Background: Atherosclerosis is associated with chronic inflammation and lipid metabolism. The neutrophil to lymphocyte ratio (NLR) as an indicator of inflammation has been confirmed to be associated with cardiovascular disease prognosis. However, few studies have explored the effects of blood lipid variability on NLR. The aim of this study was to explore the relationship between variability in blood lipid levels and NLR. Methods: The association between variability in blood lipids and NLR was assessed with both univariate and multivariate linear regression. Multivariate linear regression was also performed for a subgroup analysis. Results: The variability of high-density lipoprotein cholesterol (HDL-C) (regression coefficients [β] 4.008, standard error (SE) 0.503, P-value<0.001) and low-density lipoprotein cholesterol (LDL-C) ([β] 0.626, SE 0.164, P-value<0.001) were risk factors for the NLR value, although baseline LDL-C and HDL-C were not risk factors for NLR values. Variability of HDL-C ([β] 4.328, SE 0.578, P-value<0.001) and LDL-C ([β] 0.660, SE 0.183, P-value<0.001) were risk factors for NLR variability. Subgroup analysis demonstrated that the relationship between variability of LDL-C and NLR was consistent with the trend of the total sample for those with diabetes mellitus, controlled blood lipid, statins, atorvastatin. The relationship between the variability of HDL-C and NLR was consistent with the trend of the total sample in all subgroups. Conclusion: The variability of HDL-C and LDL-C are risk factors for the value and variability of NLR, while the relationship between variability of HDL-C and NLR is more stable than the variability of LDL-C in the subgroup analysis, which provides a new perspective for controlling inflammation in patients undergoing PCI.
In endoscopic surgery, the surgical navigation needs to calculate the internal deformation of the soft tissue by biomechanical model which needs to determine the elastic properties and boundary conditions. However, these information cannot be obtained accurately in a real operation scenario. For example, only a limited portion of a liver surface can be observed in a hepatic surgery under endoscope while its elastic properties remain unknown. In addition, simple boundary conditions such as fixed constraints and free-force constraints are not physically adequate to simulate the elastic effect of ligaments attached to the liver. Biomechanical models of the soft tissue have been thoroughly studied in recent years. In these studies, boundary conditions play an important role in identification of elastic properties for mechanical model based methods. But they rarely combine these unknown conditions together to construct the model, and instead set boundary conditions or elastic properties as known for simplification. In this paper, we present a novel method to identify the Young’s modulus and equivalent spring constraint boundary conditions of a partially observed soft object with incomplete boundary conditions. The spring constraint boundary condition is applied to alternate the conventional displacement boundary conditions (e.g. free constraint and fixed constraint) and an inverse algorithm based on the standard finite element method (FEM) and Gauss-Newton (GN) method is developed, which takes external forces and displacements of observable nodes as inputs. A series of numerical simulation experiments are implemented and the analysis of simulation results show the feasibility of the proposed method.
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