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constant pressure perfusion langendorff system  (Harvard Bioscience)

 
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    Structured Review

    Harvard Bioscience constant pressure perfusion langendorff system
    We characterize calcium transients in ventricular cardiomyocytes in response to drugs, both computationally (top) and experimentally (bottom) and identify the ion channels that most likely generate early afterdepolarizations (left). We then screen the concentration space of the two most relevant channels and identify the classification boundary between the arrhythmic and non-arrhythmic domains using high performance computing and machine learning (center). We validate our approach using electrocardiograms, both computationally and experimentally, in whole heart simulations and isolated <t>Langendorff</t> perfused hearts (right). We demonstrate the potential of our new classifier by risk stratifying 23 common drugs and comparing the result against the reported risk categories of these compounds.
    Constant Pressure Perfusion Langendorff System, supplied by Harvard Bioscience, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/constant+pressure+perfusion+langendorff+system/bio_rxiv__545863-158-24-29?v=Harvard+Bioscience
    Average 90 stars, based on 1 article reviews
    constant pressure perfusion langendorff system - by Bioz Stars, 2026-07
    90/100 stars

    Images

    1) Product Images from "Classifying drugs by their arrhythmogenic risk using machine learning"

    Article Title: Classifying drugs by their arrhythmogenic risk using machine learning

    Journal: bioRxiv

    doi: 10.1101/545863

    We characterize calcium transients in ventricular cardiomyocytes in response to drugs, both computationally (top) and experimentally (bottom) and identify the ion channels that most likely generate early afterdepolarizations (left). We then screen the concentration space of the two most relevant channels and identify the classification boundary between the arrhythmic and non-arrhythmic domains using high performance computing and machine learning (center). We validate our approach using electrocardiograms, both computationally and experimentally, in whole heart simulations and isolated Langendorff perfused hearts (right). We demonstrate the potential of our new classifier by risk stratifying 23 common drugs and comparing the result against the reported risk categories of these compounds.
    Figure Legend Snippet: We characterize calcium transients in ventricular cardiomyocytes in response to drugs, both computationally (top) and experimentally (bottom) and identify the ion channels that most likely generate early afterdepolarizations (left). We then screen the concentration space of the two most relevant channels and identify the classification boundary between the arrhythmic and non-arrhythmic domains using high performance computing and machine learning (center). We validate our approach using electrocardiograms, both computationally and experimentally, in whole heart simulations and isolated Langendorff perfused hearts (right). We demonstrate the potential of our new classifier by risk stratifying 23 common drugs and comparing the result against the reported risk categories of these compounds.

    Techniques Used: Concentration Assay, Isolation



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    Harvard Bioscience constant pressure perfusion langendorff system
    We characterize calcium transients in ventricular cardiomyocytes in response to drugs, both computationally (top) and experimentally (bottom) and identify the ion channels that most likely generate early afterdepolarizations (left). We then screen the concentration space of the two most relevant channels and identify the classification boundary between the arrhythmic and non-arrhythmic domains using high performance computing and machine learning (center). We validate our approach using electrocardiograms, both computationally and experimentally, in whole heart simulations and isolated <t>Langendorff</t> perfused hearts (right). We demonstrate the potential of our new classifier by risk stratifying 23 common drugs and comparing the result against the reported risk categories of these compounds.
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    Average 90 stars, based on 1 article reviews
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    We characterize calcium transients in ventricular cardiomyocytes in response to drugs, both computationally (top) and experimentally (bottom) and identify the ion channels that most likely generate early afterdepolarizations (left). We then screen the concentration space of the two most relevant channels and identify the classification boundary between the arrhythmic and non-arrhythmic domains using high performance computing and machine learning (center). We validate our approach using electrocardiograms, both computationally and experimentally, in whole heart simulations and isolated <t>Langendorff</t> perfused hearts (right). We demonstrate the potential of our new classifier by risk stratifying 23 common drugs and comparing the result against the reported risk categories of these compounds.
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    Image Search Results


    We characterize calcium transients in ventricular cardiomyocytes in response to drugs, both computationally (top) and experimentally (bottom) and identify the ion channels that most likely generate early afterdepolarizations (left). We then screen the concentration space of the two most relevant channels and identify the classification boundary between the arrhythmic and non-arrhythmic domains using high performance computing and machine learning (center). We validate our approach using electrocardiograms, both computationally and experimentally, in whole heart simulations and isolated Langendorff perfused hearts (right). We demonstrate the potential of our new classifier by risk stratifying 23 common drugs and comparing the result against the reported risk categories of these compounds.

    Journal: bioRxiv

    Article Title: Classifying drugs by their arrhythmogenic risk using machine learning

    doi: 10.1101/545863

    Figure Lengend Snippet: We characterize calcium transients in ventricular cardiomyocytes in response to drugs, both computationally (top) and experimentally (bottom) and identify the ion channels that most likely generate early afterdepolarizations (left). We then screen the concentration space of the two most relevant channels and identify the classification boundary between the arrhythmic and non-arrhythmic domains using high performance computing and machine learning (center). We validate our approach using electrocardiograms, both computationally and experimentally, in whole heart simulations and isolated Langendorff perfused hearts (right). We demonstrate the potential of our new classifier by risk stratifying 23 common drugs and comparing the result against the reported risk categories of these compounds.

    Article Snippet: We excised the hearts from anesthetized rats (2.5% isoflurane in 95% oxygen and 5% carbon dioxide), immediately cannulated the aorta, connected it to a constant pressure perfusion Langendorff system (Harvard Apparatus, Massachusetts) with Krebs solution (118mM NaCl, 4.75mM KCl, 25mM NaHCO 3 , 1.2mM KH 2 PO 4 , 1.2mM MgSO 4 , 1.5mM CaCl 2 , 11mM glucose, and 2mM Pyruvate), warmed to 37° C, and bubbled with 95% oxygen and 5% carbon dioxide.

    Techniques: Concentration Assay, Isolation