S-adenosyl-L-methionine supplementation alleviates aortic dissection by decreasing inflammatory infiltration.

Methionine, an indispensable amino acid crucial for dietary balance, intricately governs metabolic pathways. Disruption in its equilibrium has the potential to heighten homocysteine levels in both plasma and tissues, posing a conceivable risk of inducing inflammation and detriment to the integrity of vascular endothelial cells. The intricate interplay between methionine metabolism, with a specific focus on S-adenosyl-L-methionine (SAM), and the onset of thoracic aortic dissection (TAD) remains enigmatic despite acknowledging the pivotal role of inflammation in this vascular condition. In an established murine model induced by ß-aminopropionitrile monofumarate (BAPN), we delved into the repercussions of supplementing with S-adenosyl-L-methionine (SAM) on the progression of TAD. Our observations uncovered a noteworthy improvement in aortic dissection and rupture rates, accompanied by a marked reduction in mortality upon SAM supplementation. Notably, SAM supplementation exhibited a considerable protective effect against BAPN-induced degradation of elastin and the extracellular matrix. Furthermore, SAM supplementation demonstrated a robust inhibitory influence on the infiltration of immune cells, particularly neutrophils and macrophages. It also manifested a notable reduction in the inflammatory polarization of macrophages, evident through diminished accumulation of MHC-IIhigh macrophages and reduced expression of inflammatory cytokines such as IL1ß and TNFα in macrophages. Simultaneously, SAM supplementation exerted a suppressive effect on the activation of CD4 + and CD8 + T cells within the aorta. This was evidenced by an elevated proportion of CD44- CD62L + naïve T cells and a concurrent decrease in CD44 + CD62L- effector T cells. In summary, our findings strongly suggest that the supplementation of SAM exhibits remarkable efficacy in alleviating BAPN-induced aortic inflammation, consequently impeding the progression of thoracic aortic dissection.

MiR-181a protects the heart against myocardial infarction by regulating mitochondrial fission via targeting programmed cell death protein 4.

Worldwide, myocardial infarction (MI) is the leading cause of death and disability-adjusted life years lost. Recent researches explored new methods of detecting biomarkers that can predict the risk of developing myocardial infarction, which includes identifying genetic markers associated with increased risk. We induced myocardial infarction in mice by occluding the left anterior descending coronary artery and performed TTC staining to assess cell death. Next, we performed ChIP assays to measure the enrichment of histone modifications at the promoter regions of key genes involved in mitochondrial fission. We used qPCR and western blot to measure expression levels of relative apoptotic indicators. We report that miR-181a inhibits myocardial ischemia-induced apoptosis and preserves left ventricular function after MI. We show that programmed cell death protein 4 (PDCD4) is the target gene involved in miR-181a-mediated anti-ischemic injury, which enhanced BID recruitment to the mitochondria. In addition, we discovered that p53 inhibits the expression of miR-181a via transcriptional regulation. Here, we discovered for the first time a mitochondrial fission and apoptosis pathway which is controlled by miR-181a and involves PDCD4 and BID. This pathway may be controlled by p53 transcriptionally, and we presume that miR-181a may lead to the discovery of new therapeutic and preventive targets for ischemic heart diseases.

An artificial intelligence-assisted diagnosis modeling software (AIMS) platform based on medical images and machine learning: a development and validation study.

Background: Supervised machine learning methods [both radiomics and convolutional neural network (CNN)-based deep learning] are usually employed to develop artificial intelligence models with medical images for computer-assisted diagnosis and prognosis of diseases. A classical machine learning-based modeling workflow involves a series of interconnected components and various algorithms, but this makes it challenging, tedious, and labor intensive for radiologists and researchers to build customized models for specific clinical applications if they lack expertise in machine learning methods. Methods: We developed a user-friendly artificial intelligence-assisted diagnosis modeling software (AIMS) platform, which supplies standardized machine learning-based modeling workflows for computer-assisted diagnosis and prognosis systems with medical images. In contrast to other existing software platforms, AIMS contains both radiomics and CNN-based deep learning workflows, making it an all-in-one software platform for machine learning-based medical image analysis. The modular design of AIMS allows users to build machine learning models easily, test models comprehensively, and fairly compare the performance of different models in a specific application. The graphical user interface (GUI) enables users to process large numbers of medical images without programming or script addition. Furthermore, AIMS also provides a flexible image processing toolkit (e.g., semiautomatic segmentation, registration, morphological operations) to rapidly create lesion labels for multiphase analysis, multiregion analysis of an individual tumor (e.g., tumor mass and peritumor), and multimodality analysis. Results: The functionality and efficiency of AIMS were demonstrated in 3 independent experiments in radiation oncology, where multiphase, multiregion, and multimodality analyses were performed, respectively. For clear cell renal cell carcinoma (ccRCC) Fuhrman grading with multiphase analysis (sample size =187), the area under the curve (AUC) value of the AIMS was 0.776; for ccRCC Fuhrman grading with multiregion analysis (sample size =177), the AUC value of the AIMS was 0.848; for prostate cancer Gleason grading with multimodality analysis (sample size =206), the AUC value of the AIMS was 0.980. Conclusions: AIMS provides a user-friendly infrastructure for radiologists and researchers, lowering the barrier to building customized machine learning-based computer-assisted diagnosis models for medical image analysis.

CAPNet: Context attention pyramid network for computer-aided detection of microcalcification clusters in digital breast tomosynthesis.

BACKGROUND AND OBJECTIVE: Computer-aided detection (CADe) of microcalcification clusters (MCs) in digital breast tomosynthesis (DBT) is crucial in the early diagnosis of breast cancer. Although convolutional neural network (CNN)-based detection models have achieved excellent performance in medical lesion detection, they are subject to some limitations in MC detection: 1) Most existing models employ the feature pyramid network (FPN) for multi-scale object detection; however, the rough feature sharing between adjacent layers in the FPN may limit the detection ability for small and low-contrast MCs; and 2) the MCs region only accounts for a small part of the annotation box, so the features extracted indiscriminately within the whole box may easily be affected by the background. In this paper, we develop a novel CNN-based CADe method to alleviate the impacts of the above limitations for the accurate and rapid detection of MCs in DBT. METHODS: The proposed method has two parts: a novel context attention pyramid network (CAPNet) for intra-layer MC detection in two-dimensional (2D) slices and a three-dimensional (3D) aggregation procedure for aggregating 2D intra-layer MCs into a 3D result according to their connectivity in 3D space. The proposed CAPNet is based on an anchor-free and one-stage detection architecture and contains a context feature selection fusion (CFSF) module and a microcalcification response (MCR) branch. The CFSF module can efficiently enrich shallow layers' features by the complementary selection of local context features, aiming to reduce the missed detection of small and low-contrast MCs. The MCR branch is a one-layer branch parallel to the classification branch, which can alleviate the influence of the background region within the annotation box on feature extraction and enhance the ability of the model to distinguish MCs from normal breast tissue. RESULTS: We performed a comparison experiment on an in-house clinical dataset with 648 DBT volumes, and the proposed method achieved impressive performance with a sensitivity of 91.56 % at 1 false positive per DBT volume (FPs/volume) and 93.51 % at 2 FPs/volume, outperforming other representative detection models. CONCLUSIONS: The experimental results indicate that the proposed method is effective in the detection of MCs in DBT. This method can provide objective, accurate, and quick diagnostic suggestions for radiologists, presenting potential clinical value for early breast cancer screening.

Multi-phase-combined CECT radiomics models for Fuhrman grade prediction of clear cell renal cell carcinoma.

Objective: This study aimed to evaluate the effectiveness of multi-phase-combined contrast-enhanced CT (CECT) radiomics methods for noninvasive Fuhrman grade prediction of clear cell renal cell carcinoma (ccRCC). Methods: A total of 187 patients with four-phase CECT images were retrospectively enrolled and then were categorized into training cohort (n=126) and testing cohort (n=61). All patients were confirmed as ccRCC by histopathological reports. A total of 110 3D classical radiomics features were extracted from each phase of CECT for individual ccRCC lesion, and contrast-enhanced variation features were also calculated as derived radiomics features. These features were concatenated together, and redundant features were removed by Pearson correlation analysis. The discriminative features were selected by minimum redundancy maximum relevance method (mRMR) and then input into a C-support vector classifier to build multi-phase-combined CECT radiomics models. The prediction performance was evaluated by the area under the curve (AUC) of receiver operating characteristic (ROC). Results: The multi-phase-combined CECT radiomics model showed the best prediction performance (AUC=0.777) than the single-phase CECT radiomics model (AUC=0.711) in the testing cohort (p value=0.039). Conclusion: The multi-phase-combined CECT radiomics model is a potential effective way to noninvasively predict Fuhrman grade of ccRCC. The concatenation of first-order features and texture features extracted from corticomedullary phase and nephrographic phase are discriminative feature representations.

Investigation into the protective effects of hypaconitine and glycyrrhetinic acid against chronic heart failure of the rats.

BACKGROUND: The present study aimed to determine the protective effects of hypaconitine (HA) and glycyrrhetinic acid (GA) against chronic heart failure (CHF) in the rats and to explore the underlying molecular mechanisms. METHODS: The CHF rat model was established by transverse-aortic constriction (TAC) operation. Transthoracic echocardiography and hematoxylin eosin (HE) staining were used to evaluate the pathophysiological and histopathological changes of CHF model. The total cholesterol (TCHO) and triglyceride (TG) levels were determined by ELISA assay. The protein expression of fibroblast growth factor 2 (FGF2), vascular endothelial growth factor A (VEGFA) and endothelial nitric oxide synthase (eNOS) in the rat ventricular tissues was determined by immunohistochemistry. The serum metabolites were determined by LC-MS/MS assay. RESULTS: After applied the HA + GA, the cardiac tissue and structure were obviously improved, and the HA + GA treatment also significantly reduced the plasma levels of TCHO and TG in the CHF rats. The expression of FGF2 and VEGFA protein was up-regulated and the expression of eNOS protein was down-regulated in the ventricular tissues of CHF rats, which was significantly restored after HA + GA treatment. HA + GA treatment down-regulated serum isonicotinic acid, phosphatidylcholine, cardiolipin, estrogen glucuronide, and glycocholic acid, up-regulated serum sphingosine and deoxycholic acid in the CHF rats. CONCLUSIONS: In conclusion, HA + GA showed protective effects on CHF in the rats, and the HA + GA may exert protective effects by reducing lipid levels, up-regulating the expression of FGF2 and VEGFA proteins, attenuating eNOS protein expression, and modulating metabolic pathways. However, the molecular mechanisms underlying HA + GA-mediated effects still require further examination.

A new machine learning based user-friendly software platform for automatic radiomics modeling and analysis.

Supervised machine learning methods are usually used to build a custom model for disease diagnosis and auxiliary prognosis in radiomics studies. A classical machine learning pipeline involves a series of steps and multiple algorithms, which leads to a great challenge to find an appropriate combination of algorithms and an optimal hyper-parameter set for radiomics model building. We developed a freely available software package for radiomics model building. It can be used to lesion labeling, feature extraction, feature selection, classifier training and statistic result visualization. This software provides a user-friendly graphic interface and flexible IOs for radiologists and researchers to automatically develop radiomics models. Moreover, this software can extract features from corresponding lesion regions in multi-modality images, which is labeled by semi-automatic or full-automatic segmentation algorithms. It is designed in a loosely coupled architecture, programmed with Qt, VTK, and Python. In order to evaluate the availability and effectiveness of the software, we utilized it to build a CT-based radiomics model containing peritumoral features for malignancy grading of cell renal cell carcinoma. The final model got a good performance of grading study with AUC=0.848 on independent validation dataset.Clinical Relevance-the developed provides convenient and powerful toolboxes to build radiomics models for radiologists and researchers on clinical studies.

Diagnostic performance of noninvasive imaging modalities for localization of insulinoma: A meta-analysis.

BACKGROUND: Insulinoma is the most common functional neuroendocrine tumor found only in the pancreas. The early detection of insulinoma is of importance. Studies comparing the performance of noninvasive modalities were limited by sample size and heterogeneity between studies. The aim of this meta-analysis was to evaluate the diagnostic performance of PET/CT, SPECT/CT, CT and MRI for the localization of insulinoma, and to provide evidence for clinical practice. METHODS: PubMed, Embase, Cochrane Library, Wanfang Data and China National Knowledge Infrastructure were searched from inception to May 31, 2021. Pooled sensitivity, specificity, positive Likelihood Ratio (+LR) and negative Likelihood Ratio (-LR), diagnostic odds ratio (DOR), and concordance rate were calculated. RESULTS: A total of 19 studies including 708 patients of insulinoma reached the inclusion criteria. PET/CT imaging demonstrated a pooled sensitivity of 0.79 (95% CI: 0.54-0.92) and a pooled specificity of 0.84 (95% CI: 0.20-0.99). The pooled sensitivity and specificity of SPECT/CT were 0.77 (95% CI: 0.46-0.93) and 0.45 (95% CI: 0.22-0.70). CT showed an overall sensitivity of 0.54 (95% CI: 0.35-0.72) and specificity of 0.75 (95% CI: 0.54-0.88). The pooled sensitivity and specificity for MRI were 0.54 (95% CI: 0.31-0.75) and 0.65 (95% CI: 0.39-0.84), respectively. The concordance rates of PET, SPECT, CT, and MRI were 78% (95% CI: 66-90%), 74% (95% CI: 52-97%), 56% (95% CI: 41-72%), and 53% (95% CI: 33-73%), respectively. CONCLUSION: Results of this study indicate that PET/CT demonstrated superior performance than SPECT/CT, CT and MRI for the localization of insulinoma. GLP-1R based PET/CT manifested better diagnostic performance in comparison with SSTR based PET/CT imaging modality.

CISD1 protects against atherosclerosis by suppressing lipid accumulation and inflammation via mediating Drp1.

Atherosclerosis still remains the leading cause of morbidity and mortality worldwide, and deeper understanding of target signaling that protect from the atherosclerosis progression may provide novel therapeutic strategies. CDGSH iron-sulfur domain-containing protein 1 (CISD1) is a protein localized on the outer membrane of mitochondria, and plays key roles in regulating cell death and oxidative stress. However, its potential on atherosclerosis development and the underlying mechanisms are largely unknown. Here, in our study, we found markedly decreased CISD1 expression in lipid-laden THP1 macrophages. Notably, lentivirus (LV)-mediated CISD1 over-expression remarkably ameliorated lipid deposition in macrophages stimulated by ox-LDL. Furthermore, cellular total ROS and mitochondrial ROS generation, and impairment of mitochondrial membrane potential (MMP) were highly induced by ox-LDL in THP1 cells, while being considerably reversed upon CISD1 over-expression. Inflammatory response caused by ox-LDL was also significantly restrained in macrophages with CISD1 over-expression. Mechanistically, we found that CISD1 could interact with dynamin-related protein 1 (Drp1). Intriguingly, CISD1-improved mitochondrial dysfunction and inflammation in ox-LDL-treated macrophages were strongly abolished by Drp1 over-expression, indicating that Drp1 suppression might be necessary for CISD1 to perform its protective effects in vitro. In high fat diet (HFD)-fed apolipoprotein E-deficient (ApoE-/-) mice, tail vein injection of lentiviral vector expressing CISD1 remarkably decreased atherosclerotic lesion area, serum LDL cholesterol levels and triglyceride contents. Inflammatory response, cellular total and mitochondrial ROS production, and Drp1 expression levels in aorta tissues were also dramatically ameliorated in HFD-fed ApoE-/- mice, contributing to the inhibition of atherosclerosis in vivo. Therefore, improving CISD1 expression may be a novel therapeutic strategy for atherosclerosis treatment.

[Automatic segmentation of kidney tumor based on cascaded multiscale convolutional neural networks].

The background of abdominal computed tomography (CT) images is complex, and kidney tumors have different shapes, sizes and unclear edges. Consequently, the segmentation methods applying to the whole CT images are often unable to effectively segment the kidney tumors. To solve these problems, this paper proposes a multi-scale network based on cascaded 3D U-Net and DeepLabV3+ for kidney tumor segmentation, which uses atrous convolution feature pyramid to adaptively control receptive field. Through the fusion of high-level and low-level features, the segmented edges of large tumors and the segmentation accuracies of small tumors are effectively improved. A total of 210 CT data published by Kits2019 were used for five-fold cross validation, and 30 CT volume data collected from Suzhou Science and Technology Town Hospital were independently tested by trained segmentation models. The results of five-fold cross validation experiments showed that the Dice coefficient, sensitivity and precision were 0.796 2 ± 0.274 1, 0.824 5 ± 0.276 3, and 0.805 1 ± 0.284 0, respectively. On the external test set, the Dice coefficient, sensitivity and precision were 0.817 2 ± 0.110 0, 0.829 6 ± 0.150 7, and 0.831 8 ± 0.116 8, respectively. The results show a great improvement in the segmentation accuracy compared with other semantic segmentation methods.

Inhibition of KIF20A by transcription factor IRF6 affects the progression of renal clear cell carcinoma.

BACKGROUND: Renal clear cell carcinoma (ccRCC) is one of the most common malignant tumors, whose incidence is increasing year by year. IRF6 plays an important role in the occurrence of tumors, although there is yet no report on its expression in ccRCC. METHODS: The expression of IRF6 and KIF20A in ccRCC was predicted by GEPIA and HAP databases. In addition, GEPIA database predicted the relationship between IRF6 and KIF20A expressions and the pathological staging, overall survival, and disease-free survival of ccRCC. The possible binding sites of IRF6 and KIF20A promoters were predicted by JASPAR database and verified by luciferase and ChIP assays. The specific effects of IRF6 on ccRCC cell proliferation, invasion and apoptosis were subsequently examined at both cellular level and animal level. RESULTS: The database predicted down-regulated IRF6 expression in renal carcinoma tissues and its correlation with poor prognosis. IRF6 overexpression inhibited cRCC cell proliferation, invasion and migration. In addition, up-regulated KIF20A expression in renal carcinoma tissues and its association with prognosis were also predicted. Interference with KIF20A inhibited the proliferation, invasion, and migration of ccRCC cells. Finally, we confirmed that KIF20A is a functional target of IRF6 and can partially reverse the effects of IRF6 on the proliferation, invasion and migration of ccRCC cells. CONCLUSION: Inhibition of KIF20A by transcription factor IRF6 affects cell proliferation, invasion and migration in renal clear cell carcinoma.

CT-based peritumoral radiomics signatures for malignancy grading of clear cell renal cell carcinoma.

OBJECTIVE: To evaluate the efficiency of CT-based peritumoral radiomics signatures of clear cell renal cell carcinoma (ccRCC) for malignancy grading in preoperative prediction. MATERIALS AND METHODS: 203 patients with pathologically confirmed as ccRCC were retrospectively enrolled in this study. All patients were categorized into training set (n = 122) and validation set (n = 81). For each patient, two types of volumes of interest (VOI) were masked on CT images. One type of VOIs was defined as the tumor mass volume (TMV), which was masked by radiologists delineating the outline of all contiguous slices of the entire tumor, while the other type defined as the peritumoral tumor volume (PTV), which was automatically created by an image morphological method. 1760 radiomics features were calculated from each VOI, and then the discriminative radiomics features were selected by Pearson correlation analysis for reproducibility and redundancy. These selected features were investigated their validity for building radiomics signatures by mRMR feature ranking method. Finally, the top ranked features, which were used as radiomics signatures, were input into a classifier for malignancy grading. The prediction performance was evaluated by receiver operating characteristic (ROC) curve in an independent validation cohort. RESULTS: The radiomics signatures of PTV showed a better performance on malignancy grade prediction of ccRCC with AUC of 0.807 (95% CI 0.800-0.834) in train data and 0.848 (95% CI 0.760-0.936) in validation data, while the radiomics signatures of TMV with AUC of 0.773 (95% CI 0.744-0.802) in train data and 0.810 (95% CI 0.706-0.914) in validation data. CONCLUSION: The CT-based peritumoral radiomics signature is a potential way to be used as a noninvasive tool to preoperatively predict the malignancy grades of ccRCC.

Hypertrophic preconditioning attenuates myocardial ischemia/reperfusion injury through the deacetylation of isocitrate dehydrogenase 2.

To test the hypothesis that transient nonischemic stimulation of hypertrophy would render the heart resistant to subsequent ischemic stress, short-term transverse aortic constriction (TAC) was performed in mice and then withdrawn for several days by aortic debanding, followed by subsequent myocardial exposure to ischemia/reperfusion (I/R). Following I/R injury, the myocardial infarct size and apoptosis were markedly reduced, and contractile function was significantly improved in the TAC preconditioning group compared with the control group. Mechanistically, hypertrophic preconditioning remarkably alleviated I/R-induced oxidative stress, as evidenced by the increased reduced nicotinamide adenine dinucleotide phosphate (NADPH)/nicotinamide adenine dinucleotide phosphate (NADP) ratio, increase in the reduced glutathione (GSH)/oxidized glutathione (GSSH) ratio, and reduced mitochondrial reactive oxygen species (ROS) production. Moreover, TAC preconditioning inhibited caspase-3 activation and mitigated the mitochondrial impairment by deacetylating isocitrate dehydrogenase 2 (IDH2) via a sirtuin 3 (SIRT3)-dependent mechanism. In addition, the expression of a genetic deacetylation mimetic IDH2 mutant (IDH2 K413R) in cardiomyocytes, which increased IDH2 enzymatic activity and decreased mitochondrial ROS production, and ameliorated I/R injury, whereas the expression of a genetic acetylation mimetic (IDH2 K413Q) in cardiomyocytes abolished these protective effects of hypertrophic preconditioning. Furthermore, both the activity and expression of the SIRT3 protein were markedly increased in preconditioned mice exposed to I/R. Treatment with an adenovirus encoding SIRT3 partially emulated the actions of hypertrophic preconditioning, whereas genetic ablation of SIRT3 in mice blocked the cardioprotective effects of hypertrophic preconditioning. The present study identifies hypertrophic preconditioning as a novel endogenous self-defensive and cardioprotective strategy for cardiac I/R injury that induces IDH2 deacetylation through a SIRT3-dependent mechanism. A therapeutic strategy targeting IDH2 may be a promising treatment for cardiac ischemic injury.

BRD4 inhibition sensitizes renal cell carcinoma cells to the PI3K/mTOR dual inhibitor VS-5584.

Activation of the PI3K/AKT/mTOR pathway promotes the progression of renal cell carcinoma (RCC). This study tested the anti-RCC cell activity of the PI3K/mTOR dual inhibitor, VS-5584. We show that VS-5584 inhibited PI3K/AKT/mTORC1/2 activation in established (786-O and A498 lines) and primary RCC cells, thereby suppressing cell survival, proliferation, migration and cell cycle progression. VS-5584 induced significant apoptosis in RCC cells. A daily single oral dose of VS-5584 (20 mg/kg) significantly inhibited 786-O tumor growth in vivo. VS-5584 treatment of 786-O tumor xenografts and RCC cells resulted in feedback upregulation of bromodomain-containing protein 4 (BRD4). Furthermore, BRD4 inhibition (by JQ1 and CPI203), knockdown or complete knockout potentiated VS-5584-induced RCC cell death and apoptosis. Conversely, forced overexpression of BRD4 attenuated the cytotoxicity of VS-5584 in 786-O cells. Collectively, VS-5584 potently inhibits RCC cell proliferation and survival. Its anti-tumor activity is further enhanced by the targeted inhibition of BRD4.

De Winter syndrome as an emergency electrocardiogram sign of ST-elevation myocardial infarction: a case report.

The case report aims to reveal de Winter's electrocardiogram (ECG) pattern as an equivalent to anterior ST-segment elevation myocardial infarction (STEMI). We report a case of a 49-year-old man with a history of smoking who presented to the emergency department with a 1 day history of chest pain that was exacerbated 5 h prior to presentation. Detailed clinical investigations and coronary angiographic characteristics were recorded. The first ECG of the patient was consistent with de Winter syndrome. Acute coronary artery angiography showed that the proximal left anterior descending coronary artery was completely occluded after the first diagonal branch artery was given off. A percutaneous coronary intervention was immediately performed. Our case indicates that early identification and diagnosis of such ECGs and timely reperfusion therapy of de Winter syndrome as a STEMI equivalent are required to improve the prognosis of such patients.

Crossbar-Net: A Novel Convolutional Neural Network for Kidney Tumor Segmentation in CT Images.

Due to the unpredictable location, fuzzy texture and diverse shape, accurate segmentation of the kidney tumor in CT images is an important yet challenging task. To this end, we in this paper present a cascaded trainable segmentation model termed as Crossbar-Net. Our method combines two novel schemes: (1) we originally proposed the crossbar patches, which consists of two orthogonal non-squared patches (i.e., the vertical patch and horizontal patch). The crossbar patches are able to capture both the global and local appearance information of the kidney tumors from both the vertical and horizontal directions simultaneously. (2) With the obtained crossbar patches, we iteratively train two sub-models (i.e., horizontal sub-model and vertical sub-model) in a cascaded training manner. During the training, the trained sub-models are encouraged to become more focus on the difficult parts of the tumor automatically (i.e., mis-segmented regions). Specifically, the vertical (horizontal) sub-model is required to help segment the mis-segmented regions for the horizontal (vertical) sub-model. Thus, the two sub-models could complement each other to achieve the self-improvement until convergence. In the experiment, we evaluate our method on a real CT kidney tumor dataset which is collected from 94 different patients including 3,500 CT slices. Compared with the state-of-the-art segmentation methods, the results demonstrate the superior performance of our method on the Dice similarity coefficient, true positive fraction, centroid distance and Hausdorff distance. Moreover, to exploit the generalization to other segmentation tasks, we also extend our Crossbar-Net to two related segmentation tasks: (1) cardiac segmentation in MR images and (2) breast mass segmentation in X-ray images, showing the promising results for these two tasks. Our implementation is released at https: //github.com/Qianyu1226/Crossbar-Net.

Chronic Kidney Disease Exacerbates Myocardial Ischemia Reperfusion Injury: Role of Endoplasmic Reticulum Stress-Mediated Apoptosis.

Chronic kidney disease (CKD) is known to exacerbate myocardial ischemia reperfusion (IR) injury. However, the underlying mechanisms are still not well understood. Despite various strategies for cardioprotection, limited studies have been focused on the prevention of CKD-induced myocardial susceptibility to IR injury. Here, we hypothesized that excessive endoplasmic reticulum (ER) stress-mediated apoptosis involved in myocardial IR injury in CKD mice and pretreatment with chemical ER chaperone rendered the heart resistant to myocardial IR injury in the setting of CKD. CKD was induced by 5/6 subtotal nephrectomy (SN) in mice, whereas sham-operated mice served as control (Sham). CKD significantly aggravated the cardiac injury after IR in SN group than Sham group as reflected by more severe cardiac dysfunction, increased myocardial infarct size and the ratio of myocardial apoptosis. The expression of ER stress-mediated apoptotic proteins (Bcl-2 associated X protein (Bax), glucose-regulated protein 78 (GRP78), CCAAT/enhancer-binding protein homologous protein (CHOP), caspase-12) was markedly upregulated after IR injury in SN group than Sham group, whereas the expression of anti-apoptotic protein, Bcl-2, was obviously downregulated. In addition, the chemical ER chaperone sodium 4-phenylbutyrate (4PBA) pretreatment ameliorated cardiac dysfunction and lessened the infarct size and myocardial apoptosis after IR injury in mice with CKD. Taken together, these findings demonstrated that excessive activation of ER stress-mediated apoptosis pathway involved in the CKD-induced myocardial susceptibility to IR injury, and chemical ER chaperone 4PBA alleviated myocardial IR injury in mice with CKD.

miR-181a and miR-150 regulate dendritic cell immune inflammatory responses and cardiomyocyte apoptosis via targeting JAK1-STAT1/c-Fos pathway.

The immune inflammatory response plays a crucial role in many cardiac pathophysiological processes, including ischaemic cardiac injury and the post-infarction repair process. MicroRNAs (miRNAs) regulate the development and function of dendritic cells (DCs), which are key players in the initiation and regulation of immune responses; however, the underlying regulatory mechanisms remain unclear. Here, we used the supernatants of necrotic primary cardiomyocytes (Necrotic-S) to mimic the myocardial infarction (MI) microenvironment to investigate the role of miRNAs in the regulation of DC-mediated inflammatory responses. Our results showed that Necrotic-S up-regulated the DC maturation markers CD40, CD83 and CD86 and increased the production of inflammatory cytokines, concomitant with the up-regulation of miR-181a and down-regulation of miR-150. Necrotic-S stimulation activated the JAK/STAT pathway and promoted the nuclear translocation of c-Fos and NF-κB p65, and silencing of STAT1 or c-Fos suppressed Necrotic-S-induced DC maturation and inflammatory cytokine production. The effects of Necrotic-S on DC maturation and inflammatory responses, its activation of the JAK/STAT pathway and the induction of cardiomyocyte apoptosis under conditions of hypoxia were suppressed by miR-181a or miR-150 overexpression. Taken together, these data indicate that miR-181a and miR-150 attenuate DC immune inflammatory responses via JAK1-STAT1/c-Fos signalling and protect cardiomyocytes from cell death under conditions of hypoxia.

Hypercholesterolemia Abrogates Remote Ischemic Preconditioning-Induced Cardioprotection: Role of Reperfusion Injury Salvage Kinase Signals.

Remote ischemic preconditioning (RIPC) is one of the most powerful intrinsic cardioprotective strategies discovered so far and experimental data indicate that comorbidity may interfere with the protection by RIPC. Therefore, we investigate whether RIPC-induced cardioprotection was intact in hypercholesterolemic rat hearts exposed to ischemia reperfusion in vivo. Normal or hypercholesterolemic rat hearts were exposed to 30 min of ischemia and 2 h of reperfusion, with or without RIPC, PI3K inhibitor wortmannin, MEK-ERK1/2 inhibitor PD98059, GSK3ß inhibitor SB216763. Infarct size, apoptosis, MG53, PI3K-p85, p-Akt, p-ERK1/2, p-GSK3ß, and cleaved Caspase-3 were determined. RIPC reduced infarct size, limited cardiomyocyte apoptosis following IR that was blocked by wortmannin but not PD98059. RIPC triggered unique cardioprotective signaling including MG53, phosphorylation of Akt, and glycogen synthase kinase-3ß (GSK3ß) in concert with reduced proapoptotic active caspase-3. In contrast, RIPC failed to reduce myocardial necrosis and apoptosis as well as to increase the phosphorylated Akt and GSK3ß in hypercholestorolemic myocardium. Importantly, we found that inhibition of GSK with SB216763 reduced myocardial infarct size in healthy and hypercholesterolemic hearts, but no additional cardioprotective effect was achieved when combined with RIPC. Our results suggest that acute GSK3ß inhibition may provide a novel therapeutic strategy for hypercholesterolemic patients during acute myocardial infarction, whereas RIPC is less effective due to signaling events that adversely affect GSK3ß.

Exosomes derived from mature dendritic cells increase endothelial inflammation and atherosclerosis via membrane TNF-α mediated NF-κB pathway.

Whether dendritic cell (DC) derived exosomes play a role in the progression of endothelial inflammation and atherosclerosis remains unclear. Using a transwell system and exosome release inhibitor GW4869, we demonstrated that mature DCs contributed to endothelial inflammation and exosomes were involved in the process. To further confirm this finding, we isolated exosomes from bone marrow dendritic cell (BMDC) culture medium (named DC-exos) and stimulated human umbilical vein endothelial cell (HUVEC) with these DC-exos. We observed that mature DC-exos increased HUVEC inflammation through NF-κB pathway in a manner similar to that of lipopolysaccharide. After a protein array analysis of exosomes, we identified and confirmed tumour necrosis factor (TNF)-α on exosome membrane being the trigger of NF-κB pathway in HUVECs. We then performed an in vivo study and found that the aorta endothelial of mice could uptake intravenously injected exosomes and was activated by these exosomes. After a period of 12 weeks of mature DC-exos injection into ApoE-/- mice, the atherosclerotic lesions significantly increased. Our study demonstrates that mature DCs derived exosomes increase endothelial inflammation and atherosclerosis via membrane TNF-α mediated NF-κB pathway. This finding extends our knowledge on how DCs affect inflammation and provides a potential method to prevent endothelial inflammation and atherosclerosis.