Effect of electro-acupuncture on gene expression in heart of rats with stress-induced pre-hypertension based on gene chip technology

AbstractObjectiveTo explore electro-acupuncture's (EA's) effect on gene expression in heart of rats with stress-induced pre-hypertension and try to reveal its biological mechanism based on gene chip technology.MethodsTwenty-seven Wistar male rats were randomly divided into 3 groups. The stress-induced hypertensive rat model was prepared by electric foot-shocks combined with generated noise. Molding cycle lasted for 14 days and EA intervene was applied on rats in model + EA group during model preparation. Rat Gene 2.0 Sense Target Array technology was used for the determination of gene expression profiles and the screened key genes were verified by real-time quantitative polymerase chain reaction (RT-PCR) method.ResultsCompared with blank control group, 390 genes were changed in model group; compared with model control group, 330 genes were changed in model+EA group. Significance analysis of gene function showed that the differentially expressed genes are those involved in biological process, molecular function and cellular components. RT-PCR result of the screened key genes is consistent with that of gene chip test.ConclutionEA could significantly lower blood pressure of stress-induced pre-hypertension rats and affect its gene expression profile in heart. Genes that related to the contraction of vascular smooth muscle may be involved in EA's anti-hypertensive mechanism

Semantic Equivariant Mixup

Mixup is a well-established data augmentation technique, which can extend the training distribution and regularize the neural networks by creating ''mixed'' samples based on the label-equivariance assumption, i.e., a proportional mixup of the input data results in the corresponding labels being mixed in the same proportion. However, previous mixup variants may fail to exploit the label-independent information in mixed samples during training, which usually contains richer semantic information. To further release the power of mixup, we first improve the previous label-equivariance assumption by the semantic-equivariance assumption, which states that the proportional mixup of the input data should lead to the corresponding representation being mixed in the same proportion. Then a generic mixup regularization at the representation level is proposed, which can further regularize the model with the semantic information in mixed samples. At a high level, the proposed semantic equivariant mixup (sem) encourages the structure of the input data to be preserved in the representation space, i.e., the change of input will result in the obtained representation information changing in the same way. Different from previous mixup variants, which tend to over-focus on the label-related information, the proposed method aims to preserve richer semantic information in the input with semantic-equivariance assumption, thereby improving the robustness of the model against distribution shifts. We conduct extensive empirical studies and qualitative analyzes to demonstrate the effectiveness of our proposed method. The code of the manuscript is in the supplement.Comment: Under revie

Exploring and Exploiting Uncertainty for Incomplete Multi-View Classification

Classifying incomplete multi-view data is inevitable since arbitrary view missing widely exists in real-world applications. Although great progress has been achieved, existing incomplete multi-view methods are still difficult to obtain a trustworthy prediction due to the relatively high uncertainty nature of missing views. First, the missing view is of high uncertainty, and thus it is not reasonable to provide a single deterministic imputation. Second, the quality of the imputed data itself is of high uncertainty. To explore and exploit the uncertainty, we propose an Uncertainty-induced Incomplete Multi-View Data Classification (UIMC) model to classify the incomplete multi-view data under a stable and reliable framework. We construct a distribution and sample multiple times to characterize the uncertainty of missing views, and adaptively utilize them according to the sampling quality. Accordingly, the proposed method realizes more perceivable imputation and controllable fusion. Specifically, we model each missing data with a distribution conditioning on the available views and thus introducing uncertainty. Then an evidence-based fusion strategy is employed to guarantee the trustworthy integration of the imputed views. Extensive experiments are conducted on multiple benchmark data sets and our method establishes a state-of-the-art performance in terms of both performance and trustworthiness.Comment: CVP

USP10 is a potential mediator for vagus nerve stimulation to alleviate neuroinflammation in ischaemic stroke by inhibiting NF-κB signalling pathway

BackgroundVagus nerve stimulation (VNS) has a protective effect on neurological recovery in ischaemic stroke. However, its underlying mechanism remains to be clarified. Ubiquitin-specific protease 10 (USP10), a member of the ubiquitin-specific protease family, has been shown to inhibit the activation of the NF-κB signalling pathway. Therefore, this study investigated whether USP10 plays a key role in the protective effect of VNS against ischemic stroke and explore its mechanism.MethodsIschaemic stroke model was constructed by transient middle cerebral artery occlusion (tMCAO) in mice. VNS was performed at 30 min, 24hr, and 48hr after the establishment of tMCAO model. USP10 expression induced by VNS after tMCAO was measured. LV-shUSP10 was used to establish the model with low expression of USP10 by stereotaxic injection technique. The effects of VNS with or without USP10 silencing on neurological deficits, cerebral infarct volume, NF-κB pathway activation, glial cell activation, and release of pro-inflammation cytokines were assessed.ResultsVNS enhanced the expression of USP10 following tMCAO. VNS ameliorated neurological deficits and reduced cerebral infarct volume, but this effect was inhibited by silencing of USP10. Activation of the NF-κB pathway and the expression of inflammatory cytokines induced by tMCAO were suppressed by VNS. Moreover, VNS promoted the pro-to-anti-inflammatory response of microglia and inhibited activation of astrocytes, while silencing of USP10 prevented the neuroprotective and anti-neuroinflammatory effects of VNS.ConclusionUSP10 is a potential mediator for VNS to alleviate neurological deficits, neuroinflammation, and glial cell activation in ischaemic stroke by inhibiting NF-κB signalling pathway

Single-cell RNA sequencing reveals cancer stem-like cells and dynamics in tumor microenvironment during cholangiocarcinoma progression

Cholangiocarcinoma is a malignancy of the bile ducts that is driven by activities of cancer stem-like cells and characterized by a heterogeneous tumor microenvironment. To better understand the transcriptional profiles of cancer stem-like cells and dynamics in the tumor microenvironment during the progression of cholangiocarcinoma, we performed single-cell RNA analysis on cells collected from three different timepoints of tumorigenesis in a YAP/AKT mouse model. Bulk RNA sequencing data from TCGA (The Cancer Genome Atlas program) and ICGC cohorts were used to verify and support the finding. In vitro and in vivo experiments were performed to assess the stemness of cancer stem-like cells. We identified Tm4sf1high malignant cells as cancer stem-like cells. Across timepoints of cholangiocarcinoma formation in YAP/AKT mice, we found dynamic change in cancer stem-like cell/stromal/immune cell composition. Nevertheless, the dynamic interaction among cancer stem-like cells, immune cells, and stromal cells at different timepoints was elaborated. Collectively, these data serve as a useful resource for better understanding cancer stem-like cell and malignant cell heterogeneity, stromal cell remodeling, and immune cell reprogramming. It also sheds new light on transcriptomic dynamics during cholangiocarcinoma progression at single-cell resolution