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Article ; Online: Virtual drug screen reveals context-dependent inhibition of cardiomyocyte hypertrophy.

Eggertsen, Taylor G / Saucerman, Jeffrey J

2023 Volume 180, Issue 21, Page(s) 2721–2735

Abstract: Background and purpose: Pathological cardiomyocyte hypertrophy is a response to cardiac stress that typically leads to heart failure. Despite being a primary contributor to pathological cardiac remodelling, the therapeutic space that targets hypertrophy ...

Abstract	Background and purpose: Pathological cardiomyocyte hypertrophy is a response to cardiac stress that typically leads to heart failure. Despite being a primary contributor to pathological cardiac remodelling, the therapeutic space that targets hypertrophy is limited. Here, we apply a network model to virtually screen for FDA-approved drugs that induce or suppress cardiomyocyte hypertrophy. Experimental approach: A logic-based differential equation model of cardiomyocyte signalling was used to predict drugs that modulate hypertrophy. These predictions were validated against curated experiments from the prior literature. The actions of midostaurin were validated in new experiments using TGFβ- and noradrenaline (NE)-induced hypertrophy in neonatal rat cardiomyocytes. Key results: Model predictions were validated in 60 out of 70 independent experiments from the literature and identify 38 inhibitors of hypertrophy. We additionally predict that the efficacy of drugs that inhibit cardiomyocyte hypertrophy is often context dependent. We predicted that midostaurin inhibits cardiomyocyte hypertrophy induced by TGFβ, but not noradrenaline, exhibiting context dependence. We further validated this prediction by cellular experiments. Network analysis predicted critical roles for the PI3K and RAS pathways in the activity of celecoxib and midostaurin, respectively. We further investigated the polypharmacology and combinatorial pharmacology of drugs. Brigatinib and irbesartan in combination were predicted to synergistically inhibit cardiomyocyte hypertrophy. Conclusion and implications: This study provides a well-validated platform for investigating the efficacy of drugs on cardiomyocyte hypertrophy and identifies midostaurin for consideration as an antihypertrophic drug.
MeSH term(s)	Rats ; Animals ; Myocytes, Cardiac/metabolism ; Cardiomegaly/chemically induced ; Cardiomegaly/drug therapy ; Cardiomegaly/metabolism ; Signal Transduction ; Heart Failure/metabolism ; Transforming Growth Factor beta/metabolism ; Cells, Cultured
Chemical Substances	Transforming Growth Factor beta
Language	English
Publishing date	2023-07-05
Publishing country	England
Document type	Journal Article ; Research Support, N.I.H., Extramural
ZDB-ID	80081-8
ISSN	1476-5381 ; 0007-1188
ISSN (online)	1476-5381
ISSN	0007-1188
DOI	10.1111/bph.16163
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Article ; Online: Modeling cardiomyocyte signaling and metabolism predicts genotype-to-phenotype mechanisms in hypertrophic cardiomyopathy.

Khalilimeybodi, A / Saucerman, Jeffrey J / Rangamani, P

Computers in biology and medicine

2024 Volume 175, Page(s) 108499

Abstract: Familial hypertrophic cardiomyopathy (HCM) is a significant precursor of heart failure and sudden cardiac death, primarily caused by mutations in sarcomeric and structural proteins. Despite the extensive research on the HCM genotype, the complex and ... ...

Abstract	Familial hypertrophic cardiomyopathy (HCM) is a significant precursor of heart failure and sudden cardiac death, primarily caused by mutations in sarcomeric and structural proteins. Despite the extensive research on the HCM genotype, the complex and context-specific nature of many signaling and metabolic pathways linking the HCM genotype to phenotype has hindered therapeutic advancements for patients. Here, we have developed a computational model of HCM encompassing cardiomyocyte signaling and metabolic networks and their associated interactions. Utilizing a stochastic logic-based ODE approach, we linked cardiomyocyte signaling to the metabolic network through a gene regulatory network and post-translational modifications. We validated the model against published data on activities of signaling species in the HCM context and transcriptomes of two HCM mouse models (i.e., R403Q-αMyHC and R92W-TnT). Our model predicts that HCM mutation induces changes in metabolic functions such as ATP synthase deficiency and a transition from fatty acids to carbohydrate metabolism. The model indicated major shifts in glutamine-related metabolism and increased apoptosis after HCM-induced ATP synthase deficiency. We predicted that the transcription factors STAT, SRF, GATA4, TP53, and FoxO are the key regulators of cardiomyocyte hypertrophy and apoptosis in HCM in alignment with experiments. Moreover, we identified shared (e.g., activation of PGC1α by AMPK, and FHL1 by titin) and context-specific mechanisms (e.g., regulation of Ca2+ sensitivity by titin in HCM patients) that may control genotype-to-phenotype transition in HCM across different species or mutations. We also predicted potential combination drug targets for HCM (e.g., mavacamten plus ROS inhibitors) preventing or reversing HCM phenotype (i.e., hypertrophic growth, apoptosis, and metabolic remodeling) in cardiomyocytes. This study provides new insights into mechanisms linking genotype to phenotype in familial hypertrophic cardiomyopathy and offers a framework for assessing new treatments and exploring variations in HCM experimental models.
Language	English
Publishing date	2024-04-24
Publishing country	United States
Document type	Journal Article
ZDB-ID	127557-4
ISSN	1879-0534 ; 0010-4825
ISSN (online)	1879-0534
ISSN	0010-4825
DOI	10.1016/j.compbiomed.2024.108499
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Article ; Online: Logic-based mechanistic machine learning on high-content images reveals how drugs differentially regulate cardiac fibroblasts.

Nelson, Anders R / Christiansen, Steven L / Naegle, Kristen M / Saucerman, Jeffrey J

Proceedings of the National Academy of Sciences of the United States of America

2024 Volume 121, Issue 5, Page(s) e2303513121

Abstract: Fibroblasts are essential regulators of extracellular matrix deposition following cardiac injury. These cells exhibit highly plastic responses in phenotype during fibrosis in response to environmental stimuli. Here, we test whether and how candidate anti- ...

Abstract	Fibroblasts are essential regulators of extracellular matrix deposition following cardiac injury. These cells exhibit highly plastic responses in phenotype during fibrosis in response to environmental stimuli. Here, we test whether and how candidate anti-fibrotic drugs differentially regulate measures of cardiac fibroblast phenotype, which may help identify treatments for cardiac fibrosis. We conducted a high-content microscopy screen of human cardiac fibroblasts treated with 13 clinically relevant drugs in the context of TGFβ and/or IL-1β, measuring phenotype across 137 single-cell features. We used the phenotypic data from our high-content imaging to train a logic-based mechanistic machine learning model (LogiMML) for fibroblast signaling. The model predicted how pirfenidone and Src inhibitor WH-4-023 reduce actin filament assembly and actin-myosin stress fiber formation, respectively. Validating the LogiMML model prediction that PI3K partially mediates the effects of Src inhibition, we found that PI3K inhibition reduces actin-myosin stress fiber formation and procollagen I production in human cardiac fibroblasts. In this study, we establish a modeling approach combining the strengths of logic-based network models and regularized regression models. We apply this approach to predict mechanisms that mediate the differential effects of drugs on fibroblasts, revealing Src inhibition acting via PI3K as a potential therapy for cardiac fibrosis.
MeSH term(s)	Humans ; Actins ; Fibroblasts ; Machine Learning ; Fibrosis ; Myosins ; Phosphatidylinositol 3-Kinases
Chemical Substances	Actins ; Myosins (EC 3.6.4.1) ; Phosphatidylinositol 3-Kinases (EC 2.7.1.-)
Language	English
Publishing date	2024-01-24
Publishing country	United States
Document type	Journal Article
ZDB-ID	209104-5
ISSN	1091-6490 ; 0027-8424
ISSN (online)	1091-6490
ISSN	0027-8424
DOI	10.1073/pnas.2303513121
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Article: Logic-based modeling of biological networks with Netflux.

Clark, Alexander P / Chowkwale, Mukti / Paap, Alexander / Dang, Stephen / Saucerman, Jeffrey J

bioRxiv : the preprint server for biology

2024

Abstract: Molecular signaling networks drive a diverse range of cellular decisions, including whether to proliferate, how and when to die, and many processes in between. Such networks often connect hundreds of proteins, genes, and processes. Understanding these ... ...

Abstract	Molecular signaling networks drive a diverse range of cellular decisions, including whether to proliferate, how and when to die, and many processes in between. Such networks often connect hundreds of proteins, genes, and processes. Understanding these complex networks is greatly aided by computational modeling, but these tools require extensive programming knowledge. In this article, we describe a user-friendly, programming-free network simulation tool called Netflux (https://github.com/saucermanlab/Netflux). Over the last decade, Netflux has been used to construct numerous predictive network models that have deepened our understanding of how complex biological networks make cell decisions. Here, we provide a Netflux tutorial that covers how to construct a network model and then simulate network responses to perturbations. Upon completion of this tutorial, you will be able to construct your own model in Netflux and simulate how perturbations to proteins and genes propagate through signaling and gene-regulatory networks.
Language	English
Publishing date	2024-01-15
Publishing country	United States
Document type	Preprint
DOI	10.1101/2024.01.11.575227
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Article: Logic-based mechanistic machine learning on high-content images reveals how drugs differentially regulate cardiac fibroblasts.

Nelson, Anders R / Christiansen, Steven L / Naegle, Kristen M / Saucerman, Jeffrey J

bioRxiv : the preprint server for biology

2023

Abstract	Fibroblasts are essential regulators of extracellular matrix deposition following cardiac injury. These cells exhibit highly plastic responses in phenotype during fibrosis in response to environmental stimuli. Here, we test whether and how candidate anti-fibrotic drugs differentially regulate measures of cardiac fibroblast phenotype, which may help identify treatments for cardiac fibrosis. We conducted a high content microscopy screen of human cardiac fibroblasts treated with 13 clinically relevant drugs in the context of TGFβ and/or IL-1β, measuring phenotype across 137 single-cell features. We used the phenotypic data from our high content imaging to train a logic-based mechanistic machine learning model (LogiMML) for fibroblast signaling. The model predicted how pirfenidone and Src inhibitor WH-4-023 reduce actin filament assembly and actin-myosin stress fiber formation, respectively. Validating the LogiMML model prediction that PI3K partially mediates the effects of Src inhibition, we found that PI3K inhibition reduces actin-myosin stress fiber formation and procollagen I production in human cardiac fibroblasts. In this study, we establish a modeling approach combining the strengths of logic-based network models and regularized regression models, apply this approach to predict mechanisms that mediate the differential effects of drugs on fibroblasts, revealing Src inhibition acting via PI3K as a potential therapy for cardiac fibrosis.
Language	English
Publishing date	2023-10-23
Publishing country	United States
Document type	Preprint
DOI	10.1101/2023.03.01.530599
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Article ; Online: Network model integrated with multi-omic data predicts MBNL1 signals that drive myofibroblast activation.

Nelson, Anders R / Bugg, Darrian / Davis, Jennifer / Saucerman, Jeffrey J

iScience

2023 Volume 26, Issue 4, Page(s) 106502

Abstract: RNA-binding protein muscleblind-like1 (MBNL1) was recently identified as a central regulator of cardiac wound healing and myofibroblast activation. To identify putative MBNL1 targets, we integrated multiple genome-wide screens with a fibroblast network ... ...

Abstract	RNA-binding protein muscleblind-like1 (MBNL1) was recently identified as a central regulator of cardiac wound healing and myofibroblast activation. To identify putative MBNL1 targets, we integrated multiple genome-wide screens with a fibroblast network model. We expanded the model to include putative MBNL1-target interactions and recapitulated published experimental results to validate new signaling modules. We prioritized 14 MBNL1 targets and developed novel fibroblast signaling modules for p38 MAPK, Hippo, Runx1, and Sox9 pathways. We experimentally validated MBNL1 regulation of p38 expression in mouse cardiac fibroblasts. Using the expanded fibroblast model, we predicted a hierarchy of MBNL1 regulated pathways with strong influence on αSMA expression. This study lays a foundation to explore the network mechanisms of MBNL1 signaling central to fibrosis.
Language	English
Publishing date	2023-03-27
Publishing country	United States
Document type	Journal Article
ISSN	2589-0042
ISSN (online)	2589-0042
DOI	10.1016/j.isci.2023.106502
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Article ; Online: Intercellular model predicts mechanisms of inflammation-fibrosis coupling after myocardial infarction.

Chowkwale, Mukti / Lindsey, Merry L / Saucerman, Jeffrey J

The Journal of physiology

2022 Volume 601, Issue 13, Page(s) 2635–2654

Abstract: After myocardial infarction (MI), cardiac cells work together to regulate wound healing of the infarct. The pathological response to MI yields cardiac remodelling comprising inflammatory and fibrosis phases, and the interplay of cellular dynamics that ... ...

Abstract	After myocardial infarction (MI), cardiac cells work together to regulate wound healing of the infarct. The pathological response to MI yields cardiac remodelling comprising inflammatory and fibrosis phases, and the interplay of cellular dynamics that underlies these phases has not been elucidated. This study developed a computational model to identify cytokine and cellular dynamics post-MI to predict mechanisms driving post-MI inflammation, resolution of inflammation, and scar formation. Additionally, this study evaluated the interdependence between inflammation and fibrosis. Our model bypassed limitations of in vivo approaches in achieving cellular specificity and performing specific perturbations such as global knockouts of chemical factors. The model predicted that inflammation is a graded response to initial infarct size that is amplified by a positive feedback loop between neutrophils and interleukin 1β (IL-1β). Resolution of inflammation was driven by degradation of IL-1β, matrix metalloproteinase 9, and transforming growth factor β (TGF-β), as well as apoptosis of neutrophils. Inflammation regulated TGFβ secretion directly through immune cell recruitment and indirectly through upregulation of macrophage phagocytosis. Lastly, we found that mature collagen deposition was an ultrasensitive switch in response to inflammation, which was amplified primarily by cardiac fibroblast proliferation. These findings describe the relationship between inflammation and fibrosis and highlight how the two responses work together post-MI. This model revealed that post-MI inflammation and fibrosis are dynamically coupled, which provides rationale for designing novel anti-inflammatory, pro-resolving or anti-fibrotic therapies that may improve the response to MI. KEY POINTS: Inflammation and matrix remodelling are two processes involved in wound healing after a heart attack. Cardiac cells work together to facilitate these processes; this is done by secreting cytokines that then regulate the cells themselves or other cells surrounding them. This study developed a computational model of the dynamics of cardiac cells and cytokines to predict mechanisms through which inflammation and matrix remodelling is regulated. We show the roles of various cytokines and signalling motifs in driving inflammation, resolution of inflammation and fibrosis. The novel concept of inflammation-fibrosis coupling, based on the model prediction that inflammation and fibrosis are dynamically coupled, provides rationale for future studies and for designing therapeutics to improve the response after a heart attack.
MeSH term(s)	Animals ; Mice ; Myocardial Infarction/metabolism ; Heart ; Cytokines ; Fibrosis ; Inflammation/metabolism ; Transforming Growth Factor beta ; Mice, Inbred C57BL ; Ventricular Remodeling/physiology
Chemical Substances	Cytokines ; Transforming Growth Factor beta
Language	English
Publishing date	2022-08-08
Publishing country	England
Document type	Journal Article
ZDB-ID	3115-x
ISSN	1469-7793 ; 0022-3751
ISSN (online)	1469-7793
ISSN	0022-3751
DOI	10.1113/JP283346
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Article ; Online: Multiscale model of heart growth during pregnancy: integrating mechanical and hormonal signaling.

Yoshida, Kyoko / Saucerman, Jeffrey J / Holmes, Jeffrey W

Biomechanics and modeling in mechanobiology

2022 Volume 21, Issue 4, Page(s) 1267–1283

Abstract: Pregnancy stands at the interface of mechanics and biology. The growing fetus continuously loads the maternal organs as circulating hormone levels surge, leading to significant changes in mechanical and hormonal cues during pregnancy. In response, ... ...

Abstract	Pregnancy stands at the interface of mechanics and biology. The growing fetus continuously loads the maternal organs as circulating hormone levels surge, leading to significant changes in mechanical and hormonal cues during pregnancy. In response, maternal soft tissues undergo remarkable growth and remodeling to support the mother and baby for a healthy pregnancy. We focus on the maternal left ventricle, which increases its cardiac output and mass during pregnancy. This study develops a multiscale cardiac growth model for pregnancy to understand how mechanical and hormonal cues interact to drive this growth process. We coupled a cell signaling network model that predicts cell-level hypertrophy in response to hormones and stretch to a compartmental model of the rat heart and circulation that predicts organ-level growth in response to hemodynamic changes. We calibrated this multiscale model to data from experimental volume overload and hormonal infusions of angiotensin 2 (AngII), estrogen (E2), and progesterone (P4). We then validated the model's ability to capture interactions between inputs by comparing model predictions against published observations for the combinations of VO + E2 and AngII + E2. Finally, we simulated pregnancy-induced changes in hormones and hemodynamics to predict heart growth during pregnancy. Our model produced growth consistent with experimental data. Overall, our analysis suggests that the rise in P4 during the first half of gestation is an important contributor to heart growth during pregnancy. We conclude with suggestions for future experimental studies that will provide a better understanding of how hormonal and mechanical cues interact to drive pregnancy-induced heart growth.
MeSH term(s)	Angiotensin II ; Animals ; Cardiac Output/physiology ; Female ; Heart/anatomy & histology ; Heart/growth & development ; Heart Ventricles/anatomy & histology ; Heart Ventricles/growth & development ; Hemodynamics/physiology ; Hormones ; Models, Cardiovascular ; Myocardium/metabolism ; Pregnancy/physiology ; Rats ; Signal Transduction
Chemical Substances	Hormones ; Angiotensin II (11128-99-7)
Language	English
Publishing date	2022-06-06
Publishing country	Germany
Document type	Journal Article
ZDB-ID	2093052-5
ISSN	1617-7940 ; 1617-7959
ISSN (online)	1617-7940
ISSN	1617-7959
DOI	10.1007/s10237-022-01589-y
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Zs.A 5966: Show issues

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Article ; Online: Computational model of brain endothelial cell signaling pathways predicts therapeutic targets for cerebral pathologies.

Gorick, Catherine M / Saucerman, Jeffrey J / Price, Richard J

Journal of molecular and cellular cardiology

2021 Volume 164, Page(s) 17–28

Abstract: Brain endothelial cells serve many critical homeostatic functions. In addition to sensing and regulating blood flow, they maintain blood-brain barrier function, including precise control of nutrient exchange and efflux of xenobiotics. Many signaling ... ...

Abstract	Brain endothelial cells serve many critical homeostatic functions. In addition to sensing and regulating blood flow, they maintain blood-brain barrier function, including precise control of nutrient exchange and efflux of xenobiotics. Many signaling pathways in brain endothelial cells have been implicated in both health and disease; however, our understanding of how these signaling pathways functionally integrate is limited. A model capable of integrating these signaling pathways could both advance our understanding of brain endothelial cell signaling networks and potentially identify promising molecular targets for endothelial cell-based drug or gene therapies. To this end, we developed a large-scale computational model, wherein brain endothelial cell signaling pathways were reconstructed from the literature and converted into a network of logic-based differential equations. The model integrates 63 nodes (including proteins, mRNA, small molecules, and cell phenotypes) and 82 reactions connecting these nodes. Specifically, our model combines signaling pathways relating to VEGF-A, BDNF, NGF, and Wnt signaling, in addition to incorporating pathways relating to focused ultrasound as a therapeutic delivery tool. To validate the model, independently established relationships between selected inputs and outputs were simulated, with the model yielding correct predictions 73% of the time. We identified influential and sensitive nodes under different physiological or pathological contexts, including altered brain endothelial cell conditions during glioma, Alzheimer's disease, and ischemic stroke. Nodes with the greatest influence over combinations of desired model outputs were identified as potential druggable targets for these disease conditions. For example, the model predicts therapeutic benefits from inhibiting AKT, Hif-1α, or cathepsin D in the context of glioma - each of which are currently being studied in clinical or pre-clinical trials. Notably, the model also permits testing multiple combinations of node alterations for their effects on the network and the desired outputs (such as inhibiting AKT and overexpressing the P75 neurotrophin receptor simultaneously in the context of glioma), allowing for the prediction of optimal combination therapies. In all, our approach integrates results from over 100 past studies into a coherent and powerful model, capable of both revealing network interactions unapparent from studying any one pathway in isolation and predicting therapeutic targets for treating devastating brain pathologies.
MeSH term(s)	Brain/metabolism ; Endothelial Cells/metabolism ; Glioma/metabolism ; Glioma/pathology ; Humans ; Proto-Oncogene Proteins c-akt/metabolism ; Wnt Signaling Pathway
Chemical Substances	Proto-Oncogene Proteins c-akt (EC 2.7.11.1)
Language	English
Publishing date	2021-11-16
Publishing country	England
Document type	Journal Article ; Research Support, N.I.H., Extramural
ZDB-ID	80157-4
ISSN	1095-8584 ; 0022-2828
ISSN (online)	1095-8584
ISSN	0022-2828
DOI	10.1016/j.yjmcc.2021.11.005
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Article ; Online: Computational models of cardiovascular regulatory mechanisms.

McCulloch, Andrew D / Grandi, Eleonora / Saucerman, Jeffrey J

Journal of molecular and cellular cardiology

2021 Volume 155, Page(s) 111

MeSH term(s)	Biomarkers ; Cardiovascular Physiological Phenomena ; Cardiovascular System/drug effects ; Cardiovascular System/metabolism ; Disease Susceptibility ; Humans ; Models, Biological
Chemical Substances	Biomarkers
Language	English
Publishing date	2021-02-26
Publishing country	England
Document type	Editorial ; Introductory Journal Article
ZDB-ID	80157-4
ISSN	1095-8584 ; 0022-2828
ISSN (online)	1095-8584
ISSN	0022-2828
DOI	10.1016/j.yjmcc.2021.01.009
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