Sex-specific prevalence and risk factors of metabolic-associated fatty liver disease among 75,570 individuals in eastern China

BackgroundMetabolic-associated fatty liver disease (MAFLD) is a newly proposed definition and there is limited data on MAFLD prevalence. We aimed to investigate the prevalence of MAFLD in an eastern Chinese population.MethodsThis cross-sectional study included participants from an eastern Chinese population who underwent regular health checkups. Based on current diagnostic criteria, MAFLD was diagnosed in individuals with both hepatic steatosis and metabolic disorders. The overall and stratified prevalence derived based on sex, age, body mass index (BMI), and various metabolic disorders were estimated. Multivariate logistic regression analysis was used to determine the risk factors for MAFLD.ResultsAmong the 75,570 participants, the overall prevalence of MAFLD was 37.32%, with higher rates in men (45.66%) than in women (23.91%). MAFLD prevalence was highest in men aged 40–49 years (52.21%) and women aged 70–79 years (44.77%). In all the BMI subgroups, the prevalence was higher in men than in women. In both sexes, the prevalence of MAFLD increased as BMI levels increased. Furthermore, MAFLD was associated with metabolic disorders, especially in the female participants with severe obesity (odds ratio 58.318; 95% confidence interval: 46.978–72.397).ConclusionMAFLD is prevalent in the general adult population in eastern China. Sex-specific differences in MAFLD prevalence were identified based on age, BMI, and metabolic disorders. MAFLD is associated with metabolic disorders, particularly obesity

Prevalence, associated factors and outcomes of pressure injuries in adult intensive care unit patients: the DecubICUs study

Funder: European Society of Intensive Care Medicine; doi: http://dx.doi.org/10.13039/501100013347Funder: Flemish Society for Critical Care NursesAbstract: Purpose: Intensive care unit (ICU) patients are particularly susceptible to developing pressure injuries. Epidemiologic data is however unavailable. We aimed to provide an international picture of the extent of pressure injuries and factors associated with ICU-acquired pressure injuries in adult ICU patients. Methods: International 1-day point-prevalence study; follow-up for outcome assessment until hospital discharge (maximum 12 weeks). Factors associated with ICU-acquired pressure injury and hospital mortality were assessed by generalised linear mixed-effects regression analysis. Results: Data from 13,254 patients in 1117 ICUs (90 countries) revealed 6747 pressure injuries; 3997 (59.2%) were ICU-acquired. Overall prevalence was 26.6% (95% confidence interval [CI] 25.9–27.3). ICU-acquired prevalence was 16.2% (95% CI 15.6–16.8). Sacrum (37%) and heels (19.5%) were most affected. Factors independently associated with ICU-acquired pressure injuries were older age, male sex, being underweight, emergency surgery, higher Simplified Acute Physiology Score II, Braden score 3 days, comorbidities (chronic obstructive pulmonary disease, immunodeficiency), organ support (renal replacement, mechanical ventilation on ICU admission), and being in a low or lower-middle income-economy. Gradually increasing associations with mortality were identified for increasing severity of pressure injury: stage I (odds ratio [OR] 1.5; 95% CI 1.2–1.8), stage II (OR 1.6; 95% CI 1.4–1.9), and stage III or worse (OR 2.8; 95% CI 2.3–3.3). Conclusion: Pressure injuries are common in adult ICU patients. ICU-acquired pressure injuries are associated with mainly intrinsic factors and mortality. Optimal care standards, increased awareness, appropriate resource allocation, and further research into optimal prevention are pivotal to tackle this important patient safety threat

Correction to: Prevalence, associated factors and outcomes of pressure injuries in adult intensive care unit patients: the DecubICUs study

The original version of this article unfortunately contained a mistake

Prevalence, associated factors and outcomes of pressure injuries in adult intensive care unit patients: the DecubICUs study

Funder: European Society of Intensive Care Medicine; doi: http://dx.doi.org/10.13039/501100013347Funder: Flemish Society for Critical Care NursesAbstract: Purpose: Intensive care unit (ICU) patients are particularly susceptible to developing pressure injuries. Epidemiologic data is however unavailable. We aimed to provide an international picture of the extent of pressure injuries and factors associated with ICU-acquired pressure injuries in adult ICU patients. Methods: International 1-day point-prevalence study; follow-up for outcome assessment until hospital discharge (maximum 12 weeks). Factors associated with ICU-acquired pressure injury and hospital mortality were assessed by generalised linear mixed-effects regression analysis. Results: Data from 13,254 patients in 1117 ICUs (90 countries) revealed 6747 pressure injuries; 3997 (59.2%) were ICU-acquired. Overall prevalence was 26.6% (95% confidence interval [CI] 25.9–27.3). ICU-acquired prevalence was 16.2% (95% CI 15.6–16.8). Sacrum (37%) and heels (19.5%) were most affected. Factors independently associated with ICU-acquired pressure injuries were older age, male sex, being underweight, emergency surgery, higher Simplified Acute Physiology Score II, Braden score 3 days, comorbidities (chronic obstructive pulmonary disease, immunodeficiency), organ support (renal replacement, mechanical ventilation on ICU admission), and being in a low or lower-middle income-economy. Gradually increasing associations with mortality were identified for increasing severity of pressure injury: stage I (odds ratio [OR] 1.5; 95% CI 1.2–1.8), stage II (OR 1.6; 95% CI 1.4–1.9), and stage III or worse (OR 2.8; 95% CI 2.3–3.3). Conclusion: Pressure injuries are common in adult ICU patients. ICU-acquired pressure injuries are associated with mainly intrinsic factors and mortality. Optimal care standards, increased awareness, appropriate resource allocation, and further research into optimal prevention are pivotal to tackle this important patient safety threat

source data for microarray image segmentation

source code and data sets for microarray image griddin and segmentatio

An improved automatic gridding based on mathematical morphology

Image processing is one step of cDNA microarray analysis in which gridding is essential for the sequential spot segmentation and intensity extraction. It is necessary to introduce human intervention or inside parameter specification in most existing gridding approaches. Among these methods, the mathematical morphology one is the most rapid and simplest. However, it is easily affected by the noise. In this paper, an improved algorithm is proposed. First, a highly fluorescent noise removing method is used to 2-D microarray image signal. Secondly, the mathematical morphology dealing is applied to 1-D projection signal. Further, a refinement procedure, based on heuristic rule, is employed to improve the existing grid structure. Experiments on real images drawn from four different datasets verify that the improved approach is fully automatic and parameter-less, showing a higher accuracy in comparison with traditional mathematical morphology methods

A disease forecast and early warning system based on electronic health records

Conference Name:8th International Conference on Computer Science and Education, ICCSE 2013. Conference Address: Colombo, Sri lanka. Time:August 26, 2013 - August 28, 2013.Disease forecast and early warning have been always important but difficult tasks. Because of the drawbacks of traditional records, the electronic health records, which bring in the ICD-10, are used in our system. Input information are firstly de-duplicated to remove redundancy. After that, the system are used for disease early warning and forecast. The results show that the proposed system has great help for the health sector to prevent and control the diseases. ? 2013 IEEE

A Combinational Clustering Based Method for cDNA Microarray Image Segmentation.

Microarray technology plays an important role in drawing useful biological conclusions by analyzing thousands of gene expressions simultaneously. Especially, image analysis is a key step in microarray analysis and its accuracy strongly depends on segmentation. The pioneering works of clustering based segmentation have shown that k-means clustering algorithm and moving k-means clustering algorithm are two commonly used methods in microarray image processing. However, they usually face unsatisfactory results because the real microarray image contains noise, artifacts and spots that vary in size, shape and contrast. To improve the segmentation accuracy, in this article we present a combination clustering based segmentation approach that may be more reliable and able to segment spots automatically. First, this new method starts with a very simple but effective contrast enhancement operation to improve the image quality. Then, an automatic gridding based on the maximum between-class variance is applied to separate the spots into independent areas. Next, among each spot region, the moving k-means clustering is first conducted to separate the spot from background and then the k-means clustering algorithms are combined for those spots failing to obtain the entire boundary. Finally, a refinement step is used to replace the false segmentation and the inseparable ones of missing spots. In addition, quantitative comparisons between the improved method and the other four segmentation algorithms--edge detection, thresholding, k-means clustering and moving k-means clustering--are carried out on cDNA microarray images from six different data sets. Experiments on six different data sets, 1) Stanford Microarray Database (SMD), 2) Gene Expression Omnibus (GEO), 3) Baylor College of Medicine (BCM), 4) Swiss Institute of Bioinformatics (SIB), 5) Joe DeRisi's individual tiff files (DeRisi), and 6) University of California, San Francisco (UCSF), indicate that the improved approach is more robust and sensitive to weak spots. More importantly, it can obtain higher segmentation accuracy in the presence of noise, artifacts and weakly expressed spots compared with the other four methods

Terahertz pulse imaging: A novel denoising method by combing the ant colony algorithm with the compressive sensing

Terahertz (THz) pulse imaging exhibits a potential application in biomedicine, nondestructive detection and safety inspection. However, the THz time-domain spectroscopy system will affect the THz image quality. To improve the THz image quality, in this article we proposed a novel method by combining the ant colony algorithm with the compressive sensing method. First, the image edge is detected by using the ant colony algorithm. Subsequently, the compressive sensing method based on signal sparse representation and the reconstruction algorithm from partial Fourier is applied on the non-edge image for noise reduction. Finally, the reconstruction result is obtained by combining the noise reduced non-edge image with the edge image. The experimental results on three kinds of images prove that the proposed method can preserve the edge information during noise reduction

Automatic microarray image segmentation with clustering-based algorithms.

Image segmentation, as a key step of microarray image processing, is crucial for obtaining the spot expressions simultaneously. However, state-of-art clustering-based segmentation algorithms are sensitive to noises. To solve this problem and improve the segmentation accuracy, in this article, several improvements are introduced into the fast and simple clustering methods (K-means and Fuzzy C means). Firstly, a contrast enhancement algorithm is implemented in image preprocessing to improve the gridding precision. Secondly, the data-driven means are proposed for cluster center initialization, instead of usual random setting. The third improvement is that the multi features, including intensity features, spatial features, and shape features, are implemented in feature selection to replace the sole pixel intensity feature used in the traditional clustering-based methods to avoid taking noises as spot pixels. Moreover, the principal component analysis is adopted for various feature extraction. Finally, an adaptive adjustment algorithm is proposed based on data mining and learning for further dealing with the missing spots or low contrast spots. Experiments on real and simulation data sets indicate that the proposed improvements made our proposed method obtains higher segmented precision than the traditional K-means and Fuzzy C means clustering methods