Basic concepts of fluid responsiveness

Predicting fluid responsiveness, the response of stroke volume to fluid loading, is a relatively novel concept that aims to optimise circulation, and as such organ perfusion, while avoiding futile and potentially deleterious fluid administrations in critically ill patients. Dynamic parameters have shown to be superior in predicting the response to fluid loading compared with static cardiac filling pressures. However, in routine clinical practice the conditions necessary for dynamic parameters to predict fluid responsiveness are frequently not met. Passive leg raising as a means to alter biventricular preload in combination with subsequent measurement of the change in stroke volume can provide a fast and accurate way to guide fluid management in a broad population of critically ill patients.     

A population‐based temporal logic gate for timing and recording chemical events

Engineered bacterial sensors have potential applications in human health monitoring, environmental chemical detection, and materials biosynthesis. While such bacterial devices have long been engineered to differentiate between combinations of inputs, their potential to process signal timing and duration has been overlooked. In this work, we present a two‐input temporal logic gate that can sense and record the order of the inputs, the timing between inputs, and the duration of input pulses. Our temporal logic gate design relies on unidirectional DNA recombination mediated by bacteriophage integrases to detect and encode sequences of input events. For an E. coli strain engineered to contain our temporal logic gate, we compare predictions of Markov model simulations with laboratory measurements of final population distributions for both step and pulse inputs. Although single cells were engineered to have digital outputs, stochastic noise created heterogeneous single‐cell responses that translated into analog population responses. Furthermore, when single‐cell genetic states were aggregated into population‐level distributions, these distributions contained unique information not encoded in individual cells. Thus, final differentiated sub‐populations could be used to deduce order, timing, and duration of transient chemical events.     

An innovative approach for testing bioinformatics programs using metamorphic testing

Background  

Recent advances in experimental and computational technologies have fueled the development of many sophisticated bioinformatics programs. The correctness of such programs is crucial as incorrectly computed results may lead to wrong biological conclusion or misguide downstream experimentation. Common software testing procedures involve executing the target program with a set of test inputs and then verifying the correctness of the test outputs. However, due to the complexity of many bioinformatics programs, it is often difficult to verify the correctness of the test outputs. Therefore our ability to perform systematic software testing is greatly hindered.     

Granulocyte-macrophage colony-stimulating factor (GM-CSF) decreases left ventricular function. An echocardiographic study in cancer patients

To study the effect of granulocyte-macrophage colony-stimulating factor (GM-CSF) on the heart, echocardiographic assessments of left ventricular (LV) end-diastolic and end-systolic (ES) diameters (D), ejection fraction (EF) and cardiac output (CO) were done in six male patients (28-70 years of age) with advanced sarcoma (Group 1), prior to (day -1-0), during (day 7-9) and after (day 20-21) a first course of i.v. doxorubicin (day 0) without GM-CSF and a second course (3 weeks after the first one) with GM-CSF 250 microg/m(2)subcutaneously and daily from day 1-11. A similar study was done in ten female patients with advanced breast cancer (31-58 years of age, Group 2) for a first course of doxorubicin+cyclophosphamide with GM-CSF (same schedule as in Group 1). As compared to the mean of values prior to and after the course with GM-CSF in Group 1 and 2, the LVESD during GM-CSF administration transiently increased by median 6% (range -19 to 30%, P<0.05) vs -9% (-21 to 6%, not significant) in the first course without GM-CSF in Group 1 (P<0.05 between courses). The CO and EF tended to decrease during GM-CSF. GM-CSF thus causes a small and transient decrease of LV contractility.     

Conventional Hemodynamic Resuscitation May Fail to Optimize Tissue Perfusion: An Observational Study on the Effects of Dobutamine,Enoximone, and Norepinephrine in Patients with Acute Myocardial Infarction Complicated by Cardiogenic Shock

Aim

To investigate the effects of inotropic agents on parameters of tissue perfusion in patients with cardiogenic shock.

Methods and Results

Thirty patients with cardiogenic shock were included. Patients received dobutamine, enoximone, or norepinephrine. We performed hemodynamic measurements at baseline and after titration of the inotropic agent until cardiac index (CI) ≥2.5 L.min⁻¹.m⁻² or mixed-venous oxygen saturation (SvO₂) ≥70% (dobutamine or enoximone), and mean arterial pressure (MAP) ≥70 mmHg (norepinephrine). As parameters of tissue perfusion, we measured central-peripheral temperature gradient (delta-T) and sublingual perfused capillary density (PCD). All patients reached predefined therapeutic targets. The inotropes did not significantly change delta-T. Dobutamine did not change PCD. Enoximone increased PCD (9.1 [8.9–10.2] vs. 11.4 [8.4–13.9] mm.mm⁻²; p<0.05), and norepinephrine tended to decrease PCD (9.8 [8.5–11.9] vs. 8.8 [8.2–9.6] mm.mm⁻², p = 0.08). Fifteen patients (50%) died within 30 days after admission. Patients who had low final PCD (≤10.3 mm.mm⁻²; 64%) were more likely to die than patients who had preserved PCD (>10.3 mm.mm⁻²; mortality 72% vs. 17%, p = 0.003).

Conclusion

This study demonstrates the effects of commonly used inotropic agents on parameters of tissue perfusion in patients with cardiogenic shock. Despite hemodynamic optimization, tissue perfusion was not sufficiently restored in most patients. In these patients, mortality was high. Interventions directed at improving microcirculation may eventually help bridging the gap between improved hemodynamics and dismal patient outcome in cardiogenic shock.     

A genetic ensemble approach for gene-gene interaction identification

Background  

It has now become clear that gene-gene interactions and gene-environment interactions are ubiquitous and fundamental mechanisms for the development of complex diseases. Though a considerable effort has been put into developing statistical models and algorithmic strategies for identifying such interactions, the accurate identification of those genetic interactions has been proven to be very challenging.     

Gene-gene interaction filtering with ensemble of filters

BACKGROUND: Complex diseases are commonly caused by multiple genes and their interactions with each other. Genome-wide association (GWA) studies provide us the opportunity to capture those disease associated genes and gene-gene interactions through panels of SNP markers. However, a proper filtering procedure is critical to reduce the search space prior to the computationally intensive gene-gene interaction identification step. In this study, we show that two commonly used SNP-SNP interaction filtering algorithms, ReliefF and tuned ReliefF (TuRF), are sensitive to the order of the samples in the dataset, giving rise to unstable and suboptimal results. However, we observe that the 'unstable' results from multiple runs of these algorithms can provide valuable information about the dataset. We therefore hypothesize that aggregating results from multiple runs of the algorithm may improve the filtering performance. RESULTS: We propose a simple and effective ensemble approach in which the results from multiple runs of an unstable filter are aggregated based on the general theory of ensemble learning. The ensemble versions of the ReliefF and TuRF algorithms, referred to as ReliefF-E and TuRF-E, are robust to sample order dependency and enable a more informative investigation of data characteristics. Using simulated and real datasets, we demonstrate that both the ensemble of ReliefF and the ensemble of TuRF can generate a much more stable SNP ranking than the original algorithms. Furthermore, the ensemble of TuRF achieved the highest success rate in comparison to many state-of-the-art algorithms as well as traditional χ2-test and odds ratio methods in terms of retaining gene-gene interactions.