This paper presents the latest development of an EMT system designed for use in the metal production industry such as imaging molten steel flow profiles during continuous casting. The system that has been developed is based on a commercial data acquisition board residing in a PC host computer and programmed in the LabView graphical language. The paper reviews the new EMT hardware electronics and software. The noise effects and the detectability limits of the system are given in the paper followed by the system sensitivity map analysis. Optimal image reconstructions, including the simultaneous iterative reconstruction technique (SIRT) and non-iterative Tikhonov regularization, truncated singular value decomposition (TSVD), are also discussed and applied for the system. The system has been demonstrated in real time (10 frames s−1 for 5 kHz excitation) with test phantoms that represent typical metal flow profiles such as central, annular stream and multiple streams.
Locally acquired HEV infection is increasingly recognized in developed countries. Anti-HEV IgG seroprevalence has been shown to be high in haemodialysis patients in a number of previous studies, employing assays of uncertain sensitivity. The aim of this study was to investigate anti-HEV IgG seroprevalence in recipients of haemodialysis and renal transplants compared to a control group using a validated, highly sensitive assay. Eighty-eight patients with functioning renal transplants and 76 receiving chronic haemodialysis were tested for HEV RNA and anti-HEV IgG and IgM. Six hundred seventy controls were tested for anti-HEV IgG. Anti-HEV IgG was positive in 28/76 (36.8%) of haemodialysis and 16/88 (18.2%) of transplant patients. HEV RNA was not found in any patient. 126/670 (18.8%) of control subjects were anti-HEV IgG positive. After adjusting for age and sex, there was a significantly higher anti-HEV IgG seroprevalence amongst haemodialysis patients compared to controls (OR = 1.97, 95% CI = 1.16-3.31, P = 0.01) or transplant recipients (OR = 2.63, 95% CI = 1.18-6.07, P = 0.02). Patients with a functioning transplant showed no difference in anti-HEV IgG seroprevalence compared to controls. The duration of haemodialysis or receipt of blood products were not significant risk factors for HEV IgG positivity. Patients receiving haemodialysis have a higher seroprevalence of anti-HEV IgG than both age- and sex-matched controls and a cohort of renal transplant patients. None of the haemodialysis patients had evidence of chronic infection. The reason haemodialysis patients have a high seroprevalence remains uncertain and merits further study.
Early intervention in the management of acute kidney injury (AKI) has been shown to improve outcomes. To facilitate early review we have introduced real time reporting for AKI. An algorithm using the laboratory computer system was implemented to report AKI for inpatients. Over 6 months there were 1,906 AKI reports in 1,518 patients: 56.3% AKI1, 26.9% AKI2 and 16.8% AKI3. 51.0% were male. Median age was 78 (interquartile range [IQR] 17) years. 62.6% were from general medical wards, 16.9% from surgical wards, 6.9% from orthopaedic wards and 5.3% from specialty wards. 8.3% were from peripheral hospitals. 31% of patients with AKI reports were clinically coded for AKI. 9% (n = 139) showed progression of AKI (mortality 42%). Patients with AKI had a signifi cantly higher length of stay and mortality than those that did not. 4% of patients with AKI received acute renal replacement therapy (RRT). An e-alert system is feasible, allowing early identifi cation of inpatients with AKI.
Background The incidence of Acute Kidney Injury (AKI) continues to increase in the UK, with associated mortality rates remaining significant. Approximately one fifth of hospital admissions are associated with AKI and approximately a third of patients with AKI in hospital develop AKI during their time in hospital. A fifth of these cases are considered avoidable. Early risk detection remains key to decreasing AKI in hospitals, where sub-optimal care was noted for half of patients who developed AKI. Methods Electronic anonymised data for adults admitted into the Royal Cornwall Hospitals Trust (RCHT) between 18th March and 31st December 2015 was trimmed to that collected within the first 24 h of hospitalisation. These datasets were split according to three separate time periods: data used for training the Takagi-Sugeno Fuzzy Logic Systems (FLS) and the multivariable logistic regression (MLR) models; data used for testing; and data from a later patient spell used for validation. Three fuzzy logic models and three MLR models were developed to link characteristics of patients diagnosed with a maximum stage AKI within 7 days of admission: the first models to identify any AKI Stage (FLS I, MLR I), the second for patterns of AKI Stage 2 or 3 (FLS II, MLR II), and the third to identify AKI Stage 3 (FLS III, MLR III). Model accuracy is expressed by area under the curve (AUC). Results Accuracy for each model during internal validation was: FLS I and MLR I (AUC 0.70, 95% CI: 0.64–0.77); FLS II (AUC 0.77, 95% CI: 0.69–0.85) and MLR II (AUC 0.74, 95% CI: 0.65–0.83); FLS III and MLR III (AUC 0.95, 95% CI: 0.92–0.98). Conclusions FLS II and FLS III (and the respective MLR models) can identify with a high level of accuracy patients at high risk of developing AKI in hospital. These two models cannot be properly assessed against prior studies as this is the first attempt at quantifying the risk of developing specific Stages of AKI for a broad cohort of both medical and surgical inpatients. FLS I and MLR I performance is comparable to other existing models. Electronic supplementary material The online version of this article (10.1186/s12882-019-1237-x) contains supplementary material, which is available to authorized users.
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