Analysis of characteristics related to interstitial lung disease or pulmonary hypertension in patients with dermatomyositis.

INTRODUCTION: Dermatomyositis (DM) is often associated with interstitial lung disease (ILD) or pulmonary hypertension (PH). The aim of this study was to investigate the clinical characteristics of DM patients with ILD or PH. METHODS: This study retrospectively analysed the clinical characteristics of 372 patients with DM, including cytokines, lymphocyte subsets, immunoglobulin and complement. The DM patients were divided into different groups according to whether complicated with ILD, PH or anti-melanoma differentiation-associated gene 5 antibodies (MDA5). A qualitative and quantitative data analysis was performed. RESULTS: IgG, IgA and IgM in the DM-associated ILD (ILD-DM) were higher than that of the DM non-complicating ILD (Non-ILD-DM) (p = 0.022, 0.002 and 0.029, respectively). Meanwhile, IL-6 (p = 0.008) and IL-10 (p = 0.001) were increased in the DM-associated PH (PH-DM) than in the DM non-complicating PH (Non-PH-DM), while IL-17 (p = 0.004), double positive (DP) cell ratio and B lymphocyte ratio were reduced in the PH-DM. Moreover, the incidence of ILD and levels of C4 were higher in the DM with MDA5 (MDA5+ DM) than that of the DM without MDA5. CONCLUSION: ILD-DM has higher IgG, IgA and IgM than that of Non-ILD-DM. PH-DM has higher IL-6, IL-10 and lower IL-17, DP cell ratio and B lymphocyte ratio than that of Non-PH-DM.

Learning Unified Hyper-Network for Multi-Modal MR Image Synthesis and Tumor Segmentation With Missing Modalities.

Accurate segmentation of brain tumors is of critical importance in clinical assessment and treatment planning, which requires multiple MR modalities providing complementary information. However, due to practical limits, one or more modalities may be missing in real scenarios. To tackle this problem, existing methods need to train multiple networks or a unified but fixed network for various possible missing modality cases, which leads to high computational burdens or sub-optimal performance. In this paper, we propose a unified and adaptive multi-modal MR image synthesis method, and further apply it to tumor segmentation with missing modalities. Based on the decomposition of multi-modal MR images into common and modality-specific features, we design a shared hyper-encoder for embedding each available modality into the feature space, a graph-attention-based fusion block to aggregate the features of available modalities to the fused features, and a shared hyper-decoder for image reconstruction. We also propose an adversarial common feature constraint to enforce the fused features to be in a common space. As for missing modality segmentation, we first conduct the feature-level and image-level completion using our synthesis method and then segment the tumors based on the completed MR images together with the extracted common features. Moreover, we design a hypernet-based modulation module to adaptively utilize the real and synthetic modalities. Experimental results suggest that our method can not only synthesize reasonable multi-modal MR images, but also achieve state-of-the-art performance on brain tumor segmentation with missing modalities.

An Interpretable Early Dynamic Sequential Predictor for Sepsis-Induced Coagulopathy Progression in the Real-World Using Machine Learning.

Sepsis-associated coagulation dysfunction greatly increases the mortality of sepsis. Irregular clinical time-series data remains a major challenge for AI medical applications. To early detect and manage sepsis-induced coagulopathy (SIC) and sepsis-associated disseminated intravascular coagulation (DIC), we developed an interpretable real-time sequential warning model toward real-world irregular data. Eight machine learning models including novel algorithms were devised to detect SIC and sepsis-associated DIC 8n (1 ≤ n ≤ 6) hours prior to its onset. Models were developed on Xi'an Jiaotong University Medical College (XJTUMC) and verified on Beth Israel Deaconess Medical Center (BIDMC). A total of 12,154 SIC and 7,878 International Society on Thrombosis and Haemostasis (ISTH) overt-DIC labels were annotated according to the SIC and ISTH overt-DIC scoring systems in train set. The area under the receiver operating characteristic curve (AUROC) were used as model evaluation metrics. The eXtreme Gradient Boosting (XGBoost) model can predict SIC and sepsis-associated DIC events up to 48 h earlier with an AUROC of 0.929 and 0.910, respectively, and even reached 0.973 and 0.955 at 8 h earlier, achieving the highest performance to date. The novel ODE-RNN model achieved continuous prediction at arbitrary time points, and with an AUROC of 0.962 and 0.936 for SIC and DIC predicted 8 h earlier, respectively. In conclusion, our model can predict the sepsis-associated SIC and DIC onset up to 48 h in advance, which helps maximize the time window for early management by physicians.

MALAT1-mediated recruitment of the histone methyltransferase EZH2 to the microRNA-22 promoter leads to cardiomyocyte apoptosis in diabetic cardiomyopathy.

Diabetic patients often have a heightened risk of cardiomyopathy, even in the absence of traditional risk factors such as hypertension and atherosclerotic coronary artery disease. Diabetic cardiomyopathy is characterized by a typical cardiomyopathy specific to diabetes, the pathogenesis of which has yet to be fully elucidated. As a well-documented oncogenic long noncoding RNA (lncRNA), metastasis-associated lung adenocarcinoma transcript 1 (MALAT1) has been implicated in a variety of pathological processes, including diabetic complications. This study aimed to evaluate the functional roles of MALAT1 in the pathogenesis of diabetic cardiomyopathy. Spontaneously diabetic (db/db) C57BL/Ks mice were employed to establish diabetic cardiomyopathy models in vivo and high glucose (HG)-cultured mouse cardiomyocytes for myocardial damage models in vitro. Mouse left ventricular volume and function were evaluated by echocardiography, while the myocyte cross-sectional area was calculated to evaluate the degree of myocardial hypertrophy. TUNEL staining and flow cytometric analysis were performed to evaluate myocardial damage and cardiomyocyte apoptosis. Silencing of MALAT1 was found to attenuate cardiac dysfunction and inhibit cardiomyocyte apoptosis in db/db mice and HG-cultured mouse cardiomyocytes. MALAT1 recruited the histone methyltransferase EZH2 to the miR-22 promoter region and inhibited its expression. EZH2 induced an increased in the expression of ATP-binding cassette transporter A1 (ABCA1), which was identified to be a target gene of miR-22. Silencing of EZH2 was found to improve cardiac function and prevent cardiomyocyte apoptosis in db/db mice and HG-cultured mouse cardiomyocytes in the presence of MALAT1, suggesting that MALAT1 mediated myocardial damage by recruiting EZH2 to the miR-22 promoter. Taken together, this study's findings provide evidence confirming our hypothesis, suggesting the involvement of MALAT1 in the processes of cardiac function and cardiomyocyte apoptosis via the EZH2/miR-22/ABCA1 signaling cascade, which has potential therapeutic implications for the understanding of diabetic cardiomyopathy.

The role of the globular heads of the C1q receptor in TcdA-induced human colonic epithelial cell apoptosis via a mitochondria-dependent pathway.

BACKGROUND: Clostridioides (formerly Clostridium) difficile infection is the leading cause of antibiotic-associated colitis. Studies have demonstrated that C. difficile toxin A (TcdA) can cause apoptosis of many human cell types. The purpose of this study was to investigate the relationships among exposure to TcdA, the role of the receptor for the globular heads of C1q (gC1qR) gene and the underlying intracellular apoptotic mechanism in human colonic epithelial cells (NCM 460). In this study, gC1qR expression was examined using real-time polymerase chain reaction (PCR), western blotting and immunohistochemical staining. Cell viability was assessed by the water-soluble tetrazolium salt (WST-1) assay, and cell apoptosis was assessed by flow cytometry and the terminal deoxynucleotidyl transferase-mediated deoxyuridine triphosphate nick-end labeling (TUNEL) assay. Mitochondrial function was assessed based on reactive oxygen species (ROS) generation, changes in the mitochondrial membrane potential (ΔΨm) and the content of ATP. RESULTS: Our study demonstrated that increasing the concentration of TcdA from 10 ng/ml to 20 ng/ml inhibited cell viability and induced cell apoptosis (p < 0.01). Moreover, the TcdA-induced gC1qR expression and enhanced expression of gC1qR caused mitochondrial dysfunction (including production of ROS and decreases in the ΔΨm and the content of ATP) and cell apoptosis. However, silencing of the gC1qR gene reversed TcdA-induced cell apoptosis and mitochondrial dysfunction. CONCLUSION: These data support a mechanism by which gC1qR plays a crucial role in TcdA-induced apoptosis of human colonic epithelial cells in a mitochondria-dependent manner.

Unsupervised MR-to-CT Synthesis Using Structure-Constrained CycleGAN.

Synthesizing a CT image from an available MR image has recently emerged as a key goal in radiotherapy treatment planning for cancer patients. CycleGANs have achieved promising results on unsupervised MR-to-CT image synthesis; however, because they have no direct constraints between input and synthetic images, cycleGANs do not guarantee structural consistency between these two images. This means that anatomical geometry can be shifted in the synthetic CT images, clearly a highly undesirable outcome in the given application. In this paper, we propose a structure-constrained cycleGAN for unsupervised MR-to-CT synthesis by defining an extra structure-consistency loss based on the modality independent neighborhood descriptor. We also utilize a spectral normalization technique to stabilize the training process and a self-attention module to model the long-range spatial dependencies in the synthetic images. Results on unpaired brain and abdomen MR-to-CT image synthesis show that our method produces better synthetic CT images in both accuracy and visual quality as compared to other unsupervised synthesis methods. We also show that an approximate affine pre-registration for unpaired training data can improve synthesis results.

Inhibition of long non-coding RNA metastasis-associated lung adenocarcinoma transcript 1 attenuates high glucose-induced cardiomyocyte apoptosis via regulation of miR-181a-5p.

Diabetic cardiomyopathy (DCM) is one of the cardiovascular complications of diabetes mellitus independent of hypertension, coronary disease, and other heart diseases. The development of DCM is multifactorial and hard to detect at an early stage. Long non-coding RNA metastasis-associated lung adenocarcinoma transcript 1 (Malat1) is emerging as a regulator of DCM, the underlying mechanism of its role in DCM has not been elaborated yet. In this study, we established a mouse DCM model via streptozocin injection as evidenced by cell hypertrophy and cell apoptosis of myocardial tissue, and found that Malat1 expression was upregulated in the myocardium in DCM mice. Meanwhile, elevated expression of pro-apoptotic factors p53, p21, cleaved caspase 3, cleaved caspase 9 and BAX, and down-regulation of anti-apoptotic BCL-2 were observed in DCM myocardium. We further investigated the effect of Malat1 on cardiomyocytes under high glucose condition by silencing Malat1 with its specific short-hairpin RNA. Like in vivo, expression of Malat1 in cardiomyocytes was notably raised, remarkable cell apoptosis and changes in apoptosis-related factors were also observed following high glucose treatment. Besides, we validated that Malat1 acted as a sponge of miR-181a-5p. Inhibition of miR-181a-5p could, at least partially, abolish Malat1 knockdown-induced alteration in cardiomyocytes. In addition, p53, a critical regulator of apoptosis, was validated to be a downstream target of miR-181a-5p. In summary, our findings reveal that Malat1 knockdown attenuates high glucose-induced cardiomyocyte apoptosis via releasing miR-181a-5p, and this mechanism may provide us with new diagnosis target of DCM.

Measurement of Free-Form Curved Surfaces Using Laser Triangulation.

Laser triangulation (LT) is widely used in many fields due to its good stability, high resolution and fast speed. However, the accuracy in these applications suffers from severe constraints on the data acquisition accuracy of LT. To solve this problem, the optical triangulation principle, the object equation of the optical path relationship and the deviation of the laser spot centroid are applied to deduce a mathematical model. Therefore, the image sensor inclination errors can be quantitatively calculated, and the collected data are compensated in real time. Further, a threshold sub-pixel gray-gravity (GG) extraction algorithm is proposed; the gradient function and Gaussian fit algorithm are used to set thresholds to remove the impact of the spot edge noise area on the center location; and polynomial interpolation is employed to enhance the data density of the traditional GG method, thus improving the data acquisition accuracy of LT. Finally, the above methods are applied to on-machine measurement of the American Petroleum Institute (API) thread and the screw rotor, respectively. The experimental results prove that the proposed method can significantly improve the measurement accuracy of free-form curved surfaces using LT and that the improved laser spot center extraction algorithm is more suitable for free-form curved surfaces with smaller curvature and more uniform curvature changes.

Downregulation of microRNA-1 attenuates glucose-induced apoptosis by regulating the liver X receptor α in cardiomyocytes.

Diabetic cardiomyopathy (DCM) is characterized by abnormal myocardial structure or performance. It has been suggested that microRNA-1 (miR-1) may be abnormally expressed in the hearts of patients with diabetes. In the present study, the role of miR-1 in glucose-induced apoptosis and its underlying mechanism of action was investigated in rat cardiomyocyte H9C2 cells. Cells were transfected with anti-miR-1 or miR-1-overexpression plasmids and the expression of miR-1 and liver X receptor α (LXRα) were determined by reverse transcription-quantitative polymerase chain reaction analysis. The proportion of apoptotic cells was determined using an Annexin-V-FITC apoptosis detection kit and the mitochondrial membrane potential (ΔΨ) was measured following staining with rhodamine 123. In addition, the expression of apoptosis-associated proteins was measured by western blot analysis. The results demonstrated that expression of miR-1 was significantly increased, whereas the expression of LXRα was significantly decreased in H9C2 cells following treatment with glucose. miR-1 knockdown significantly inhibited apoptosis, increased the ΔΨ and suppressed the cleavage of poly (adenosine diphosphate-ribose) polymerase, caspase-3 and caspase-9. It also significantly downregulated the expression of Bcl-2 and upregulated the expression of Bax. In addition, it was demonstrated that miR-1 regulates LXRα; transfection with anti-miR-1 significantly increased the expression of LXRα. Furthermore, treatment of cells with the LXR agonist GW3965 inhibited apoptosis in glucose-induced anti-miR-1 cells. These results suggest a novel function of miR-1: The regulation of cardiomyocyte apoptosis via LXRα, and provide novel insights into regarding the complex mechanisms involved in DCM.

Neural multi-atlas label fusion: Application to cardiac MR images.

Multi-atlas segmentation approach is one of the most widely-used image segmentation techniques in biomedical applications. There are two major challenges in this category of methods, i.e., atlas selection and label fusion. In this paper, we propose a novel multi-atlas segmentation method that formulates multi-atlas segmentation in a deep learning framework for better solving these challenges. The proposed method, dubbed deep fusion net (DFN), is a deep architecture that integrates a feature extraction subnet and a non-local patch-based label fusion (NL-PLF) subnet in a single network. The network parameters are learned by end-to-end training for automatically learning deep features that enable optimal performance in a NL-PLF framework. The learned deep features are further utilized in defining a similarity measure for atlas selection. By evaluating on two public cardiac MR datasets of SATA-13 and LV-09 for left ventricle segmentation, our approach achieved 0.833 in averaged Dice metric (ADM) on SATA-13 dataset and 0.95 in ADM for epicardium segmentation on LV-09 dataset, comparing favorably with the other automatic left ventricle segmentation methods. We also tested our approach on Cardiac Atlas Project (CAP) testing set of MICCAI 2013 SATA Segmentation Challenge, and our method achieved 0.815 in ADM, ranking highest at the time of writing.

Application of a double-colour upconversion nanofluorescent probe for targeted imaging of mantle cell lymphoma.

Upconversion nanoparticles are a new type of fluorescent marker in biomedical imaging that can convert a longer wavelength (such as near-infrared fluorescence) into a shorter wavelength (such as visible light). Mantle cell lymphoma, which is derived from B-cell lymphoma, is a subtype of non-Hodgkin's lymphoma, and the immune phenotype is a mature B-cell phenotype (CD20+, CD5+). To develop the use of nanomaterials as specific markers for the medical imaging of mantle cell lymphoma, we modified the surface of UCNPs by oxidation so that the CD20 or CD5 antibody could covalently attach to the upconversion nanoparticles to form antibody-UCNP conjugates. These antibody-UCNP conjugates were used as fluorescent probes to detect the CD20 or CD5 antigen. Due to the excessive expression of these antigens on the surface of MCL cells and successful strong connection between the antibody and UCNPs, the latter could specifically combine with mantle cell lymphoma cells. Upon near-infrared excitation at 980 nm, cells labelled with UCNPs emitted bright upconversion fluorescence.