Salmonella enterica: a surprisingly well-adapted intracellular lifestyle

The infectious intracellular lifestyle of Salmonella enterica relies on the adaptation to nutritional conditions within the Salmonella-containing vacuole (SCV) in host cells. We summarize latest results on metabolic requirements for Salmonella during infection. This includes intracellular phenotypes of mutant strains based on metabolic modeling and experimental tests, isotopolog profiling using (13)C-compounds in intracellular Salmonella, and complementation of metabolic defects for attenuated mutant strains towards a comprehensive understanding of the metabolic requirements of the intracellular lifestyle of Salmonella. Helpful for this are also genomic comparisons. We outline further recent studies and which analyses of intracellular phenotypes and improved metabolic simulations were done and comment on technical required steps as well as progress involved in the iterative refinement of metabolic flux models, analyses of mutant phenotypes, and isotopolog analyses. Salmonella lifestyle is well-adapted to the SCV and its specific metabolic requirements. Salmonella metabolism adapts rapidly to SCV conditions, the metabolic generalist Salmonella is quite successful in host infection

Dynamic Reconstruction with Statistical Ray Weighting for C-Arm CT Perfusion Imaging

Abstract—Tissue perfusion measurement using C-arm angiography systems is a novel technique with potential high benefit for catheter-guided treatment of stroke in the interventional suite. However, perfusion C-arm CT (PCCT) is challenging: the slow C-arm rotation speed only allows measuring samples of contrast time attenuation curves (TACs) every 5 – 6 s if reconstruction algorithms for static data are used. Furthermore, the peaks of the tissue TACs typically lie in a range of 5 – 30 HU, thus perfusion imaging is very sensitive to noise. Recently we presented a dynamic, iterative reconstruction (DIR) approach to reconstruct TACs described by a weighted sum of linear spline functions with a regularization based on joint bilateral filtering (JBF). In this work we incorporate statistical ray weighting into the algorithm and show how this helps to improve the reconstructed cerebral blood flow (CBF) maps in a simulation study with a realistic dynamic brain phantom. The Pearson correlation of the CBF maps to ground truth maps increases from 0.85 (FDK), 0.87 (FDK with JBF), and 0.90 (DIR with JBF) to 0.92 (DIR with JBF and ray weighting). The results suggest that the statistical ray weighting approach improves the diagnostic accuracy of PCCT based on DIR. I

Deconvolution-Based CT and MR Brain Perfusion Measurement: Theoretical Model Revisited and Practical Implementation Details

Deconvolution-based analysis of CT and MR brain perfusion data is widely used in clinical practice and it is still a topic of ongoing research activities. In this paper, we present a comprehensive derivation and explanation of the underlying physiological model for intravascular tracer systems. We also discuss practical details that are needed to properly implement algorithms for perfusion analysis. Our description of the practical computer implementation is focused on the most frequently employed algebraic deconvolution methods based on the singular value decomposition. In particular, we further discuss the need for regularization in order to obtain physiologically reasonable results. We include an overview of relevant preprocessing steps and provide numerous references to the literature. We cover both CT and MR brain perfusion imaging in this paper because they share many common aspects. The combination of both the theoretical as well as the practical aspects of perfusion analysis explicitly emphasizes the simplifications to the underlying physiological model that are necessary in order to apply it to measured data acquired with current CT and MR scanners

DCE-MRI perfusion and permeability parameters as predictors of tumor response to CCRT in patients with locally advanced NSCLC

In this prospective study, 36 patients with stage III non-small cell lung cancers (NSCLC), who underwent dynamic contrast-enhanced MRI (DCE-MRI) before concurrent chemo-radiotherapy (CCRT) were enrolled. Pharmacokinetic analysis was carried out after non-rigid motion registration. The perfusion parameters including Blood Flow (BF), Blood Volume (BV), Mean Transit Time (MTT) and permeability parameters including endothelial transfer constant (Ktrans), reflux rate (Kep), fractional extravascular extracellular space volume (Ve), fractional plasma volume (Vp) were calculated, and their relationship with tumor regression was evaluated. The value of these parameters on predicting responders were calculated by receiver operating characteristic (ROC) curve. Multivariate logistic regression analysis was conducted to find the independent variables. Tumor regression rate is negatively correlated with V e and its standard variation V e-SD and positively correlated with K trans and Kep. Significant differences between responders and non-responders existed in Ktrans, Kep, Ve, Ve-SD, MTT, BV-SD and MTT-SD (P < 0.05). ROC indicated that Ve < 0.24 gave the largest area under curve of 0.865 to predict responders. Multivariate logistic regression analysis also showed Ve was a significant predictor. Baseline perfusion and permeability parameters calculated from DCE-MRI were seen to be a viable tool for predicting the early treatment response after CCRT of NSCLC. © 2016 The Author(s)

Diaqua­bis(2,2′-biimidazole)cobalt(II) dichloride

There are independent cations and four chloride anions in the crystal structure of the title complex, [Co(C6H6N4)2(H2O)2]Cl2. In each cation, the CoII cation is coordinated by four N atoms from two biimidazole and two O atoms of two water mol­ecules; one Co atom is at a position of site symmetry m, the other at a position of site symmetry 2/m. All Cl− ions and water mol­ecules are also located on the mirror plane. Each structural unit is connected through O—H⋯Cl and N—H⋯Cl inter­molecular hydrogen bonds, forming a three–dimensional supramolecular structure

Pneumothorax detection in chest radiographs: optimizing artificial intelligence system for accuracy and confounding bias reduction using in-image annotations in algorithm training

OBJECTIVES Diagnostic accuracy of artificial intelligence (AI) pneumothorax (PTX) detection in chest radiographs (CXR) is limited by the noisy annotation quality of public training data and confounding thoracic tubes (TT). We hypothesize that in-image annotations of the dehiscent visceral pleura for algorithm training boosts algorithm's performance and suppresses confounders. METHODS Our single-center evaluation cohort of 3062 supine CXRs includes 760 PTX-positive cases with radiological annotations of PTX size and inserted TTs. Three step-by-step improved algorithms (differing in algorithm architecture, training data from public datasets/clinical sites, and in-image annotations included in algorithm training) were characterized by area under the receiver operating characteristics (AUROC) in detailed subgroup analyses and referenced to the well-established \textquotedblCheXNet\textquotedbl algorithm. RESULTS Performances of established algorithms exclusively trained on publicly available data without in-image annotations are limited to AUROCs of 0.778 and strongly biased towards TTs that can completely eliminate algorithm's discriminative power in individual subgroups. Contrarily, our final \textquotedblalgorithm 2\textquotedbl which was trained on a lower number of images but additionally with in-image annotations of the dehiscent pleura achieved an overall AUROC of 0.877 for unilateral PTX detection with a significantly reduced TT-related confounding bias. CONCLUSIONS We demonstrated strong limitations of an established PTX-detecting AI algorithm that can be significantly reduced by designing an AI system capable of learning to both classify and localize PTX. Our results are aimed at drawing attention to the necessity of high-quality in-image localization in training data to reduce the risks of unintentionally biasing the training process of pathology-detecting AI algorithms. KEY POINTS • Established pneumothorax-detecting artificial intelligence algorithms trained on public training data are strongly limited and biased by confounding thoracic tubes. • We used high-quality in-image annotated training data to effectively boost algorithm performance and suppress the impact of confounding thoracic tubes. • Based on our results, we hypothesize that even hidden confounders might be effectively addressed by in-image annotations of pathology-related image features

A dynamic reconstruction approach for cerebral blood flow quantification with an interventional C-arm CT

Tomographic perfusion imaging is a well accepted method for stroke diagnosis that is available with current CT and MRI scanners. A challenging new method, which is currently not available, is perfusion imaging with an interventional C-arm CT that can acquire 4-D images using a C-arm angiography system. This method may help to optimize the workflow du-ring catheter-guided stroke treatment. The main challenge in perfusion C-arm CT is the comparably slow rotational speed of the C-arm (approximately 5 seconds) which decreases the overall temporal resolution. In this work we present a dyna-mic reconstruction approach optimized for perfusion C-arm CT based on temporal estimation of partially backprojected volumes. We use numerical simulations to validate the algo-rithm: For a typical configuration the relative error in estima-ted arterial peak enhancement decreases from 14.6 % to 10.5% using the dynamic reconstruction. Furthermore we present in-itial results obtained with a clinical C-arm CT in a pig model. 1

Interventional 4-D C-Arm CT Perfusion Imaging Using Interleaved Scanning and Partial Reconstruction Interpolation

Abstract—Tissue perfusion measurement during catheter-guided stroke treatment in the interventional suite is currently not possible. In this work, we present a novel approach that uses a C-arm angiography system capable of CT-like imaging (C-arm CT) for this purpose. With C-arm CT one reconstructed volume can be obtained every 4–6 s which makes it challenging to measure the flow of an injected contrast bolus. We have developed an interleaved scanning (IS) protocol that uses several scan sequences to increase temporal sampling. Using a dedicated 4-D reconstruction approach based on partial reconstruction interpolation (PRI) we can optimally process our data. We evaluated our combined approach (IS-PRI) with simu-lations and a study in 5 healthy pigs. In our simulations, the cerebral blood flow values (unit: ml/100g/min) were 60 (healthy tissue) and 20 (pathological tissue). For one scan sequence the values were estimated with standard deviations of 14.3 and 2.9, respectively. For two interleaved sequences the standard devia-tions decreased to 3.6 and 1.5, respectively. We used perfusion CT to validate the in vivo results. With two interleaved sequences we achieved promising correlations ranging from r=0.63 to r=0.94. The results suggest that C-arm CT tissue perfusion imaging is feasible with two interleaved scan sequences. Index Terms—Perfusion imaging, dynamic reconstruction, C-arm CT, stroke treatment. I

Topical antibiotics as a major contextual hazard toward bacteremia within selective digestive decontamination studies: a meta-analysis

BACKGROUND: Among methods for preventing pneumonia and possibly also bacteremia in intensive care unit (ICU) patients, Selective Digestive Decontamination (SDD) appears most effective within randomized concurrent controlled trials (RCCT’s) although more recent trials have been cluster randomized. However, of the SDD components, whether protocolized parenteral antibiotic prophylaxis (PPAP) is required, and whether the topical antibiotic actually presents a contextual hazard, remain unresolved. The objective here is to compare the bacteremia rates and patterns of isolates in SDD-RCCT’s versus the broader evidence base. METHODS: Bacteremia incidence proportion data were extracted from component (control and intervention) groups decanted from studies investigating antibiotic (SDD) or non-antibiotic methods of VAP prevention and summarized using random effects meta-analysis of study and group level data. A reference category of groups derived from purely observational studies without any prevention method under study provided a benchmark incidence. RESULTS: Within SDD RCCTs, the mean bacteremia incidence among concurrent component groups not exposed to PPAP (27 control; 17.1%; 13.1-22.1% and 12 intervention groups; 16.2%; 9.1-27.3%) is double that of the benchmark bacteremia incidence derived from 39 benchmark groups (8.3; 6.8-10.2%) and also 20 control groups from studies of non-antibiotic methods (7.1%; 4.8 – 10.5). There is a selective increase in coagulase negative staphylococci (CNS) but not in Pseudomonas aeruginosa among bacteremia isolates within control groups of SDD-RCCT’s versus benchmark groups with data available. CONCLUSIONS: The topical antibiotic component of SDD presents a major contextual hazard toward bacteremia against which the PPAP component partially mitigates. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12879-014-0714-x) contains supplementary material, which is available to authorized users