UNMF: a unified nonnegative matrix factorization for multi-dimensional omics data.

Factor analysis, ranging from principal component analysis to nonnegative matrix factorization, represents a foremost approach in analyzing multi-dimensional data to extract valuable patterns, and is increasingly being applied in the context of multi-dimensional omics datasets represented in tensor form. However, traditional analytical methods are heavily dependent on the format and structure of the data itself, and if these change even slightly, the analyst must change their data analysis strategy and techniques and spend a considerable amount of time on data preprocessing. Additionally, many traditional methods cannot be applied as-is in the presence of missing values in the data. We present a new statistical framework, unified nonnegative matrix factorization (UNMF), for finding informative patterns in messy biological data sets. UNMF is designed for tidy data format and structure, making data analysis easier and simplifying the development of data analysis tools. UNMF can handle a wide range of data structures and formats, and works seamlessly with tensor data including missing observations and repeated measurements. The usefulness of UNMF is demonstrated through its application to several multi-dimensional omics data, offering user-friendly and unified features for analysis and integration. Its application holds great potential for the life science community. UNMF is implemented with R and is available from GitHub (https://github.com/abikoushi/moltenNMF).

CYBERTRACK2.0: zero-inflated model-based cell clustering and population tracking method for longitudinal mass cytometry data.

SUMMARY: Recent advancements in high-dimensional single-cell technologies, such as mass cytometry, enable longitudinal experiments to track dynamics of cell populations and identify change points where the proportions vary significantly. However, current research is limited by the lack of tools specialized for analyzing longitudinal mass cytometry data. In order to infer cell population dynamics from such data, we developed a statistical framework named CYBERTRACK2.0. The framework's analytic performance was validated against synthetic and real data, showing that its results are consistent with previous research. AVAILABILITY AND IMPLEMENTATION: CYBERTRACK2.0 is available at https://github.com/kodaim1115/CYBERTRACK2. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Ethoxyquin is a Competent Radical-Trapping Antioxidant for Preventing Ferroptosis in Doxorubicin Cardiotoxicity.

ABSTRACT: Doxorubicin (DOX) is an effective anti-cancer agent for various malignancies. Nevertheless, it has a side effect of cardiotoxicity, referred to as doxorubicin-induced cardiomyopathy (DIC), that is associated with a poorer prognosis. This cardiotoxicity limits the clinical use of DOX as a therapeutic agent for malignancies. Recently, ferroptosis, a form of regulated cell death induced by the accumulation of lipid peroxides, has been recognized as a major pathophysiology of DIC. Ethoxyquin is a lipophilic antioxidant widely used for food preservation and thus may be a potential therapeutic drug for preventing DIC. However, the efficacy of ethoxyquin against ferroptosis and DIC remains to be fully elucidated. Here, we investigated the inhibitory action of ethoxyquin against GPx4-deficient ferroptosis and its therapeutic efficacy against DOX-induced cell death in cultured cardiomyocytes and cardiotoxicity in a murine model of DIC. In cultured cardiomyocytes, ethoxyquin treatment effectively prevented GPx4-deficient ferroptosis. Ethoxyquin also prevented DOX-induced cell death, accompanied by the suppression of malondialdehyde (MDA) and mitochondrial lipid peroxides, which were induced by DOX. Furthermore, ethoxyquin significantly prevented DOX-induced cell death without any suppression of caspase cleavages representing apoptosis. In DIC mice, ethoxyquin treatment ameliorated cardiac impairments, such as contractile dysfunction and myocardial atrophy, and lung congestion. Ethoxyquin also suppressed serum lactate dehydrogenase and creatine kinase activities, decreased the levels of lipid peroxides such as MDA and acrolein, inhibited cardiac fibrosis, and reduced TUNEL-positive cells in the hearts of DIC mice. Collectively, ethoxyquin is a competent antioxidant for preventing ferroptosis in DIC and can be its prospective therapeutic drug.

Heart Rate Reduction with Ivabradine Prevents Cardiac Rupture after Myocardial Infarction in Mice.

PURPOSE: Cardiac rupture is a fatal complication following myocardial infarction (MI). An increase in heart rate (HR) is reportedly an independent risk factor for cardiac rupture during acute MI. However, the role of HR reduction in cardiac rupture after MI remains to be fully elucidated. We aimed to evaluate the therapeutic efficacy of HR reduction with ivabradine (IVA) on post-MI cardiac rupture in mice. METHODS: We induced MI in mice by ligating the left anterior descending coronary artery. Subsequently, we subcutaneously implanted osmotic pumps filled with IVA solution or vehicle (Veh) in the surviving MI mice at 24 h postoperatively. We biochemically analyzed the myocardium on day 5, additionally observed the mice for 10 days, and analyzed the rates of cardiac rupture and non-cardiac rupture death, and survival after MI. RESULTS: HR was significantly lower in the IVA-treated mice, whereas blood pressure was comparable between the two groups. Compared to the Veh-treated mice, apoptosis was significantly reduced in the MI border zone in the IVA-treated mice. Although there were no differences in the infarct size of the surviving MI mice between the two groups, HR reduction with IVA significantly reduced cardiac rupture (rupture rate 26 and 8% in the Veh-treated and IVA-treated groups, respectively) and improved survival after MI. CONCLUSION: Our findings suggest that HR reduction with IVA prevents cardiac rupture after MI. This may be particularly effective in MI patients with a high HR who are either unable to adequately tolerate ß-blockers or whose HR remains high despite receiving ß-blockers.

Hierarchical non-negative matrix factorization using clinical information for microbial communities.

BACKGROUND: The human microbiome forms very complex communities that consist of hundreds to thousands of different microorganisms that not only affect the host, but also participate in disease processes. Several state-of-the-art methods have been proposed for learning the structure of microbial communities and to investigate the relationship between microorganisms and host environmental factors. However, these methods were mainly designed to model and analyze single microbial communities that do not interact with or depend on other communities. Such methods therefore cannot comprehend the properties between interdependent systems in communities that affect host behavior and disease processes. RESULTS: We introduce a novel hierarchical Bayesian framework, called BALSAMICO (BAyesian Latent Semantic Analysis of MIcrobial COmmunities), which uses microbial metagenome data to discover the underlying microbial community structures and the associations between microbiota and their environmental factors. BALSAMICO models mixtures of communities in the framework of nonnegative matrix factorization, taking into account environmental factors. We proposes an efficient procedure for estimating parameters. A simulation then evaluates the accuracy of the estimated parameters. Finally, the method is used to analyze clinical data. In this analysis, we successfully detected bacteria related to colorectal cancer. CONCLUSIONS: These results show that the method not only accurately estimates the parameters needed to analyze the connections between communities of microbiota and their environments, but also allows for the effective detection of these communities in real-world circumstances.

Changes in lung to finger circulation time measured via cardiopulmonary polygraphy in patients with varying types of heart disease.

Cardiopulmonary polygraphy (PG) demonstrates not only parameters for sleep disordered breathing (SDB) but also hemodynamics. We previously developed a software that detects lung to fingertip circulation time (LFCT) derived from PG dataset and reported that those LFCT reflected the cardiac output. The purpose of this study is to investigate how the LFCT changes during clinical course and whether reflects the impact of in-hospital treatment on cardiac function. Consecutive patients (N = 89) who admitted to the cardiovascular division, underwent PG at the early and late phase of admission. Parameters for SDB and LFCT were compared between an acute decompensated heart failure (ADHF) group (n = 51) and non-ADHF group (n = 38). ADHF group was further divided into subgroups: preserved ejection fraction (pEF) (EF > 40%) and reduced EF (rEF) (EF ≤ 40%). Using our original algorithm, we obtained LFCT values from all of the patients, though 29.4% of ADHF and 44.7% of non-ADHF had no or mild SDB. LFCT significantly shortened in the ADHF-rEF group, in contrast to ADHF-pEF group or non-ADHF group (ADHF-rEF group: 26.9 ± 7.6 to 24.2 ± 6.1 s, p = 0.01; ADHF-pEF group: 25.3 ± 7.3 to 25.3 ± 6.9 s, p = 0.98; non-ADHF group: 21.5 ± 5.5 to 21.9 ± 5.0 s, p = 0.65). The respiratory disorder index in the ADHF group improved after treatment, irrespective of EF (pEF: 26.9 ± 16.1 to 15.8 ± 11.9/h, p < 0.01; rEF: 27.0 ± 16.5 to 20.7 ± 13.6/h, p = 0.03). Automatic detection of LFCT was feasible in almost all cardiac patients. LFCT value changed according to the heart failure treatment in ADHF-rEF patients and reflected cardiac function. LFCT might be a useful indicator of effective cardiac disease treatment.

Model-based clustering for flow and mass cytometry data with clinical information.

BACKGROUND: High-dimensional flow cytometry and mass cytometry allow systemic-level characterization of more than 10 protein profiles at single-cell resolution and provide a much broader landscape in many biological applications, such as disease diagnosis and prediction of clinical outcome. When associating clinical information with cytometry data, traditional approaches require two distinct steps for identification of cell populations and statistical test to determine whether the difference between two population proportions is significant. These two-step approaches can lead to information loss and analysis bias. RESULTS: We propose a novel statistical framework, called LAMBDA (Latent Allocation Model with Bayesian Data Analysis), for simultaneous identification of unknown cell populations and discovery of associations between these populations and clinical information. LAMBDA uses specified probabilistic models designed for modeling the different distribution information for flow or mass cytometry data, respectively. We use a zero-inflated distribution for the mass cytometry data based the characteristics of the data. A simulation study confirms the usefulness of this model by evaluating the accuracy of the estimated parameters. We also demonstrate that LAMBDA can identify associations between cell populations and their clinical outcomes by analyzing real data. LAMBDA is implemented in R and is available from GitHub ( https://github.com/abikoushi/lambda ).

Model-based cell clustering and population tracking for time-series flow cytometry data.

BACKGROUND: Modern flow cytometry technology has enabled the simultaneous analysis of multiple cell markers at the single-cell level, and it is widely used in a broad field of research. The detection of cell populations in flow cytometry data has long been dependent on "manual gating" by visual inspection. Recently, numerous software have been developed for automatic, computationally guided detection of cell populations; however, they are not designed for time-series flow cytometry data. Time-series flow cytometry data are indispensable for investigating the dynamics of cell populations that could not be elucidated by static time-point analysis. Therefore, there is a great need for tools to systematically analyze time-series flow cytometry data. RESULTS: We propose a simple and efficient statistical framework, named CYBERTRACK (CYtometry-Based Estimation and Reasoning for TRACKing cell populations), to perform clustering and cell population tracking for time-series flow cytometry data. CYBERTRACK assumes that flow cytometry data are generated from a multivariate Gaussian mixture distribution with its mixture proportion at the current time dependent on that at a previous timepoint. Using simulation data, we evaluate the performance of CYBERTRACK when estimating parameters for a multivariate Gaussian mixture distribution, tracking time-dependent transitions of mixture proportions, and detecting change-points in the overall mixture proportion. The CYBERTRACK performance is validated using two real flow cytometry datasets, which demonstrate that the population dynamics detected by CYBERTRACK are consistent with our prior knowledge of lymphocyte behavior. CONCLUSIONS: Our results indicate that CYBERTRACK offers better understandings of time-dependent cell population dynamics to cytometry users by systematically analyzing time-series flow cytometry data.

ENIGMA: an enterotype-like unigram mixture model for microbial association analysis.

BACKGROUND: One of the major challenges in microbial studies is detecting associations between microbial communities and a specific disease. A specialized feature of microbiome count data is that intestinal bacterial communities form clusters called as "enterotype", which are characterized by differences in specific bacterial taxa, making it difficult to analyze these data under health and disease conditions. Traditional probabilistic modeling cannot distinguish between the bacterial differences derived from enterotype and those related to a specific disease. RESULTS: We propose a new probabilistic model, named as ENIGMA (Enterotype-like uNIGram mixture model for Microbial Association analysis), which can be used to address these problems. ENIGMA enabled simultaneous estimation of enterotype-like clusters characterized by the abundances of signature bacterial genera and the parameters of environmental effects associated with the disease. CONCLUSION: In the simulation study, we evaluated the accuracy of parameter estimation. Furthermore, by analyzing the real-world data, we detected the bacteria related to Parkinson's disease. ENIGMA is implemented in R and is available from GitHub ( https://github.com/abikoushi/enigma ).

A latent allocation model for the analysis of microbial composition and disease.

BACKGROUND: Establishing the relationship between microbiota and specific diseases is important but requires appropriate statistical methodology. A specialized feature of microbiome count data is the presence of a large number of zeros, which makes it difficult to analyze in case-control studies. Most existing approaches either add a small number called a pseudo-count or use probability models such as the multinomial and Dirichlet-multinomial distributions to explain the excess zero counts, which may produce unnecessary biases and impose a correlation structure taht is unsuitable for microbiome data. RESULTS: The purpose of this article is to develop a new probabilistic model, called BERnoulli and MUltinomial Distribution-based latent Allocation (BERMUDA), to address these problems. BERMUDA enables us to describe the differences in bacteria composition and a certain disease among samples. We also provide a simple and efficient learning procedure for the proposed model using an annealing EM algorithm. CONCLUSION: We illustrate the performance of the proposed method both through both the simulation and real data analysis. BERMUDA is implemented with R and is available from GitHub ( https://github.com/abikoushi/Bermuda ).

Deferasirox Targeting Ferroptosis Synergistically Ameliorates Myocardial Ischemia Reperfusion Injury in Conjunction With Cyclosporine A.

BACKGROUND: Ferroptosis, an iron-dependent form of regulated cell death, is a major cell death mode in myocardial ischemia reperfusion (I/R) injury, along with mitochondrial permeability transition-driven necrosis, which is inhibited by cyclosporine A (CsA). However, therapeutics targeting ferroptosis during myocardial I/R injury have not yet been developed. Hence, we aimed to investigate the therapeutic efficacy of deferasirox, an iron chelator, against hypoxia/reoxygenation-induced ferroptosis in cultured cardiomyocytes and myocardial I/R injury. METHODS AND RESULTS: The effects of deferasirox on hypoxia/reoxygenation-induced iron overload in the endoplasmic reticulum, lipid peroxidation, and ferroptosis were examined in cultured cardiomyocytes. In a mouse model of I/R injury, the infarct size and adverse cardiac remodeling were examined after treatment with deferasirox, CsA, or both in combination. Deferasirox suppressed hypoxia- or hypoxia/reoxygenation-induced iron overload in the endoplasmic reticulum, lipid peroxidation, and ferroptosis in cultured cardiomyocytes. Deferasirox treatment reduced iron levels in the endoplasmic reticulum and prevented increases in lipid peroxidation and ferroptosis in the I/R-injured myocardium 24 hours after I/R. Deferasirox and CsA independently reduced the infarct size after I/R injury to a similar degree, and combination therapy with deferasirox and CsA synergistically reduced the infarct size (infarct area/area at risk; control treatment: 64±2%; deferasirox treatment: 48±3%; CsA treatment: 48±4%; deferasirox+CsA treatment: 37±3%), thereby ameliorating adverse cardiac remodeling on day 14 after I/R. CONCLUSIONS: Combination therapy with deferasirox and CsA may be a clinically feasible and effective therapeutic approach for limiting I/R injury and ameliorating adverse cardiac remodeling after myocardial infarction.

Hypoxia-induced oxidative stress promotes therapy resistance via upregulation of heme oxygenase-1 in multiple myeloma.

BACKGROUND: Multiple myeloma (MM) is a hematopoietic malignancy for which proteasome inhibitors have become available in recent years. However, many patients develop resistance to these drugs during treatment. Therefore, it is important to elucidate the mechanisms underlying resistance acquisition by proteasome inhibitors. Side population (SP) cells, which have a high drug efflux capacity and hypoxic responses in the microenvironment have both provided important insights into drug resistance in MM; however, little is known about the characteristics of SP cells in hypoxic microenvironments. METHODS: We performed cDNA microarray analysis for SP and non-SP obtained from RPMI-8226 and KMS-11 cell lines cultured for 48 h in normoxic and hypoxic conditions (1% O2 ). Genes specifically upregulated in hypoxic SP were examined. RESULTS: Our comprehensive gene expression analysis identified HMOX1, BACH2, and DUX4 as protein-coding genes that are specifically highly expressed in SP cells under hypoxic conditions. We have shown that HMOX1/heme oxygenase-1 (HMOX1/HO-1) is induced by hypoxia-inducible reactive oxygen species (ROS) and reduces ROS levels. Furthermore, we found that HMOX1 contributes to hypoxia-induced resistance to proteasome inhibitors in vitro and in vivo. Excessive ROS levels synergistically enhance bortezomib sensitivity. In clinical datasets, HMOX1 had a strong and significantly positive correlation with MAFB but not MAF. Interestingly, hypoxic stimulation increased MAFB/MafB expression in myeloma cells; in addition, the knockdown of MAFB under hypoxic conditions suppressed HMOX1 expression. CONCLUSION: These results suggest that the hypoxia-ROS-HMOX1 axis and hypoxia-induced MafB may be important mechanisms of proteasome inhibitor resistance in hypoxic microenvironments.

Acute Myeloid Leukemia Harboring the t(16;21)(p11;q22) Translocation Treated With Venetoclax Plus Azacitidine After Cord Blood Transplantation.

A 62-year-old female was diagnosed with acute myeloid leukemia (AML) with t(16;21)(p11;q22). She achieved complete hematological remission after induction therapy and underwent umbilical cord blood stem cell transplantation (CBT). At 150 days after the CBT, a bone marrow examination revealed relapse. We treated the patient with venetoclax plus azacitidine as salvage therapy. After five cycles of venetoclax and azacitidine therapy, the patient died due to disease progression. The prognosis of AML with t(16;21)(p11;q22) is very poor owing to the high rate of early relapse even after hematopoietic stem cell transplantation. Therefore, a novel therapeutic approach is required to improve patient outcomes.

Cardiac Autoantibodies Against Cardiac Troponin I in Post-Myocardial Infarction Heart Failure: Evaluation in a Novel Murine Model and Applications in Therapeutics.

BACKGROUND: Cardiac autoantibodies (cAAbs) are involved in the progression of adverse cardiac remodeling in heart failure (HF). However, our understanding of cAAbs in HF is limited owing to the absence of relevant animal models. Herein, we aimed to establish and characterize a murine model of cAAb-positive HF after myocardial infarction (MI), thereby facilitating the development of therapeutics targeting cAAbs in post-MI HF. METHODS: MI was induced in BALB/c mice. Plasma cAAbs were evaluated using modified Western blot-based methods. Prognosis, cardiac function, inflammation, and fibrosis were compared between cAAb-positive and cAAb-negative MI mice. Rapamycin was used to inhibit cAAb production. RESULTS: Common cAAbs in BALB/c MI mice targeted cTnI (cardiac troponin I). Herein, 71% (24/34) and 44% (12/27) of the male and female MI mice, respectively, were positive for cAAbs against cTnI (cTnIAAb). Germinal centers were formed in the spleens and mediastinal lymph nodes of cTnIAAb-positive MI mice. cTnIAAb-positive MI mice showed progressive cardiac remodeling with a worse prognosis (P=0.014, by log-rank test), which was accompanied by cardiac inflammation, compared with that in cTnIAAb-negative MI mice. Rapamycin treatment during the first 7 days after MI suppressed cTnIAAb production (cTnIAAb positivity, 59% [29/49] and 7% [2/28] in MI mice treated with vehicle and rapamycin, respectively; P<0.001, by Pearson χ2 test), consequently improving the survival and ameliorating cardiac inflammation, cardiac remodeling, and HF in MI mice. CONCLUSIONS: The present post-MI HF model may accelerate our understanding of cTnIAAb and support the development of therapeutics against cTnIAAbs in post-MI HF.

Iron Overload via Heme Degradation in the Endoplasmic Reticulum Triggers Ferroptosis in Myocardial Ischemia-Reperfusion Injury.

Ischemia-reperfusion (I/R) injury is a promising therapeutic target to improve clinical outcomes after acute myocardial infarction. Ferroptosis, triggered by iron overload and excessive lipid peroxides, is reportedly involved in I/R injury. However, its significance and mechanistic basis remain unclear. Here, we show that glutathione peroxidase 4 (GPx4), a key endogenous suppressor of ferroptosis, determines the susceptibility to myocardial I/R injury. Importantly, ferroptosis is a major mode of cell death in I/R injury, distinct from mitochondrial permeability transition (MPT)-driven necrosis. This suggests that the use of therapeutics targeting both modes is an effective strategy to further reduce the infarct size and thereby ameliorate cardiac remodeling after I/R injury. Furthermore, we demonstrate that heme oxygenase 1 up-regulation in response to hypoxia and hypoxia/reoxygenation degrades heme and thereby induces iron overload and ferroptosis in the endoplasmic reticulum (ER) of cardiomyocytes. Collectively, ferroptosis triggered by GPx4 reduction and iron overload in the ER is distinct from MPT-driven necrosis in both in vivo phenotype and in vitro mechanism for I/R injury. The use of therapeutics targeting ferroptosis in conjunction with cyclosporine A can be a promising strategy for I/R injury.

Doxorubicin causes ferroptosis and cardiotoxicity by intercalating into mitochondrial DNA and disrupting Alas1-dependent heme synthesis.

Clinical use of doxorubicin (DOX) is limited because of its cardiotoxicity, referred to as DOX-induced cardiomyopathy (DIC). Mitochondria-dependent ferroptosis, which is triggered by iron overload and excessive lipid peroxidation, plays a pivotal role in the progression of DIC. Here, we showed that DOX accumulated in mitochondria by intercalating into mitochondrial DNA (mtDNA), inducing ferroptosis in an mtDNA content-dependent manner. In addition, DOX disrupted heme synthesis by decreasing the abundance of 5'-aminolevulinate synthase 1 (Alas1), the rate-limiting enzyme in this process, thereby impairing iron utilization, resulting in iron overload and ferroptosis in mitochondria in cultured cardiomyocytes. Alas1 overexpression prevented this outcome. Administration of 5-aminolevulinic acid (5-ALA), the product of Alas1, to cultured cardiomyocytes and mice suppressed iron overload and lipid peroxidation, thereby preventing DOX-induced ferroptosis and DIC. Our findings reveal that the accumulation of DOX and iron in mitochondria cooperatively induces ferroptosis in cardiomyocytes and suggest that 5-ALA can be used as a potential therapeutic agent for DIC.

An analysis modality for vascular structures combining tissue-clearing technology and topological data analysis.

The blood and lymphatic vasculature networks are not yet fully understood even in mouse because of the inherent limitations of imaging systems and quantification methods. This study aims to evaluate the usefulness of the tissue-clearing technology for visualizing blood and lymphatic vessels in adult mouse. Clear, unobstructed brain/body imaging cocktails and computational analysis (CUBIC) enables us to capture the high-resolution 3D images of organ- or area-specific vascular structures. To evaluate these 3D structural images, signals are first classified from the original captured images by machine learning at pixel base. Then, these classified target signals are subjected to topological data analysis and non-homogeneous Poisson process model to extract geometric features. Consequently, the structural difference of vasculatures is successfully evaluated in mouse disease models. In conclusion, this study demonstrates the utility of CUBIC for analysis of vascular structures and presents its feasibility as an analysis modality in combination with 3D images and mathematical frameworks.

Immunomodulatory Cell Therapy Using αGalCer-Pulsed Dendritic Cells Ameliorates Heart Failure in a Murine Dilated Cardiomyopathy Model.

BACKGROUND: Dilated cardiomyopathy (DCM) is a life-threatening disease, resulting in refractory heart failure. An immune disorder underlies the pathophysiology associated with heart failure progression. Invariant natural killer T (iNKT) cell activation is a prospective therapeutic strategy for ischemic heart disease. However, its efficacy in nonischemic cardiomyopathy, such as DCM, remains to be elucidated, and the feasible modality for iNKT cell activation in humans is yet to be validated. METHODS: Dendritic cells isolated from human volunteers were pulsed with α-galactosylceramide ex vivo, which were used as α-galactosylceramide-pulsed dendritic cells (αGCDCs). We treated DCM mice harboring mutated troponin TΔK210/ΔK210 with αGCDCs and evaluated the efficacy of iNKT cell activation on heart failure in DCM mice. Furthermore, we investigated the molecular basis underlying its therapeutic effects in these mice and analyzed primary cardiac cells under iNKT cell-secreted cytokines. RESULTS: The number of iNKT cells in the spleens of DCM mice was reduced compared with that in wild-type mice, whereas αGCDC treatment activated iNKT cells, prolonged survival of DCM mice, and prevented decline in the left ventricular ejection fraction for 4 weeks, accompanied by suppressed interstitial fibrosis. Mechanistically, αGCDC treatment suppressed TGF (transforming growth factor)-ß signaling and expression of fibrotic genes and restored vasculature that was impaired in DCM hearts by upregulating angiopoietin 1 (Angpt1) expression. Consistently, IFNγ (interferon gamma) suppressed TGF-ß-induced Smad2/3 signaling and the expression of fibrotic genes in cardiac fibroblasts and upregulated Angpt1 expression in cardiomyocytes via Stat1. CONCLUSIONS: Immunomodulatory cell therapy with αGCDCs is a novel therapeutic strategy for heart failure in DCM.

A mixture-of-experts deep generative model for integrated analysis of single-cell multiomics data.

The recent development of single-cell multiomics analysis has enabled simultaneous detection of multiple traits at the single-cell level, providing deeper insights into cellular phenotypes and functions in diverse tissues. However, currently, it is challenging to infer the joint representations and learn relationships among multiple modalities from complex multimodal single-cell data. Here, we present scMM, a novel deep generative model-based framework for the extraction of interpretable joint representations and crossmodal generation. scMM addresses the complexity of data by leveraging a mixture-of-experts multimodal variational autoencoder. The pseudocell generation strategy of scMM compensates for the limited interpretability of deep learning models, and the proposed approach experimentally discovered multimodal regulatory programs associated with latent dimensions. Analysis of recently produced datasets validated that scMM facilitates high-resolution clustering with rich interpretability. Furthermore, we show that crossmodal generation by scMM leads to more precise prediction and data integration compared with the state-of-the-art and conventional approaches.

Methotrexate-induced Transient Encephalopathy in an Adolescent and Young Adult Patient with Acute Lymphoblastic Leukemia.

A 17-year-old girl was diagnosed with acute lymphoblastic leukemia (ALL). After the administration of high-dose methotrexate (MTX) or intrathecal MTX, the patient experienced transient hemiparesis and motor aphasia. Diffusion-weighted magnetic resonance imaging showed a high-intensity lesion in the bilateral centrum semiovale, and a vasospasm was detected in the proximal segment of bilateral A1 on magnetic resonance angiography. Edaravone was administered, and leucovorin rescue treatment was continued; eventually, the patient's neurological symptoms completely resolved. This finding suggested that vasospasm might be a mechanism underlying MTX-induced transient encephalopathy in adolescent and young adult patients with ALL.