Cardiovascular disease encompasses a wide range of conditions, resulting in the highest number of deaths worldwide. The underlying pathologies surrounding cardiovascular disease include a vast and complicated network of both cellular and molecular mechanisms. Unique phenotypic alterations in specific cell types, visualized as varying RNA expression-levels (both coding and non-coding), have been identified as crucial factors in the pathology underlying conditions such as heart failure and atherosclerosis. Recent advances in single-cell RNA sequencing (scRNA-seq) have elucidated a new realm of cell subpopulations and transcriptional variations that are associated with normal and pathological physiology in a wide variety of diseases. This breakthrough in the phenotypical understanding of our cells has brought novel insight into cardiovascular basic science. scRNA-seq allows for separation of widely distinct cell subpopulations which were, until recently, simply averaged together with bulk-tissue RNA-seq. scRNA-seq has been used to identify novel cell types in the heart and vasculature that could be implicated in a variety of disease pathologies. Furthermore, scRNA-seq has been able to identify significant heterogeneity of phenotypes within individual cell subtype populations. The ability to characterize single cells based on transcriptional phenotypes allows researchers the ability to map development of cells and identify changes in specific subpopulations due to diseases at a very high throughput. This review looks at recent scRNA-seq studies of various aspects of the cardiovascular system and discusses their potential value to our understanding of the cardiovascular system and pathology.
Despite substantial advances in the study, treatment, and prevention of cardiovascular disease, numerous challenges relating to optimally screening, diagnosing, and managing patients remain. Simultaneous improvements in computing power, data storage, and data analytics have led to the development of new techniques to address these challenges. One powerful tool to this end is machine learning (ML), which aims to algorithmically identify and represent structure within data. Machine learning’s ability to efficiently analyze large and highly complex data sets make it a desirable investigative approach in modern biomedical research. Despite this potential and enormous public and private sector investment, few prospective studies have demonstrated improved clinical outcomes from this technology. This is particularly true in cardiology, despite its emphasis on objective, data-driven results. This threatens to stifle ML’s growth and use in mainstream medicine. We outline the current state of ML in cardiology and outline methods through which impactful and sustainable ML research can occur. Following these steps can ensure ML reaches its potential as a transformative technology in medicine.
The tumor microenvironment of ovarian cancer is the peritoneal cavity wherein adipose tissue is a major component. The role of the adipose tissue in support of ovarian cancer progression has been elucidated in several studies from the past decades. The adipocytes, in particular, are a major source of factors, which regulate all facets of ovarian cancer progression such as acquisition of chemoresistance, enhanced metastatic potential, and metabolic reprogramming. In this review, we summarize the relevant studies, which highlight the role of adipocytes in ovarian cancer progression and offer insights into unanswered questions and possible future directions of research.
Introduction B-cells have been strongly implicated in cardiac allograft rejection (CAR). Recently, however, the CTOT-11 trial showed that depleting mature CD20+ B-cells did not reduce rates of rejection in cardiac allograft recipients and unexpectedly increased the severity of allograft vasculopathy. Therefore, it can be hypothesized that differing phenotypic subtypes of B-cells correspond with different biological mechanisms relating to CAR. Though, current applications to quantify these subtypes of immune cells, i.e with immunohistochemistry or flow cytometry, are often restricted by limited cell markers and cost-burden; therefore, we demonstrate a novel deconvolution method, FARDEEP, that has been validated to accurately enumerate peripheral blood mononuclear cell-subtypes (PBMCs) in a quicker and more cost-effective manner. Purpose To better understand the association of different B-cell subtypes in CAR by identifying the B-cell subtype most predictive for pathologically defined rejection. Methods The machine learning tool, FARDEEP, was trained with the transcriptomic signatures of 29 PBMC subtypes, characterized by previous single-cell RNA experiments. FARDEEP then was used to deconvolute data-mined RNA from 259 blood samples from 98 cardiac allograft recipients enrolled in the CARGO study (GSE2445). Random forest tree (RF) was then used to analyze the levels of deconvoluted subtypes to predict the severity of rejection assessed by endomyocardial biopsy. Finally, RF was used to identify the subtypes of PBMCs most valuable in predicting rejection. Results Out of the 259 samples with consensus pathological readings, 140 had a consensus International Society of Heart and Lung Transplant grade of 0, 63 with grade 1a, 31 with grade 1b, and 25 with grade 3a or higher. We grouped biopsy samples with grade 0, 1a, and 1b as “low-risk” rejection (n=234). 3a or higher samples were grouped as “high-risk” (n=25). There were no grade 2s in the dataset. According to the dataset, blood was extracted from patients on average 72.5 days post-transplant. The RF had good performance in predicting rejection severity. (Figure 1a) CD20- plasmablast cells were stronger predictors for differentiating high-risk from low-risk compared to CD20+ B-cell populations (i.e B Naive and B Memory cells). (Figure 1b) Overall, however, dendritic cells (DCs), neutrophils, monocytes, and basophils were the strongest predictors for rejection. Conclusion Our findings support the results from the CTOT-11 trial showing that CD20+ B-cells may not contribute to CAR as significantly as seen with other PBMC subtypes. Instead, we showed that among B-cells, CD20- plasmablasts were more likely associated with CAR, possibly explaining why targeting CD20 was ineffective in preventing rejection. Thus, targeting plasmablast-associated markers could potentially be more useful to prevent CAR. Model Performance with Variables Funding Acknowledgement Type of funding source: Private grant(s) and/or Sponsorship. Main funding source(s): 1) Society of Academic Emergency Medicine Foundation; 2) The Jewish Fund
Study Objective: Performance of HEART, GRACE, and TIMI to predict emergent cardiac testing needs, and major adverse cardiac event outcomes (MACE) occurrences, in low risk chest pain patients was compared.Methods: Emergency department observation unit (EDOU) chest pain patients were included. Cardiac imaging tests included different non-invasive cardiac stress and invasive coronary angiography. HEART, GRACE, and TIMI scores were categorized as low (HEART 3, GRACE 108, and TIMI 1) versus above-low risks. Patients were followed for 6-months post-discharge. The results of cardiac tests, EDOU length of stay (LOS), and MACE occurrences were compared. ANOVA was used to compare groups with continuous data and Chi square testing was used for categorical data.Results: Of 986 patients, emergent cardiac tests were performed on 62%. The patients were low risk by all tools (85% by HEART, 81% by GRACE, and 80% by TIMI). The low-risk patients had low abnormal cardiac test results as compared to above-low risks patients (1% versus 11% in HEART, 1% versus 9% in TIMI, and 2% versus 4% in GRACE, p<0.05). The average LOS was 33 hours for patients with, and 26 hours for patients without emergent cardiac tests. MACE occurrence demonstrated no significant difference regardless of whether cardiac stress test was performed emergently or not (0.31% versus 0.97% in HEART, 0.27% versus 0.95% in TIMI, and 0% versus 0.81% in GRACE, p>0.05).Conclusion: Decision tools minimize emergent cardiac testing needs with less than 1% MACE occurrence especially when the HEART tool was used. Study Objective: Venous thromboembolism (VTE) affects 1 in 1000 population in the United States with a 3-, 6-, and 12-month mortality rate of 23.2%, 30.2% and 37.1%. The rate of recurrent pulmonary embolism (PE) within 90 days in patients with VTE is 12%. Most PE's are diagnosed in the ED. Risk stratification into massive, sub massive and nonmassive is done based on clinical presentation and clot burden. Catheter directed thrombolysis (CDT) has been one of the newer strategies for management of massive PE (Class IIa) and has also been recommended by AHA for patients with submassive PE (Class IIb). Ultrasound-enhanced (UE) CDT combines ultrasonic clot destruction with local thrombolysis and has been shown to reduce the RV/LV ratio and clot burden.The objective was to evaluate the overall effectiveness of an ED-based protocol including UE CDT in the management of massive and submassive PEs.Methods: A retrospective cohort of consecutive patients with suspected massive or submassive PE presenting to the ED between 2010-2015, diagnosed utilizing a clinical protocol based on POC ultrasound were included in the study. Demographic data along with mortality rate (primary endpoint), echocardiographic RV dilation (secondary endpoint) and complications were abstracted. The Pulmonary Embolism Severity Index (PESI) score was used to grade PE severity. Mortality from our study cohort was compared to the historic cohort from the PESI study.Results: A total of 84 patients with a mean ...
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