A dynamic learning-based ECG feature extraction method for myocardial infarction detection.

Objective.Myocardial infarction (MI) is one of the leading causes of human mortality in all cardiovascular diseases globally. Currently, the 12-lead electrocardiogram (ECG) is widely used as a first-line diagnostic tool for MI. However, visual inspection of pathological ECG variations induced by MI remains a great challenge for cardiologists, since pathological changes are usually complex and slight.Approach.To have an accuracy of the MI detection, the prominent features extracted from in-depth mining of ECG signals need to be explored. In this study, a dynamic learning algorithm is applied to discover prominent features for identifying MI patients via mining the hidden inherent dynamics in ECG signals. Firstly, the distinctive dynamic features extracted from the multi-scale decomposition of dynamic modeling of the ECG signals effectively and comprehensibly represent the pathological ECG changes. Secondly, a few most important dynamic features are filtered through a hybrid feature selection algorithm based on filter and wrapper to form a representative reduced feature set. Finally, different classifiers based on the reduced feature set are trained and tested on the public PTB dataset and an independent clinical data set.Main results.Our proposed method achieves a significant improvement in detecting MI patients under the inter-patient paradigm, with an accuracy of 94.75%, sensitivity of 94.18%, and specificity of 96.33% on the PTB dataset. Furthermore, classifiers trained on PTB are verified on the test data set collected from 200 patients, yielding a maximum accuracy of 84.96%, sensitivity of 85.04%, and specificity of 84.80%.Significance.The experimental results demonstrate that our method performs distinctive dynamic feature extraction and may be used as an effective auxiliary tool to diagnose MI patients.

Observer-based dynamical pattern recognition via deterministic learning.

In this paper, based on the sampled-data observer and the deterministic learning theory, a rapid dynamical pattern recognition approach is proposed for univariate time series composed of the output signals of the dynamical systems. Specifically, locally-accurate identification of inherent dynamics of univariate time series is first achieved by using the sampled-data observer and the radial basis function (RBF) networks. The dynamical estimators embedded with the learned knowledge are then designed by resorting to the sampled-data observer. It is proved that generated estimator residuals can reflect the difference between the system dynamics of the training and test univariate time series. Finally, a recognition decision-making scheme is proposed based on the residual norms of the dynamical estimators. Through rigorous analysis, recognition conditions are given to guarantee the accurate recognition of the dynamical pattern of the test univariate time series. The significance of this paper lies in that the difficult problems of dynamical modeling and rapid recognition for univariate time series are solved by incorporating the sampled-data observer design and the deterministic learning theory. The effectiveness of the proposed approach is confirmed by a numerical example and compressor stall warning experiments.

Early detection of myocardial ischemia in 12-lead ECG using deterministic learning and ensemble learning.

BACKGROUND AND OBJECTIVE: Early detection of myocardial ischemia is a necessary but difficult problem in cardiovascular diseases. Approaches that exclusively rely on classical ST and T wave changes on the standard 12-lead electrocardiogram (ECG) lack sufficient accuracy in detecting myocardial ischemia. This study aims to construct generalizable models for the detection of myocardial ischemia in patients with subtle ECG waveform changes (namely non-diagnostic ECG) using ensemble learning to integrate ECG dynamic features acquired via deterministic learning. METHODS: First, cardiodynamicsgram (CDG), a noninvasive spatiotemporal electrocardiographic method, is generated through dynamic modeling of ECG signals using the deterministic learning algorithm. Then, the spectral fitting exponent, Lyapunov exponent, and Lempel-Ziv complexity are extracted from CDG. Subsequently, the bagging-based heterogeneous ensemble algorithm is applied on CDG features to generate diverse base classifiers and aggregate them with weighted voting to obtain an ensemble model for myocardial ischemia detection. Finally, we train and test the proposed heterogeneous ensemble model on a real-world clinical dataset. This dataset consists of 499 non-diagnostic 12-lead ECG records from 499 patients collected from three independent medical centers, including 383 patients with myocardial ischemia and 116 patients without ischemia. RESULTS: With 10-times 5-fold cross-validation technology, our proposed method achieves an average accuracy of 89.10%, sensitivity of 91.72%, and specificity of 82.69% using the heterogeneous ensemble algorithm on the real-world clinical dataset. On three independent medical centers, our ensemble model also achieves accuracy performance over 82% for patients with non-diagnostic ECG. Furthermore, our ensemble model trained with real-world clinical data yields promising results of 91.11% accuracy, 90.49% sensitivity, and 92.88% specificity on the external test set of the public PTB dataset. CONCLUSION: The experimental results demonstrate that the proposed model combining ensemble learning and deterministic learning presents excellent diagnostic accuracy and generalization in clinical practice, and could be implemented as a complement to the standard ECG in the clinical diagnosis of myocardial ischemia.

PHOSPHO1 Serves as a Key Metabolism-Related Biomarker in the Tumorigenesis of Diffuse Large B-cell Lymphoma.

OBJECTIVE: Diffuse large B-cell lymphoma (DLBCL) is an aggressive type of non-Hodgkin lymphoma. Due to its genetic heterogeneity and abnormal metabolism, many DLBCL patients have a poor prognosis. This study investigated the key metabolism-related genes and potential mechanisms. METHODS: Differentially expressed genes, differentially expressed transcription factors (TFs), and differentially expressed metabolism-related genes (DEMRGs) of glucose and lipid metabolic processes were identified using the edgeR package. Key DEMRGs were screened by Lasso regression, and a prediction model was constructed. The cell type identification by estimating relative subsets of RNA transcripts algorithm was utilized to assess the fraction of immune cells, and Gene Set Enrichment Analysis was used to determine immune-related pathways. A regulatory network was constructed with significant co-expression interactions among TFs, DEMRGs, immune cells/pathways, and hallmark pathways. RESULTS: A total of 1551 DEMRGs were identified. A prognostic model with a high applicability (area under the curve=0.921) was constructed with 13 DEMRGs. Tumorigenesis of DLBCL was highly related to the neutrophil count. Four DEMRGs (PRXL2AB, CCN1, DECR2 and PHOSPHO1) with 32 TF-DEMRG, 36 DEMRG-pathway, 14 DEMRG-immune-cell, 9 DEMRG-immune-gene-set, and 67 DEMRG-protein-chip interactions were used to construct the regulatory network. CONCLUSION: We provided a prognostic prediction model based on 13 DEMRGs for DLBCL. We found that phosphatase, orphan 1 (PHOSPHO1) is positively regulated by regulatory factor X5 (RFX5) and mediates MYC proto-oncogene (MYC) targeting the V2 pathway and neutrophils.

Erratum: Metformin induces cell cycle arrest, apoptosis and autophagy through ROS/JNK signaling pathway in human osteosarcoma: Erratum.

[This corrects the article DOI: 10.7150/ijbs.33787.].

A Novel Multitasking Ant Colony Optimization Method for Detecting Multiorder SNP Interactions.

MOTIVATION: Linear or nonlinear interactions of multiple single-nucleotide polymorphisms (SNPs) play an important role in understanding the genetic basis of complex human diseases. However, combinatorial analytics in high-dimensional space makes it extremely challenging to detect multiorder SNP interactions. Most classic approaches can only perform one task (for detecting k-order SNP interactions) in each run. Since prior knowledge of a complex disease is usually not available, it is difficult to determine the value of k for detecting k-order SNP interactions. METHODS: A novel multitasking ant colony optimization algorithm (named MTACO-DMSI) is proposed to detect multiorder SNP interactions, and it is divided into two stages: searching and testing. In the searching stage, multiple multiorder SNP interaction detection tasks (from 2nd-order to kth-order) are executed in parallel, and two subpopulations that separately adopt the Bayesian network-based K2-score and Jensen-Shannon divergence (JS-score) as evaluation criteria are generated for each task to improve the global search capability and the discrimination ability for various disease models. In the testing stage, the G test statistical test is adopted to further verify the authenticity of candidate solutions to reduce the error rate. RESULT: Three multiorder simulated disease models with different interaction effects and three real age-related macular degeneration (AMD), rheumatoid arthritis (RA) and type 1 diabetes (T1D) datasets were used to investigate the performance of the proposed MTACO-DMSI. The experimental results show that the MTACO-DMSI has a faster search speed and higher discriminatory power for diverse simulation disease models than traditional single-task algorithms. The results on real AMD data and RA and T1D datasets indicate that MTACO-DMSI has the ability to detect multiorder SNP interactions at a genome-wide scale. Availability and implementation: https://github.com/shouhengtuo/MTACO-DMSI/.

Fault Identification for a Class of Nonlinear Systems of Canonical Form via Deterministic Learning.

In this article, through a combination of the deterministic learning (DL) method and the adaptive high gain observer (AHGO) technology, a fault identification approach for a class of nonlinear systems in canonical form is proposed. By using the DL method, the partial persistent excitation condition of the identification system is satisfied, and then, the AHGO technology is exploited to estimate the states and the neural network weights simultaneously. To analyze the convergence of the proposed method, we first analyze the uniformed completely observability (UCO) property of the linear part of the nonlinear identification system. Then, by using the Lipschitz property of the nonlinear item and the Bellman-Gronwall lemma, we show that the UCO property of the nonlinear identification system is depended on the UCO property of the linear part when the observer gain is chosen large. Therefore, by using the UCO property of the nonlinear identification system and the Lyapunov stability theorem, the convergence of the proposed learning observer is proven. The attraction of this article is based on the analysis of the UCO property of the identification system, and the convergence of the proposed learning observer can be directly proven. The simulation example is given to demonstrate the effectiveness of the proposed method.

Rapid Sensor Fault Diagnosis for a Class of Nonlinear Systems via Deterministic Learning.

In this article, a rapid sensor fault diagnosis (SFD) method is presented for a class of nonlinear systems. First, by exploiting the linear adaptive observer technology and the deterministic learning method (DLM), an adaptive neural network (NN) observer is constructed to capture the information of the unknown sensor fault function. Second, when the NN input orbit is a period or recurrent one, the partial persistent excitation (PE) condition of the NNs can be guaranteed through the DLM. Based on the partial PE condition and the uniformly completely observable property of a linear time-varying system, the accurate state estimation and the sensor fault identification can be achieved by properly choosing the observer gain. Third, a bank of dynamical observers utilizing the experiential knowledge is constructed to achieve rapid SFD and data recovery. The attractions of the proposed approach are that accurate approximations of sensor faults can be achieved through the DLM, and the data that are destroyed by the sensor faults can be recovered by using the learning results. Simulation studies of a robot system are utilized to show the effectiveness of the proposed method.

Synergistic Effect of Statins and Abiraterone Acetate on the Growth Inhibition of Neuroblastoma via Targeting Androgen Receptor.

Neuroblastoma is the most common extracranial neuroendocrine tumor in childhood. Although many studies have tried to find effective treatments, there are still numerous limitations in current clinical targeted therapy. So, it is important to find new therapeutic targets and strategies from a new perspective. Our previous study reported that the androgen receptor (AR) promotes the growth of neuroblastoma in vitro and in vivo. Based on documentary investigation, we postulated that the AR-SCAP-SREBPs-CYP17/HMGCR axis may regulate cholesterol and androgens synthesis and form a positive enhancement loop promoting NB progression. Clinical samples and Oncomine database analysis proved the activation of AR-SCAP-SREBPs-CYP17/HMGCR axis in neuroblastoma. The combination of inhibitors of HMGCR (statins) and CYP17A1 (abiraterone acetate) showed synergistic effect that significantly inhibited the proliferation and migration with decreased expression of related genes detected in vitro and in vivo suggesting the dual-targeted therapy had the potential to inhibit the progression of neuroblastoma in spite of its MYCN status. This study provides new ideas for clinical treatment of neuroblastoma with efficacy and reduced toxicity.

The neuroprotection of deproteinized calf blood extractives injection against Alzheimer's disease via regulation of Nrf-2 signaling.

Alzheimer's disease (AD) is characterized by cognitive decline due to the accumulation of extracellular ß-amyloid (Aß) plaques and neurofibrillary tangles in the brain, which impair glutamate (Glu) metabolism. Deproteinized Calf Blood Extractive Injection (DCBEI) is a biopharmaceutical that contains 17 types of amino acids and 5 types of nucleotides. In this study, we found that DCBEI pretreatment reduced L-Glu-dependent neuroexcitation toxicity by maintaining normal mitochondrial function in HT22 cells. DCBEI treatment also reduced the expression of pro-apoptosis proteins and increased the expression of anti-apoptosis proteins. Furthermore, DCBEI attenuated AD-like behaviors (detected via the Morris water maze test) in B6C3-Tg (APPswePSEN1dE9)/Nju double transgenic (APP/PS1) mice; this effect was associated with a reduction in the amount of Aß and neurofibrillary tangle deposition and the concomitant reduction of phospho-Tau in the hippocampus. Metabonomic profiling revealed that DCBEI regulated the level of neurotransmitters in the hippocampus of APP/PS1 mice. Label-free proteomics revealed that DCBEI regulated the expression of Nrf-2 and its downstream targets, as well as the levels of phospho-protein kinase B and mitogen-activated protein kinase. Together, these data show that DCBEI can ameliorate AD symptoms by upregulating Nrf2-mediated antioxidative pathways and thus preventing mitochondrial apoptosis.

The roles of metastasis-related proteins in the development of giant cell tumor of bone, osteosarcoma and Ewing's sarcoma.

BACKGROUND: Giant cell tumor of bone (GC), osteosarcoma (OS) and Ewing's sarcoma (ES) are three different types of bone cancer with common and specific pathology features. OBJECTIVE: The purpose of the study was to examine the relationship and differences of the three bone tumors using clinical samples. METHODS: Through screening the profiles of clinical samples from GC, OS and ES patients using a humanoncology array, we found 26, 25 and 15 tumorigenesis factors significantly increased in GS, OS and ES tissues compared to normal individuals. eNOS, endostatin, HIF-1α, IL-6, CCL2/MCP-1, CCL8/MCP-2, CCL7/MCP-3, Tie and VEGF directly or indirectly involve in the metastasis Therefore, expression levels of the 6 factors were further determined by Western blot. RESULTS: The results showed levels of MCP1, MCP2, MCP3 or IL-6 in the GS, OS and ES significantly increased, and the expression levels of angiogenesis and anti-angiogenesis factors containing eNOS, endostatin, HIF-1α, Tie or VEGF were enhanced. CONCLUSIONS: Our results suggest that eNOS, endostatin, HIF-1α, IL-6, CCL2/MCP-1, CCL8/MCP-2, CCL7/MCP-3, Tie and VEGF may play important roles in tumorigenesis, reveal the expression differences of tumor-associated cytokines and angiogenesis related factors, and provide clinical evidence for studying the mechanisms on the metastasis in GC, OS and ES.

Fault Detection for a Class of Uncertain Sampled-Data Systems Using Deterministic Learning.

In this article, we propose a learning-based fault diagnosis approach for a class of nonlinear sampled-data systems. First, the unmodeled sampled dynamics is acquired by the using deterministic learning method. The knowledge of the sampled dynamics of the normal and fault patterns is stored in the form of constant neural networks. Second, a fault detection scheme is designed in which memories of the learned knowledge can be recalled to give a rapid response to a fault. Third, analytical results concerning the fault detection condition and detection time are derived. It is shown that the mismatch function plays an important role in the performance properties of the diagnosis scheme. To analyze the effect of mismatch function on the residual, the concept of duty ratio is developed. Moreover, by comparing the constant neural networks of the normal and fault patterns, an extraction operator is designed to capture the feature of the mismatch function. By using this method, the performance of the diagnosis scheme can be improved. A simulation study is included to demonstrate the effectiveness of the approach.

Assessing vitamin D related genetic variants, status, and influence factors in pregnant women in Eastern and Central China.

Vitamin D deficiency has recently become a global public health problem. However, it is still unclear if gene polymorphisms in the vitamin D pathway influence vitamin D levels among pregnant women in Eastern and Central China. The objective of this study was to assess factors influencing vitamin D levels in pregnant women. A total of 326 participants in Shandong and Henan provinces in China were enrolled from August 2017 to April 2019. Serum 25(OH)D levels and single nucleotide polymorphisms (SNPs) in the vitamin D pathway were measured using the blood samples collected in the first trimester, second trimester, and third trimester. Data on demographics, lifestyle, and health behavior were collected using a questionnaire. Statistical analyses were performed using the R software. The prevalence of 25(OH)D deficiency was significantly more severe in pregnant women. The average 25(OH)D value of all enrolled pregnant women was 14.57 ± 7.21 ng/ml (deficiency). Only 15 (4.60%) participants had a 25(OH)D concentration ≥30 ng/ml (sufficient). The prevalence of four ranks of vitamin D levels from severe 25(OH)D deficiency to 25(OH)D sufficiency (<10, 10-20, 20-30, and ≥30 ng/ml) was 29.14%, 52.45%, 13.80%, and 4.60%, respectively. Variants of GC (rs1155563) and CYP24A1 (rs6013897) were significantly associated with both 25(OH)D concentrations and vitamin D deficiency among pregnant women, respectively. Our findings suggest that pregnant women in Eastern and Central China are at high risk of vitamin D deficiency. Genetic mutants in the vitamin D pathway (GC and CYP24A1) were significantly associated with 25(OH)D levels in pregnant women in Eastern and Central China.

Correction to: microRNA-124 inhibits bone metastasis of breast cancer by repressing Interleukin-11.

An amendment to this paper has been published and can be accessed via the original article.

Facile preparation of high conductive silver electrodes by dip-coating followed by quick sintering.

With polyol-synthesized silver nanoparticles (AgNPs) as raw materials, the silver electrodes with high conductivity were fabricated via a dip-coating method followed by sintering process, and the effects of the dip-coating and sintering process on the conductivity and surface roughness of silver electrodes were investigated in detail. The silver film with a thickness of 1.97 µm and a roughness of about 2 nm can be prepared after dip-coating at a pulling rate of 500 µm s-1 for 40 coating times. The non-conductive dip-coated silver films are transformed into conductive silver electrodes after conventional sintering in a muffle oven, infrared sintering and microwave sintering, respectively. Compared with high sintering temperature and long sintering time of conventional sintering and infrared sintering, microwave sintering can achieve quick sintering of silver films to fabricate high conductive silver electrodes. The silver electrodes with a sheet resistance of 0.75 Ω sq-1 and a surface roughness of less than 1 nm can be obtained after microwave sintering at 500 W for 50 s. The adjustable dip-coating method followed by quick microware sintering is an appropriate approach to prepare high conductive AgNPs-based electrodes for organic light-emitting diodes or other devices.

Metformin induces cell cycle arrest, apoptosis and autophagy through ROS/JNK signaling pathway in human osteosarcoma.

Metformin, an ancient drug commonly used for treating type II diabetes, has been associated to anti-cancer capacity in a variety of developing cancers, though the mechanism remains elusive. Here, we aimed to examine the inhibitory effect of metformin in osteosarcoma. Herein, we demonstrated that metformin treatment blocked proliferation progression by causing accumulation of G2/M phase in U2OS and 143B cells. Furthermore, metformin treatment triggered programmed cell death process in osteosarcoma cell lines. Further research indicated the induction of apoptosis and autophagy triggered by metformin could remarkably attenuate after the treatment of ROS scavenger NAC and JNK inhibitor SP600125. Additionally, our results showed that NAC-suppressed JNK/c-Jun signaling pathway could have been activated through metformin treatment. Lastly, metformin could inhibit osteosarcoma growth under safe dose in vivo. Thus, we propose that metformin could induce cell cycle arrest as well as programmed cell death, including apoptosis and autophagy, through ROS-dependent JNK/c-Jun cascade in human osteosarcoma. This metformin-induced pathway provides further insights into its antitumor potential molecular mechanism and illuminates potential cancer targets for osteosarcoma.

Tin dioxide-based nanomaterials as anodes for lithium-ion batteries.

The development of new electrode materials for lithium-ion batteries (LIBs) has attracted significant attention because commercial anode materials in LIBs, like graphite, may not be able to meet the increasing energy demand of new electronic devices. Tin dioxide (SnO2) is considered as a promising alternative to graphite due to its high specific capacity. However, the large volume changes of SnO2 during the lithiation/delithiation process lead to capacity fading and poor cycling performance. In this review, we have summarized the synthesis of SnO2-based nanomaterials with various structures and chemical compositions, and their electrochemical performance as LIB anodes. This review addresses pure SnO2 nanomaterials, the composites of SnO2 and carbonaceous materials, the composites of SnO2 and transition metal oxides, and other hybrid SnO2-based materials. By providing a discussion on the synthesis methods and electrochemistry of some representative SnO2-based nanomaterials, we aim to demonstrate that electrochemical properties can be significantly improved by modifying chemical composition and morphology. By analyzing and summarizing the recent progress in SnO2 anode materials, we hope to show that there is still a long way to go for SnO2 to become a commercial LIB electrode and more research has to be focused on how to enhance the cycling stability.

Systematic evaluation of lithium-excess polyanionic compounds as multi-electron reaction cathodes.

Polyanion cathodes with multi-electron redox always facilitate wider application in a metal ion-based battery system because of their high capacity and safety. However, the irreversible phase transformation and interfacial deterioration remain major impediments. Herein, using monoclinic Li3V2(PO4)3 as a model, the impact of excess lithium on its electrochemical properties are demonstrated. It was determined that a maximum of 5% excess lithium could be incorporated into the monoclinic structure, and a further overdose of lithium led to the formation of secondary phase Li3PO4. The excess Li+ ions are located at both octahedral and interstitial sites, which enable enhanced redox kinetics that are mainly attributed to accelerated ionic movement induced by alternate diffusion behavior of Li+ ions in a three-dimensional permeation path. Moreover, Li-excess local configurations can stabilize the lattice oxygen and provide a favorable cathode-electrolyte interface, which synergistically relieves the structural degradation during electrochemical cycling, thus guaranteeing exceptional cycling stability (e.g., 82.5% after 1000 cycles at 1000 mA g-1). These findings provide a comprehensive understanding of defect/electronic structure/ion transport and the intrinsic properties of polyanionic Li3V2(PO4)3 and may help to pave the way for other highly stable electrodes for rechargeable batteries.

Small Fault Detection for a Class of Closed-Loop Systems via Deterministic Learning.

In this paper, based on the deterministic learning (DL) theory, an approach for detection for small faults in a class of nonlinear closed-loop systems is proposed. First, the DL-based neural control approach and identification approach are employed to extract the knowledge of the control effort that compensates the fault dynamics (change of the control effort) and the fault dynamics (the change of system dynamics due to fault). Second, two types of residuals are constructed. One is to measure the change of system dynamics, another one is to measure change of the control effort. By combining these residuals, an enhanced residual is generated, in which the fault dynamics and the control effort are combined to diagnose the fault. It is shown that the major fault information is compensated by the control, and the major fault information is double in the enhanced residual. Therefore, the fault information in the diagnosis residual is enhanced. Finally, an analysis of the fault detectability condition of the diagnosis scheme is given. Simulation studies are included to demonstrate the effectiveness of the approach.