BackgroundThe aim of this study was to determine if the post-operative serum arterial lactate concentration is associated with mortality, length of hospital stay or complications following hepatic resection.MethodsSerum lactate concentration was recorded at the end of liver resection in a consecutive series of 488 patients over a seven-year period. Liver function, coagulation and electrolyte tests were performed post-operatively. Renal dysfunction was defined as a creatinine rise of >1.5x the pre-operative value.ResultsThe median lactate was 2.8 mmol/L (0.6 to 16 mmol/L) and was elevated (≥2 mmol/L) in 72% of patients. The lactate concentration was associated with peak post-operative bilirubin, prothrombin time, renal dysfunction, length of hospital stay and 90-day mortality (P < 0.001). The 90-day mortality in patients with a post-operative lactate ≥6 mmol/L was 28% compared to 0.7% in those with lactate ≤2 mmol/L. Pre-operative diabetes, number of segments resected, the surgeon’s assessment of liver parenchyma, blood loss and transfusion were independently associated with lactate concentration.ConclusionsInitial post-operative lactate concentration is a useful predictor of outcome following hepatic resection. Patients with normal post-operative lactate are unlikely to suffer significant hepatic or renal dysfunction and may not require intensive monitoring or critical care.
A substantial body of literature has accumulated on the topic of using remotely sensed data to map impervious surfaces which are widely recognized as an important indicator of urbanization. However, the remote sensing of impervious surface growth has not been successfully addressed. This study proposes a new framework for deriving and summarizing urban expansion and re-densification using time series of impervious surface fractions (ISFs) derived from remotely sensed imagery. This approach integrates multiple endmember spectral mixture analysis (MESMA), analysis of regression residuals, spatial statistics (Getis_Ord) and urban growth theories; hence, the framework is abbreviated as MRGU. The performance of MRGU was compared with commonly used change detection techniques in order to evaluate the effectiveness of the approach. The results suggested that the ISF regression residuals were optimal for detecting impervious surface changes while Getis_Ord was effective for mapping hotspot regions in the regression residuals image. Moreover, the MRGU outputs agreed with the mechanisms proposed in several existing urban growth theories, but importantly the outputs enable the refinement of such models by explicitly accounting for the spatial distribution of both expansion and re-densification mechanisms.Based on Landsat data, the MRGU is somewhat restricted in its ability to measure re-densification in the urban core but this may be improved through the use of higher spatial resolution satellite imagery. The paper ends with an assessment of the present gaps in remote sensing of impervious surface growth and suggests some solutions. The application of impervious surface fractions in urban change detection is a stimulating new research idea which is driving future research with new models and algorithms.
Many countries have been constructing modern ground transportation projects. This raises questions about the impacts of such projects on development of impervious surfaces, yet there have been few attempts to systematically analyze these impacts. This paper attempts to narrow this information gap using the Hangzhou Bay Bridge project, China, as an exploratory case study. Using remotely sensed data, we developed a framework based on statistical techniques, wavelet multi-resolution analysis and Theil-Sen slope analysis to measure the changes in impervious surfaces. The derived changes were then linked to the bridge project with respect to socioeconomic factors and land use development activities. The findings highlight that the analytical framework could reliably quantify the area, pattern and form of new urban area and urban intensification. Change detection analysis showed that urban area, GDP and the length of highways increased moderately in the pre-Hangzhou Bay Bridge period (1995-2002) while all of these variables increased more substantially during (2002-2009) and after (2009-2013) the bridge construction. The results indicate that the development of impervious surfaces due to new urban area came at the expense of permeable surfaces in the urban fringe and within rural regions, while urban intensification occurred mainly in the form of the redevelopment of older structures to modern high-rise buildings within existing urban regions. In the context of improved transportation infrastructure, our findings suggest that new urban area and urban intensification can be attributed to consecutive events which act like a chain reaction: construction of improved transportation projects, their impacts on land use development policies, effects of both systems on socioeconomic variables, and finally all these changes influence new urban area and urban intensification. However, more research is needed to better understand this sequential process and to examine the broader applicability of the concept in other developing regions.
Self-organising map (SOM), an unsupervised machine learning algorithm based on neural networks, is applied to introduce a novel approach for the analysis of XRF spectral imaging data. This method automatically reduced hundreds of thousands of XRF spectra in a spectral image dataset to a handful of distinct clusters that share similar spectra. In this study, we show how clustering and the combination of spatial and spectral information can be used to aid materials identification and deduce the paint sequence. The efficiency and accuracy of the method is presented through the analysis of a Peruvian watercolour painting from the Getty Research Institute collection. Confirmation of the interpretation was provided by complementary non-invasive techniques, such as optical microscopy, reflectance and Raman spectroscopies.
Background. This study aimed to assess the relationship between diabetes, obesity, and hepatic steatosis in patients undergoing liver resection and to determine if these factors are independent predictors of major complications. Materials and Methods. Analysis of a prospectively maintained database of patients undergoing liver resection between 2005 and 2012 was undertaken. Background liver was assessed for steatosis and classified as <33% and ≥33%. Major complications were defined as Grade III–V complications using the Dindo-Clavien classification. Results. 504 patients underwent liver resection, of whom 56 had diabetes and 61 had steatosis ≥33%. Median BMI was 26 kg/m2 (16–54 kg/m2). 94 patients developed a major complication (18.7%). BMI ≥ 25 kg/m2 (P = 0.001) and diabetes (P = 0.018) were associated with steatosis ≥33%. Only insulin dependent diabetes was a risk factor for major complications (P = 0.028). Age, male gender, hypoalbuminaemia, synchronous bowel procedures, extent of resection, and blood transfusion were also independent risk factors. Conclusions. Liver surgery in the presence of steatosis, elevated BMI, and non-insulin dependent diabetes is not associated with major complications. Although diabetes requiring insulin therapy was a significant risk factor, the major risk factors relate to technical aspects of surgery, particularly synchronous bowel procedures.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.