Thyroid ultrasound diagnosis improvement via multi-view self-supervised learning and two-stage pre-training.

Thyroid nodule classification and segmentation in ultrasound images are crucial for computer-aided diagnosis; however, they face limitations owing to insufficient labeled data. In this study, we proposed a multi-view contrastive self-supervised method to improve thyroid nodule classification and segmentation performance with limited manual labels. Our method aligns the transverse and longitudinal views of the same nodule, thereby enabling the model to focus more on the nodule area. We designed an adaptive loss function that eliminates the limitations of the paired data. Additionally, we adopted a two-stage pre-training to exploit the pre-training on ImageNet and thyroid ultrasound images. Extensive experiments were conducted on a large-scale dataset collected from multiple centers. The results showed that the proposed method significantly improves nodule classification and segmentation performance with limited manual labels and outperforms state-of-the-art self-supervised methods. The two-stage pre-training also significantly exceeded ImageNet pre-training.

Automated identification of protein expression intensity and classification of protein cellular locations in mouse brain regions from immunofluorescence images.

Knowledge of protein expression in mammalian brains at regional and cellular levels can facilitate understanding of protein functions and associated diseases. As the mouse brain is a typical mammalian brain considering cell type and structure, several studies have been conducted to analyze protein expression in mouse brains. However, labeling protein expression using biotechnology is costly and time-consuming. Therefore, automated models that can accurately recognize protein expression are needed. Here, we constructed machine learning models to automatically annotate the protein expression intensity and cellular location in different mouse brain regions from immunofluorescence images. The brain regions and sub-regions were segmented through learning image features using an autoencoder and then performing K-means clustering and registration to align with the anatomical references. The protein expression intensities for those segmented structures were computed on the basis of the statistics of the image pixels, and patch-based weakly supervised methods and multi-instance learning were used to classify the cellular locations. Results demonstrated that the models achieved high accuracy in the expression intensity estimation, and the F1 score of the cellular location prediction was 74.5%. This work established an automated pipeline for analyzing mouse brain images and provided a foundation for further study of protein expression and functions.

The characteristics of invasive cardiac lipoma: case report and literature review.

Invasive cardiac lipoma is a rare type of primary cardiac tumor that is composed of adipose tissue but infiltrating the adjacent structures. It is a benign tumor that can cause significant morbidity and mortality due to its size and location within the heart. We describe a giant invasive intracardiac lipoma across atrial wall extending to the ascending aorta and the superior vena cava. This review will provide an overview of invasive cardiac lipoma, including its clinical presentation, diagnosis, and management.

Automatic recognition of protein subcellular location patterns in single cells from immunofluorescence images based on deep learning.

With the improvement of single-cell measurement techniques, there is a growing awareness that individual differences exist among cells, and protein expression distribution can vary across cells in the same tissue or cell line. Pinpointing the protein subcellular locations in single cells is crucial for mapping functional specificity of proteins and studying related diseases. Currently, research about single-cell protein location is still in its infancy, and most studies and databases do not annotate proteins at the cell level. For example, in the human protein atlas database, an immunofluorescence image stained for a particular protein shows multiple cells, but the subcellular location annotation is for the whole image, ignoring intercellular difference. In this study, we used large-scale immunofluorescence images and image-level subcellular locations to develop a deep-learning-based pipeline that could accurately recognize protein localizations in single cells. The pipeline consisted of two deep learning models, i.e. an image-based model and a cell-based model. The former used a multi-instance learning framework to comprehensively model protein distribution in multiple cells in each image, and could give both image-level and cell-level predictions. The latter firstly used clustering and heuristics algorithms to assign pseudo-labels of subcellular locations to the segmented cell images, and then used the pseudo-labels to train a classification model. Finally, the image-based model was fused with the cell-based model at the decision level to obtain the final ensemble model for single-cell prediction. Our experimental results showed that the ensemble model could achieve higher accuracy and robustness on independent test sets than state-of-the-art methods.

Long Noncoding RNA SNHG4 Attenuates the Injury of Myocardial Infarction via Regulating miR-148b-3p/DUSP1 Axis.

Objective: Long noncoding RNAs (lncRNAs), including some members of small nucleolar RNA host gene (SNHG), are important regulators in myocardial injury, while the role of SNHG4 in myocardial infarction (MI) is rarely known. This study is aimed at exploring the regulatory role and mechanisms of SNHG4 on MI. Methods: Cellular and rat models of MI were established. The expression of relating genes was measured by qRT-PCR and/or western blot. In vitro, cell viability was detected by MTT assay, and cell apoptosis was assessed by caspase-3 level, Bax/Bcl-2 expression, and/or flow cytometry. The inflammation was evaluated by TNF-α, IL-1ß, and IL-6 levels. The myocardial injury in MI rats was evaluated by echocardiography, TTC/HE/MASSON/TUNEL staining, and immunohistochemistry (Ki67). DLR assay was performed to confirm the target relationships. Results: SNHG4 was downregulated in hypoxia-induced H9c2 cells and MI rats, and its overexpression enhanced cell viability and inhibited cell apoptosis and inflammation both in vitro and in vivo. SNHG4 overexpression also decreased infarct and fibrosis areas, relieved pathological changes, and improved heart function in MI rats. In addition, miR-148b-3p was an action target of SNHG4, and its silencing exhibited consistent results with SNHG4 overexpression in vitro. DUSP1 was a target of miR-148b-3p, which inhibited the apoptosis of hypoxia-induced H9c2 cells. Both miR-148b-3p overexpression and DUSP1 silencing weakened the effects of SNHG4 overexpression on protecting H9c2 cells against hypoxia. Conclusions: Overexpression of SNHG4 relieved MI through regulating miR-148b-3p/DUSP1, providing potential therapeutic targets.

Rosendaal linear interpolation method appraising of time in therapeutic range in patients with 12-week follow-up interval after mechanical heart valve replacement.

Background: The objective of this study was to evaluate the quality of anticoagulation by the time in therapeutic range (TTR) for patients with 12-week INR follow-up interval. Materials and methods: From January 2018 to December 2020, a selective group of patients who underwent mechanical valve replacement and followed up at our anticoagulation clinic for adjustment of warfarin dose were enrolled. The incidences of complications of anticoagulation therapy were reported by linearized rates. TTR was calculated by the Rosendaal linear interpolation method. Results: Two hundred and seventy-four patients were eligible for this study. The mean age of these patients was 52.8 ± 12.7 years, and 65.7% (180 cases) of them were females. The mean duration of warfarin therapy was 16.7 ± 28.1 months. A total of 1309 INR values were collected, representing 66789 patient days. In this study, the mean TTR was 63.7% ± 18.6%, weekly doses of warfarin were 20.6 ± 6.0 mg/weekly, and the mean monitoring interval for the patient was 53.6 ± 27.1 days. There were 153 cases in good TTR group (TTR ≥ 60%) and 121 cases in poor TTR group (TTR < 60%). The calculated mean TTR in both groups was 42.6% ± 22.1% and 74.8% ± 10.4%, respectively. Compared with the TTR ≥ 60% group, the TTR < 60% group exhibited a more prevalence of female gender (p = 0.001), atrial fibrillation (p < 0.001), NYHA ≥ III (p < 0.001), and lower preoperative left ventricular ejection fraction (LVEF, p = 0.032). In multivariate analysis, female gender (p = 0.023) and atrial fibrillation (p = 0.011) were associated with TTR < 60%. The incidence of major bleeding and thromboembolic events was 2.7% and 1.1% patient-years, respectively. There was one death which resulted from cerebral hemorrhage. The incidence of death was 0.5% patient-years. The difference in anticoagulation-related complications between the TTR < 60% group and the TTR ≥ 60% group was not statistically significant. Conclusion: For patients with stable international normalized ratio monitoring results who are follow-up at anticoagulation clinics, a 12-week monitoring interval has an acceptable quality of anticoagulation. The female gender and atrial fibrillation were associated with TTR < 60%.

Improving segmentation and classification of renal tumors in small sample 3D CT images using transfer learning with convolutional neural networks.

PURPOSE: Computed tomography (CT) images can display internal organs of patients and are particularly suitable for preoperative surgical diagnoses. The increasing demands for computer-aided systems in recent years have facilitated the development of many automated algorithms, especially deep convolutional neural networks, to segment organs and tumors or identify diseases from CT images. However, performances of some systems are highly affected by the amount of training data, while the sizes of medical image data sets, especially three-dimensional (3D) data sets, are usually small. This condition limits the application of deep learning. METHODS: In this study, given a practical clinical data set that has 3D CT images of 20 patients with renal carcinoma, we designed a pipeline employing transfer learning to alleviate the detrimental effect of the small sample size. A dual-channel fine segmentation network (FS-Net) was constructed to segment kidney and tumor regions, with 210 publicly available 3D images from a competition employed during the training phase. We also built discriminative classifiers to classify the benign and malignant tumors based on the segmented regions, where both handcrafted and deep features were tested. RESULTS: Our experimental results showed that the Dice values of segmented kidney and tumor regions were 0.9662 and 0.7685, respectively, which were better than those of state-of-the-art methods. The classification model using radiomics features can classify most of the tumors correctly. CONCLUSIONS: The designed FS-Net was demonstrated to be more effective than simply fine-tuning on the practical small size data set given that the model can borrow knowledge from large auxiliary data without diluting the signal in primary data. For the small data set, radiomics features outperformed deep features in the classification of benign and malignant tumors. This work highlights the importance of architecture design in transfer learning, and the proposed pipeline is anticipated to provide a reference and inspiration for small data analysis.

DULoc: quantitatively unmixing protein subcellular location patterns in immunofluorescence images based on deep learning features.

MOTIVATION: Knowledge of subcellular locations of proteins is of great significance for understanding their functions. The multi-label proteins that simultaneously reside in or move between more than one subcellular structure usually involve with complex cellular processes. Currently, the subcellular location annotations of proteins in most studies and databases are descriptive terms, which fail to capture the protein amount or fractions across different locations. This highly limits the understanding of complex spatial distribution and functional mechanism of multi-label proteins. Thus, quantitatively analyzing the multiplex location patterns of proteins is an urgent and challenging task. RESULTS: In this study, we developed a deep-learning-based pattern unmixing pipeline for protein subcellular localization (DULoc) to quantitatively estimate the fractions of proteins localizing in different subcellular compartments from immunofluorescence images. This model used a deep convolutional neural network to construct feature representations, and combined multiple nonlinear decomposing algorithms as the pattern unmixing method. Our experimental results showed that the DULoc can achieve over 0.93 correlation between estimated and true fractions on both real and synthetic datasets. In addition, we applied the DULoc method on the images in the human protein atlas database on a large scale, and showed that 70.52% of proteins can achieve consistent location orders with the database annotations. AVAILABILITY AND IMPLEMENTATION: The datasets and code are available at: https://github.com/PRBioimages/DULoc. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

CircUBXN7 mitigates H/R-induced cell apoptosis and inflammatory response through the miR-622-MCL1 axis.

BACKGROUND: Hypoxia/reoxygenation (H/R)-mediated apoptosis and inflammation are major causes of tissue injury in acute myocardial infarction (AMI). Exploring the underlying mechanisms of cardiomyocyte injury induced by H/R is important for AMI treatment. Circular RNAs have been demonstrated to paly vital roles in the pathogenesis of AMI. Our study aimed to explore the function of circular RNA UBXN7 (circUBXN7) in regulating H/R-induced cardiomyocyte injury. METHODS: H/R-treated H9c2 cells and a mouse model of AMI were used to investigate the function of circUBXN7 in H/R damage and AMI. The expressions of circUNXN7, miR-622 and MCL1 were analyzed by RT-qPCR. CCK-8 was used for examining cell viability. Cell apoptosis was evaluated with caspase 3 activity and Annexin V/PI staining. MCL1, Bax, Bcl-2 and cleaved-caspase 3 were examined with western blot. ELISA was used to examine the secretion of IL-6, TNF-α and IL-1ß. RESULTS: CircUBXN7 was downregulated in patients and mice with AMI, as well as in H/R-treated cells. Overexpression of circUBXN7 mitigated H/R-mediated apoptosis and secretion of inflammatory factors including IL-6, TNF-α and IL-1ß. CircUBXN7 suppressed cell apoptosis and inflammatory reaction induced by H/R via targeting miR-622. MiR-622 targeted MCL1 to restrain its expression in H9c2 cells. Knockdown of MCL1 abrogated circUBXN7-mediated alleviation of apoptosis and inflammation after H/R treatment. CONCLUSION: CircUBXN7 mitigates cardiomyocyte apoptosis and inflammatory reaction in H/R injury by targeting miR-622 and maintaining MCL1 expression. Our study provides novel potential therapeutic targets for AMI treatment.

Atrial fibrillation detection based on multi-feature extraction and convolutional neural network for processing ECG signals.

BACKGROUND AND OBJECTIVE: The incidence of atrial fibrillation is increasing annually. We develop an automatic detection system, which is of great significance for the early detection and treatment of atrial fibrillation. This can lead to the reduction of the incidence of critical illnesses and mortality. METHODS: We propose an atrial fibrillation detection algorithm based on multi-feature extraction and convolutional neural network of atrial activity via electrocardiograph signals, and compare its detection based on cluster analysis, one-versus-one rule and support vector machine, using accuracy, specificity, sensitivity and true positive rate as evaluation criteria. RESULTS: The atrial fibrillation detection algorithm proposed in this paper has an accuracy rate of 98.92%, a specificity of 97.04%, a sensitivity of 97.19%, and a true positive rate of 96.47%. The average accuracy of the algorithms we compared is 80.26%, and the accuracy of our algorithm is 23.25% higher than this average pertaining to the other algorithms. CONCLUSION: We implemented an atrial fibrillation detection algorithm that meets the requirements of high accuracy, robustness and generalization ability. It has important clinical and social significance for early detection of atrial fibrillation, improvement of patient treatment plans and improvement of medical diagnosis.

Coronary angiography image segmentation based on PSPNet.

PURPOSE: Coronary artery disease (CAD) is known to have high prevalence, high disability and mortality. The incidence and mortality of cardiovascular disease are also gradually increasing worldwide. Therefore, our paper proposes to use a more efficient image processing method to extract accurate vascular structures from vascular images by combining computer vision and deep learning. METHOD: Our proposed segmentation of coronary angiography images based on PSPNet network was compared with FCN, and analyzed and discussed the experimental results using three evaluation indicators of precision, recall and Fl-score. Aiming at the complex and changeable structure of coronary angiography images and over-fitting or parameter structure destruction, we implemented the parallel multi-scale convolutional neural network model using PSPNet, using small sample transfer learning that limits parameter learning method. RESULTS: The accuracy of our technique proposed in this paper is 0.957. The accuracy of PSPNet is 26.75% higher than the traditional algorithm and 4.59% higher than U-Net. The average segmentation accuracy of the PSPNet model using transfer learning on the test set increased from 0.926 to 0.936, the sensitivity increased from 0.846 to 0.865, and the specificity increased from 0.921 to 0.949. The segmentation effect in this paper is closest to the segmentation result of the human expert, and is smoother than that of U-Net segmentation. CONCLUSION: The PSPNet network reduces manual interaction in diagnosis, reduces dependence on medical personnel, improves the efficiency of disease diagnosis, and provides auxiliary strategies for subsequent medical diagnosis systems based on cardiac coronary angiography.

Comparative analysis of active contour and convolutional neural network in rapid left-ventricle volume quantification using echocardiographic imaging.

In cardiology, ultrasound is often used to diagnose heart disease associated with myocardial infarction. This study aims to develop robust segmentation techniques for segmenting the left ventricle (LV) in ultrasound images to check myocardium movement during heartbeat. The proposed technique utilizes machine learning (ML) techniques such as the active contour (AC) and convolutional neural networks (CNNs) for segmentation. Medical experts determine the consistency between the proposed ML approach, which is a state-of-the-art deep learning method, and the manual segmentation approach. These methods are compared in terms of performance indicators such as the ventricular area (VA), ventricular maximum diameter (VMXD), ventricular minimum diameter (VMID), and ventricular long axis angle (AVLA) measurements. Furthermore, the Dice similarity coefficient, Jaccard index, and Hausdorff distance are measured to estimate the agreement of the LV segmented results between the automatic and visual approaches. The obtained results indicate that the proposed techniques for LV segmentation are useful and practical. There is no significant difference between the use of AC and CNN in image segmentation; however, the AC method could obtain comparable accuracy as the CNN method using less training data and less run-time.

Human umbilical cord blood-derived MSCs exosome attenuate myocardial injury by inhibiting ferroptosis in acute myocardial infarction mice.

The exosome of MSCs derived from human umbilical cord blood (HUCB-MSC) has been reported to have cardioprotective effects on mouse models of acute myocardial infarction (AMI) and cardiomyocyte hypoxia injury, but the exact mechanisms involved require further investigation. This paper aimed to study the role of HUCB-MSC-exosomes in inhibiting ferroptosis to attenuate myocardial injury. Compared with sham or normoxia groups, RT-PCR and western blotting showed that divalent metal transporter 1 (DMT1) expression was significantly increased, and Prussian blue staining, ferrous iron (Fe2+), MDA, and GSH level detection demonstrated that ferroptosis occurred in the infraction myocardium and in cardiomyocyte following hypoxia-induced injury. Overexpression of DMT1 promoted H/R-induced myocardial cell ferroptosis, while knockdown of DMT1 significantly inhibited the ferroptosis. HUCB-MSCs-derived exosomes inhibited ferroptosis and reduced myocardial injury, which was abolished in exosome with miR-23a-3p knockout. Moreover, dual luciferase reporter assay confirmed that DMT1 was a target gene of miR-23a-3p. In conclusion, HUCB-MSCs-exosomes may suppress DMT1 expression by miR-23a-3p to inhibit ferroptosis and attenuate myocardial injury.

Knockdown of Long Noncoding RNA SNHG14 Protects H9c2 Cells Against Hypoxia-induced Injury by Modulating miR-25-3p/KLF4 Axis in Vitro.

ABSTRACT: Cyanotic congenital heart disease (CCHD) is the main cause of death in infants worldwide. Long noncoding RNAs (lncRNAs) have been pointed to exert crucial roles in development of CHD. The current research is designed to illuminate the impact and potential mechanism of lncRNA SNHG14 in CCHD in vitro. The embryonic rat ventricular myocardial cells (H9c2 cells) were exposed to hypoxia to establish the model of CCHD in vitro. Quantitative real-time polymerase chain reaction was conducted to examine relative expressions of SNHG14, miR-25-3p, and KLF4. Cell viability was determined by the MTT assay. Lactate dehydrogenase (LDH) was measured by an LDH assay kit. Apoptosis-related proteins (Bax and Bcl-2) and KLF4 were detected by Western Blot. The targets of SNHG14 and miR-25-3p were verified by the dual-luciferase reporter assay. SNHG14 and KLF4 were upregulated, whereas miR-25-3p was downregulated in hypoxia-induced H9c2 cells and cardiac tissues of patients with CCHD compared with their controls. Knockdown of SNHG14 or overexpression of miR-25-3p facilitated cell viability, while depressing cell apoptosis and release of LDH in hypoxia-induced H9c2 cells. MiR-25-3p was a target of SNHG14 and inversely modulated by SNHG14. MiR-25-3p could directly target KLF4 and negatively regulate expression of KLF4. Repression of miR-25-3p or overexpression of KLF4 reversed the suppression impacts of sh-SNHG14 on cell apoptosis and release of LDH as well as the promotion impact of sh-SNHG14 on cell viability in hypoxia-induced H9c2 cells. Sh-SNHG14 protected H9c2 cells against hypoxia-induced injury by modulating miR-25-3p/KLF4 axis in vitro.

The circular RNA TLK1 exacerbates myocardial ischemia/reperfusion injury via targeting miR-214/RIPK1 through TNF signaling pathway.

PURPOSE: Myocardial ischemia/reperfusion injury (IRI) induces cardiomyocytes death and leads to loss of cardiac function. Circular RNAs (circRNA) have gain increasing interests in modulating myocardial IRI. In this study, we aim to investigate the role and exact mechanism of circTLK1 in the pathogenesis of myocardial IRI. METHODS: Myocardial IRI was developed in mice with measuring hemodynamic parameters and the activity of serum myocardial enzymes to evaluate cardiac function. HE and TTC staining were performed to assess infarct area. Expression patterns of circTLK1 and miR-214 were investigated using qRT-PCR assay. Gene expression of circTLK1, miR-214 or RIPK was altered by transfecting with their overexpression or knockdown vectors. The apoptosis of cardimyocytes was assessed by TUNEL staining and Caspase-3 activity analysis. Apoptosis-related markers Bcl-2, Bax, and caspase3, as well as TNF-α signals were determined by western blotting. The interactions of circTLK1/miR-214 and miR-214/RIPK1 were verified using luciferase reporter assay. RNA immunoprecipitation (RIP) was subjected to further definite the direct binding of circTLK1/miR-214. The regulatory network of circTLK1/miR-214/RIPK1 was further validated in vivo. RESULTS: circTLK1 was an up-regulated circRNA found in a myocardial IRI mouse model. Mice with silencing circTLK1 significantly alleviated the impaired cardiac function indexes and decreased infarct area, thus attenuating the pathogenesis of myocardial IRI. Knockdown of circTLK1 dramatically decreased cardiomyocytes apoptosis, which was determined by apoptosis-related proteins. miR-214 was identified as a downstream effector to reverse circTLK1-mediated damage effects in myocardial IRI. miR-214 could directly target RIPK1 via binding to its' 3'-UTR. Overexpression of RIPK1 led to impaired cardiac function indexes, increased infarct area, and cell apoptosis, which abolished the protective effects of miR-214. The TNF signaling pathway was demonstrated to be involved in the circTLK1/miR-214/RIPK1 regulatory network in myocardial IRI. CONCLUSION: Taken together, our study revealed an up-regulated circRNA, circTLK1, could exacerbate myocardial IRI via targeting miR-214/RIPK1-mediated TNF signaling pathway, which may provide therapeutic targets for treatment.

LncRNA OIP5-AS1 inhibits osteoblast differentiation of valve interstitial cells via miR-137/TWIST11 axis.

OIP5-AS1, a highly abundant imprinted long non-coding RNA (lncRNA), has been implicated in calcific aortic valve disease (CAVD). However, the function and underlying mechanism of OIP5-AS1 in CAVD progression remains unknown. In this study, osteoblastic differentiation of valve interstitial cells (VICs) isolated from human calcific aortic valves was induced by osteogenic medium. The protein levels of osteogenic markers were determined by immunofluorescence and western blotting. OIP5-AS1, miR-137 and TWIST-related protein 1 (TWIST1) expressions were detected by quantitative real-time PCR (qRT-PCR). ALP activity was evaluated by spectrophotometry. Mineralized bone matrix formation was assessed by Alizarin Red S staining. The interaction between OIP5-AS1 and miR-137 was studied using luciferase reporter assay, RNA pull-down assay and RNA-binding protein immunoprecipitation (RIP) assay. Luciferase reporter assay was also used to identify the possible interaction between miR-137 and TWIST11. The results showed that downregulated expression of OIP5-AS1 was observed in human aortic VICs after osteogenic induction. In vitro experiments revealed that OIP5-AS1 acted as a negative regulator of osteogenic differentiation. Mechanistically, we further showed that OIP5-AS1 could relieve osteogenic differentiation of VICs via upregulating miR-137 target gene TWIST1. Our study provides novel mechanistic insights into the cross-talk between OIP5-AS1, miR-137, and TWIST11, shedding light on the therapy for CAVD.

Primary malignant schwannoma of the heart.

Primary malignant schwannoma of the heart is an extremely rare disease. We, herein, report a 42-year-old female who underwent successful surgical excision of such a tumor.

MicroRNA-26b relieves inflammatory response and myocardial remodeling of mice with myocardial infarction by suppression of MAPK pathway through binding to PTGS2.

BACKGROUND: Myocardial infarction (MI) is a common cardiovascular disease caused by myocardial ischemia. Also, microRNA (miRNA) participates in the pathophysiology of many cardiovascular diseases, which can affect stem cell transplantation in the treatment of MI. In this study, our aim is to explore effect of miR-26b on inflammatory response and myocardial remodeling through the MAPK pathway by targeting PTGS2 in mice with MI. METHODS: Microarray data analysis was conducted to screen MI-related differentially expressed gens (DEGs). Relationship between miR-26b and PTGS2 was testified. Cardiac function, inflammatory reaction, infarct size, and myocardial fibrosis were observed. The miR-26b expression and mRNA and protein levels of, PTGS2, ERK, JNK and p38 and Bcl-2/Bax were examined. The effect of miR-26b on cell apoptosis was also analyzed. RESULTS: MiR-26b was predicted to target PTGS2 further to mediate the MAPK pathway, thus affecting MI. MiR-26b negatively targeted PTGS2. MI mice showed decreased cardiac function, as well as increased inflammatory reaction, myocardial injury, area of fibrosis and myocardial cell apoptosis. After injection of miR-26b agomir or NS-398 (PTGS2 inhibitor), inflammatory response of MI mice was attenuated and myocardial remodeling induced by MI was alleviated. CONCLUSION: These findings indicate that miR-26b inhibits PTGS2 to activate the MAPK pathway, so as to reduce inflammatory response and improve myocardial remodeling in mice with MI.

Ten-year experience of tricuspid valve replacement with the St. Jude medical valve.

Bioprosthetic valves for tricuspid valve replacement (TVR) have become increasingly popular in recent years, but mechanical valves remain valuable, particularly for the patients who want to avoid reoperation for bioprostheses malfunction. The aim of this study was to review our 10-year experience in adult patients who underwent TVR with the St. Jude Medical (SJM) valve. From 2005 to 2015, 265 TVRs with SJM valves were performed at our institution. The mean age at operation was 44.1 ± 9.7 years, and 207 cases (78.1%) were female. The mean follow-up was 4.9 ± 2.7 years. Preoperative atrial fibrillation was present in 199 cases (75.1%) and ascites in 26 (9.8%). Of all cases, 88.7% were characterized as New York Heart Association class III or IV. The hospital mortality was 6.4%. There were 9 deaths (3.8%) during late follow-up. The overall survival rates were 89.2% ± 2.2% at 5 years and 86.6% ± 2.9% at 10 years. The linearized rates of valve thrombosis and bleeding events were 0.8%/patient-year and 1.5%/patient-year, respectively. Three cases (1.3%) were reoperated due to prosthetic valve thrombosis. There was no reoperation for sperivalvular leakage and structural failure. The freedom from reoperation was 98.6% ± 0.8% at 5 years and 98.6% ± 0.8% at 10 years. The SJM valve in the tricuspid position is a reliable mechanical prosthesis with a low rate of valve thrombosis and reoperation. It is a reasonable choice for the patients who require mechanical valve replacement in the tricuspid position.