Optimal imaging with adaptive mesh refinement in electrical tomography

In non-linear electrical impedance tomography the goodness of fit of the trial images is assessed by the well-established statistical χ2 criterion applied to the measured and predicted datasets. Further selection from the range of images that fit the data is effected by imposing an explicit constraint on the form of the image, such as the minimization of the image gradients. In particular, the logarithm of the image gradients is chosen so that conductive and resistive deviations are treated in the same way. In this paper we introduce the idea of adaptive mesh refinement to the 2D problem so that the local scale of the mesh is always matched to the scale of the image structures. This improves the reconstruction resolution so that the image constraint adopted dominates and is not perturbed by the mesh discretization. The avoidance of unnecessary mesh elements optimizes the speed of reconstruction without degrading the resulting images. Starting with a mesh scale length of the order of the electrode separation it is shown that, for data obtained at presently achievable signal-to-noise ratios of 60 to 80 dB, one or two refinement stages are sufficient to generate high quality images

Convergence issues of non-linear 3D reconstruction in EIT

The convergence of non-linear reconstruction algorithms for Electrical Impedance Tomography (EIT) depends on many factors, such as the noise of the measurement system, the numerical discretization of the object and the starting value of the iterative approximation. We have shown previously that the result can be made independent of the underlying numerical discretization of the object when adaptive mesh refinement methods are used in combination with a logarithmic image smoothness constraint. The non-linear nature of EIT reconstruction and the unavoidable measurement noise, however, cause several differing solutions in the image space to satisfy the conditions for a 'best-fit' conductivity distribution. A verification of the correctness of an obtained image can only be carried out if the true distribution is known. This, however, is not possible when in-vivo measurements are made and hence we need to establish a measure of similarity between the reconstructed result and a simulated object to characterize the algorithmic behaviour for real experiments. By distributing and reconstructing a large number of initial 3D conductivity estimates on a commodity cluster of PCs, we can determine the quality of the final images and correlate the results with the true images within a short time-span. This gives an indication of the quality of image and provides a measure of convergence properties of the tested algorithms. In this paper, we present results from these investigations into the characteristics and efficient reconstruction of the final image and comment on the clinical importance of choice of starting valu

Comparison of algorithms for non-linear inverse 3D electrical tomography reconstruction

Non-linear electrical impedance tomography reconstruction algorithms usually employ the Newton–Raphson iteration scheme to image the conductivity distribution inside the body. For complex 3D problems, the application of this method is not feasible any more due to the large matrices involved and their high storage requirements. In this paper we demonstrate the suitability of an alternative conjugate gradient reconstruction algorithm for 3D tomographic imaging incorporating adaptive mesh refinement and requiring less storage space than the Newton–Raphson scheme. We compare the reconstruction efficiency of both algorithms for a simple 3D head model. The results show that an increase in speed of about 30% is achievable with the conjugate gradient-based method without loss of accuracy

P2X7 receptors regulate multiple types of membrane trafficking responses and non-classical secretion pathways

Activation of the P2X7 receptor (P2X7R) triggers a remarkably diverse array of membrane trafficking responses in leukocytes and epithelial cells. These responses result in altered profiles of cell surface lipid and protein composition that can modulate the direct interactions of P2X7R-expressing cells with other cell types in the circulation, in blood vessels, at epithelial barriers, or within sites of immune and inflammatory activation. Additionally, these responses can result in the release of bioactive proteins, lipids, and large membrane complexes into extracellular compartments for remote communication between P2X7R-expressing cells and other cells that amplify or modulate inflammation, immunity, and responses to tissue damages. This review will discuss P2X7R-mediated effects on membrane composition and trafficking in the plasma membrane (PM) and intracellular organelles, as well as actions of P2X7R in controlling various modes of non-classical secretion. It will review P2X7R regulation of: (1) phosphatidylserine distribution in the PM outer leaflet; (2) shedding of PM surface proteins; (3) release of PM-derived microvesicles or microparticles; (4) PM blebbing; (5) cell–cell fusion resulting in formation of multinucleate cells; (6) phagosome maturation and fusion with lysosomes; (7) permeability of endosomes with internalized pathogen-associated molecular patterns; (8) permeability/integrity of mitochondria; (9) exocytosis of secretory lysosomes; and (10) release of exosomes from multivesicular bodies

Evaluation of prognostic risk models for postoperative pulmonary complications in adult patients undergoing major abdominal surgery: a systematic review and international external validation cohort study

Background Stratifying risk of postoperative pulmonary complications after major abdominal surgery allows clinicians to modify risk through targeted interventions and enhanced monitoring. In this study, we aimed to identify and validate prognostic models against a new consensus definition of postoperative pulmonary complications. Methods We did a systematic review and international external validation cohort study. The systematic review was done in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. We searched MEDLINE and Embase on March 1, 2020, for articles published in English that reported on risk prediction models for postoperative pulmonary complications following abdominal surgery. External validation of existing models was done within a prospective international cohort study of adult patients (≥18 years) undergoing major abdominal surgery. Data were collected between Jan 1, 2019, and April 30, 2019, in the UK, Ireland, and Australia. Discriminative ability and prognostic accuracy summary statistics were compared between models for the 30-day postoperative pulmonary complication rate as defined by the Standardised Endpoints in Perioperative Medicine Core Outcome Measures in Perioperative and Anaesthetic Care (StEP-COMPAC). Model performance was compared using the area under the receiver operating characteristic curve (AUROCC). Findings In total, we identified 2903 records from our literature search; of which, 2514 (86·6%) unique records were screened, 121 (4·8%) of 2514 full texts were assessed for eligibility, and 29 unique prognostic models were identified. Nine (31·0%) of 29 models had score development reported only, 19 (65·5%) had undergone internal validation, and only four (13·8%) had been externally validated. Data to validate six eligible models were collected in the international external validation cohort study. Data from 11 591 patients were available, with an overall postoperative pulmonary complication rate of 7·8% (n=903). None of the six models showed good discrimination (defined as AUROCC ≥0·70) for identifying postoperative pulmonary complications, with the Assess Respiratory Risk in Surgical Patients in Catalonia score showing the best discrimination (AUROCC 0·700 [95% CI 0·683–0·717]). Interpretation In the pre-COVID-19 pandemic data, variability in the risk of pulmonary complications (StEP-COMPAC definition) following major abdominal surgery was poorly described by existing prognostication tools. To improve surgical safety during the COVID-19 pandemic recovery and beyond, novel risk stratification tools are required. Funding British Journal of Surgery Society