Classification of motor imagery using chaotic entropy based on sub-band EEG source localization.

Objective.Electroencephalography (EEG) has been widely used in motor imagery (MI) research by virtue of its high temporal resolution and low cost, but its low spatial resolution is still a major criticism. The EEG source localization (ESL) algorithm effectively improves the spatial resolution of the signal by inverting the scalp EEG to extrapolate the cortical source signal, thus enhancing the classification accuracy.Approach.To address the problem of poor spatial resolution of EEG signals, this paper proposed a sub-band source chaotic entropy feature extraction method based on sub-band ESL. Firstly, the preprocessed EEG signals were filtered into 8 sub-bands. Each sub-band signal was source localized respectively to reveal the activation patterns of specific frequency bands of the EEG signals and the activities of specific brain regions in the MI task. Then, approximate entropy, fuzzy entropy and permutation entropy were extracted from the source signal as features to quantify the complexity and randomness of the signal. Finally, the classification of different MI tasks was achieved using support vector machine.Main result.The proposed method was validated on two MI public datasets (brain-computer interface (BCI) competition III IVa, BCI competition IV 2a) and the results showed that the classification accuracies were higher than the existing methods.Significance.The spatial resolution of the signal was improved by sub-band EEG localization in the paper, which provided a new idea for EEG MI research.

Investigation of the predictive value of a novel algorithm based on coronary CT angiography regarding fractional flow reserve and revascularization in patients with stable coronary artery disease.

Fractional flow reserve (FFR) has been established as a gold standard for functional coronary ischemia. At present, the FFR can be calculated from coronary computed tomography angiography (CCTA) images (CT-FFR). Previous studies have suggested that CT-FFR outperforms CCTA and invasive coronary angiography (ICA) in determining hemodynamic significance of stenoses. Recently, a novel automatical algorithm of CT-FFR called RuiXin-FFR has been developed. The present study is designed to investigate the predictive value of this algorithm and its value in therapeutic decision making. The present study retrospectively included 166 patients with stable coronary artery disease (CAD) who underwent CCTA screening and diagnostic ICA examination at Peking University People's Hospital, in 73 of whom wire-derived FFR was also measured. CT-FFR analyses were performed with a dedicated software. All patients were followed up for at least 1 year. We validated the accuracy of RuiXin-FFR with invasive FFR as the standard of reference, and investigated the role of RuiXin-FFR in predicting treatment strategy and long-term prognosis. The mean age of the patients was 63.3 years with 63.9% male. The CT-FFR showed a moderate correlation with wire-derived FFR (r = 0.542, p < 0.0001) and diagnostic accuracy of 87.6% to predict myocardial ischemia (AUC: 0.839, 95% CI 0.728-0.950), which was significantly higher than CCTA and ICA. In the multivariate logistic regression analysis, CT-FFR ≤ 0.80 was an independent predictor of undergoing coronary revascularization (OR: 45.54, 95% CI 12.03-172.38, p < 0.0001), whereas CT-FFR > 0.80 was an independent predictor of non-obstructive CAD (OR: 14.67, 95% CI 5.42-39.72, p < 0.0001). Reserving ICA and revascularization for vessels with positive CT-FFR could have reduced the rate of ICA by 29.6%, lowered the rate of ICA in vessels without stenosis > 50% by 11.7%, and increased the rate of revascularization in patients receiving ICA by 21.2%. The average follow-up was 23.7 months, and major adverse cardiovascular events (MACE) occurred in 11 patients. The rate of MACE was significantly lower in patients with CT-FFR > 0.80. The new algorithm of CT-FFR can be used to predict the invasive FFR. The RuiXin-FFR can also provide useful information for the screening of patients in whom further ICA is indeed needed and prognosis evaluation.

DNMT1 regulates hypermethylation and silences hsa_circ_401351 in hydroquinone-induced malignant TK6 cells.

BACKGROUND: Benzene and its metabolite hydroquinone (HQ) are widely used in daily life, and long-term exposure to benzene or HQ can induce acute myeloid leukemia (AML). Circular RNAs (circRNAs) are mostly produced by reverse splicing of gene exon mRNA precursors. The modulation of circRNA expression is connected to leukemia progression; however, the molecular mechanism is still unknown. MATERIALS AND METHODS: In this study, the cells were divided into four groups: PBS control group (PBS-TK6), TK6 malignantly transformed cells induced by 10.0 µmol/L HQ (HQ-TK6), and HQ-TK6 cells treated with 5 µmol/L 5-AzaC (DNA methyltransferase inhibitor) for 24 h (HQ + 5-AzaC). HQ-TK6 cells were treated with 200 nmol/L TSA (histone deacetylation inhibitor) for 24 h (HQ + TSA). qRT-PCR was used to identify the differential hsa_circ_401351 expression between the four groups. We further determined the hsa_circ_401351 promoter methylation level with methylation-specific PCR. DNMT1 and DNMT3b were knocked down by CRISPR/Cas9 to elucidate the specific molecular mechanism of hsa_circ_401351 in HQ-TK6 cells. CCK-8 and flow cytometry detected cell proliferation and apoptosis, respectively, after hsa_circ_401351 was overexpressed in HQ-TK6 cells. RESULTS: Compared with the PBS-TK6 group, the expression of hsa_circ_401351 was found to be lower in the HQ-TK6 group. Nevertheless, treatment with 5-AzaC or TSA increased hsa_circ_401351 expression, with the upregulation being more pronounced in the TSA group. The expression of hsa_circ_401351 in the DNMT1 knockdown group was dramatically increased by 50% compared to that in the control group, and the DNA methylation level of the hsa_circ_401351 promoter region was decreased. When hsa_circ_401351 was overexpressed, HQ-TK6 cell proliferation was significantly slowed after 48 h compared with the control group. Flow cytometry showed that cells were mainly arrested in G1 phase, and apoptosis was significantly enhanced. Similarly, qRT-PCR and Western blot data showed significant reductions in Caspase-3 mRNA and protein production, and Bcl-2 mRNA levels were also elevated. CONCLUSIONS: Overall, our research showed that elevated DNMT1 expression in HQ-TK6 cells increased methylation levels and decreased expression of the hsa_circ_401351 promoter region, limiting its ability to suppress HQ-TK6 cell growth and enhance apoptosis.

Abnormal inter-ventricular diastolic mechanical delay in patients with ST-segment elevation myocardial infarction.

BACKGROUND: This study aimed to investigate the ventricular mechanical relaxation pattern and its clinical influence in patients with ST-segment elevation myocardial infarction (STEMI). METHODS: Echocardiography was performed to measure mitral and tricuspid diastolic opening times. Left ventricular diastolic mechanical delay (LVMDd) was defined as diastolic filling of the right ventricle earlier than that of the left ventricle, and right ventricular diastolic mechanical delay (RVMDd) was defined as the right ventricular diastolic filling later than left ventricular filling. RESULTS: Among 152 patients with STEMI, 100 (65.8%) had LVMDd, and 47 (30.9%) had RVMDd. In-hospital complications were significantly increased in patients with RVMDd (61.6% vs. 41.0%, P = 0.017). Those with RVMDd exhibited significantly lower left ventricular global longitudinal strain (11.7 ± 4.1% vs. 13.2 ± 4.0%, P = 0.035), global work index (913.8 ± 365.9 vs. 1098.9 ± 358.8 mmHg%, P = 0.005) and global constructive work (1218.6 ± 392.8 vs. 1393.7 ± 432.7 mmHg%, P = 0.021). Mitral deceleration time significantly decreased (127.4 ± 33.5 vs. 145.6 ± 41.7 ms, P = 0.012), and the ratio of early mitral inflow to early mitral annular velocity (E/E') significantly increased [13.0(11.0-20.0) vs. 11.9(9.3-14.3), P = 0.006] in the RVMDd group. Logistic regression analysis showed that age (odds ratio [OR]:0.920; P = 0.001), brain natriuretic peptide level (OR: 1.1002; P = 0.036) and mitral E/E' (OR: 1.187; P = 0.003) were independently associated with RVMDd. CONCLUSIONS: Delayed right ventricular filling is related to more severe left ventricular systolic and diastolic dysfunction in STEMI patients. More attention should be paid to patients with RVMDd to prevent adverse events during hospitalization.

High-Density Surface EMG Decomposition by Combining Iterative Convolution Kernel Compensation With an Energy-Specific Peel-off Strategy.

Objective- This study aims to develop a novel framework for high-density surface electromyography (HD-sEMG) signal decomposition with superior decomposition yield and accuracy, especially for low-energy MUs. Methods- An iterative convolution kernel compensation-peel off (ICKC-P) framework is proposed, which consists of three steps: decomposition of the motor units (MUs) with relatively large energy by using the iterative convolution kernel compensation (ICKC) method and extraction of low-energy MUs with a Post-Processor and novel 'peel-off' strategy. Results- The performance of the proposed framework was evaluated by both simulated and experimental HD-sEMG signals. Our simulation results demonstrated that, with 120 simulated MUs, the proposed framework extracts more MUs compared to K-means convolutional kernel compensation (KmCKC) approach across six noise levels. And the proposed 'peel-off' strategy estimates more accurate MUAP waveforms at six noise levels than the 'peel-off' strategy proposed in the progressive FastICA peel-off (PFP) framework. For the experimental sEMG signals recorded from biceps brachii, an average of 16.1 ±3.4 MUs were identified from each contraction, while only 10.0 ± 2.8 MUs were acquired by the KmCKC method. Conclusion- The high yield and accuracy of MUs decomposed from simulated and experimental HD-sEMG signals demonstrate the superiority of the proposed framework in decomposing low-energy MUs compared to existing methods for HD-sEMG signal decomposition. Significance- The proposed framework enables us to construct a more representative motor unit pool, consequently enhancing our understanding pertaining to various neuropathological conditions and providing invaluable information for the diagnosis and treatment of neuromuscular disorders and motor neuron diseases.

Cross-Subject Emotion Recognition Based on Domain Similarity of EEG Signal Transfer Learning.

For solving the problem of the inevitable decline in the accuracy of cross-subject emotion recognition via Electroencephalograph (EEG) signal transfer learning due to the negative transfer of data in the source domain, this paper offers a new method to dynamically select the data suitable for transfer learning and eliminate the data that may lead to negative transfer. The method which is called cross-subject source domain selection (CSDS) consists of the next three parts. 1) First, a Frank-copula model is established according to Copula function theory to study the correlation between the source domain and the target domain, which is described by the Kendall correlation coefficient. 2) The calculation method for the Maximum Mean Discrepancy is improved to determine the distance between classes in a single source. After normalization, the Kendall correlation coefficient is superimposed, and the threshold is set to identify the source-domain data most suitable for transfer learning. 3) In the process of transfer learning, on the basis of Manifold Embedded Distribution Alignment, the Local Tangent Space Alignment method is used to provide a low-dimensional linear estimation of the local geometry of nonlinear manifolds, which maintains the local characteristics of the sample data after dimensionality reduction. Experimental results show that compared with the traditional methods, the CSDS increases the accuracy of emotion classification by approximately 2.8% and reduces the runtime by approximately 65%.

Dual-Encoder VAE-GAN With Spatiotemporal Features for Emotional EEG Data Augmentation.

The current data scarcity problem in EEG-based emotion recognition tasks leads to difficulty in building high-precision models using existing deep learning methods. To tackle this problem, a dual encoder variational autoencoder-generative adversarial network (DEVAE-GAN) incorporating spatiotemporal features is proposed to generate high-quality artificial samples. First, EEG data for different emotions are preprocessed as differential entropy features under five frequency bands and divided into segments with a 5s time window. Secondly, each feature segment is processed in two forms: the temporal morphology data and the spatial morphology data distributed according to the electrode position. Finally, the proposed dual encoder is trained to extract information from these two features, concatenate the two pieces of information as latent variables, and feed them into the decoder to generate artificial samples. To evaluate the effectiveness, a systematic experimental study was conducted in this work on the SEED dataset. First, the original training dataset is augmented with different numbers of generated samples; then, the augmented training datasets are used to train the deep neural network to construct the sentiment model. The results show that the augmented datasets generated by the proposed method have an average accuracy of 97.21% on all subjects, which is a 5% improvement compared to the original dataset, and the similarity between the generated data and the original data distribution is proved. These results demonstrate that our proposed model can effectively learn the distribution of raw data to generate high-quality artificial samples, which can effectively train a high-precision affective model.

Self-Supervised EEG Emotion Recognition Models Based on CNN.

Emotion plays crucial roles in human life. Recently, emotion classification from electroencephalogram (EEG) signal has attracted attention by researchers due to the rapid development of brain computer interface (BCI) techniques and machine learning algorithms. However, recent studies on emotion classification show resource utilization because they use the fully-supervised learning methods. Therefore, in this study, we applied the self-supervised learning methods to improve the efficiency of resources usage. We employed a self-supervised approach to train deep multi-task convolutional neural network (CNN) for EEG-based emotion classification. First, six signal transformations were performed on unlabeled EEG data to construct the pretext task. Second, a multi-task CNN was used to perform signal transformation recognition on the transformed signals together with the original signals. After the signal transformation recognition network was trained, the convolutional layer network was frozen and the fully connected layer was reconstructed as emotion recognition network. Finally, the EEG data with affective labels were used to train the emotion recognition network to clarify the emotion. In this paper, we conduct extensive experiments from the data scaling perspective using the SEED, DEAP affective dataset. Results showed that the self-supervised learning methods can learn the internal representation of data and save computation time compared to the fully-supervised learning methods. In conclusion, our study suggests that the self-supervised machine learning model can improve the performance of emotion classification compared to the conventional fully supervised model.

ULFAC-Net: Ultra-Lightweight Fully Asymmetric Convolutional Network for Skin Lesion Segmentation.

Segmentation of skin lesions is a critical step in the process of skin lesion diagnosis. Such segmentation is challenging due to the irregular shape, fuzzy contours and severe noise interference in the skin lesion region. Existing deep learning-based skin lesion segmentation methods are usually computationally expensive, hindering their deployment in dermoscopic devices with poor computational power. To address these challenges, we propose an ultralightweight fully asymmetric convolutional network for skin lesion segmentation, called ULFAC-Net. we use a parallel asymmetric convolutional (PAC) module to extract features instead of the traditional square convolution, and innovatively propose a PAC module with dual attention (Att-PAC) to enhance the feature representation. Based on the PAC and Att-PAC modules, we further propose a lightweight textual information submodule. To balance the number of parameters and performance of the model, we also hand-design an asymmetric encoder-decoder architecture. In this paper, we validate the effectiveness and robustness of the proposed ULFAC-Net on four publicly available skin lesion segmentation datasets (ISIC2018, ISBI2017, ISIC2016 and PH2 datasets). The experimental results show that ULFAC-Net achieves competitive segmentation performance with only 0.842 million(0.842M) parameters and 3.71 gigabytes of floating point operations (GFLOPs) compared to other state-of-the-art methods.

Cross-subject EEG emotion recognition using multi-source domain manifold feature selection.

Recent researches on emotion recognition suggests that domain adaptation, a form of transfer learning, has the capability to solve the cross-subject problem in Affective brain-computer interface (aBCI) field. However, traditional domain adaptation methods perform single to single domain transfer or simply merge different source domains into a larger domain to realize the transfer of knowledge, resulting in negative transfer. In this study, a multi-source transfer learning framework was proposed to promote the performance of multi-source electroencephalogram (EEG) emotion recognition. The method first used the data distribution similarity ranking (DDSA) method to select the appropriate source domain for each target domain off-line, and reduced data drift between domains through manifold feature mapping on Grassmann manifold. Meanwhile, the minimum redundancy maximum correlation algorithm (mRMR) was employed to select more representative manifold features and minimized the conditional distribution and marginal distribution of the manifold features, and then learned the domain-invariant classifier by summarizing structural risk minimization (SRM). Finally, the weighted fusion criterion was applied to further improve recognition performance. We compared our method with several state-of-the-art domain adaptation techniques using the SEED and DEAP dataset. Results showed that, compared with the conventional MEDA algorithm, the recognition accuracy of our proposed algorithm on SEED and DEAP dataset were improved by 6.74% and 5.34%, respectively. Besides, compared with TCA, JDA, and other state-of-the-art algorithms, the performance of our proposed method was also improved with the best average accuracy of 86.59% on SEED and 64.40% on DEAP. Our results demonstrated that the proposed multi-source transfer learning framework is more effective and feasible than other state-of-the-art methods in recognizing different emotions by solving the cross-subject problem.

Multi-source online transfer algorithm based on source domain selection for EEG classification.

The non-stationary nature of electroencephalography (EEG) signals and individual variability makes it challenging to obtain EEG signals from users by utilizing brain-computer interface techniques. Most of the existing transfer learning methods are based on batch learning in offline mode, which cannot adapt well to the changes generated by EEG signals in the online situation. To address this problem, a multi-source online migrating EEG classification algorithm based on source domain selection is proposed in this paper. By utilizing a small number of labeled samples from the target domain, the source domain selection method selects the source domain data similar to the target data from multiple source domains. After training a classifier for each source domain, the proposed method adjusts the weight coefficients of each classifier according to the prediction results to avoid the negative transfer problem. This algorithm was applied to two publicly available motor imagery EEG datasets, namely, BCI Competition Ⅳ Dataset Ⅱa and BNCI Horizon 2020 Dataset 2, and it achieved average accuracies of 79.29 and 70.86%, respectively, which are superior to those of several multi-source online transfer algorithms, confirming the effectiveness of the proposed algorithm.

Continuous estimation of multi-DOF movement from sEMG based on non-negative matrix factorization and L2 regulation.

Accurate continuous estimation of multi-DOF movement is crucial for simultaneous control of advanced myoelectric prosthetic. The decoupling of multi-DOF is a challenge for continuous estimation. In this paper, we propose a model combined non-negative matrix factorization (NMF) with Hadamard product and L2 regulation to suppress the non-active DOF and achieve the multi-DOF movement continuous estimation. The L2 regulation of non-active DOF activation coefficient was added to the object function of NMF with the benefit of Hadamard product. The angles were estimated by a linear combination of the activation coefficients. We performed a set of continuous estimation experiments for single-DOF and multi-DOF movements of wrist flexion/extend and hand open/close. The results illustrated that the novel model could suppress non-active DOF in single-DOF movement better than other methods based on muscle synergy theory. Moreover, we investigated the robustness of suppression effect and the similarity of synergy matrices at different speeds for NMF-based methods, and the results showed that the proposed method had a superior performance.

Coronary microcirculation dysfunction evaluated by myocardial contrast echocardiography predicts poor prognosis in patients with ST-segment elevation myocardial infarction after percutaneous coronary intervention.

BACKGROUND: The mortality rate of acute ST-segment elevation myocardial infarction (STEMI) remains substantial, despite advances in treatment strategies. Coronary microcirculation dysfunction (CMD) persists after percutaneous coronary intervention (PCI) in a substantial proportion of STEMI patients. The association between CMD assessed using myocardial contrast echocardiography (MCE) and prognosis requires further elucidation. This study aimed to evaluate the impact of CMD after successful PCI on the prognosis of patients with STEMI. METHODS: We enrolled 167 patients with STEMI after PCI who underwent MCE during hospitalization between January 2018 and March 2022. Patients were classified into the CMD and non-CMD groups according to the results of MCE. The clinical data and MCE results of both groups were analyzed. Follow-up was conducted for major adverse cardiac events. RESULTS: MCE detected CMD in 105 patients (62.9%). The CMD group contained fewer hypertensive patients (55.2% versus 74.2%, P = 0.015). Patients with CMD exhibited significantly higher levels of plasma troponin I (TnI) [73.2 (23.0-124.0) versus 28.9 (12.7-80.2) ng/mL, P = 0.004], higher levels of plasma B-type natriuretic peptide [255 (99-641) versus 193 (59-389) pg/mL, P = 0.004], poorer Killip classification (P = 0.038), and different culprit vessels (P < 0.001) compared to the non-CMD group. Patients with CMD exhibited lower left ventricular ejection fraction [50 (43-58) versus 61 (54-67) %, P < 0.001], poorer wall motion score index values (1.68 ± 0.4 versus 1.31 ± 0.26, P < 0.001) and poorer left ventricular global longitudinal strain [-11.2 (-8.7 to -14.1) versus -13.9 (-11.0 to -17.2) %, P < 0.001] compared to the non-CMD group. Patients underwent follow-up for 13 (7-20) months. After adjusting for hypertension, peak TnI level, culprit vessel, and Killip classification, CMD was an independent predictor of total major adverse cardiac events at 13 months' follow-up [adjusted odds ratio (OR), 2.457; 95% confidence interval (CI), 1.042-5.790; P = 0.040], and patients with CMD had a higher risk of hospitalization for heart failure (adjusted OR, 5.184; 95% CI, 1.044-25.747; P = 0.044) and repeat myocardial infarction (adjusted OR, 2.896; 95% CI, 1.109-7.565; P = 0.030). CONCLUSIONS: MCE is a safe and effective method for detecting CMD in patients with STEMI. CMD detected by MCE after successful PCI in patients with STEMI is a common occurrence, which is associated with a significantly worse prognosis, especially hospitalization for heart failure and repeat myocardial infarction.

A novel optical coherence tomography­based calcium scoring system can predict the stent expansion of moderate and severe calcified lesions.

Coronary calcified lesions can exert serious effects on stent expansion. A calcium scoring system, based on optical coherence tomography (OCT), has been previously developed to identify relatively mild calcified lesions that would benefit from plaque modification procedures. Therefore, the present study aimed to establish a novel OCT-based scoring system to predict the stent expansion of moderate and severe calcified lesions. A total of 33 patients who underwent percutaneous coronary intervention (PCI; 34 calcified lesions were observed using coronary angiography) were retrospectively included in the present study. Coronary angiography and OCT images were subsequently reviewed and analyzed. Furthermore, a calcium scoring system was developed based on the results of multivariate analysis before the optimal threshold for the prediction of stent underexpansion in patients with moderate and severe calcified lesions was determined. The mean age of the patients was 67±10 years. The present analysis demonstrated that the final post-PCI median stent expansion was 70.74%, where stent underexpansion (defined as stent expansion <80%) was observed in 23 lesions. The mean maximum calcium arc, length and thickness, which were assessed using OCT, were found to be 230˚, 25.10 mm and 1.18 mm, respectively. A multivariate logistic regression model demonstrated that age and the maximum calcium arc were independent predictors of stent underexpansion. A novel calcium scoring system was thereafter established using the following formula: (0.16 x age) + (0.03 x maximum calcium arc) according to the ß-coefficients in the multivariate analysis, with the optimal cut-off value for the prediction of stent underexpansion being 16.87. Receiver operating characteristic curve analysis demonstrated that this novel scoring system yielded a larger area under the curve value compared with that from a previous study's scoring system. Therefore, in conclusion, since the calcium scoring system of the present study based on age and the maximum calcium arc obtained from OCT was specifically developed in the subjects with moderate and severe calcified lesions, it may be more accurate in predicting the risk of stent underexpansion in these patients.

Left ventricular function and coronary microcirculation in patients with mild reduced ejection fraction after STEMI.

BACKGROUND: The characteristics of heart failure (HF) with mildly reduced ejection fraction (EF) (HFmrEF) overlap with those of HF with reduced EF (HFrEF) and HF with preserved EF (HFpEF) and need to be further explored. This study aimed to evaluate left ventricular (LV) function and coronary microcirculation in patients with mildly reduced ejection fraction after acute ST-segment elevation myocardial infarction (STEMI). METHODS: We enrolled 119 patients with STEMI who had undergone speckle tracking imaging and myocardial contrast echocardiography during hospitalization from June 2016 to June 2021. They were classified into normal, HFmrEF, and HFrEF groups according to their left ventricular EF (LVEF): ≥ 50%, 40-50%, and ≤ 40%, respectively. The data of the HFmrEF group were analyzed and compared with those of the normal and HFrEF groups. RESULTS: HFmrEF was observed in 32 patients (26.9%), HFrEF in 17 (14.3%), and normal LVEF in 70 patients (58.8%). The mean global longitudinal strain (GLS) of all patients was - 11.9 ± 3.8%. The GLS of HFmrEF patients was not significantly different from that of the HFrEF group (- 9.9 ± 2.5% and - 8.0 ± 2.3%, respectively, P = 0.052), but they were both lower than that of the normal group (- 13.8% ± 3.5%, P < 0.001). The HFmrEF group exhibited significantly poorer myocardial perfusion index (1.24 ± 0.33) than the normal group (1.08 ± 0.14, P = 0.005) but displayed no significant difference from the HFrEF group (1.18 ± 0.19, P = 0.486). Moreover, a significant difference in the incidence of regional wall motion (WM) abnormalities in the three groups was observed (P = 0.009), and the WM score index of patients with HFmrEF was 1.76 ± 0.30, similar to that of patients with HFrEF (1.81 ± 0.43, P = 0.618), but poorer than that in the normal group (1.33 ± 0.25, P < 0.001). CONCLUSIONS: GLS is a more sensitive tool than LVEF for detecting LV systolic dysfunction. The LV systolic function, coronary microcirculation, and WM in patients with HFmrEF was poorer than that of patients with normal LVEF, but comparable to that in patients with HFrEF. Patients with HFmrEF after STEMI require more attention and appropriate management.

The Coronary Angiography-Derived Index of Microcirculatory Resistance Predicts Left Ventricular Performance Recovery in Patients with ST-Segment Elevation Myocardial Infarction.

Objectives: The present study is designed to investigate the impact of coronary angiography-derived index of microcirculatory resistance (caIMR) on left ventricular performance recovery. Background: IMR has been established as a gold standard for coronary microvascular assessment and a predictor of left ventricular recovery after ST-segment elevation myocardial infarction (STEMI). CaIMR is a novel and accurate alternative of IMR. Methods: The present study retrospectively included 80 patients with STEMI who underwent primary percutaneous coronary intervention (PCI). We offline performed the post-PCI caIMR analysis of the culprit vessel. Echocardiography was performed within the first 24 hours and at 3 months after the index procedure. Left ventricular recovery was defined as the change in left ventricular ejection fraction (LVEF) more than zero. Results: The mean age of the patients was 58.0 years with 80.0% male. The average post-PCI caIMR was 43.2. Overall left ventricular recovery was seen in 41 patients. Post-PCI caIMR (OR: 0.948, 95% CI: 0.916-0.981, p = 0.002), left anterior descending as the culprit vessel (OR: 3.605, 95% CI: 1.23-10.567, p = 0.019), and male (OR: 0.254, 95% CI: 0.066-0.979, p = 0.047) were independent predictors of left ventricular recovery at 3 months follow-up. A predictive model was established with the best cutoff value for the prediction of left ventricular recovery 2.33 (sensitivity 0.610, specificity 0.897, and area under the curve 0.765). In patients with a predictive model score less than 2.33, the LVEF increased significantly at 3 months. Conclusions: The post-PCI caIMR can accurately predict left ventricular functional recovery at 3 months follow-up in patients with STEMI treated by primary PCI, supporting its use in clinical practice.

Hydroxychloroquine induces apoptosis of myeloid-derived suppressor cells via up-regulation of CD81 contributing to alleviate lupus symptoms.

BACKGROUND: Systemic lupus erythematosus (SLE) is a chronic autoimmune disorder that results from widespread immune complex deposition and secondary tissue injury. Hydroxychloroquine (HCQ) has been used clinically to treat SLE, while its exact mechanism has still remained elusive. Some studies have shown that myeloid-derived suppressor cells (MDSCs) play a vital role in the regulation of SLE. In this study, we aimed to explore the effects of HCQ on the apoptosis of MDSCs in lupus mice and its possible molecular regulatory mechanism. METHODS: We constructed the imiquimod (IMQ)-induced lupus model in mice. The proportion and apoptosis of MDSCs were measured by flow cytometry. CD81-overexpressed adeno-associated virus was intraperitoneally injected into the lupus mice. We also transfected the CD81 siRNA into bone marrow-derived MDSCs, and employed qRT-PCR and Western blotting to quantify the level of CD81. RESULTS: The results showed that HCQ ameliorated IMQ-induced lupus symptoms, and simultaneously inhibited the expansion of MDSCs. In particular, HCQ induced the apoptosis of MDSCs, and also up-regulated the expression level of CD81 in MDSCs, which might indicate the relationship between the expression level of CD81 and the apoptosis of MDSCs. CD81 was further confirmed to participate in the apoptosis of MDSCs and lupus disease progression by overexpressing CD81 in vivo. Molecular docking experiment further proved the targeting effect of HCQ on CD81. And then we interfered CD81 in bone marrow derived MDSCs in vitro, and it was revealed that HCQ rescued the decreased expression level of CD81 and relieved the immune imbalance of Th17/Treg cells. CONCLUSION: In summary, HCQ promoted the apoptosis of MDSCs by up-regulating the expression level of CD81 in MDSCs, and ultimately alleviated lupus symptoms. Our results may assist scholars to develop further effective therapies for SLE.

A new feature selection approach for driving fatigue EEG detection with a modified machine learning algorithm.

This study aims to identify new electroencephalography (EEG) features for the detection of driving fatigue. The most common EEG feature in driving fatigue detection is the power spectral density (PSD) of five frequency bands, i.e., alpha, beta, gamma, delta, and theta bands. PSD has proved to be useful, however its flaw is that it covers much implicit information of the time domain. In this study we propose a new approach, which combines ensemble empirical mode decomposition (EEMD) and PSD, to explore new EEG features for driving fatigue detection. Through EEMD we get a series of intrinsic mode function (IMF) components, from which we can extract PSD features. We used six features to compare with the proposed features, including the PSD of five frequency bands, PSD of empirical mode decomposition (EMD)-IMF components, PSD, permutation entropy (PE), sample entropy (SE), and fuzzy entropy (FE) of EEMD-IMF components, and common spatial pattern. Feature overlap ratio and multiple machine learning methods were applied to evaluate these feature extraction approaches. The results show that the classification accuracy and overlap ratio of experiments based on IMF's energy spectrum is far superior to other features. Through channel optimization and a comparison of accuracy, we conclude that our new feature selection approach has a better performance based on the modified hierarchical extreme learning machine algorithm with Particle Swarm Optimization (PSO-H-ELM) classifier, which has the highest average accuracy of 97.53%.

Usefulness of echocardiographic myocardial work in evaluating the microvascular perfusion in STEMI patients after revascularization.

BACKGROUND: Left ventricular myocardial work (MW) assessed by echocardiography has recently been introduced as a new index of global and regional myocardial performance. The presence of microvascular obstruction after revascularization in ST-segment elevation myocardial infarction (STEMI) patients predicts poor clinical outcomes. This study aimed to explore the usefulness of MW in identifying impaired microvascular perfusion (MVP) in the patients with STEMI after revascularization. METHODS: One hundred and sixty STEMI patients who underwent myocardial contrast echocardiography (MCE) within 48 h after percutaneous coronary intervention (PCI) were included. Patients were divided into normal MVP and impaired MVP groups according to the myocardial perfusion score. The clinical data, coronary angiography results and echocardiographic data including Global work index (GWI), global constructive work (GCW), global wasted work (GWW), and global work efficiency (GWE) were collected. RESULTS: Impaired MVP was found in 60% of patients. Compared with the normal MVP group, GWI (909.2 ± 287.6 mmHg% vs. 1191.2 ± 378.2 mmHg%), GCW (1198.3 ± 339.6 mmHg% vs. 1525.9 ± 420.5 mmHg%), GWE (82.7 ± 7.8% vs. 86.8 ± 5.6%) and GLS (- 11.0 ± 3.4% vs. - 14.4 ± 3.8%) were significantly reduced in the impaired MVP group. Whereas there was no statistically significant difference in left ventricular ejection fraction (LVEF) and GWW, multivariate logistic regression analysis showed that peak troponin I (OR 1.017, 95% CI 1.006-1.029; P = 0.004), final TIMI flow ≤ 2 (OR 16.366, 95% CI 1.998-134.06; P = 0.009), left ventricular end-diastolic volume index (LVEDVi) (OR 1.139 95% CI 1.048-1.239; P = 0.002), and GWI (OR 0.997 95% CI 0.994-1.000; P = 0.029) were independently associated with impaired MVP. GWI showed a good sensitivity (86.8%) but low specificity (53.7%) in identifying impaired MVP (AUC 0.712, 95% CI 0.620-0.804; P < 0.001). Combination with GWI can improve the diagnostic value of TNI or LVEVi for impaired MVP. CONCLUSION: Impaired MVP is relatively common in STEMI patients after revascularization and independently associated with left ventricular GWI assessed by echocardiography. GWI confer incremental value to MVP assessment in STEMI patients.