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House A, Cornick J, Butt Q, Guvendiren M. Elastomeric platform with surface wrinkling patterns to control cardiac cell alignment. J Biomed Mater Res A 2023; 111:1228-1242. [PMID: 36762538 DOI: 10.1002/jbm.a.37511] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2022] [Revised: 12/07/2022] [Accepted: 01/29/2023] [Indexed: 02/11/2023]
Abstract
There is a growing interest in creating 2D cardiac tissue models that display native extracellular matrix (ECM) cues of the heart tissue. Cellular alignment alone is known to be a crucial cue for cardiac tissue development by regulating cell-cell and cell-ECM interactions. In this study, we report a simple and robust approach to create lamellar surface wrinkling patterns enabling spatial control of pattern dimensions with a wide range of pattern amplitude (A ≈ 2-55 μm) and wavelength (λ ≈ 35-100 μm). For human cardiomyocytes (hCMs) and human cardiac fibroblasts (hCFs), our results indicate that the degree of cellular alignment and pattern recognition are correlated with pattern A and λ. We also demonstrate fabrication of devices composed of micro-well arrays with user-defined lamellar patterns on the bottom surface of each well for high-throughput screening studies. Results from a screening study indicate that cellular alignment is strongly diminished with increasing seeding density. In another study, we show our ability to vary hCM/hCF seeding ratio for each well to create co-culture systems where seeding ratio is independent of cellular alignment.
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Affiliation(s)
- Andrew House
- Otto H. York Department of Chemical and Materials Engineering, New Jersey Institute of Technology, Newark, New Jersey, USA
| | - Jason Cornick
- Otto H. York Department of Chemical and Materials Engineering, New Jersey Institute of Technology, Newark, New Jersey, USA
| | - Quratulain Butt
- Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, New Jersey, USA
| | - Murat Guvendiren
- Otto H. York Department of Chemical and Materials Engineering, New Jersey Institute of Technology, Newark, New Jersey, USA
- Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, New Jersey, USA
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Wang L, Dela Rosa JGL, Munshi NV. Protocol for inducing cellular ablation in the mouse atrioventricular conduction system. STAR Protoc 2023; 4:102145. [PMID: 36933220 PMCID: PMC10024233 DOI: 10.1016/j.xpro.2023.102145] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2022] [Revised: 01/25/2023] [Accepted: 02/08/2023] [Indexed: 03/13/2023] Open
Abstract
Damage to the atrioventricular conduction system (AVCS), the main electrical connection between the atrial and ventricular chambers, can result in a variety of cardiac conduction disorders. Here, we provide a protocol for selective damage of the mouse AVCS to study its response during injury. We describe tamoxifen-induced cellular ablation, detection of AV block through electrocardiography, and quantification of histological and immunofluorescence markers to analyze the AVCS. This protocol can be used to study mechanisms associated with AVCS injury repair and regeneration. For complete details on the use and execution of this protocol, please refer to Wang et al. (2021).1.
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Affiliation(s)
- Lin Wang
- Department of Internal Medicine, Cardiology Division, UT Southwestern Medical Center, Dallas, TX 75390, USA
| | - Jared Gabriel L Dela Rosa
- Department of Internal Medicine, Cardiology Division, UT Southwestern Medical Center, Dallas, TX 75390, USA; College of Medicine, University of the Philippines Manila, Metro Manila 1000, Philippines
| | - Nikhil V Munshi
- Department of Internal Medicine, Cardiology Division, UT Southwestern Medical Center, Dallas, TX 75390, USA; Department of Molecular Biology, UT Southwestern Medical Center, Dallas, TX 75390, USA; McDermott Center for Human Growth and Development, UT Southwestern Medical Center, Dallas, TX 75390, USA; Hamon Center for Regenerative Science and Medicine, UT Southwestern Medical Center, Dallas, TX 75390, USA.
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Salyha N, Oliynyk I. Hypoxia modeling techniques: A review. Heliyon 2023; 9:e13238. [PMID: 36718422 PMCID: PMC9877323 DOI: 10.1016/j.heliyon.2023.e13238] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2022] [Revised: 01/08/2023] [Accepted: 01/23/2023] [Indexed: 01/27/2023] Open
Abstract
Hypoxia is the main cause and effect of a large number of diseases, including the most recent one facing the world, the coronavirus disease (COVID-19). Hypoxia is divided into short-term, long-term, and periodic, it can be the result of diseases, climate change, or living and traveling in the high mountain regions of the world. Since each type of hypoxia can be a cause and a consequence of various physiological changes, the methods for modeling these hypoxias are also different. There are many techniques for modeling hypoxia under experimental conditions. The most common animal for modeling hypoxia is a rat. Hypoxia models (hypoxia simulations) in rats are a tool to study the effect of various conditions on the oxygen supply of the body. These models can provide a necessary information to understand hypoxia and also provide effective treatment, highlighting the importance of various reactions of the body to hypoxia. The main parameters when choosing a model should be reproducibility and the goal that the scientist wants to achieve. Hypoxia in rats can be reproduced both ways exogenously and endogenously. The reason for writing this review was the aim to systematize the models of rats available in the literature in order to facilitate their selection by scientists. The relative strengths and limitations of each model need to be identified and understood in order to evaluate the information obtained from these models and extrapolate these results to humans to develop the necessary generalizations. Despite these problems, animal models have been and remain vital to understanding the mechanisms involved in the development and progression of hypoxia. The eligibility criteria for the selected studies was a comprehensive review of the methods and results obtained from the studies. This made it possible to make generalizations and give recommendations on the application of these methods. The review will assist scientists in choosing an appropriate hypoxia simulation method, as well as assist in interpreting the results obtained with these methods.
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Affiliation(s)
- Nataliya Salyha
- Institute of Animal Biology NAAS, Lviv, Ukraine,Corresponding author
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Jiang M, Qiu Y, Zhang W, Zhang J, Wang Z, Ke W, Wu Y, Wang Z. Visualization deep learning model for automatic arrhythmias classification. Physiol Meas 2022; 43. [PMID: 35882225 DOI: 10.1088/1361-6579/ac8469] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2022] [Accepted: 07/26/2022] [Indexed: 11/11/2022]
Abstract
With the improvement of living standards, heart disease has become one of the common diseases that threaten human health. As a widely used reliable, non-invasive technology, the Electrocardiography (ECG) data has been increasingly used for diagnosing cardiovascular diseases. With the rapid growth of ECG examinations and the shortage of cardiologists, accurate and automatic recognition of ECG patterns has become a research hotspot. In order to improve the accuracy in detecting abnormal ECG patterns, this paper proposes a hybrid 1D Resnet-GRU consisting of the Resnet and gated recurrent unit (GRU) modules to implement classification of arrhythmias from 12-lead ECG recordings. In addition, the focal Loss function is used to solve the problem of unbalanced datasets. Based on the proposed 1D Resnet-GRU model, the Grad-CAM++ mechanism, one of class-discriminative visualization methods, has been employed to the trained network model and generate thermal images superimposed on raw signals, so as to improve interpretability and transparency for arrhythmia classification. Experimental results show the proposed Resnet-GRU method can improve the F_1 score and accuracy effectively, and Grad-CAM++ can provide interpretability and clinical information for arrhythmia classification.
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Affiliation(s)
- Mingfeng Jiang
- School of Information Science and Technology, Zhejiang Sci-Tech University, No. 928, 2th street, Hangzhou, China, Hangzhou, Zhejiang, 310018, CHINA
| | - Yujie Qiu
- School of Information Science and Technology, Zhejiang Sci-Tech University, School of Information Science and Technology, Zhejiang Sci-Tech University, Hangzhou 310018, China, Hangzhou, Zhejiang, 310018, CHINA
| | - Wei Zhang
- Zhejiang Sci-Tech University, School of Information Science and Technology, Zhejiang Sci-Tech University, Hangzhou 310018, China, Hangzhou, Zhejiang, 310018, CHINA
| | - Jucheng Zhang
- Department of Clinical Engineering, Zhejiang University School of Medicine Second Affiliated Hospital, Hangzhou, Zhejiang, Hangzhou, Zhejiang, 310009, CHINA
| | - Zefeng Wang
- Department of Cardiology, Beijing An Zhen Hospital, No. 2 Anzhen Road, Chaoyang District, Beijing 100029, China, Chaoyang-qu, Beijing, 100029, CHINA
| | - Wei Ke
- School of Applied Sciences, Macau Polytechnic Institute, School of Applied Sciences, Macao Polytechnic Institute, Macao SAR 999078, China, Macau, Macau, 999078, CHINA
| | - Yongquan Wu
- Department of Cardiology,, Beijing An Zhen Hospital, No. 2 Anzhen Road, Chaoyang District, Beijing 100029, China, Chaoyang-qu, Beijing, 100029, CHINA
| | - Zhikang Wang
- Zhejiang University School of Medicine Second Affiliated Hospital, Department of Clinical Engineering, The Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou 310019, China, Hangzhou, Zhejiang, 310009, CHINA
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