HCMV-miR-US33-5p promotes apoptosis of aortic vascular smooth muscle cells by targeting EPAS1/SLC3A2 pathway.

BACKGROUND: In patients with acute aortic dissection (AAD), increased vascular smooth muscle cell (VSMC) apoptosis has been found. Human cytomegalovirus (HCMV)-miR-US33-5p was significantly increased in the plasma of patients with AAD. However, the roles of miR-US33-5p in human aortic VSMC (HA-VSMC) apoptosis remain to be elucidated. METHODS: In the current study, cell apoptosis was analyzed by flow cytometry, cell proliferation by CCK-8 assay, and differentially expressed genes by RNA sequencing. Luciferase reporter assay was used for binding analysis between miR-US33-5p and endothelial PAS domain protein 1 (EPAS1), and EPAS1 and amino acid transporter heavy chain, member 2 (SLC3A2). The enrichment degree of SLC3A2 promoter DNA was analyzed by chromatin immunoprecipitation assay. Quantitative reverse-transcription polymerase chain reaction (qRT-PCR) and immunoblotting were performed for measuring messenger RNA (mRNA) and protein levels, respectively. RESULTS: It was found that HCMV infection inhibited proliferation but promoted HA-VSMC apoptosis by upregulating HCMV-miR-US33-5p. Transfection of HCMV-miR-US33-5p mimics the significant effect on several signaling pathways including integrin signaling as shown in the RNA sequencing data. Western blotting analysis confirmed that HCMV-miR-US33-5p mimics suppression of the activity of key factors of the integrin signal pathway including FAK, AKT, CAS, and Rac. Mechanistic study showed that HCMV-miR-US33-5p bound to the 3'-untranslated region of EPAS1 to suppress its expression, leading to suppression of SLC3A2 expression, which ultimately promoted cell apoptosis and inhibited cell proliferation. This was confirmed by the findings that silencing EPAS1 significantly reduced the SLC3A2 expression and inhibited proliferation and key factors of integrin signal pathway. CONCLUSIONS: HCMV-miR-US33-5p suppressed proliferation, key factors of integrin signal pathway, and EPAS1/SLC3A2 expression, but promoted HA-VSMC apoptosis. These findings highlighted the importance of HCMV-miR-US33-5p/EPAS1/SCL3A2 signaling and may provide new insights into therapeutic strategies for AAD.

Consistency-guided Differential Decoding for Enhancing Semi-supervised Medical Image Segmentation.

Semi-supervised learning (SSL) has been proven beneficial for mitigating the issue of limited labeled data, especially on volumetric medical image segmentation. Unlike previous SSL methods which focus on exploring highly confident pseudo-labels or developing consistency regularization schemes, our empirical findings suggest that differential decoder features emerge naturally when two decoders strive to generate consistent predictions. Based on the observation, we first analyze the treasure of discrepancy in learning towards consistency, under both pseudo-labeling and consistency regularization settings, and subsequently propose a novel SSL method called LeFeD, which learns the feature-level discrepancies obtained from two decoders, by feeding such information as feedback signals to the encoder. The core design of LeFeD is to enlarge the discrepancies by training differential decoders, and then learn from the differential features iteratively. We evaluate LeFeD against eight state-of-the-art (SOTA) methods on three public datasets. Experiments show LeFeD surpasses competitors without any bells and whistles, such as uncertainty estimation and strong constraints, as well as setting a new state of the art for semi-supervised medical image segmentation. Code has been released at https://github.com/maxwell0027/LeFeD.

Subject-Independent Wearable P300 Brain-Computer Interface Based on Convolutional Neural Network and Metric Learning.

OBJECTIVE: The calibration procedure for a wearable P300 brain-computer interface (BCI) greatly impact the user experience of the system. Each user needs to spend additional time establishing a decoder adapted to their own brainwaves. Therefore, achieving subject independent is an urgent issue for wearable P300 BCI needs to be addressed. METHODS: A dataset of electroencephalogram (EEG) signals was constructed from 100 individuals by conducting a P300 speller task with a wearable EEG amplifier. A framework is proposed that initially improves cross-subject consistency of EEG features through a common feature extractor. Subsequently, a simple and compact convolutional neural network (CNN) architecture is employed to learn an embedding sub-space, where the mapped EEG features are maximally separated, while pursuing the minimum distance within the same class and the maximum distance between different classes. Finally, the model's generalization capability was further optimized through fine-tuning. RESULTS: The proposed method significantly boosts the average accuracy of wearable P300 BCI to 73.23 ±7.62% without calibration and 78.75±6.37% with fine-tuning. CONCLUSION: The results demonstrate the feasibility and excellent performance of our dataset and framework. SIGNIFICANCE: A calibration-free wearable P300 BCI system is feasible, suggesting significant potential for practical applications of the wearable P300 BCI system.

Shared Three-Dimensional Robotic Arm Control Based on Asynchronous BCI and Computer Vision.

OBJECTIVE: A brain-computer interface (BCI) can be used to translate neuronal activity into commands to control external devices. However, using noninvasive BCI to control a robotic arm for movements in three-dimensional (3D) environments and accomplish complicated daily tasks, such as grasping and drinking, remains a challenge. APPROACH: In this study, a shared robotic arm control system based on hybrid asynchronous BCI and computer vision was presented. The BCI model, which combines steady-state visual evoked potentials (SSVEPs) and blink-related electrooculography (EOG) signals, allows users to freely choose from fifteen commands in an asynchronous mode corresponding to robot actions in a 3D workspace and reach targets with a wide movement range, while computer vision can identify objects and assist a robotic arm in completing more precise tasks, such as grasping a target automatically. RESULTS: Ten subjects participated in the experiments and achieved an average accuracy of more than 92% and a high trajectory efficiency for robot movement. All subjects were able to perform the reach-grasp-drink tasks successfully using the proposed shared control method, with fewer error commands and shorter completion time than with direct BCI control. SIGNIFICANCE: Our results demonstrated the feasibility and efficiency of generating practical multidimensional control of an intuitive robotic arm by merging hybrid asynchronous BCI and computer vision-based recognition.

Thoracic aneurysm and dissection gene variants increase the risk of aortic-related adverse events in early-onset isolated Stanford type B aortic dissection after endovascular aortic repair.

Background: Researches on Marfan syndrome and Ehlers-Danlos syndrome leading to early-onset aortic dissection (AD) emphasize the importance of gene variants, but the genetic pathogenesis, clinical characteristics and outcomes of early-onset isolated Stanford type B aortic dissection (iTBAD) patients remain unclear and need to be further elucidated. Methods: Isolated type B AD patients with an onset age of less than 50 years were enrolled in this study. Whole exome sequencing (WES) was performed to detect 11 known thoracic aortic aneurysm and dissection (TAAD) gene variants. Clinical characteristics and outcomes were compared between patients with and without gene variants. Multivariate Cox regression analysis was performed to identify independent risk factors for aortic-related adverse events (ARAEs) after endovascular aortic repair. Results: A total of 37 patients were included. Ten patients carried 10 variants in five TAAD genes, four of whom carried pathogenic or likely pathogenic variants. Compared to patients without the variants, patients with variants had a lower incidence of hypertension (50.0% vs. 88.9%, P=0.021), a higher incidence of other vascular abnormalities (60.0% vs. 18.5%, P=0.038), all-cause mortality (40.0% vs. 3.7%, P=0.014) and aortic related mortality (30.0% vs. 3.7%, P=0.052). Multivariate analysis confirmed the presence of TAAD gene variants as the only independent risk factor for ARAEs [hazard ratio (HR) =4.00; 95% confidence interval (CI): 1.26-12.74; P=0.019]. Conclusions: Routine genetic testing is necessary for early-onset iTBAD patients. Individuals with a high risk of ARAEs can be identified by detecting TAAD gene variants, which is important for risk stratification and proper management.

Smart Adhesive Peptide Nanofibers for Cell Capture and Release.

Efficient cell capture and release methods are important for single-cell analysis of pathological samples. It requires not only strong cell binding but also mild cell release to maximize the number of collected cells while maintaining their viability. Here, we report a smart cell capture and release system based on self-assembling adhesive peptide nanofibers. We installed a versatile surface binding motif, 3, 4-dihydroxyphenylalanine to the C-terminus of a self-assembling peptide. We show that the designed peptide can self-assemble at physiological pH to establish strong cell and substrate binding. The binding strength is dramatically reduced upon the dissembling of the peptide fibers triggered by raising the pH to slightly basic. We demonstrate the efficient capture of four different cells using this system. The capture rates are comparable to fibrin glue and the released cells retain higher viability than those released by enzymatic digestion approaches. Given that this method is highly efficient, biocompatible, and easy to implement, we anticipate that this approach can be widely applied to cell capture and release for single cell analysis and cell therapy.