Mitochondrial biological function and risk of atrial fibrillation and atrial flutter: A 2-sample Mendelian randomization study.

Current research suggests that mitochondrial dysfunction can be a contributing factor in the development of cardiac arrhythmias. In pursuit of elucidating the causal link between the biological functions of mitochondria and the occurrence of atrial fibrillation/flutter, we conducted a 2-sample Mendelian randomization (MR) study. Mitochondrial proteins were selected for exposure in this study. To enhance the accuracy of our study, we selected data on AF/AFL from the FinnGen study and the UK Biobank for MR analysis, respectively. The inverse variance-weighted method was utilized as the primary analysis technique for MR. In addition, we performed a series of sensitivity analyses to detect heterogeneity and horizontal pleiotropy. MR results indicated a significant positive association between NAD-dependent protein deacylase sirtuin-5 and AF/AFL (odds ratio = 1.084, 95% confidence interval: 1.037-1.133, P = 3.679 × 10-4, Adjusted P = .024), with consistent outcomes observed in replication analysis (odds ratio = 1.002, 95% confidence interval: 1.001-1.003, P = 4.808 × 10-4, Adjusted P = .032). NAD-dependent protein deacylase sirtuin-5 can significantly promote the occurrence of AF/AFL, and its specific mechanisms warrant further investigation.

Circulating soluble suppression of tumorigenicity-2 and the recurrence of atrial fibrillation after catheter ablation: A meta-analysis.

Soluble suppression of tumorigenicity-2 (sST-2), a marker of myocardial fibrosis and remodeling, has been related to the development of atrial fibrillation (AF). The aim of this meta-analysis was to evaluate the relationship between baseline serum sST-2 levels and the risk of AF recurrence after ablation. Relevant observational studies were retrieved from PubMed, Web of Science, Embase, Wanfang and China National Knowledge Infrastructure (CNKI). A random-effects model was used to combine the data, accounting for between-study heterogeneity. Fourteen prospective cohorts were included. Pooled results showed higher sST-2 levels before ablation in patients with AF recurrence compared to those without AF recurrence (standardized mean difference = 1.15, 95% confidence interval [CI] = 0.67 to 1.63, P < 0.001; I2 = 92%). Meta-regression analysis suggested that the proportion of patients with paroxysmal AF (PaAF) was positively related to the difference in serum sST-2 levels between patients with and without AF recurrence (coefficient = 0.033, P < 0.001). Subgroup analysis showed a more remarkable difference in serum sST-2 levels between patients with and without AF recurrence in studies where PaAF was ≥ 60% compared to those where it was < 60% (P = 0.007). Further analyses showed that high sST-2 levels before ablation were associated with an increased risk of AF recurrence (odds ratio [OR] per 1 ng/mL increment of sST-2 =1.05, OR for high versus low sST-2 = 1.73, both P values < 0.05). In conclusion, high sST-2 baseline levels may be associated with an increased risk of AF recurrence after catheter ablation.

FetchEEG: a hybrid approach combining feature extraction and temporal-channel joint attention for EEG-based emotion classification.

Objective. Electroencephalogram (EEG) analysis has always been an important tool in neural engineering, and the recognition and classification of human emotions are one of the important tasks in neural engineering. EEG data, obtained from electrodes placed on the scalp, represent a valuable resource of information for brain activity analysis and emotion recognition. Feature extraction methods have shown promising results, but recent trends have shifted toward end-to-end methods based on deep learning. However, these approaches often overlook channel representations, and their complex structures pose certain challenges to model fitting.Approach. To address these challenges, this paper proposes a hybrid approach named FetchEEG that combines feature extraction and temporal-channel joint attention. Leveraging the advantages of both traditional feature extraction and deep learning, the FetchEEG adopts a multi-head self-attention mechanism to extract representations between different time moments and channels simultaneously. The joint representations are then concatenated and classified using fully-connected layers for emotion recognition. The performance of the FetchEEG is verified by comparison experiments on a self-developed dataset and two public datasets.Main results. In both subject-dependent and subject-independent experiments, the FetchEEG demonstrates better performance and stronger generalization ability than the state-of-the-art methods on all datasets. Moreover, the performance of the FetchEEG is analyzed for different sliding window sizes and overlap rates in the feature extraction module. The sensitivity of emotion recognition is investigated for three- and five-frequency-band scenarios.Significance. FetchEEG is a novel hybrid method based on EEG for emotion classification, which combines EEG feature extraction with Transformer neural networks. It has achieved state-of-the-art performance on both self-developed datasets and multiple public datasets, with significantly higher training efficiency compared to end-to-end methods, demonstrating its effectiveness and feasibility.

Self-supervised contrastive learning for EEG-based cross-subject motor imagery recognition.

Objective. The extensive application of electroencephalography (EEG) in brain-computer interfaces (BCIs) can be attributed to its non-invasive nature and capability to offer high-resolution data. The acquisition of EEG signals is a straightforward process, but the datasets associated with these signals frequently exhibit data scarcity and require substantial resources for proper labeling. Furthermore, there is a significant limitation in the generalization performance of EEG models due to the substantial inter-individual variability observed in EEG signals.Approach. To address these issues, we propose a novel self-supervised contrastive learning framework for decoding motor imagery (MI) signals in cross-subject scenarios. Specifically, we design an encoder combining convolutional neural network and attention mechanism. In the contrastive learning training stage, the network undergoes training with the pretext task of data augmentation to minimize the distance between pairs of homologous transformations while simultaneously maximizing the distance between pairs of heterologous transformations. It enhances the amount of data utilized for training and improves the network's ability to extract deep features from original signals without relying on the true labels of the data.Main results. To evaluate our framework's efficacy, we conduct extensive experiments on three public MI datasets: BCI IV IIa, BCI IV IIb, and HGD datasets. The proposed method achieves cross-subject classification accuracies of 67.32%, 82.34%, and 81.13%on the three datasets, demonstrating superior performance compared to existing methods.Significance. Therefore, this method has great promise for improving the performance of cross-subject transfer learning in MI-based BCI systems.

Robust deep learning from incomplete annotation for accurate lung nodule detection.

Deep learning plays a significant role in the detection of pulmonary nodules in low-dose computed tomography (LDCT) scans, contributing to the diagnosis and treatment of lung cancer. Nevertheless, its effectiveness often relies on the availability of extensive, meticulously annotated dataset. In this paper, we explore the utilization of an incompletely annotated dataset for pulmonary nodules detection and introduce the FULFIL (Forecasting Uncompleted Labels For Inexpensive Lung nodule detection) algorithm as an innovative approach. By instructing annotators to label only the nodules they are most confident about, without requiring complete coverage, we can substantially reduce annotation costs. Nevertheless, this approach results in an incompletely annotated dataset, which presents challenges when training deep learning models. Within the FULFIL algorithm, we employ Graph Convolution Network (GCN) to discover the relationships between annotated and unannotated nodules for self-adaptively completing the annotation. Meanwhile, a teacher-student framework is employed for self-adaptive learning using the completed annotation dataset. Furthermore, we have designed a Dual-Views loss to leverage different data perspectives, aiding the model in acquiring robust features and enhancing generalization. We carried out experiments using the LUng Nodule Analysis (LUNA) dataset, achieving a sensitivity of 0.574 at a False positives per scan (FPs/scan) of 0.125 with only 10% instance-level annotations for nodules. This performance outperformed comparative methods by 7.00%. Experimental comparisons were conducted to evaluate the performance of our model and human experts on test dataset. The results demonstrate that our model can achieve a comparable level of performance to that of human experts. The comprehensive experimental results demonstrate that FULFIL can effectively leverage an incomplete pulmonary nodule dataset to develop a robust deep learning model, making it a promising tool for assisting in lung nodule detection.

Multi modality fusion transformer with spatio-temporal feature aggregation module for psychiatric disorder diagnosis.

Bipolar disorder (BD) is characterized by recurrent episodes of depression and mild mania. In this paper, to address the common issue of insufficient accuracy in existing methods and meet the requirements of clinical diagnosis, we propose a framework called Spatio-temporal Feature Fusion Transformer (STF2Former). It improves on our previous work - MFFormer by introducing a Spatio-temporal Feature Aggregation Module (STFAM) to learn the temporal and spatial features of rs-fMRI data. It promotes intra-modality attention and information fusion across different modalities. Specifically, this method decouples the temporal and spatial dimensions and designs two feature extraction modules for extracting temporal and spatial information separately. Extensive experiments demonstrate the effectiveness of our proposed STFAM in extracting features from rs-fMRI, and prove that our STF2Former can significantly outperform MFFormer and achieve much better results among other state-of-the-art methods.

Protein Disorder in Plant Stress Adaptation: From Late Embryogenesis Abundant to Other Intrinsically Disordered Proteins.

Global climate change has caused severe abiotic and biotic stresses, affecting plant growth and food security. The mechanical understanding of plant stress responses is critical for achieving sustainable agriculture. Intrinsically disordered proteins (IDPs) are a group of proteins without unique three-dimensional structures. The environmental sensitivity and structural flexibility of IDPs contribute to the growth and developmental plasticity for sessile plants to deal with environmental challenges. This article discusses the roles of various disordered proteins in plant stress tolerance and resistance, describes the current mechanistic insights into unstructured proteins such as the disorder-to-order transition for adopting secondary structures to interact with specific partners (i.e., cellular membranes, membrane proteins, metal ions, and DNA), and elucidates the roles of liquid-liquid phase separation driven by protein disorder in stress responses. By comparing IDP studies in animal systems, this article provides conceptual principles of plant protein disorder in stress adaptation, reveals the current research gaps, and advises on the future research direction. The highlighting of relevant unanswered questions in plant protein disorder research aims to encourage more studies on these emerging topics to understand the mechanisms of action behind their stress resistance phenotypes.

Predicting miRNA-disease association via graph attention learning and multiplex adaptive modality fusion.

miRNAs are a class of small non-coding RNA molecules that play important roles in gene regulation. They are crucial for maintaining normal cellular functions, and dysregulation or dysfunction of miRNAs which are linked to the onset and advancement of multiple human diseases. Research on miRNAs has unveiled novel avenues in the realm of the diagnosis, treatment, and prevention of human diseases. However, clinical trials pose challenges and drawbacks, such as complexity and time-consuming processes, which create obstacles for many researchers. Graph Attention Network (GAT) has shown excellent performance in handling graph-structured data for tasks such as link prediction. Some studies have successfully applied GAT to miRNA-disease association prediction. However, there are several drawbacks to existing methods. Firstly, most of the previous models rely solely on concatenation operations to merge features of miRNAs and diseases, which results in the deprivation of significant modality-specific information and even the inclusion of redundant information. Secondly, as the number of layers in GAT increases, there is a possibility of excessive smoothing in the feature extraction process, which significantly affects the prediction accuracy. To address these issues and effectively complete miRNA disease prediction tasks, we propose an innovative model called Multiplex Adaptive Modality Fusion Graph Attention Network (MAMFGAT). MAMFGAT utilizes GAT as the main structure for feature aggregation and incorporates a multi-modal adaptive fusion module to extract features from three interconnected networks: the miRNA-disease association network, the miRNA similarity network, and the disease similarity network. It employs adaptive learning and cross-modality contrastive learning to fuse more effective miRNA and disease feature embeddings as well as incorporates multi-modal residual feature fusion to tackle the problem of excessive feature smoothing in GATs. Finally, we employ a Multi-Layer Perceptron (MLP) model that takes the embeddings of miRNA and disease features as input to anticipate the presence of potential miRNA-disease associations. Extensive experimental results provide evidence of the superior performance of MAMFGAT in comparison to other state-of-the-art methods. To validate the significance of various modalities and assess the efficacy of the designed modules, we performed an ablation analysis. Furthermore, MAMFGAT shows outstanding performance in three cancer case studies, indicating that it is a reliable method for studying the association between miRNA and diseases. The implementation of MAMFGAT can be accessed at the following GitHub repository: https://github.com/zixiaojin66/MAMFGAT-master.

Microtubule Regulation in Plants: From Morphological Development to Stress Adaptation.

Microtubules (MTs) are essential elements of the eukaryotic cytoskeleton and are critical for various cell functions. During cell division, plant MTs form highly ordered structures, and cortical MTs guide the cell wall cellulose patterns and thus control cell size and shape. Both are important for morphological development and for adjusting plant growth and plasticity under environmental challenges for stress adaptation. Various MT regulators control the dynamics and organization of MTs in diverse cellular processes and response to developmental and environmental cues. This article summarizes the recent progress in plant MT studies from morphological development to stress responses, discusses the latest techniques applied, and encourages more research into plant MT regulation.

SSA-ICL: Multi-domain adaptive attention with intra-dataset continual learning for Facial expression recognition.

Facial expression recognition (FER) is a kind of affective computing that identifies the emotional state represented in facial photographs. Various methods have been developed for completing this critical task. In spite of this progress, three significant obstacles, the interaction between spatial action units, the inadequacy of semantic information about spectral expressions and the unbalanced data distribution, are not well addressed. In this work, we propose SSA-ICL, a novel approach for FER, and solve these three difficulties inside a coherent framework. To address the first two challenges, we develop a Spectral and Spatial Attention (SSA) module that integrates spectral semantics with spatial locations to improve the performance of the model. We provide an Intra-dataset Continual Learning (ICL) module to combat the issue of long-tail distribution in FER datasets. By subdividing a single long-tail dataset into multiple sub-datasets, ICL repeatedly trains well-balanced representations from each subset and finally develop a independent classifier. We performed extensive experiments on two publicly available datasets, AffectNet and RAFDB. In comparison to existing attention modules, our SSA achieves an accuracy improvement of 3.8%∼6.7%, as evidenced by testing results. In the meanwhile, our proposed SSA-ICL can achieve superior or comparable performance to state-of-the-art FER methods (65.78% on AffectNet and 89.44% on RAFDB).

Dietary Grape Seed Proanthocyanidin Alleviates the Liver Injury Induced by Long-Term High-Fat Diets in Sprague Dawley Rats.

In mammals, the liver is the most important organ that plays a vital function in lipid metabolism. Grape seed proanthocyanidin (GSPE) is a kind of natural polyphenolic compound primarily obtained from grape skin and seeds. Recent research found it had high bioavailability in defending against obesity, hyperlipidemia, inflammatory, oxidative stress, and targeting liver tissue. However, the mechanism of GSPE in regulating obesity induced by dietary high-fat (HF) was not fully understood, particularly the influences on liver functions. Therefore, this study aimed to investigate the effects of GSPE supplementation on the liver function and lipid metabolic parameters in rats fed HF diets long-term. A total of 40 healthy female Sprague Dawley rats were selected. After 8 weeks of obesity model feeding, the rats were randomly divided into four treatments: NC, standard diet; NC + GSPE, standard diet + 500 mg/kg body weight GSPE; HF, high-fat diet; HG + GSPE, high fat diet + 500 mg/kg body weight GSPE. Results indicated that long-term HF feeding caused severe liver problems including megalohepatia, steatosis, inflammation, and hepatocyte apoptosis. The supplementation of GSPE alleviated these symptoms. The results of the current experiment confirmed that GSPE addition up-regulated the expression of the Wnt3a/ß-catenin signaling pathway, thereby restraining the liver cell endoplasmic reticulum stress and hepatocyte apoptosis. Furthermore, the microRNA-103 may play a role in this signal-regulated pathway. In summary, GSPE had a protective effect on the liver and the current experiment provided a reference for the application of GSPE in animal nutrition as a kind of natural feed additive.

Enhancing adoptive T cell therapy for solid tumor with cell-surface anchored immune checkpoint inhibitor nanogels.

The efficacy of Adoptive Cell Therapy (ACT) for solid tumor is still mediocre. This is mainly because tumor cells can hijack ACT T cells' immune checkpoint pathways to exert immunosuppression in the tumor microenvironment. Immune Checkpoint Inhibitors such as anti-PD-1 (aPD1) can counter the immunosuppression, but the synergizing effects of aPD1 to ACT was still not satisfactory. Here we demonstrate an approach to safely anchor aPD1-formed nanogels onto T cell surface via bio-orthogonal click chemistry before adoptive transfer. The spatial-temporal co-existence of aPD1 with ACT T cells and the responsive drug release significantly improved the treatment outcome of ACT in murine solid tumor model. The average tumor weight of the group treated by cell-surface anchored aPD1 was only 18 % of the group treated by equivalent dose of free aPD1 and T cells. The technology can be broadly applicable in ACTs employing natural or Chimeric Antigen Receptor (CAR) T cells.

Curcumin alleviated lipopolysaccharide-induced lung injury via regulating the Nrf2-ARE and NF-κB signaling pathways in ducks.

BACKGROUND: Under the intensive modern poultry farming system, the lung of duck is one of the main target organs for various bacterial and viral infections. Curcumin is a kind of natural polyphenol compound for which various beneficial biological functions exist, including being an anti-inflammatory, antioxidant, and antiviral. The aim of this work was to investigate the mechanism of curcumin-alleviated lipopolysaccharide (LPS)-induced lung damage by the nuclear erythroid 2-related factor 2 (Nrf2)-antioxidant reaction element (ARE) and nuclear factor kappa B (NF-κB) signaling pathway in ducks. RESULTS: In total, 450 one-day-old male specific pathogen-free ducks were randomly assigned into three dietary treatments: CON, basal diet; LPS, basal diet + LPS treatment; LPS + CUR, basal diet + LPS + 500 mg kg-1 of curcumin. At the end of the experiment (21 days), ducks in LPS treatment were challenged with 5 mg LPS per kilogram of body weight and the other two treatments were injected with the same dose of phosphate-buffered saline solution. The results showed that LPS caused acute inflammation, oxidation stress, and lung injury. Dietary addition of curcumin significantly relieved the oxidation stress and inflammation parameters. Moreover, the results showed that remission may be through the signaling pathways of both Nrf2-ARE and NF-κB. CONCLUSION: In conclusion, dietary supplementation of 500 mg kg-1 of curcumin exhibited a lung-protective effect in ducks. This experiment broadens the mode of metabolism actions of curcumin in the target organs and provides an insight for the application of curcumin in waterfowl feed. © 2022 Society of Chemical Industry.

Comparison of Neoadjuvant Chemotherapy Efficiency in Advanced Ovarian Cancer Patients Treated With Paclitaxel Plus Carboplatin and Intraperitoneal Bevacizumab vs. Paclitaxel With Carboplatin.

Objective: This study evaluated the role of neoadjuvant chemotherapy (NACT) with bevacizumab intraperitoneal perfusion in advanced ovarian cancer (AOC). Methods: In this study, 80 patients with advanced epithelial ovarian cancer (stage IIIc or IV) who received NACT at the Central Hospital of Zhuzhou between February 2019 and October 2020 were enrolled. Patients were randomized to receive paclitaxel plus carboplatin (TC) or TC plus intraperitoneal perfusion of bevacizumab (TCB). The effect of chemotherapy was assessed following two cycles of chemotherapy. Cancer antigen 125 (CA125), tumor size, ascites volume, bleeding volume, duration of operation, surgical satisfaction rate, complication rate, and residual tumor were assessed to monitor response to chemotherapy. Results: Treatment with TCB regimen significantly reduced serum levels of CA125 and ascites volume (p < 0.001). Meanwhile, the TCB group had significantly lower intraoperative blood loss and shorter operation time (p < 0.001). Most importantly, patients treated with TCB regimen had a higher surgical satisfaction rate (p < 0.01). Moreover, the incidence of postoperative wound infection, hypoproteinemia, abdominal distension, and fever was lower in the TCB group compared with the TC group. Assessment of adverse reactions during chemotherapy showed no severe complications between the two groups. Conclusions: The results demonstrated that the TCB regimen is superior to the TC regimen alone in the treatment of AOC. These findings could help improve the surgical satisfaction rate, provide more effective treatment strategies to prolong progression-free survival and reduce postoperative complications, and promote surgical recovery in AOC.

Fetal Heart and Descending Aorta Detection in Four-Chamber View of Fetal Echocardiography.

Automatic analysis of fetal heart and related components in fetal echocardiography can help cardiologists to reach a diagnosis for Congenital Heart Disease (CHD). Previous studies mainly focused on cardiac chamber segmentation, while few researches deal with the cardiac component detection. In this paper, we tackle the task of simultaneous detection of the fetal heart and descending aorta in four-chamber view of fetal echocardiography, which is useful to analyze some kinds of CHD, such as left/right atrial isomerism, dextroversion of heart, etc. Several CNN-based object detection methods with different backbones are thoroughly evaluated, and finally, the Hybrid Task Cascade method with HRNet is selected as the detection method. Experiments on a fetal echocardiography dataset show that the method can achieve superior performance according to common-used evaluation metrics.Clinical relevance-This can be used to help the cardiologists to estimate the position of the fetal heart and the descending aorta, which is also useful to estimate the direction of the cardiac axis and apex and analyze some kinds of CHD, such as left/right atrial isomerism, dextroversion of heart, etc.

Simultaneous Segmentation of Four Cardiac Chambers in Fetal Echocardiography.

Accurate segmentation of cardiac chambers is helpful for the diagnosis of Congenital Heart Disease (CHD) in fetal echocardiography. Previous studies mainly focused on single cardiac chamber segmentation, which cannot provide sufficient information for the cardiologists. In this paper, we present an instance segmentation approach capable of segmenting four cardiac chambers accurately and simultaneously. A novel object proposal recovery strategy is further deployed to retrieve possible missing objects. To alleviate the shortage of medical data and further improve the segmentation performance, we utilize a rotation and distortion method for data augmentation. Experiments on a fetal echocardiography dataset of 319 fetuses demonstrate that the proposed approach can achieve superior performance according to common-used evaluation metrics.Clinical relevance-This can be used to help the cardiologists to better analyze the structure and function of the fetal heart.

A category attention instance segmentation network for four cardiac chambers segmentation in fetal echocardiography.

Fetal echocardiography is an essential and comprehensive examination technique for the detection of fetal heart anomalies. Accurate cardiac chambers segmentation can assist cardiologists to analyze cardiac morphology and facilitate heart disease diagnosis. Previous research mainly focused on the segmentation of single cardiac chambers, such as left ventricle (LV) segmentation or left atrium (LA) segmentation. We propose a generic framework based on instance segmentation to segment the four cardiac chambers accurately and simultaneously. The proposed Category Attention Instance Segmentation Network (CA-ISNet) has three branches: a category branch for predicting the semantic category, a mask branch for segmenting the cardiac chambers, and a category attention branch for learning category information of instances. The category attention branch is used to correct instance misclassification of the category branch. In our collected dataset, which contains echocardiography images with four-chamber views of 319 fetuses, experimental results show our method can achieve superior segmentation performance against state-of-the-art methods. Specifically, using fivefold cross-validation, our model achieves Dice coefficients of 0.7956, 0.7619, 0.8199, 0.7470 for the four cardiac chambers, and with an average precision of 45.64%.

An Intrinsically Disordered Protein Interacts with the Cytoskeleton for Adaptive Root Growth under Stress.

Intrinsically disordered proteins function as flexible stress modulators in vivo through largely unknown mechanisms. Here, we elucidated the mechanistic role of an intrinsically disordered protein, REPETITIVE PRO-RICH PROTEIN (RePRP), in regulating rice (Oryza sativa) root growth under water deficit. With nearly 40% Pro, RePRP is induced by water deficit and abscisic acid (ABA) in the root elongation zone. RePRP is sufficient and necessary for repression of root development by water deficit or ABA. We showed that RePRP interacts with the highly ordered cytoskeleton components actin and tubulin both in vivo and in vitro. Binding of RePRP reduces the abundance of actin filaments, thus diminishing noncellulosic polysaccharide transport to the cell wall and increasing the enzyme activity of Suc synthase. RePRP also reorients the microtubule network, which leads to disordered cellulose microfibril organization in the cell wall. The cell wall modification suppresses root cell elongation, thereby generating short roots, whereas increased Suc synthase activity triggers starch accumulation in "heavy" roots. Intrinsically disordered proteins control cell elongation and carbon reserves via an order-by-disorder mechanism, regulating the highly ordered cytoskeleton for development of "short-but-heavy" roots as an adaptive response to water deficit in rice.