AI Article Synopsis
- Myocardial ischemia and arrhythmias are common in congestive heart failure, and the study investigates how these conditions affect action potential duration (APD) in myocytes during simulated ischemia (SI).
- The researchers found that while baseline APD was similar in both heart failure (HF) and normal myocytes, the variability in APD during SI increased more significantly in HF cells.
- Rigor, or muscle stiffness, occurred faster in normal cells, but the variance in the timing of rigor was greater in HF myocytes, suggesting that HF cells display more inconsistency under stress.
- Interestingly, when normal and HF cells were coupled, the APD and timing of rigor were synchronized, indicating that intercellular connections can influence
Article Abstract
Objective: Myocardial ischemia and ventricular arrhythmias often complicate congestive heart failure. Ischemia-induced dispersion in repolarization is an important arrhythmogenic factor that might be caused by intrinsic cellular differences in response to simulated ischemia (SI) or by changed coupling of myocytes. We hypothesized that intrinsic heterogeneity in action potential duration (APD) or the occurrence of rigor is larger in failing than in normal rabbit myocytes during SI.
Methods: Heart failure (HF) was induced with volume and pressure overload. Left ventricular myocytes from apex, free wall and base were enzymatically isolated and exposed to SI with NaCN.
Results: There were no baseline differences in APD before SI. During SI no differences in time to inexcitability occurred but the range in APD increased more in HF than in normal cells. Rigor occurred after 16.8+/-3.5 and 23.0+/-7.5 min (P<0.05) in normal and HF myocytes, with no differences between apical, free wall or base cells. Variance in time to rigor was larger in HF than in normal cells (55.7 versus 12.4 min(2)). Blockade of anaerobic reserve decreased variance in time to rigor, also when normalized to mean, in HF and normal myocytes. In coupled normal and HF cell pairs, no delay in action potential propagation or differences in APD occurred during SI, and time to rigor was synchronized (P<0.05 vs. single cells).
Conclusions: Intercellular differences in APD and in time of rigor arise in normal and HF myocytes subjected to SI, and are inhibited by blockade of anaerobic glycolysis. Dispersion in APD and tolerance to SI is increased in HF cells. APD and time to rigor are completely synchronized in coupled cell pairs.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1016/s0008-6363(03)00460-7 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Evolution of parasite transmission dispersion.
R Soc Open Sci
January 2025
Institute for Integrative Biology, ETH Zürich, 8005 Zürich, Switzerland.
An open question in epidemiology is why transmission is often overdispersed, meaning that most new infections are driven by few infected individuals. For example, around 10% of COVID-19 cases cause 80% of new COVID-19 cases. This overdispersion in parasite transmission is likely driven by intrinsic heterogeneity among hosts, i.
View Article and Find Full Text PDFSingle-cell RNA-seq data augmentation using generative Fourier transformer.
Commun Biol
January 2025
Precision Medicine and Computational Biology, Sanofi, Cambridge, MA, 02141, USA.
Single-cell RNA sequencing (scRNA-seq) provides a powerful tool for dissecting cellular complexity and heterogeneity. However, its full potential to achieve statistically reliable conclusions is often constrained by the limited number of cells profiled, particularly in studies of rare diseases, specialized tissues, and uncommon cell types. Deep learning-based generative models (GMs) designed to address data scarcity often face similar limitations due to their reliance on pre-training or fine-tuning, inadvertently perpetuating a cycle of data inadequacy.
View Article and Find Full Text PDFInvestigating the intrinsic top-down dynamics of deep generative models.
Sci Rep
January 2025
Department of General Psychology and Padova Neuroscience Center, University of Padova, Padova, Italy.
Hierarchical generative models can produce data samples based on the statistical structure of their training distribution. This capability can be linked to current theories in computational neuroscience, which propose that spontaneous brain activity at rest is the manifestation of top-down dynamics of generative models detached from action-perception cycles. A popular class of hierarchical generative models is that of Deep Belief Networks (DBNs), which are energy-based deep learning architectures that can learn multiple levels of representations in a completely unsupervised way exploiting Hebbian-like learning mechanisms.
View Article and Find Full Text PDFN-detected TROSY for H-N heteronuclear correlation to study intrinsically disordered proteins: strategies to increase spectral quality.
J Biomol NMR
January 2025
Department of Chemistry "Ugo Schiff" and Magnetic Resonance Center (CERM), University of Florence, Florence, Italy.
Intrinsically disordered proteins and protein regions are central to many biological processes but difficult to characterize at atomic resolution. Nuclear magnetic resonance is particularly well-suited for providing structural and dynamical information on intrinsically disordered proteins, but existing NMR methodologies need to be constantly refined to provide greater sensitivity and resolution, particularly to capitalise on the potential of high magnetic fields to investigate large proteins. In this paper, we describe how N-detected 2D NMR experiments can be optimised for better performance.
View Article and Find Full Text PDFInsights into Landscape Structure Change in Urbanising Rainforest and Guinea Savanna Ecological Regions of Nigeria.
Environ Manage
January 2025
Department of Geoecology, Institute of Geosciences and Geography, Martin Luther University, Halle-Wittenberg, Halle (Saale), Germany.
In the face of unabated urban expansion, understanding the intrinsic characteristics of landscape structure is pertinent to preserving ecological diversity and managing the supply of ecosystem services. This study integrates machine-learning-based geospatial and landscape ecological techniques to assess the dynamics of landscape structure in cities of the rainforest (Akure and Owerri) and Guinea savanna (Makurdi and Minna) ecological regions of Nigeria between 1986 and 2022. Supervised classification using the random forest (RF) machine-learning classifier was performed on Landsat images on the Google Earth Engine (GEE) platform, and landscape metrics were calculated with FRAGSTATS to assess landscape composition, configuration, and connectivity.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!