Diffuse Large B-Cell Lymphoma With Cardiac Invasion Presented as Acute Myocardial Infarction and Left Ventricular Hypertrophy: A Case Report.

Primary cardiac lymphoma is an exceedingly rare malignant tumor, with diffuse large B-cell lymphoma (DLBCL) being the most prevalent histological subtype. This disease has non-specific clinical manifestations, making early diagnosis crucial. However, DLBCL diagnosis is commonly delayed, and its prognosis is typically poor. Herein, we report the case of a 51-year-old male patient with DLBCL who presented with recurrent chest tightness for 4 months as the primary clinical symptom. The patient was admitted to the hospital and diagnosed with acute myocardial infarction and left ventricular hypertrophy with heart failure. Echocardiography revealed a progression from left ventricular thickening to local pericardial thickening and adhesion in the inferior and lateral walls of the left ventricle. Finally, pathological analysis of myocardial biopsy confirmed the diagnosis of DLBCL. After treatment with the R-CHOP chemotherapy regimen, the patient's chest tightness improved, and he was discharged. After 2 months, the patient succumbed to death owing to sudden ventricular tachycardia, ventricular fibrillation, and decreased blood pressure despite rescue efforts. Transthoracic echocardiography is inevitable for the early diagnosis of DLBCL, as it can narrow the differential and guide further investigations and interventions, thereby improving the survival of these patients.

CEG: A joint model for causal commonsense events enhanced story ending generation.

With the success of pre-trained language models, the performance of story ending generation has been dramatically improved while remaining challenging due to the lack of commonsense reasoning ability. Most previous works mainly focus on using commonsense knowledge to enhance the implicit correlations between words but ignore the hidden causality of sentences or events. In this paper, we propose Causal commonsense Enhanced joint model for story ending Generation (CEG), which incorporates causal commonsense events knowledge to generate a reasonable story ending. Specifically, we first develop a commonsense events inference model trained on GLUCOSE, which converts static knowledge into a dynamic generation model to discover unseen knowledge. It uses prompts to produce various commonsense events behind the stories as pseudo-labels of the dataset. Then, we propose a joint model for the causal events inference task and the story ending generation task to inject inference knowledge into the generation, which consists of a shared encoder, an inference decoder, and a generation decoder. In the causal events inference task, we use the shared encoder and the inference decoder to reason the causal events behind each sentence of the story context to help the model better understand the story and provide long-distance dependencies for the story ending generation. In story ending generation, we combine the hidden states of the causal events with the story context to generate the story ending by the shared encoder and the generation decoder. We jointly train the model on two tasks so that the generation decoder produces the story endings that better match the clues. Experimental results on the ROCStories dataset show that our model outperforms the previous works, demonstrating the effectiveness of the joint model and the generated causal events.

Glycyrrhizic acid ameliorates myocardial ischemia-reperfusion injury in rats through inhibiting endoplasmic reticulum stress.

The purpose of this study was to investigate the role of glycyrrhizic acid (GA) in regulating myocardial ischemia-reperfusion injury (MIRI) in rats as well as the underlying mechanism. H9c2 cells were subjected to hypoxia/re-oxygenation (H/R) to mimic the MIRI in vitro, while a rat model of ischemia-reperfusion (I/R) was constructed by occlusion of the left anterior descending coronary artery for 0.5 h followed by 2 h of reperfusion. While flow cytometry and TUNEL assay were performed to analyze apoptosis in cells and myocardial tissue, echocardiography, hematoxylin and eosin staining, and Masson's trichrome staining were conducted to evaluate cardiac function and pathological changes, respectively. The levels of serum CK, CK-MB, LDH, AST, TNF-α, and IL-6 as well as the contents of MDA and SOD in tissues were measured by ELISA, while Western blot analysis was performed to detect the expression of endoplasmic reticulum stress (ERS)-related proteins. GA treatment significantly reduced apoptosis in H9c2 cells, while it alleviated left ventricular dysfunction, fibrosis and myocardial apoptosis, down-regulated the levels of CK, CK-MB, LDH, AST, TNF-α, IL-6, and MDA, and up-regulated SOD levels in I/R rats. Moreover, GA treatment led to a decrease in the expression of CHOP, GRP78, and p-PERK in both H/R cells and I/R rats. This study demonstrates that cardioprotective role of GA in MIRI may involve the attenuation of ERS-induced apoptosis and inflammation, potentially providing an alternative strategy for intervention of MIRI.

Multiomics Analysis of Transcriptome, Epigenome, and Genome Uncovers Putative Mechanisms for Dilated Cardiomyopathy.

OBJECTIVE: Multiple genes have been identified to cause dilated cardiomyopathy (DCM). Nevertheless, there is still a lack of comprehensive elucidation of the molecular characteristics for DCM. Herein, we aimed to uncover putative molecular features for DCM by multiomics analysis. METHODS: Differentially expressed genes (DEGs) were obtained from different RNA sequencing (RNA-seq) datasets of left ventricle samples from healthy donors and DCM patients. Furthermore, protein-protein interaction (PPI) analysis was then presented. Differentially methylated genes (DMGs) were identified between DCM and control samples. Following integration of DEGs and DMGs, differentially expressed and methylated genes were acquired and their biological functions were analyzed by the clusterProfiler package. Whole exome sequencing of blood samples from 69 DCM patients was constructed in our cohort, which was analyzed the maftools package. The expression of key mutated genes was verified by three independent datasets. RESULTS: 1407 common DEGs were identified for DCM after integration of the two RNA-seq datasets. A PPI network was constructed, composed of 171 up- and 136 downregulated genes. Four hub genes were identified for DCM, including C3 (degree = 24), GNB3 (degree = 23), QSOX1 (degree = 21), and APOB (degree = 17). Moreover, 285 hyper- and 321 hypomethylated genes were screened for DCM. After integration, 20 differentially expressed and methylated genes were identified, which were associated with cell differentiation and protein digestion and absorption. Among single-nucleotide variant (SNV), C>T was the most frequent mutation classification for DCM. MUC4 was the most frequent mutation gene which occupied 71% across 69 samples, followed by PHLDA1, AHNAK2, and MAML3. These mutated genes were confirmed to be differentially expressed between DCM and control samples. CONCLUSION: Our findings comprehensively analyzed molecular characteristics from the transcriptome, epigenome, and genome perspectives for DCM, which could provide practical implications for DCM.

Multiomics Analysis of Genetics and Epigenetics Reveals Pathogenesis and Therapeutic Targets for Atrial Fibrillation.

OBJECTIVE: This study is aimed at understanding the molecular mechanisms and exploring potential therapeutic targets for atrial fibrillation (AF) by multiomics analysis. METHODS: Transcriptomics and methylation data of AF patients were retrieved from the Gene Expression Omnibus (GEO). Differentially expressed genes (DEGs) and differentially methylated sites between AF and normal samples were screened. Then, highly expressed and hypomethylated and lowly expressed and hypermethylated genes were identified for AF. Weighted gene coexpression network analysis (WGCNA) was presented to construct AF-related coexpression networks. 52 AF blood samples were used for whole exome sequence. The mutation was visualized by the maftools package in R. Key genes were validated in AF using independent datasets. RESULTS: DEGs were identified between AF and controls, which were enriched in neutrophil activation and regulation of actin cytoskeleton. RHOA, CCR2, CASP8, and SYNPO2L exhibited abnormal expression and methylation, which have been confirmed to be related to AF. PCDHA family genes had high methylation and low expression in AF. We constructed two AF-related coexpression modules. Single-nucleotide polymorphism (SNP) was the most common mutation type in AF, especially T > C. MUC4 was the most frequent mutation gene, followed by PHLDA1, AHNAK2, and MAML3. There was no statistical difference in expression of AHNAK2 and MAML3, for AF. PHLDA1 and MUC4 were confirmed to be abnormally expressed in AF. CONCLUSION: Our findings identified DEGs related to DNA methylation and mutation for AF, which may offer possible therapeutic targets and a new insight into the pathogenesis of AF from a multiomics perspective.