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1. Computational models of ventricular mechanics and adaptation in response to right-ventricular pressure overload

   https://doi.org/10.3389/fphys.2022.948936  

   Odeigah, Oscar O.; Valdez-Jasso, Daniela; Wall, Samuel T.; Sundnes, Joakim (August 2022, Frontiers in Physiology)

   Pulmonary arterial hypertension (PAH) is associated with substantial remodeling of the right ventricle (RV), which may at first be compensatory but at a later stage becomes detrimental to RV function and patient survival. Unlike the left ventricle (LV), the RV remains understudied, and with its thin-walled crescent shape, it is often modeled simply as an appendage of the LV. Furthermore, PAH diagnosis is challenging because it often leaves the LV and systemic circulation largely unaffected. Several treatment strategies such as atrial septostomy, right ventricular assist devices (RVADs) or RV resynchronization therapy have been shown to improve RV function and the quality of life in patients with PAH. However, evidence of their long-term efficacy is limited and lung transplantation is still the most effective and curative treatment option. As such, the clinical need for improved diagnosis and treatment of PAH drives a strong need for increased understanding of drivers and mechanisms of RV growth and remodeling (G&R), and more generally for targeted research into RV mechanics pathology. Computational models stand out as a valuable supplement to experimental research, offering detailed analysis of the drivers and consequences of G&R, as well as a virtual test bench for exploring and refining hypotheses of growth mechanisms. In this review we summarize the current efforts towards understanding RV G&R processes using computational approaches such as reduced-order models, three dimensional (3D) finite element (FE) models, and G&R models. In addition to an overview of the relevant literature of RV computational models, we discuss how the models have contributed to increased scientific understanding and to potential clinical treatment of PAH patients. 

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2. Premature Ventricular Contraction Beat Classification via Hyperdimensional Computing

   https://doi.org/10.1109/IEEECONF56349.2022.10052044  

   Ung, Brandon W.; Ge, Lulu; Parhi, Keshab K. (October 2022, Proc. 2022 Asilomar Conference on Signals, Systems and Computers)

   Hyperdimensional computing (HD) is an emerging brain-inspired paradigm used for machine learning classification tasks. It manipulates ultra-long vectors-hypervectors- using simple operations, which allows for fast learning, energy efficiency, noise tolerance, and a highly parallel distributed framework. HD computing has shown a significant promise in the area of biological signal classification. This paper addresses group-specific premature ventricular contraction (PVC) beat detection with HD computing using the data from the MIT-BIH arrhythmia database. Temporal, heart rate variability (HRV), and spectral features are extracted, and minimal redundancy maximum relevance (mRMR) is used to rank and select features for classification. Three encoding approaches are explored for mapping the features into the HD space. The HD computing classifiers can achieve a PVC beat detection accuracy of 97.7 % accuracy, compared to 99.4% achieved by more computationally complex methods such as convolutional neural networks (CNNs). 

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3. Tnni3k alleles influence ventricular mononuclear diploid cardiomyocyte frequency

   https://doi.org/10.1371/journal.pgen.1008354  

   Gan, Peiheng; Patterson, Michaela; Velasquez, Alexa; Wang, Kristy; Tian, Di; Windle, Jolene J.; Tao, Ge; Judge, Daniel P.; Makita, Takako; Park, Thomas J.; et al (October 2019, PLOS Genetics)
4. Ventricular Helix Angle Trends and Long-Range Connectivity

   https://doi.org/10.1007/978-3-031-35302-4_7  

   Wilson, Alexander J.; Han, Q. Joyce; Perotti, Luigi E.; Ennis, Daniel B. (June 2023, Lecture notes in computer science)

   Bernard, Olivier; Clarysse, Patrick; Duchateau, Nicolas; Ohayon, Jacques; Viallon, Magalie (Ed.)

   Porcine hearts (N = 14) underwent ex vivo diffusion tensor imaging (DTI) at 3T. DTI analysis showed regional differences in helix angle (HA) range. The HA range in the posterior free wall was significantly greater than that of the anterior free wall (p = 0.02), the lateral free wall (p < 0.001) and the septum (p = 0.008). The best-fit transmural HA function also varied by region, with eight regions best described by an arctan function, seven by an arcsine function, and a single region by a linear function. Tractography analysis was performed, and the length that the tracts spanned within the epicardial, midwall, and endocardial segments was measured. A high number of tracts span the epicardial and mid-wall thirds, with fewer tracts spanning the mid-wall and endocardial thirds. Connectivity analysis of the number of tracts connecting different ventricular regions showed a high prevalence of oblique tracts that may be critical for long-range connectivity. 

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5. A portable multi-sensor module for monitoring external ventricular drains

   https://doi.org/10.1007/s10544-021-00579-8  

   Hudson, Trevor Q.; Baldwin, Alex; Samiei, Aria; Lee, Priya; McComb, J. Gordon; Meng, Ellis (December 2021, Biomedical Microdevices)