Background: Arterial calcification is a significant cardiovascular risk factor in hemodialysis patients. A series of factors are involved in the process of arterial calcification; however, the relationship between malnutrition and arterial calcification is still unclear. Methods: 68 hemodialysis patients were enrolled in this study. Nutrition status was evaluated using modified quantitative subjective global assessment (MQSGA). Related serum biochemical parameters were measured. And the radial artery samples were collected during the arteriovenous fistula surgeries. Hematoxylin/eosin stain was used to observe the arterial structures while Alizarin red stain to observe calcified depositions and classify calcified degree. The expressions of bone morphogenetic protein 2 (BMP2) and matrix Gla protein (MGP) were detected by immunohistochemistry and western blot methods. Results: 66.18% hemodialysis patients were malnutrition. In hemodialysis patients, the calcified depositions were mainly located in the medial layer of the radial arteries and the expressions of BMP2 and MGP were both increased in the calcified areas. The levels of serum albumin were negatively associated with calcification score and the expressions of BMP2 and MGP. While MQSGA score, serum phosphorus and calcium × phosphorus product showed positive relationships with calcification score and the expressions of BMP2 and MGP. Conclusions: Malnutrition is prevalent in hemodialysis patients and is associated with arterial calcification and the expressions of BMP2 and MGP in calcified radial arteries. Malnutrition may be a new inducer candidate for arterial calcification in hemodialysis patients.

Sample entropy, an approximation of the Kolmogorov entropy, was proposed to characterize complexity of a time series, which is essentially defined as $-log(B/A)$, where B denotes the number of matched template pairs with length m and A denotes the number of matched template pairs with $m+1$, for a predetermined positive integer m. It has been widely used to analyze physiological signals. As computing sample entropy is time consuming, the box-assisted, bucket-assisted, x-sort, assisted sliding box, and kd-tree-based algorithms were proposed to accelerate its computation. These algorithms require $O\left(N^2\right)$ or $O\left(N^{2-\frac{1}{m+1}}\right)$ computational complexity, where N is the length of the time series analyzed. When N is big, the computational costs of these algorithms are large. We propose a super fast algorithm to estimate sample entropy based on Monte Carlo, with computational costs independent of N (the length of the time series) and the estimation converging to the exact sample entropy as the number of repeating experiments becomes large. The convergence rate of the algorithm is also established. Numerical experiments are performed for electrocardiogram time series, electroencephalogram time series, cardiac inter-beat time series, mechanical vibration signals (MVS), meteorological data (MD), and $1/f$ noise. Numerical results show that the proposed algorithm can gain 100–1000 times speedup compared to the kd-tree and assisted sliding box algorithms while providing satisfactory approximate accuracy.

Existing single-photon emission computed tomography (SPECT) reconstruction methods are mostly based on discrete models that may be viewed as piecewise constant approximations of a continuous data acquisition process. Due to low accuracy order of piecewise constant approximations, a traditional discrete model introduces irreducible model errors which are a bottleneck of the quality improvement of reconstructed images in clinical applications. To overcome this drawback, we develop a higher-order polynomial method for SPECT reconstruction. Specifically, we represent the data acquisition of SPECT imaging by using an integral equation model, approximate the solution of the underlying integral equation by higher-order piecewise polynomials leading to a new discrete system and introduce two novel regularizers for the system, by exploring the a priori knowledge of the radiotracer distribution, suitable for the approximation. The proposed higher-order polynomial method outperforms significantly the cutting edge reconstruction method based on a traditional discrete model in terms of model error reduction, noise suppression, and artifact reduction. In particular, the coefficient of variation of images reconstructed by the piecewise linear polynomial method is reduced by a factor of 10 in comparison to that of a traditional discrete model-based method.

Network embedding, which is the task of learning low-dimensional representations of vertices, has attracted increasing attention recently. Evidences have been found that the hidden metric space of many realistic complex networks is hyperbolic. The topology and weight emerge naturally as reflections of the hyperbolic metric property. A common objective of hyperbolic embedding is to maximize the likelihood function of the hyperbolic network model. The difficulty is that the likelihood function is non-concave which is difficult to optimize. In this paper, we propose a hyperbolic embedding method for weighted networks. To prevent the optimization from falling into numerous local optima, initial embedding is obtained by approximation. A proposed gradient algorithm then improves the embedding according to the likelihood function. Experiments on synthetic and real networks show that the proposed method achieves good embedding performance with respect to different quality metrics and applications.

Sjögren’s Syndrome (SS) is a rare chronic autoimmune disorder primarily affecting adult females, characterized by chronic inflammation and salivary and lacrimal gland dysfunction. It is often associated with systemic lupus erythematosus, rheumatoid arthritis and kidney disease, which can lead to increased mortality. Early diagnosis is critical, but traditional methods for diagnosing SS, mainly through histopathological evaluation of salivary gland tissue, have limitations. The study used 100 labial gland biopsy, creating whole-slide images (WSIs) for analysis. The proposed model, named Cell-tissue-graph-based pathological image analysis model (CTG-PAM) and based on graph theory, characterizes single-cell feature, cell-cell feature, and cell-tissue feature. Building upon these features, CTG-PAM achieves cellular-level classification, enabling lymphocyte recognition. Furthermore, it leverages connected component analysis techniques in the cell graph structure to perform SS diagnosis based on lymphocyte counts. CTG-PAM outperforms traditional deep learning methods in diagnosing SS. Its area under the receiver operating characteristic curve (AUC) is 1.0 for the internal validation dataset and 0.8035 for the external test dataset. This indicates high accuracy. The sensitivity of CTG-PAM for the external dataset is 98.21%, while the accuracy is 93.75%. In comparison, the sensitivity and accuracy for traditional deep learning methods (ResNet-50) are lower. The study also shows that CTG-PAM’s diagnostic accuracy is closer to skilled pathologists compared to beginners. Our findings indicate that CTG-PAM is a reliable method for diagnosing SS. Additionally, CTG-PAM shows promise in enhancing the prognosis of SS patients and holds significant potential for the differential diagnosis of both non-neoplastic and neoplastic diseases. The AI model potentially extends its application to diagnosing immune cells in tumor microenvironments.