Dietary fiber may benefit chondrocyte activity maintenance.

The understanding of the link between the gut-bone axis is growing yearly, but the mechanisms involved are not yet clear. Our study analyzed the role of Sestrin2 (SESN2)pathway in the gut-bone axis. We established an osteoarthritis (OA) model in Sprague-Dawley (SD) rats using the anterior cruciate ligament transection (ACLT) procedure, followed by a dietary intervention with varying levels of dietary fiber content for 8 weeks. By 16S rRNA sequencing of the gut microbiota, we found that high dietary fiber (HDF) intake could significantly increase the Bacillota-dominant gut microbiota. Meanwhile, enzyme linked immunosorbent assay (ELISA) and histological analysis showed that intervention with HDF could reduce the degree of bone and joint lesions and inflammation. We hypothesize that HDF increased the dominant flora of Bacillota, up-regulated the expression of SESN2 in knee joint, and reduced gut permeability, thereby reducing systemic inflammatory response and the degree of bone and joint lesions. Therefore, the present study confirms that changes in gut microbiota induced by increased dietary fiber intake delayed the onset of OA by promoting up-regulation of SESN2 expression at the knee joint to maintain chondrocyte activity and reduce synovial inflammation.

PBX/Knotted 1 homeobox-2 (PKNOX2) is a novel regulator of myocardial fibrosis.

Much effort has been made to uncover the cellular heterogeneities of human hearts by single-nucleus RNA sequencing. However, the cardiac transcriptional regulation networks have not been systematically described because of the limitations in detecting transcription factors. In this study, we optimized a pipeline for isolating nuclei and conducting single-nucleus RNA sequencing targeted to detect a higher number of cell signal genes and an optimal number of transcription factors. With this unbiased protocol, we characterized the cellular composition of healthy human hearts and investigated the transcriptional regulation networks involved in determining the cellular identities and functions of the main cardiac cell subtypes. Particularly in fibroblasts, a novel regulator, PKNOX2, was identified as being associated with physiological fibroblast activation in healthy hearts. To validate the roles of these transcription factors in maintaining homeostasis, we used single-nucleus RNA-sequencing analysis of transplanted failing hearts focusing on fibroblast remodelling. The trajectory analysis suggested that PKNOX2 was abnormally decreased from fibroblast activation to pathological myofibroblast formation. Both gain- and loss-of-function in vitro experiments demonstrated the inhibitory role of PKNOX2 in pathological fibrosis remodelling. Moreover, fibroblast-specific overexpression and knockout of PKNOX2 in a heart failure mouse model induced by transverse aortic constriction surgery significantly improved and aggravated myocardial fibrosis, respectively. In summary, this study established a high-quality pipeline for single-nucleus RNA-sequencing analysis of heart muscle. With this optimized protocol, we described the transcriptional regulation networks of the main cardiac cell subtypes and identified PKNOX2 as a novel regulator in suppressing fibrosis and a potential therapeutic target for future translational studies.

Revealing the Subzero-Temperature Electrochemical Kinetics Behaviors in Ni-Rich Cathode.

The sluggish kinetics in Ni-rich cathodes at subzero temperatures causes decreased specific capacity and poor rate capability, resulting in slow and unstable charge storage. So far, the driving force of this phenomenon remains a mystery. Herein, with the help of in-situ X-ray diffraction and time of flight secondary ion mass spectrometry techniques, the continuous accumulation of both the cathode electrolyte interphase (CEI) film formation and the incomplete structure evolution during cycling under subzero temperature are proposed. It is presented that excessively uniform and thick CEI film generated at subzero temperatures would block the diffusion of Li+ -ions, resulting in incomplete phase evolution and clear charge potential delay. The incomplete phase evolution throughout the Li+ -ion intercalation/de-intercalation processes would further cause low depth of discharge and poor electrochemical reversibility with low initial Coulombic efficiency, as well. In addition, the formation of the thick and uniform CEI film would also consume Li+ -ions during the charging process. This discovery highlights the effects of the CEI film formation behavior and incomplete phase evolution in restricting electrochemical kinetics under subzero temperatures, which the authors believe would promote the further application of the Ni-rich cathodes.

Use of a novel tapered, scoring balloon to treat juxta-anastomotic stenosis in AV fistula.

BACKGROUND: Juxta-anastomotic stenosis, the most common lesion in arteriovenous fistula, creates a dilemma for optimal balloon size choosing during PTA. OBJECTIVES: To descript the effect of a novel tapered scoring balloon catheter (DK Medtech, Suzhou, China) which has a conical shape with an increasing diameter from the front part of the balloon to the end, and three non-slip elements attached on the surface of the balloon. RESEARCH DESIGN: Case series of 10 patients used this balloon catheter was retrospectively analyzed. SUBJECTS: Patients with juxta-anastomotic stenosis. MEASURES: A retrospective review of 10 cases using the novel tapered, scoring balloon catheter in our center was performed. RESULTS: All cases were clinical technique success. The average total procedure time was 16.7 min with 2:14 of fluoroscopy time. No complications such as vascular rupture or dissection occurred. The primary patency rates at 6 and 12 month were 80% and 50% separately. CONCLUSIONS: This tapered scoring balloon provides an economical, safe, and efficient management for juxta-anastomotic stenosis.

Batch alignment of single-cell transcriptomics data using deep metric learning.

scRNA-seq has uncovered previously unappreciated levels of heterogeneity. With the increasing scale of scRNA-seq studies, the major challenge is correcting batch effect and accurately detecting the number of cell types, which is inevitable in human studies. The majority of scRNA-seq algorithms have been specifically designed to remove batch effect firstly and then conduct clustering, which may miss some rare cell types. Here we develop scDML, a deep metric learning model to remove batch effect in scRNA-seq data, guided by the initial clusters and the nearest neighbor information intra and inter batches. Comprehensive evaluations spanning different species and tissues demonstrated that scDML can remove batch effect, improve clustering performance, accurately recover true cell types and consistently outperform popular methods such as Seurat 3, scVI, Scanorama, BBKNN, Harmony et al. Most importantly, scDML preserves subtle cell types in raw data and enables discovery of new cell subtypes that are hard to extract by analyzing each batch individually. We also show that scDML is scalable to large datasets with lower peak memory usage, and we believe that scDML offers a valuable tool to study complex cellular heterogeneity.

Structure-preserved dimension reduction using joint triplets sampling for multi-batch integration of single-cell transcriptomic data.

Dimension reduction (DR) plays an important role in single-cell RNA sequencing (scRNA-seq), such as data interpretation, visualization and other downstream analysis. A desired DR method should be applicable to various application scenarios, including identifying cell types, preserving the inherent structure of data and handling with batch effects. However, most of the existing DR methods fail to accommodate these requirements simultaneously, especially removing batch effects. In this paper, we develop a novel structure-preserved dimension reduction (SPDR) method using intra- and inter-batch triplets sampling. The constructed triplets jointly consider each anchor's mutual nearest neighbors from inter-batch, k-nearest neighbors from intra-batch and randomly selected cells from the whole data, which capture higher order structure information and meanwhile account for batch information of the data. Then we minimize a robust loss function for the chosen triplets to obtain a structure-preserved and batch-corrected low-dimensional representation. Comprehensive evaluations show that SPDR outperforms other competing DR methods, such as INSCT, IVIS, Trimap, Scanorama, scVI and UMAP, in removing batch effects, preserving biological variation, facilitating visualization and improving clustering accuracy. Besides, the two-dimensional (2D) embedding of SPDR presents a clear and authentic expression pattern, and can guide researchers to determine how many cell types should be identified. Furthermore, SPDR is robust to complex data characteristics (such as down-sampling, duplicates and outliers) and varying hyperparameter settings. We believe that SPDR will be a valuable tool for characterizing complex cellular heterogeneity.

CuCl2 -Catalyzed α-Chloroketonation of Aromatic Alkenes via Visible-Light-Induced LMCT.

Here we report a copper-catalyzed protocol for the synthesis of α-chloroketones from aromatic alkenes including electron-deficient olefins under visible-light irradiation. Preliminary mechanistic studies show that the peroxo Cu(II) species is the key intermediate and hydroperoxyl (HOO⋅) and chlorine (Cl⋅) radicals can be generated by ligand-to-metal charge transfer (LMCT).

Propensity score matching enables batch-effect-corrected imputation in single-cell RNA-seq analysis.

Developments of single-cell RNA sequencing (scRNA-seq) technologies have enabled biological discoveries at the single-cell resolution with high throughput. However, large scRNA-seq datasets always suffer from massive technical noises, including batch effects and dropouts, and the dropout is often shown to be batch-dependent. Most existing methods only address one of the problems, and we show that the popularly used methods failed in trading off batch effect correction and dropout imputation. Here, inspired by the idea of causal inference, we propose a novel propensity score matching method for scRNA-seq data (scPSM) by borrowing information and taking the weighted average from similar cells in the deep sequenced batch, which simultaneously removes the batch effect, imputes dropout and denoises data in the entire gene expression space. The proposed method is testified on two simulation datasets and a variety of real scRNA-seq datasets, and the results show that scPSM is superior to other state-of-the-art methods. First, scPSM improves clustering accuracy and mixes cells of the same type, suggesting its ability to keep cell type separation while correcting for batch. Besides, using the scPSM-integrated data as input yields results free of batch effects or dropouts in the differential expression analysis. Moreover, scPSM not only achieves ideal denoising but also preserves real biological structure for downstream gene-based analyses. Furthermore, scPSM is robust to hyperparameters and small datasets with a few cells but enormous genes. Comprehensive evaluations demonstrate that scPSM jointly provides desirable batch effect correction, imputation and denoising for recovering the biologically meaningful expression in scRNA-seq data.

Novel Risk Prediction Model to Determine Adverse Heart Failure Outcomes in Arrhythmogenic Right Ventricular Cardiomyopathy.

Background Patients with arrhythmogenic right ventricular cardiomyopathy are at risk for life-threatening ventricular tachyarrhythmias, but progressive heart failure (HF) may occur in later stages of disease. This study aimed to characterize potential risk predictors and develop a model for individualized assessment of adverse HF outcomes in arrhythmogenic right ventricular cardiomyopathy. Methods and Results Longitudinal and observational cohorts with 290 patients with arrhythmogenic right ventricular cardiomyopathy from the Fuwai Hospital in Beijing, China, and 99 patients from the University Heart Center in Zurich, Switzerland, with follow-up data were studied. The primary end point of the study was heart transplantation or death attributable to HF. The model was developed by Cox regression analysis for predicting risk and was internally validated. During 4.92±3.03 years of follow-up, 48 patients reached the primary end point. The determinants of the risk prediction model were left ventricular ejection fraction, serum creatinine levels, moderate-to-severe tricuspid regurgitation, and atrial fibrillation. Implantable cardioverter-defibrillators did not reduce the occurrence of adverse HF outcomes. Conclusions A novel risk prediction model for arrhythmogenic right ventricular cardiomyopathy has been developed using 2 large and well-established cohorts, incorporating common clinical parameters such as left ventricular ejection fraction, serum creatinine levels, tricuspid regurgitation, and atrial fibrillation, which can identify patients who are at risk for terminal HF events, and may guide physicians to assess individualized HF risk and to optimize management strategies.

Inhibition of IRAK 1/4 alleviates colitis by inhibiting TLR4/ NF-κB pathway and protecting the intestinal barrier.

Interleukin-1 receptor-associated kinase 1/4 (IRAK1/4) is the main kinase of the Toll-like receptor (TLR)-mediated pathway, considered a new target for treating inflammatory diseases. Studies showed a significant correlation between TLRs and inflammatory responses in ulcerative colitis (UC). Therefore, in this study, after inducing experimental colitis in mice with 3% dextran sulfate sodium (DSS), different concentrations of IRAK1/4 inhibitors were administered intraperitoneally. Then, the disease activity index was assessed, including the degree of pathological damage, by HE staining. Subsequently, while western blotting detected the TLR4/NF-κB pathway and intestinal barrier protein expression (Zonula-1, Occludin, Claudin-1, JAM-A), real-time polymerase chain reaction (RT-PCR) detected the mRNA expression levels of IRAK1/4 and mucin1/2. Furthermore, the expression levels of Zonula-1 and occludin were detected by immunofluorescence, including the plasma FITC-dextran 4000 concentration, to evaluate intestinal barrier permeability. However, ELISA measured the expression of inflammatory factors to reflect intestinal inflammation in mice. Investigations showed that the IRAK 1/4 inhibitor significantly reduced clinical symptoms and pathological DSS-induced colitis damage in mice and then inhibited the cytoplasmic and nuclear translocation of NF-κB p65, including the phosphorylation of IκBα and reduction in downstream inflammatory factor production. Therefore, we established that the IRAK1/4 inhibitor effectively improves colitis induced by DSS, partly by inhibiting the TLR4/NF-κB pathway, reducing inflammation, and maintaining the integrity of the colonic barrier.

Clinical Efficacy of Endoscopic Submucosal Dissection for the Treatment of Duodenal Lesions in Terms of Operative Technique and Management of Complications.

Background and Aims: Duodenal endoscopic submucosal dissection (ESD) has been considered to be the most challenging because of its high incidence of complications, which has hindered the development of duodenal ESD. The aim of this study is to discuss operation tips for duodenal ESD and to assess the efficacy and safety of duodenal ESD. Patients and Methods: Eighty-two patients who underwent ESD in the digestive endoscope center for superficial duodenal epithelial tumors (SDETs) from January 2017 to June 2021 were studied. Patients were divided into three groups according to the occurrence of complications, and the clinical characteristics and surgical efficacy of each group were compared. Results: SDETs in 82 patients were completely removed by ESD, with a 97.5% R0 resection rate. The average size of resected lesions was 23.8 ± 6.5 mm. There were significant differences in lesion size and operation time between the normal and intraprocedural complication groups (P < .05). Similarly, between the normal and delayed complication groups, significant differences were noted in lesion location, size, operation time, occupied circumference, and postoperative hospitalization duration (P < .05). Conclusion: Duodenal ESD is prone to complications that increase the complexity of the procedure. By improving the necessary technique and skills, duodenal ESD remains safe and effective.

ScCAEs: deep clustering of single-cell RNA-seq via convolutional autoencoder embedding and soft K-means.

Clustering and cell type classification are a vital step of analyzing scRNA-seq data to reveal the complexity of the tissue (e.g. the number of cell types and the transcription characteristics of the respective cell type). Recently, deep learning-based single-cell clustering algorithms become popular since they integrate the dimensionality reduction with clustering. But these methods still have unstable clustering effects for the scRNA-seq datasets with high dropouts or noise. In this study, a novel single-cell RNA-seq deep embedding clustering via convolutional autoencoder embedding and soft K-means (scCAEs) is proposed by simultaneously learning the feature representation and clustering. It integrates the deep learning with convolutional autoencoder to characterize scRNA-seq data and proposes a regularized soft K-means algorithm to cluster cell populations in a learned latent space. Next, a novel constraint is introduced to the clustering objective function to iteratively optimize the clustering results, and more importantly, it is theoretically proved that this objective function optimization ensures the convergence. Moreover, it adds the reconstruction loss to the objective function combining the dimensionality reduction with clustering to find a more suitable embedding space for clustering. The proposed method is validated on a variety of datasets, in which the number of clusters in the mentioned datasets ranges from 4 to 46, and the number of cells ranges from 90 to 30 302. The experimental results show that scCAEs is superior to other state-of-the-art methods on the mentioned datasets, and it also keeps the satisfying compatibility and robustness. In addition, for single-cell datasets with the batch effects, scCAEs can ensure the cell separation while removing batch effects.

Single-cell transcriptomic identified HIF1A as a target for attenuating acute rejection after heart transplantation.

Acute rejection (AR) is an important contributor to graft failure, which remains a leading cause of death after heart transplantation (HTX). The regulation of immune metabolism has become a new hotspot in the development of immunosuppressive drugs. In this study, Increased glucose metabolism of cardiac macrophages was found in patients with AR. To find new therapeutic targets of immune metabolism regulation for AR, CD45+ immune cells extracted from murine isografts, allografts, and untransplanted donor hearts were explored by single-cell RNA sequencing. Total 20 immune cell subtypes were identified among 46,040 cells. The function of immune cells in AR were illustrated simultaneously. Cardiac resident macrophages were substantially replaced by monocytes and proinflammatory macrophages during AR. Monocytes/macrophages in AR allograft were more active in antigen presentation and inflammatory recruitment ability, and glycolysis. Based on transcription factor regulation analysis, we found that the increase of glycolysis in monocytes/macrophages was mainly regulated by HIF1A. Inhibition of HIF1A could alleviate inflammatory cells infiltration in AR. To find out the effect of HIF1A on AR, CD45+ immune cells extracted from allografts after HIF1A inhibitor treatment were explored by single-cell RNA sequencing. HIF1A inhibitor could reduce the antigen presenting ability and pro-inflammatory ability of macrophages, and reduce the infiltration of Cd4+ and Cd8a+ T cells in AR. The expression of Hif1α in AR monocytes/macrophages was regulated by pyruvate kinase 2. Higher expression of HIF1A in macrophages was also detected in human hearts with AR. These indicated HIF1A may serve as a potential target for attenuating AR.

Detailed Analyses of the Expression Patterns of Potential Severe Acute Respiratory Syndrome Coronavirus 2 Receptors in the Human Heart Using Single-Nucleus RNA Sequencing.

Cardiac injury is a common complication of coronavirus disease 2019 (COVID-19), but the exact mechanisms have not been completely elucidated. The virus receptors on subsets of cells are key determinants of susceptibility to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. Due to its high sequence similarity to SARS-CoV, SARS-CoV-2 also utilizes ACE2 as the cell entry receptor. A growing number of studies have indicated that other receptors apart from ACE2 are involved in SARS-CoV-2 infection. This study aimed to elucidate the expression characteristics of SARS-CoV-2 cellular receptors in the heart. We first investigated ACE2 expression in a comprehensive transcriptional landscape of the human heart comprising single-nucleus RNA-seq (snRNA-seq) data for >280,000 cells. Then, the expression distributions of novel SARS-CoV-2 receptors were analyzed at the single-cell level to clarify the cardiovascular complications in COVID-19. We observed a higher percentage of ACE2-positive cells in pericytes (8.3%), fibroblasts (5.1%), and adipocytes (4.4%) in the human heart, compared to other cell types. The frequency of ACE2-positive cells in each cell type from the ventricles was significantly higher than that in the atria, suggesting that the ventricular cells are more susceptible to SARS-CoV-2 infection. The distribution patterns of other receptors (BSG, HSPA5, KREMEN1, NRP1, ANPEP, AXL) were significantly different from those of ACE2, demonstrating higher expression levels in ventricular cardiomyocytes. Moreover, our results suggest that fibroblasts and adipocytes, aside from pericytes, may be vulnerable targets for SARS-CoV-2 infection in the human heart. Our study presents potential targets for future clinical studies and interventions for cardiac injury in patients with COVID-19.

SpaGCN: Integrating gene expression, spatial location and histology to identify spatial domains and spatially variable genes by graph convolutional network.

Recent advances in spatially resolved transcriptomics (SRT) technologies have enabled comprehensive characterization of gene expression patterns in the context of tissue microenvironment. To elucidate spatial gene expression variation, we present SpaGCN, a graph convolutional network approach that integrates gene expression, spatial location and histology in SRT data analysis. Through graph convolution, SpaGCN aggregates gene expression of each spot from its neighboring spots, which enables the identification of spatial domains with coherent expression and histology. The subsequent domain guided differential expression (DE) analysis then detects genes with enriched expression patterns in the identified domains. Analyzing seven SRT datasets using SpaGCN, we show it can detect genes with much more enriched spatial expression patterns than competing methods. Furthermore, genes detected by SpaGCN are transferrable and can be utilized to study spatial variation of gene expression in other datasets. SpaGCN is computationally fast, platform independent, making it a desirable tool for diverse SRT studies.

Identification of reference genes for gene expression studies among different developmental stages of murine hearts.

BACKGROUND: Real-time quantitative polymerase chain reaction (RT-qPCR) is a widely-used standard assay for assessing gene expression. RT-qPCR data requires reference genes for normalization to make the results comparable. Therefore, the selected reference gene should be highly stable in its expression throughout the experimental datasets. So far, reports about the optimal set of reference genes in murine left ventricle (LV) across embryonic and postnatal stages are few. The objective of our research was to identify the appropriate reference genes in murine LV among different developmental stages. METHODS: We investigated the gene expression profiles of 21 widely used housekeeping genes in murine LV from 7 different developmental stages (almost throughout the whole period of the mouse lifespan). The stabilities of the potential reference genes were evaluated by five methods: GeNorm, NormFinder, BestKeeper, Delta-Ct and RefFinder. RESULTS: We proposed a set of reliable reference genes for normalization of RT-qPCR experimental data in different conditions. Furthermore, our results showed that 6 genes (18S, Hmbs, Ubc, Psmb4, Tfrc and Actb) are not recommended to be used as reference genes in murine LV development studies. The data also suggested that the Rplp0 gene might serve as an optimal reference gene in gene expression analysis. CONCLUSIONS: Our study investigated the expression stability of the commonly used reference genes in process of LV development and maturation. We proposed a set of optimal reference genes that are suitable for accurate normalization of RT-qPCR data in specific conditions. Our findings may be helpful in future studies for investigating the gene expression patterns and mechanism of mammalian heart development.

A joint deep learning model enables simultaneous batch effect correction, denoising, and clustering in single-cell transcriptomics.

Recent developments of single-cell RNA-seq (scRNA-seq) technologies have led to enormous biological discoveries. As the scale of scRNA-seq studies increases, a major challenge in analysis is batch effects, which are inevitable in studies involving human tissues. Most existing methods remove batch effects in a low-dimensional embedding space. Although useful for clustering, batch effects are still present in the gene expression space, leaving downstream gene-level analysis susceptible to batch effects. Recent studies have shown that batch effect correction in the gene expression space is much harder than in the embedding space. Methods such as Seurat 3.0 rely on the mutual nearest neighbor (MNN) approach to remove batch effects in gene expression, but MNN can only analyze two batches at a time, and it becomes computationally infeasible when the number of batches is large. Here, we present CarDEC, a joint deep learning model that simultaneously clusters and denoises scRNA-seq data while correcting batch effects both in the embedding and the gene expression space. Comprehensive evaluations spanning different species and tissues showed that CarDEC outperforms Scanorama, DCA + Combat, scVI, and MNN. With CarDEC denoising, non-highly variable genes offer as much signal for clustering as the highly variable genes (HVGs), suggesting that CarDEC substantially boosted information content in scRNA-seq. We also showed that trajectory analysis using CarDEC's denoised and batch-corrected expression as input revealed marker genes and transcription factors that are otherwise obscured in the presence of batch effects. CarDEC is computationally fast, making it a desirable tool for large-scale scRNA-seq studies.

Pristimerin reduces dextran sulfate sodium-induced colitis in mice by inhibiting microRNA-155.

Pristimerin (Pris), which is a natural triterpenoid compound extracted from the Celastraceae plant, has an effect on intestinal inflammation, but its mechanism needs further study. Human genome-wide analysis found that the expression of microRNA-155 in the blood and colon tissue of patients with IBD was increased. Therefore, we studied the effect of Pris on a model of DSS-induced colitis in mice and investigated whether Pris inhibited the expression of microRNA-155. We obtained a mouse model of acute experimental colitis by allowing the mice to drink a 3% by mass DSS solution freely for 7 days. Pris solutions at different concentrations were injected via the abdomen to simulate the therapeutic effect of Pris on colitis. The body weight and faeces were measured and recorded daily. The mice were sacrificed by the cervical dislocation method after the experiment, and the colon length and histological changes, as well as the changes in Nrf2 in the colon tissue, were measured. The activities of the antioxidant enzymes GSH, GSH-Px and SOD were examined. The expression levels of microRNA-155, IL-1ß, IL-6, IL-17, and TNF-α in the colon were detected by RT-PCR technology, and the expression of NF-κB p65 in the colon was detected by western blotting. Our study shows that Pris can reduce the DAI score, obviously alleviate weight loss, and decrease the colon pathological tissue damage caused by DSS. Pris can inhibit the increase in microRNA-155 expression induced by DSS-induced colitis. Our study has shown that Pris may reduce DSS-induced colitis in mice by inhibiting the expression of microRNA-155, and further inhibition of the inflammatory response and oxidative stress occurred.

Plasma testosterone and arrhythmic events in male patients with arrhythmogenic right ventricular cardiomyopathy.

AIMS: Arrhythmogenic right ventricular cardiomyopathy (ARVC) is associated with life-threatening ventricular arrhythmia and progressive ventricular dysfunction. Previous studies suggested that sex hormones play an important role in the onset and prognosis of ARVC. This study aimed to investigate the role of testosterone in predicting major adverse cardiac events in the Chinese ARVC cohort. METHODS AND RESULTS: Ninety-nine ARVC patients (median age, 40 years; 70.7% male) and 96 healthy controls (median age, 41 years; 62.5% male) were enrolled. The circulating levels of testosterone were measured by enzyme-linked immunosorbent assays (ELISA). The median follow-up time of all ARVC male patients was 17 months (interquartile range/IQR 9-29). Cox proportional hazards regression was used to analyse the effect of plasma testosterone and other well-described risk factors on malignant arrhythmic events in male ARVC patients. The male ARVC patients had significantly elevated levels of total testosterone [TT, 6.390 (4.438-8.768) ng/mL vs. 3.617 (2.073-4.479) ng/mL, P < 0.0001, data shown as the median with IQR], bioavailable testosterone [BT, 4.11 (1.990-6.545) ng/mL vs. 1.32 (0.7965-2.0350) ng/mL, P < 0.0001, median with IQR], and free testosterone [FT, 0.2055 (0.1000-0.4073) ng/mL vs. 0.0768 (0.0405-0.1105) ng/mL, P < 0.0001, median with IQR] than healthy male volunteer, whereas no differences were observed among female counterparts. There was no significant correlation between the baseline clinical characteristics and testosterone levels in male ARVC patients (Spearman's correlation test, P > 0.05). During the follow-up, the levels of testosterone were higher in male patients who experienced malignant arrhythmic events (N = 22) than in those who did not (N = 25) [TT, 9.034 (7.222-15.370) ng/mL vs. 4.633 (3.363-6.375) ng/mL, P < 0.001; BT, 7.485 (2.070-9.163) ng/mL vs. 3.300 (1.685-4.690) ng/mL, P < 0.05; FT, 0.453 (0.221-0.758) ng/mL vs. 0.161 (0.075-0.337) ng/mL P < 0.05, data expressed as median (IQR) and adjusted by Dunn's multiple comparisons test], whereas such distinction was not observed among patients with significant structural progression events (N = 16). Through multivariable adjustments, the Cox regression analysis showed the level of plasma total testosterone (HR = 1.325, 95% confidence interval = 1.171-1.498, P < 0.001) was an independent predictor for malignant arrhythmic events. CONCLUSIONS: The levels of plasma testosterone in ARVC male patients are higher than those in healthy males. Testosterone level, without relation to the baseline cardiac function and future significant structural progression events, is a strong predictor of future adverse arrhythmic events in male patients with ARVC. Therefore, our results suggest that testosterone may be a useful biomarker in arrhythmic risk prediction in the ARVC.

Deep learning enables accurate clustering with batch effect removal in single-cell RNA-seq analysis.

Single-cell RNA sequencing (scRNA-seq) can characterize cell types and states through unsupervised clustering, but the ever increasing number of cells and batch effect impose computational challenges. We present DESC, an unsupervised deep embedding algorithm that clusters scRNA-seq data by iteratively optimizing a clustering objective function. Through iterative self-learning, DESC gradually removes batch effects, as long as technical differences across batches are smaller than true biological variations. As a soft clustering algorithm, cluster assignment probabilities from DESC are biologically interpretable and can reveal both discrete and pseudotemporal structure of cells. Comprehensive evaluations show that DESC offers a proper balance of clustering accuracy and stability, has a small footprint on memory, does not explicitly require batch information for batch effect removal, and can utilize GPU when available. As the scale of single-cell studies continues to grow, we believe DESC will offer a valuable tool for biomedical researchers to disentangle complex cellular heterogeneity.