in cirrhosis is impaired, which is tolerated even though the ejection fraction and cardiac output are elevated because of the low peripheral resistance. However, during surgery the cirrhotic patient can decompensate because of the physiological changes and stress of surgery. Recently, we showed that the FloTrac/Vigileo™ failed to perform in cirrhotic patients undergoing transplant surgery. In response, the company upgraded their software. Therefore, we have assessed the accuracy and reliability of this new third-generation (version 3.02) FloTrac/Vigileo algorithm software in the same setting. Methods The cardiac index was measured simultaneously by single-bolus thermodilution (CI(TD)), using a pulmonary artery catheter, and pulse contour analysis, using the FloTrac/Vigileo (CI(V)). Readings were made at 10 time points during and after liver transplant surgery in 21 patients. Comparisons with data from our 2009 study, which used second-generation (version 01.10) software, were also made. Results Our new data show that version 3.02 software significantly reduced the adverse effect on pulse contour cardiac output reading bias in low peripheral resistance states, and thus improves the overall precision and trending ability of the system. Regression analysis between CI(TD) and CI(V) showed that the correlation was moderate (r =0.67, 95% confidence interval, 0.40 to 0.86). The Bland and Altman analysis showed that bias was 0.4 L.min(-1) · m(-2), and the percentage error was 52% (95% confidence interval, 49% to 55%). Trending ability of the new software also was improved but was still well below the current benchmarks. Conclusion The new software (version 3.02) provided substantial improvements over the previous versions with better overall precision and trending ability. Further algorithm refinements will increase this technology's reliability to be extensively used in the highly complex setting of cirrhotic patients undergoing liver transplantation.

Background Thermodilution cardiac output using a pulmonary artery catheter is the reference method against which all new methods of cardiac output measurement are judged. However, thermodilution lacks precision and has a quoted precision error of ± 20%. There is uncertainty about its true precision and this causes difficulty when validating new cardiac output technology. Our aim in this investigation was to determine the current precision error of thermodilution measurements. Methods A test rig through which water circulated at different constant rates with ports to insert catheters into a flow chamber was assembled. Flow rate was measured by an externally placed transonic flowprobe and meter. The meter was calibrated by timed filling of a cylinder. Arrow and Edwards 7Fr thermodilution catheters, connected to a Siemens SC9000 cardiac output monitor, were tested. Thermodilution readings were made by injecting 5 mL of ice-cold water. Precision error was divided into random and systematic components, which were determined separately. Between-readings (random) variability was determined for each catheter by taking sets of 10 readings at different flow rates. Coefficient of variation (CV) was calculated for each set and averaged. Between-catheter systems (systematic) variability was derived by plotting calibration lines for sets of catheters. Slopes were used to estimate the systematic component. Performances of 3 cardiac output monitors were compared: Siemens SC9000, Siemens Sirecust 1261, and Philips MP50. Results Five Arrow and 5 Edwards catheters were tested using the Siemens SC9000 monitor. Flow rates between 0.7 and 7.0 L/min were studied. The CV (random error) for Arrow was 5.4% and for Edwards was 4.8%. The random precision error was ± 10.0% (95% confidence limits). CV (systematic error) was 5.8% and 6.0%, respectively. The systematic precision error was ± 11.6%. The total precision error of a single thermodilution reading was ± 15.3% and ± 13.0% for triplicate readings. Precision error increased by 45% when using the Sirecust monitor and 100% when using the Philips monitor. Conclusion In vitro testing of pulmonary artery catheters enabled us to measure both the random and systematic error components of thermodilution cardiac output measurement, and thus calculate the precision error. Using the Siemens monitor, we established a precision error of ± 15.3% for single and ± 13.0% for triplicate reading, which was similar to the previous estimate of ± 20%. However, this precision error was significantly worsened by using the Sirecust and Philips monitors. Clinicians should recognize that the precision error of thermodilution cardiac output is dependent on the selection of catheter and monitor model.

Objective To perform gene expression profiles comparison so that to identify and understand the potential differences in pathogenesis between the pandemic and seasonal A (H1N1) influenza viruses. Methods A549 cells were infected with A/California/07/09 (H1N1) and A/GuangdongBaoan/51/08 (H1N1) respectively at the same MOI of 2 and collected at 2, 4, 8, and 24 h post infection (p.i.). Gene expression profiles of A549 cells were obtained using the 22 K Human Genome Oligo Array, and differentially expressed genes were analyzed at selected time points. Results Microarrays results indicated that both of the viruses suppressed host immune response related pathways including cytokine production while pandemic H1N1 virus displayed weaker suppression of host immune response than seasonal H1N1 virus. Observation on similar anti-apoptotic events such as activation of apoptosis inhibitor and down-regulation of key genes of apoptosis pathways in both infections showed that activities of promoting apoptosis were different in later stage of infection. Conclusion The immuno-suppression and anti-apoptosis events of pandemic H1N1 virus were similar to those seen by seasonal H1N1 virus. The pandemic H1N1 virus had an ability to inhibit biological pathways associated with cytokine responses, NK activation and macrophage recognition .

Influenza is a contagious, acute respiratory disease caused by influenza viruses and has caused substantial human morbidity and mortality over the past century (24, 27). The 1918-1919 pandemic caused by influenza virus type A H1N1 was responsible for an estimated 50 million deaths (21). In recent years, novel subtype influenza viruses, such as H5N1 and the 2009 pandemic H1N1, have been transmitted directly from animals to the human population. These infections were characterized by unusually high rates of severe respiratory disease and mortality among young patients (8, 18). Various genetic shifts have occurred in these viruses, allowing them to evade the host protective effects of specific antihemagglutinin (HA) or antineuraminidase (NA) antibodies (27). Therefore, host innate immunity in the early phase of infection, which includes a variety of pattern recognition molecules, inflammatory cytokines, and immune cells, such as macrophages and natural killer (NK) cells, plays a critical role in host defense. NK cells are bone marrow-derived, large, granular lymphocytes and are key effector cells in innate immunity for host defense against invading infectious pathogens and malignant transformation through cytolytic activity and production of cytokines, such as gamma interferon (IFN-γ) (10, 28, 43, 51). In humans, NK cells account for approximately 10% of all blood lymphocytes and are identified by their expression of the CD56 surface antigen and their lack of CD3. Two distinct subsets of human NK cells have been defined according to the cell surface density of CD56 expression (10). The majority (∼90% in blood) of human NK cells are CD56dim, and a minor population (∼10% in blood) is CD56bright. These NK subsets are functionally distinct, with the immunoregulatory CD56bright cells producing abundant cytokines and the cytotoxic CD56dim cells probably functioning as efficient effectors of natural and antibody-dependent target cell lysis (11). Many lines of evidence suggest that NK cells can be functionally activated by the interaction between natural cytotoxicity receptors (NCRs) on the cell surface and influenza virus HA protein or stress-induced proteins from infected cells (2, 13, 33, 44, 46). On the other hand, influenza virus is able to evade host immunity by infecting NK cells and triggering cell apoptosis or by attenuating NK cell lysis of H3N2-infected cells, owing to alterations in HA binding properties (35, 39). The infiltration of macrophages and lymphocytes into the lung and strong inflammatory responses were detected in H5N1 and the 1918 and 2009 pandemic H1N1 infections. Nevertheless, little is known about the precise roles of NK cells in these infections. In this study, the responses of NK cells to 1918 H1N1, 2009 H1N1, and H5N1 influenza A viruses were evaluated using three strains of influenza A virus pseudotyped particles (pps). Our findings may aid in understanding the pathogenicity of influenza viruses and its correlation with clinical severity.