25 research outputs found

    PNI Biomarkers and Health Outcomes in College Women

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    Sleep disturbance has been found to trigger a stress response with a subsequent activation of the psychoneuroimmunological (PNI) pathway associated with adverse health outcomes. This study aimed to assess the association among selected PNI biomarkers, sleep disturbances, and adverse health outcomes (depressive symptoms, physical symptoms). A stratified, quota sample (14 poor sleepers and 15 good sleepers) was drawn from a pool of healthy college women from a larger scale of study. The participants reported their sleep, stress, depressive, and physical symptoms. Wrist actigraphy was used to collect objective sleep data, and the Enzyme-Linked ImmunoSorbent Assay was used to assess PNI biomarkers. Poor sleep quality, higher stress perception, elevated serum serotonin, and lower serum interleukin-10 explained 75.3% of the variances for the depressive symptoms. Poor sleep quality along with delayed peak activity rhythms accounted 31.4% of the physical symptoms. High serotonin and tumor necrosis factor-α were the significant predictors for poor sleep efficiency, and serotonin was the single significant predictor for poor daytime functioning. Stress and sleep disturbances negatively impact the health of college women and should be as part of regular check-ups on campus. PNI effects on health outcomes should be further explored. Educational materials in the areas of sleep hygiene, health impacts from sleep disturbances, and strategies to maintain synchronized circadian rhythms should be mandatorily included in the college curriculum

    Efficacy of robot-assisted full-endoscopic transforaminal lumbar interbody fusion in the treatment of degree Ⅰand Ⅱ lumbar spondylolisthesis

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    Objective To evaluate the efficacy of robot-assisted full-endoscopic transforaminal lumbar interbody fusion (Endo-TLIF) in the treatment of grade Ⅰ and Ⅱ lumbar spondylolisthesis. Methods The clinical data of 17 lumbar spondylolisthesis patients underwent robot-assisted Endo-TLIF treatment in Gansu Provincial Hospital from January 2020 to January 2022 were retrospectively collected. The postoperative hospital stay and complications were recorded. The accuracy of pedicle screw placement and the time of single screw placement were calculated. The clinical efficacy was evaluated by modified MacNab and lumbar interbody fusion was evaluated by Suk method. The related scores were recorded before operation, 1 month after operation and at the last follow-up. Results There were (126.45±17.28) min in duration of surgery, (90.00±11.25) mL in intraoperative blood loss, (4.10±0.95) times in intraoperative X-ray fluoroscopy, and (3.54±0.37) days in postoperative hospital stay. The accuracy of pedicle screw placement was 97.06 %, and average pedicle screw placement time was 9.75 min. At the last follow-up, all patients were evaluated to have good lumbar interbody fusion, with the excellent and good rate was 94.12%. One month after operation and at the last follow-up, the visual analogue scale (VAS) score was lower than that before operation, while the total score of Japanese Orthopaedic Association (JOA) was significantly higher than that before operation (P<0.05). No dura mater injury, nerve tissue injury, screw loosening and fracture were found in all patients. Conclusion Robot-assisted Endo-TLIF in the treatment of grade Ⅰ and Ⅱ lumbar spondylolisthesis has good efficacy, and has the advantages of accurate pedicle screw placement, less trauma, short operation time, and less intraoperative blood loss, but there are problems of high cost and high radiation to patients

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

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    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

    Domain decomposition methods with graph cuts algorithms for total variation minimization

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    Recently, graph cuts algorithms have been used to solve variational image restoration problems, especially for noise removal and segmentation. Compared to time-marching PDE methods, graph cuts based methods are more efficient and able to obtain the global minimizer. However, for high resolution and large-scale images, the cost of both memory and computational time increases dramatically. In this paper, we combine the domain decomposition method and the graph cuts algorithm for solving the total variation minimizations with L1 and L2 fidelity term. Numerous numerical experiments on large-scale data demonstrate the proposed algorithm yield good results in terms of computational time and memory usage

    Computing and Information DOMAIN DECOMPOSITION METHODS WITH GRAPH CUTS ALGORITHMS FOR IMAGE SEGMENTATION

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    Abstract. Recently, it is shown that graph cuts algorithms can be used to solve some variational image restoration problems, especially connected with noise removal and segmentation. For very large size images, the usage for memory and computation increases dramatically. We propose a domain decomposition method with graph cuts algorithms. We show that the new approach costs effective both for memory and computation. Experiments with large size 2D and 3D data are supplied to show the efficiency of the algorithms

    Augmented Lagrangian method for total variation based image restoration and segmentation over triangulated surfaces

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    Recently total variation (TV) regularization has been proven very successful in image restoration and segmentation. In image restoration, TV based models offer a good edge preservation property. In image segmentation, TV (or vectorial TV) helps to obtain convex formulations of the problems and thus provides global minimizations. Due to these advantages, TV based models have been extended to image restoration and data segmentation on manifolds. However, TV based restoration and segmentation models are difficult to solve, due to the nonlinearity and non-differentiability of the TV term. Inspired by the success of operator splitting and the augmented Lagrangian method (ALM) in 2D planar image processing, we extend the method to TV and vectorial TV based image restoration and segmentation on triangulated surfaces, which are widely used in computer graphics and computer vision. In particular, we will focus on the following problems. First, several Hilbert spaces will be given to describe TV and vectorial TV based variational models in the discrete setting. Second, we present ALM applied to TV and vectorial TV image restoration on mesh surfaces, leading to efficient algorithms for both gray and color image restoration. Third, we discuss ALM for vectorial TV based multi-region image segmentation, which also works for both gray and color images. The proposed method benefits from fast solvers for sparse linear systems and closed form solutions to subproblems. Experiments on both gray and color images demonstrate the efficiency of our algorithms

    Supplementary Material_OL_Revised.docx

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    Response to reviewers’ comments_Supplementary materia
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