Evaluation of left ventricular blood flow kinetic energy in patients with acute myocardial infarction by 4D Flow MRI: a preliminary study.

PURPOSE: To evaluate the intracavity left ventricular (LV) blood flow kinetic energy (KE) parameters using four-dimensional (4D) flow cardiovascular magnetic resonance (CMR) in patients with acute myocardial infarction (AMI). METHODS: Thirty AMI patients and twenty controls were examined via CMR, which included cine imaging, late gadolinium enhancement (LGE) and global heart 4D flow imaging. The KE parameters were indexed to LV end-diastolic volume (EDV) to obtain average, systolic and diastolic KE as well as the proportion of LV in-plane KE (%). These parameters were compared between the AMI patients and controls and between the two subgroups. RESULTS: Analysis of the LV blood flow KE parameters at different levels of the LV cavity and in different segments of the same level showed that the basal level had the highest blood flow KE while the apical level had the lowest in the control group. There were no significant differences in diastolic KE, systolic in-plane KE and diastolic in-plane KE between the anterior wall and posterior wall (p > 0.05), only the systolic KE had a significant difference between them (p < 0.05). Compared with those in the control group, the average (10.7 ± 3.3 µJ/mL vs. 14.7 ± 3.6 µJ/mL, p < 0.001), systolic (14.6 ± 5.1 µJ/mL vs. 18.9 ± 3.9 µJ/mL, p = 0.003) and diastolic KE (7.9 ± 2.5 µJ/mL vs. 10.6 ± 3.8 µJ/mL, p = 0.018) were significantly lower in the AMI group. The average KE in the infarct segment was lower than that in the noninfarct segment in the AMI group (49.5 ± 18.7 µJ/mL vs. 126.3 ± 50.7 µJ/mL, p < 0.001), while the proportion of systolic in-plane KE increased significantly (61.8%±11.5 vs. 42.9%±14.4, p = 0.001). CONCLUSION: The 4D Flow MRI technique can be used to quantitatively evaluate LV regional hemodynamic parameters. There were differences in the KE parameters of LV blood flow at different levels and in different segments of the same level in healthy people. In AMI patients, the average KE of the infarct segment decreased, while the proportion of systolic in-plane KE significantly increased.

Influence of cultivation substrate on antioxidant activities and triterpenoid profiles of the fruiting body of Ganoderma lucidum.

Introduction: The fruiting body of Ganoderma lucidum has been believed to possess a wide range of therapeutic effects. There are two main methods for artificial cultivation of G. lucidum to produce the fruiting body, namely wood log cultivation and substitute cultivation. The impact of cultivation substrates on the composition of bioactive compounds remains largely unexplored. This study aims to compare the antioxidant activities and triterpenoid profiles of the fruiting bodies of G. lucidum that cultivated through wood log cultivation (WGL) and substitute cultivation (SGL) methods. Methods: The antioxidant activities, including the DPPH radical scavenging, hydroxyl radical scavenging, superoxide radical scavenging, and total antioxidant activities, were assessed in both WGL and SGL samples. Furthermore, the UHPLC-Q-Orbitrap-MS technique was employed to compare their phytochemical profiles, with a specific emphasis on triterpenoid constituents. Results and discussion: It was found that WGL samples exhibited significantly higher total triterpenoid content, DPPH radical scavenging activity, and total antioxidant activity. Furthermore, an untargeted metabolomics approach employing UHPLC-Q-Orbitrap-MS tentatively identified a total of 96 triterpenoids. Distinguishingly different triterpenoid profiles between the two types of G. lucidum samples were revealed via the utilization of principal component analysis (PCA) and hierarchical cluster analysis (HCA). Specifically, 17 triterpenoids showed significant differences. Of these triterpenoids, 6 compounds, such as ganosporelactone B, ganoderol A, ganoderic acid A, ganoderic acid alpha, were significantly higher in SGL samples; 11 compounds, such as lucidenic acid A, lucidenic acid D1, lucidenic acid F, lucidenic acid G, lucidenic acid J, ganoderic acid E, and ganoderic acid O, were significantly higher in WGL samples. These findings expand our knowledge regarding the impact of cultivation substrate on the antioxidant activities and triterpenoid profiles of G. lucidum, and offer practical implications for its cultivation.

Effects of oregano essential oil Pickering emulsion and ZnO nanoparticles on the properties and antibacterial activity of konjac glucomannan/carboxymethyl chitosan nanocomposite films.

Green and environmentally friendly natural bio-based food packaging films are increasingly favored by consumers. This study incorporated carboxylated-cellulose nanocrystal stabilized oregano essential oil (OEO) Pickering emulsion and ZnO nanoparticles (ZNPs) into konjac glucomannan (KGM)/carboxymethyl chitosan (CMCS) complexes to develop active food packaging films. The effects of OEO Pickering emulsion and ZNPs on the physical, structural, and antimicrobial activities of the nanocomposite films were evaluated. The OEO Pickering emulsion had a droplet size of 48.43 ± 3.56 µm and showed excellent dispersion and stability. Fourier transform infrared and X-ray diffraction analyses suggested that the interactions between the Pickering emulsion, ZNPs and KGM/CMCS matrix were mainly through hydrogen bonding. SEM observations confirmed that the Pickering emulsion and ZNPs were well incorporated into the KGM/CMCS matrix, forming tiny pores within the nanocomposite films. The incorporation of the OEO Pickering emulsion and/or ZNPs obviously increased the light and water vapor barrier ability, thermal stability, mechanical strength and antimicrobial properties of the KGM/CMCS nanocomposite film. Notably, KGM/CMCS/ZNPs/OEO Pickering emulsion films exhibited the highest barrier, and mechanical and antimicrobial activities due to the synergistic effect between the OEO Pickering emulsion and ZNPs. These results suggest that KGM/CMCS/ZNPs/OEO Pickering emulsion films can be utilized as novel active food packaging materials to extend the shelf life of packaged foods.

Efficacy and safety of aspirin antiplatelet therapy within 48 h of symptom onset in patients with acute stroke.

BACKGROUND: Aspirin is a widely used antiplatelet agent that reduces the risk of recurrent ischemic stroke and other vascular events. However, the optimal timing and dose of aspirin initiation after an acute stroke remain controversial. AIM: To evaluate the efficacy and safety of aspirin antiplatelet therapy within 48 h of symptom onset in patients with acute stroke. METHODS: We conducted a randomized, open-label, controlled trial in 60 patients with acute ischemic or hemorrhagic stroke who were admitted to our hospital within 24 h of symptom onset. Patients were randomly assigned to receive either aspirin 300 mg daily or no aspirin within 48 h of stroke onset. The primary outcome was the occurrence of recurrent stroke, myocardial infarction, or vascular death within 90 d. The secondary outcomes were functional outcomes at 90 d measured using the modified Rankin Scale (mRS), incidence of bleeding complications, and mortality rate. RESULTS: The mean age of the patients was 67.8 years and 55% of them were male. The median time from stroke onset to randomization was 12 h. The baseline characteristics were well balanced between the two groups. The primary outcome occurred in 6.7% of patients in the aspirin group and 16.7% of patients in the no aspirin group (relative risk = 0.40, 95% confidence interval: 0.12-1.31, P = 0.13). The mRS score at 90 d was significantly lower in the aspirin group than in the no aspirin group (median, 2 vs 3, respectively; P = 0.04). The incidence of bleeding complications was similar between the groups (6.7% vs 6.7%, P = 1.00). The mortality rates were also comparable between the two groups (10% vs 13.3%, P = 0.69). CONCLUSION: Aspirin use is associated with favorable functional outcomes but does not significantly reduce the risk of recurrent vascular events. Its acceptable safety profile is comparable to that of no aspirin. Further studies with larger sample sizes and longer follow-up periods are needed to confirm these findings.

Temporal transformer-spatial graph convolutional network: an intelligent classification model for anti N-methyl-D-aspartate receptor encephalitis based on electroencephalogram signal.

Encephalitis is a disease typically caused by viral infections or autoimmunity. The most common type of autoimmune encephalitis is anti-N-methyl-D-aspartate receptor (NMDAR) antibody-mediated, known as anti-NMDA receptor encephalitis, which is a rare disease. Specific EEG patterns, including "extreme delta brush" (EDB), have been reported in patients with anti-NMDA receptor encephalitis. The aim of this study was to develop an intelligent diagnostic model for encephalitis based on EEG signals. A total of 131 Participants were selected based on reasonable inclusion criteria and divided into three groups: health control (35 participants), viral encephalitis (58 participants), and anti NMDAR receptor encephalitis (55 participants). Due to the low prevalence of anti-NMDAR receptor encephalitis, it took several years to collect participants' EEG signals while they were in an awake state. EEG signals were collected and analyzed following the international 10-20 system layout. We proposed a model called Temporal Transformer-Spatial Graph Convolutional Network (TT-SGCN), which consists of a Preprocess Module, a Temporal Transformer Module (TTM), and a Spatial Graph Convolutional Module (SGCM). The raw EEG signal was preprocessed according to traditional procedures, including filtering, averaging, and Independent Component Analysis (ICA) method. The EEG signal was then segmented and transformed using short-time Fourier transform (STFT) to produce concatenated power density (CPD) maps, which served as inputs for the proposed model. TTM extracted the time-frequency features of each channel, and SGCM fused these features using graph convolutional methods based on the location of electrodes. The model was evaluated in two experiments: classification of the three groups and pairwise classification among the three groups. The model was trained using two stages and achieved the performance, with an accuracy of 82.23%, recall of 80.75%, precision of 82.51%, and F1 score of 81.23% in the classification of the three groups. The proposed model has the potential to become an intelligent auxiliary diagnostic tool for encephalitis.

Gradient Learning under Tilted Empirical Risk Minimization.

Gradient Learning (GL), aiming to estimate the gradient of target function, has attracted much attention in variable selection problems due to its mild structure requirements and wide applicability. Despite rapid progress, the majority of the existing GL works are based on the empirical risk minimization (ERM) principle, which may face the degraded performance under complex data environment, e.g., non-Gaussian noise. To alleviate this sensitiveness, we propose a new GL model with the help of the tilted ERM criterion, and establish its theoretical support from the function approximation viewpoint. Specifically, the operator approximation technique plays the crucial role in our analysis. To solve the proposed learning objective, a gradient descent method is proposed, and the convergence analysis is provided. Finally, simulated experimental results validate the effectiveness of our approach when the input variables are correlated.

Highly expressed centromere protein L indicates adverse survival and associates with immune infiltration in hepatocellular carcinoma.

BACKGROUND: Hepatocellular carcinoma (HCC) is characterized by rapid progression, high recurrence rate and poor prognosis. Early prediction for the prognosis and immunotherapy efficacy is of great significance to improve the survival of HCC patients. However, there is still no reliable predictor at present. This study is aimed to explore the role of centromere protein L (CENPL) in predicting prognosis and its association with immune infiltration in HCC. METHODS: The expression of CENPL was identified through analyzing the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) data. The association between CENPL expression and clinicopathological features was investigated by the Wilcoxon signed-rank test or Kruskal Wallis test and logistic regression. The role of CENPL in prognosis was examined via Kaplan-Meier method and Log-rank test as well as univariate and multivariate Cox regression analysis. Besides, in TIMER and GEPIA database, we investigated the correlation between CENPL level and immunocyte and immunocyte markers, and the prognostic-related methylation sites in CENPL were identified by MethSurv. RESULTS: CENPL had a high expression in HCC samples. Increased CENPL was prominently associated with unfavorable survival, and maybe an independent prognostic factor of worse overall survival (OS), disease-specific survival (DSS), disease-free interval (DFI), progression-free interval (PFI). Additionally, CENPL expression was significantly correlated with immune cell infiltration and some markers. CENPL also contained a methylation site that was notably related to poor prognosis. CONCLUSIONS: Elevated CENPL may be a promising prognostic marker and associate with immune infiltration in HCC.

Error Bound of Mode-Based Additive Models.

Due to their flexibility and interpretability, additive models are powerful tools for high-dimensional mean regression and variable selection. However, the least-squares loss-based mean regression models suffer from sensitivity to non-Gaussian noises, and there is also a need to improve the model's robustness. This paper considers the estimation and variable selection via modal regression in reproducing kernel Hilbert spaces (RKHSs). Based on the mode-induced metric and two-fold Lasso-type regularizer, we proposed a sparse modal regression algorithm and gave the excess generalization error. The experimental results demonstrated the effectiveness of the proposed model.

A Training Data Set Cleaning Method by Classification Ability Ranking for the k -Nearest Neighbor Classifier.

The k -nearest neighbor (KNN) rule is a successful technique in pattern classification due to its simplicity and effectiveness. As a supervised classifier, KNN classification performance usually suffers from low-quality samples in the training data set. Thus, training data set cleaning (TDC) methods are needed for enhancing the classification accuracy by cleaning out noisy, or even wrong, samples in the original training data set. In this paper, we propose a classification ability ranking (CAR)-based TDC method to improve the performance of a KNN classifier, namely CAR-based TDC method. The proposed classification ability function ranks a training sample in terms of its contribution to correctly classify other training samples as a KNN through the leave-one-out (LV1) strategy in the cleaning stage. The training sample that likely misclassifies the other samples during the KNN classifications according to the LV1 strategy is considered to have lower classification ability and will be cleaned out from the original training data set. Extensive experiments, based on ten real-world data sets, show that the proposed CAR-based TDC method can significantly reduce the classification error rates of KNN-based classifiers, while reducing computational complexity thanks to a smaller cleaned training data set.

Correlation-driven direct sampling method for geostatistical simulation and training image evaluation.

Multiple-point geostatistics (MPS) is a competitive algorithm that produces a set of geologically realistic models. Viewing a training image (TI) as a prior model, MPS extracts patterns from the TI and reproduces patterns which are compatible with the hard data (HD). However, two challenges within the MPS framework are the geologically complex simulation and the TI evaluation. With the objective to achieve a high-quality simulation, we explore a way to address these two issues. First, correlation-driven direct sampling (CDS) is proposed to realize geostatistical simulation. We introduce the correlation-driven distance as a measure of similarity between two patterns. The weights in our distance measurement are derived by the concepts of the ellipse, correlation coefficient, Gaussian function, and affine transformation. Second, we fulfill TI evaluation on the basis of the consistency between TI and HD. Inspired by CDS, the minimum correlation-driven distance (MCD) is proposed to improve the evaluation accuracy. We suggest a conditioning pattern extraction history strategy to speed up the evaluation program. Third, the local consistency is presented to address nonstationarity. The program automatically divides the simulation domain into several subareas. A two-dimensional (2D) channelized reservoir image and a three-dimensional (3D) rock image are used to validate our proposed method. In comparison with previous methods, CDS yields better simulation quality. The further applications include a set of 2D TI evaluations and a 3D simulation domain segmentation. MCD exhibits sensible evaluation accuracy, excellent computational efficiency, and the ability to deal with nonstationarity.

[Clinical research of auricular gold-needle therapy in treatment of chronic fatigue syndrome of qi deficiency constitution].

OBJECTIVE: To observe the clinical therapeutic effects of auricular gold-needle therapy on chronic fatigue syndrome of qi deficiency constitution and explore its potential mechanism. METHODS: A total of 120 patients were randomized into an auricular gold-needle therapy group, an auricular point pressure therapy group and a Chinese herb group, 40 cases in each one. Additionally, a health control group (40 cases) was set up, without any intervention. In the auricular gold-needle therapy group, the gold needle was used to stimulate the auricular points on one side and the cowherb seed pressure therapy on the other side. In the auricular point pressure therapy group, the cowherb seed pressure therapy was adopted only on one side. The auricular points were shen (CO10), xin (CO15), fei (CO14), pizhixia (AT4), etc. in the two groups. The auricular points on both sides were used alternatively. The treatment was given once a week, 4 treatments as one course and the consecutive 3 courses of treatment were required. In the Chinese herb group, buzhong yiqi wan was prescribed for oral administration, 6 g, twice a day, the medication for 1 month was as one session and the consecutive 3 sessions of medication were required. Before and after treatment, separately, the clinical symptom score, the levels of the serum immunoglobulins, i.e. IgA, IgG and IgM were observed in the patients of the three groups. The therapeutic effects were evaluated in the three groups. RESULTS: The total effective rate was 90.0% (36/40) in the auricular gold-needle therapy group, better than 80.0% (32/40) in the auricular point pressure therapy group and 82.5% (33/40) in the Chinese herb group (both P<0.05). Before treatment, the clinical symptom scores of the patients in the three groups were obviously higher than the health control group (all P<0.001). After treatment, the symptom scores were all reduced as compared the scores before treatment in the three groups (all P<0.001) and the symptom scores in the auricular gold-needle therapy group were better than the auricular point pressure therapy group and the Chinese herb group (both P<0.01). Before treatment, the levels of serum IgA, IgG and IgM of the patients in the three groups were lower than the health control group (all P<0.001). The levels were all improved after treatment in the three groups (all P<0.01), and the levels in the auricular gold-needle therapy group was better than the auricular point pressure therapy group and the Chinese herb group (all P<0.05). CONCLUSION: The auricular gold-needle therapy achieves the significant therapeutic effects on chronic fatigue syndrome of qi deficiency constitution and its mechanism is probably related to the regulation of immune function.

Compressive sensing image recovery using dictionary learning and shape-adaptive DCT thresholding.

Compressed sensing (CS) has shown to be a successful technique for image recovery. Designing an effective regularization term reflecting the image sparse prior information plays a critical role in this field. Dictionary learning (DL) strategy alleviates the drawback of fixed bases. But the structure information of the image is easy to be blurred in complex regions due to the absence of sparsity in dictionary learning. This paper proposes a novel joint dictionary learning and Shape-Adaptive DCT (SADCT) thresholding method. We first propose to exploit sparsity of image in shape-adaptive regions, which is beneficial to medical images of complex textures. In this framework, the local sparsity depicts the smoothness redundancies exploited by dictionary learning. Moreover, the sparsity is enhanced especially in detail areas by the newly introduced SADCT thresholding. The attenuated SADCT coefficients are used to reconstruct a local estimation of the signal within the adaptive-shape support. Image is represented sparser in SADCT transform domain and the details of the image information can be kept with a much larger probability. Based on split Bregman iterations, an efficient alternating minimization algorithm is developed to solve the proposed CS medical image recovery problem. The results of various experiments on MR images consistently demonstrate that the proposed algorithm efficiently recovers MR images and shows advantages over the current leading CS reconstruction approaches.

Accelerating simulation for the multiple-point statistics algorithm using vector quantization.

Multiple-point statistics (MPS) is a prominent algorithm to simulate categorical variables based on a sequential simulation procedure. Assuming training images (TIs) as prior conceptual models, MPS extracts patterns from TIs using a template and records their occurrences in a database. However, complex patterns increase the size of the database and require considerable time to retrieve the desired elements. In order to speed up simulation and improve simulation quality over state-of-the-art MPS methods, we propose an accelerating simulation for MPS using vector quantization (VQ), called VQ-MPS. First, a variable representation is presented to make categorical variables applicable for vector quantization. Second, we adopt a tree-structured VQ to compress the database so that stationary simulations are realized. Finally, a transformed template and classified VQ are used to address nonstationarity. A two-dimensional (2D) stationary channelized reservoir image is used to validate the proposed VQ-MPS. In comparison with several existing MPS programs, our method exhibits significantly better performance in terms of computational time, pattern reproductions, and spatial uncertainty. Further demonstrations consist of a 2D four facies simulation, two 2D nonstationary channel simulations, and a three-dimensional (3D) rock simulation. The results reveal that our proposed method is also capable of solving multifacies, nonstationarity, and 3D simulations based on 2D TIs.

Generalization Performance of Regularized Ranking With Multiscale Kernels.

The regularized kernel method for the ranking problem has attracted increasing attentions in machine learning. The previous regularized ranking algorithms are usually based on reproducing kernel Hilbert spaces with a single kernel. In this paper, we go beyond this framework by investigating the generalization performance of the regularized ranking with multiscale kernels. A novel ranking algorithm with multiscale kernels is proposed and its representer theorem is proved. We establish the upper bound of the generalization error in terms of the complexity of hypothesis spaces. It shows that the multiscale ranking algorithm can achieve satisfactory learning rates under mild conditions. Experiments demonstrate the effectiveness of the proposed method for drug discovery and recommendation tasks.

Texture Classification Using Local Pattern Based on Vector Quantization.

Local binary pattern (LBP) is a simple and effective descriptor for texture classification. However, it has two main disadvantages: (1) different structural patterns sometimes have the same binary code and (2) it is sensitive to noise. In order to overcome these disadvantages, we propose a new local descriptor named local vector quantization pattern (LVQP). In LVQP, different kinds of texture images are chosen to train a local pattern codebook, where each different structural pattern is described by a unique codeword index. Contrarily to the original LBP and its many variants, LVQP does not quantize each neighborhood pixel separately to 0/1, but aims at quantizing the whole difference vector between the central pixel and its neighborhood pixels. Since LVQP deals with the structural pattern as a whole, it has a high discriminability and is less sensitive to noise. Our experimental results, achieved by using four representative texture databases of Outex, UIUC, CUReT, and Brodatz, show that the proposed LVQP method can improve classification accuracy significantly and is more robust to noise.

Extreme learning machine for ranking: generalization analysis and applications.

The extreme learning machine (ELM) has attracted increasing attention recently with its successful applications in classification and regression. In this paper, we investigate the generalization performance of ELM-based ranking. A new regularized ranking algorithm is proposed based on the combinations of activation functions in ELM. The generalization analysis is established for the ELM-based ranking (ELMRank) in terms of the covering numbers of hypothesis space. Empirical results on the benchmark datasets show the competitive performance of the ELMRank over the state-of-the-art ranking methods.

Convergence rate of the semi-supervised greedy algorithm.

This paper proposes a new greedy algorithm combining the semi-supervised learning and the sparse representation with the data-dependent hypothesis spaces. The proposed greedy algorithm is able to use a small portion of the labeled and unlabeled data to represent the target function, and to efficiently reduce the computational burden of the semi-supervised learning. We establish the estimation of the generalization error based on the empirical covering numbers. A detailed analysis shows that the error has O(n(-1)) decay. Our theoretical result illustrates that the unlabeled data is useful to improve the learning performance under mild conditions.

Error analysis of coefficient-based regularized algorithm for density-level detection.

In this letter, we consider a density-level detection (DLD) problem by a coefficient-based classification framework with [Formula: see text]-regularizer and data-dependent hypothesis spaces. Although the data-dependent characteristic of the algorithm provides flexibility and adaptivity for DLD, it leads to difficulty in generalization error analysis. To overcome this difficulty, an error decomposition is introduced from an established classification framework. On the basis of this decomposition, the estimate of the learning rate is obtained by using Rademacher average and stepping-stone techniques. In particular, the estimate is independent of the capacity assumption used in the previous literature.