Investigation in haemodynamic stability during intermittent haemodialysis in the critically ill

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An investigation into the effects of commencing haemodialysis in the critically ill

<b>Introduction:</b> We have aimed to describe haemodynamic changes when haemodialysis is instituted in the critically ill. 3 hypotheses are tested: 1)The initial session is associated with cardiovascular instability, 2)The initial session is associated with more cardiovascular instability compared to subsequent sessions, and 3)Looking at unstable sessions alone, there will be a greater proportion of potentially harmful changes in the initial sessions compared to subsequent ones. <b>Methods:</b> Data was collected for 209 patients, identifying 1605 dialysis sessions. Analysis was performed on hourly records, classifying sessions as stable/unstable by a cutoff of >+/-20% change in baseline physiology (HR/MAP). Data from 3 hours prior, and 4 hours after dialysis was included, and average and minimum values derived. 3 time comparisons were made (pre-HD:during, during HD:post, pre-HD:post). Initial sessions were analysed separately from subsequent sessions to derive 2 groups. If a session was identified as being unstable, then the nature of instability was examined by recording whether changes crossed defined physiological ranges. The changes seen in unstable sessions could be described as to their effects: being harmful/potentially harmful, or beneficial/potentially beneficial. <b>Results:</b> Discarding incomplete data, 181 initial and 1382 subsequent sessions were analysed. A session was deemed to be stable if there was no significant change (>+/-20%) in the time-averaged or minimum MAP/HR across time comparisons. By this definition 85/181 initial sessions were unstable (47%, 95% CI SEM 39.8-54.2). Therefore Hypothesis 1 is accepted. This compares to 44% of subsequent sessions (95% CI 41.1-46.3). Comparing these proportions and their respective CI gives a 95% CI for the standard error of the difference of -4% to 10%. Therefore Hypothesis 2 is rejected. In initial sessions there were 92/1020 harmful changes. This gives a proportion of 9.0% (95% CI SEM 7.4-10.9). In the subsequent sessions there were 712/7248 harmful changes. This gives a proportion of 9.8% (95% CI SEM 9.1-10.5). Comparing the two unpaired proportions gives a difference of -0.08% with a 95% CI of the SE of the difference of -2.5 to +1.2. Hypothesis 3 is rejected. Fisher’s exact test gives a result of p=0.68, reinforcing the lack of significant variance. <b>Conclusions:</b> Our results reject the claims that using haemodialysis is an inherently unstable choice of therapy. Although proportionally more of the initial sessions are classed as unstable, the majority of MAP and HR changes are beneficial in nature

Explaining anomalous responses to treatment in the Intensive Care Unit

The Intensive Care Unit (ICU) provides treatment to critically ill patients. When a patient does not respond as expected to such treatment it can be challenging for clinicians, especially junior clinicians, as they may not have the relevant experience to understand the patient’s anomalous response. Datasets for 10 patients from Glasgow Royal Infirmary’s ICU have been made available to us. We asked several ICU clinicians to review these datasets and to suggest sequences which include anomalous or unusual reactions to treatment. Further, we then asked two ICU clinicians if they agreed with their colleagues’ assessments, and if they did to provide possible explanations for these anomalous sequences. Subsequently we have developed a system which is able to replicate the clinicians’ explanations based on the knowledge contained in its several ontologies; further the system can suggest additional explanations which will be evaluated by the senior consultant

Strategies for Wildlife Disease Surveillance

Epidemiologic surveillance is defined by the Centers for Disease Control and Prevention (CDC) as the ongoing systematic and continuous collection, analysis, and interpretation of health data\u27: The objective of surveillance is to generate data for rapid response to the detection of a disease of concern to apply prevention, control, or eradication measures as well as to evaluate such interventions. This is distinct from disease monitoring, which usually does not involve a particular response to disease detection. Surveillance for wildlife diseases has increased in importance due to the emergence and re-emergence of wildlife diseases that are threats to human, animal, and ecosystem health, or could potentially have a negative economic impact. It has been estimated that 75% of emerging human diseases are zoonotic in origin, of which the majority originate from wildlife (Taylor et al. 2001). However, there are unique challenges concerning wildlife disease surveillance such that disease and pathogens can be very difficult to detect and measure in wild animals. These challenges have been described previously (Wobeser 2006), but one of the primary issues is that disease in wildlife often goes unrecognized, especially in remote locations. Furthermore, sick and dead animals are very difficult to detect, as animals will disguise the signs of illness or hide when diseased. Carcasses from diseased animals are also rapidly removed by scavengers or will rapidly decompose, rendering them suboptimal for diagnostic purposes. There is also a lack of validated diagnostic tests for most wildlife disease agents as well as baseline data. The paucity of laboratory capacity with expertise in wildlife disease diagnostic investigation is also an impediment. Finally, surveillance networks for wildlife diseases that perform field investigations and report disease events are under-developed in most regions of the world. Despite these challenges, a number of very important epidemiological surveillance projects have been ongoing or recently developed, and some examples are described in this chapter. The examples are mostly drawn from the experiences of the U.S. Geological Survey National Wildlife Health Center (NWHC) and are provided to illustrate the different surveillance strategies and sampling techniques that can be used and have proven successful. Some future directions for wildlife disease surveillance are also suggested

Isospectral domains with mixed boundary conditions

We construct a series of examples of planar isospectral domains with mixed Dirichlet-Neumann boundary conditions. This is a modification of a classical problem proposed by M. Kac.Comment: 9 figures. Statement of Theorem 5.1 correcte

Granulocyte macrophage colony-stimulating factor receptor α expression and its targeting in antigen-induced arthritis and inflammation

A Representative FACS plots showing Ly6G and Ly6C staining of CD45+ myeloid populations in the AIA knee joint. F4/80intSSchi eosinophils (Eos), F4/80+CD11c+MHCII+ Mo-DCs (R1), F4/80+CD11c-MHCII+ macrophages (Macs) (R2), F4/80+CD11c-MHCII- macrophages (R3), F4/80-CD11c+ MHCII+ cDCs, F4/80-CD11b+Ly6G+ neutrophils, which are also Ly6C+, and F4/80-CD11b+Ly6G-SScloLy6C+/- monocytes. B Representative FACS plots of CD45+ myeloid populations in the AIA knee joint showing Ly6G+ neutrophils are CD64- and F4/80+ macrophages/Mo-DCs are CD64+. (PDF 235 kb

BASIS: High-performance bioinformatics platform for processing of large-scale mass spectrometry imaging data in chemically augmented histology

Mass Spectrometry Imaging (MSI) holds significant promise in augmenting digital histopathologic analysis by generating highly robust big data about the metabolic, lipidomic and proteomic molecular content of the samples. In the process, a vast quantity of unrefined data, that can amount to several hundred gigabytes per tissue section, is produced. Managing, analysing and interpreting this data is a significant challenge and represents a major barrier to the translational application of MSI. Existing data analysis solutions for MSI rely on a set of heterogeneous bioinformatics packages that are not scalable for the reproducible processing of large-scale (hundreds to thousands) biological sample sets. Here, we present a computational platform (pyBASIS) capable of optimized and scalable processing of MSI data for improved information recovery and comparative analysis across tissue specimens using machine learning and related pattern recognition approaches. The proposed solution also provides a means of seamlessly integrating experimental laboratory data with downstream bioinformatics interpretation/analyses, resulting in a truly integrated system for translational MSI

Asymptotics of near-cloaking

This paper describes how asymptotic analysis can be used to gain new insights into the theory of cloaking of spherical and cylindrical targets within the context of acoustic waves in a class of linear elastic materials. In certain cases these configurations allow solutions to be written down in terms of eigenfunction expansions from which high-frequency asymptotics can be extracted systematically. These asymptotics are compared with the predictions of ray theory and are used to describe the scattering that occurs when perfect cloaking models are regularised