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Innervated human cardiac muscle model reveals sympathetic drivers of KCNH2-associated arrhythmias.

Code ↔ Paper

3 matches between paragraphs of the paper and lines of its authors' code, computed by the harvester (lexical-v1). Click a colored paragraph or line to see its counterpart.

The 3 matches
  1. [1] § Methods › Orientation analysis of axons and vessels in iEHM ↔ bio_image_unet/siam_unet/train.py, lines 17–51 · score 0.57 · BCE Dice, neural network, convolutional, trained, loss, models
  2. [2] § Results › Generation and characterization of innervated human cardiac muscle (iEHM) ↔ src/iehm/analysis.py, lines 97–176 · score 0.57 · orientation correlation, Scale bar, angles, axons, CM, vascular
  3. [3] § Methods › Orientation analysis of axons and vessels in iEHM ↔ src/iehm/analysis.py, lines 69–95 · score 0.54 · Hough, Pearson, angles, orientation, histogram, probabilistic

Paper

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The authors' code

Python · 299 lines · 11 KB · MIT · 2 matches

  1. import pickle
  2. from pathlib import Path
  4. import matplotlib
  5. import matplotlib as mpl
  6. import os.path
  8. from bio_image_unet import unet # pip install bio-image-unet
  9. import numpy as np
  10. import matplotlib.pyplot as plt
  12. import tifffile
  13. from matplotlib.ticker import MultipleLocator, FormatStrFormatter
  14. from matplotlib_scalebar.scalebar import ScaleBar
  15. from skimage.transform import probabilistic_hough_line
  16. from skimage.morphology import skeletonize
  17. from scipy.stats import pearsonr
  19. MODELS_DIR = Path(__file__).resolve().parents[2] / "models"
  22. class iEHM_Analysis:
  23. def __init__(self, filename, pixelsize=0.2076):
  24. self.filename = filename
  25. self.pixelsize = pixelsize
  26. self.folder = os.path.splitext(filename)[0] + '/'
  27. os.makedirs(self.folder, exist_ok=True)
  29. self.filename_nf = self.folder + 'nf.tif'
  30. self.filename_vs = self.folder + 'vs.tif'
  31. self.filename_nc = self.folder + 'nc.tif'
  32. self.filename_pc = self.folder + 'pc.tif'
  33. self.filename_mask_nf = self.folder + 'mask_nf.tif'
  34. self.filename_mask_vs = self.folder + 'mask_vs.tif'
  36. self.data = {}
  37. self.load_data()
  39. def save_data(self):
  40. with open(self.folder + 'data.pk', 'wb') as handle:
  41. pickle.dump(self.data, handle, protocol=pickle.HIGHEST_PROTOCOL)
  43. def load_data(self):
  44. if os.path.exists(self.folder + 'data.pk'):
  45. with open(self.folder + 'data.pk', 'rb') as handle:
  46. self.data = pickle.load(handle)
  48. def maxproj(self):
  49. data = tifffile.imread(self.filename)
  51. # split channels (neurofil, vasculature, nuclei, pericytes)
  52. data_nf, data_vs, data_nc, data_pc = data[:, 0], data[:, 1], data[:, 2], data[:, 3]
  54. # save mips
  55. tifffile.imwrite(self.filename_nf, np.max(data_nf, axis=0))
  56. tifffile.imwrite(self.filename_vs, np.max(data_vs, axis=0))
  57. tifffile.imwrite(self.filename_nc, np.max(data_nc, axis=0))
  58. tifffile.imwrite(self.filename_pc, np.max(data_pc, axis=0))
  60. def predict_NF_VS(self, model_nf_path=None, model_vs_path=None):
  61. model_NF = model_nf_path or str(MODELS_DIR / 'model_NF.pth')
  62. model_VS = model_vs_path or str(MODELS_DIR / 'model_VS.pth')
  64. unet.Predict(self.filename_nf, self.filename_mask_nf, model_params=model_NF, resize_dim=(2048, 2048),
  65. network='Unet_v0')
  66. unet.Predict(self.filename_vs, self.filename_mask_vs, model_params=model_VS, resize_dim=(2048, 2048),
  67. network='Unet_v0')
  69. def orientation_analysis(self):
  70. # hough line transform
  72. img_mask_vs = tifffile.imread(self.filename_mask_vs) > 255 * 0.5
  73. img_mask_nf = tifffile.imread(self.filename_mask_nf) > 255 * 0.5
  75. img_mask_vs_skel = skeletonize(img_mask_vs)
  76. lines_vs = np.asarray(probabilistic_hough_line(img_mask_vs_skel, line_gap=50))
  78. img_mask_nf_skel = skeletonize(img_mask_nf)
  79. lines_nf = np.asarray(probabilistic_hough_line(img_mask_nf_skel, line_gap=50))
  81. angles_lines_vs = np.asarray([np.arctan(np.diff(line.T[0]) / np.diff(line.T[1])) for line in lines_vs])[:,
  82. 0] + np.pi / 2
  83. angles_lines_nf = np.asarray([np.arctan(np.diff(line.T[0]) / np.diff(line.T[1])) for line in lines_nf])[:,
  84. 0] + np.pi / 2
  86. hist_vs, hist_nf = np.histogram(angles_lines_vs, density=True, bins=20), np.histogram(angles_lines_nf,
  87. density=True, bins=20)
  89. R, p = pearsonr(hist_vs[0], hist_nf[0])
  91. _dict = {'lines_vs': lines_vs, 'lines_nf': lines_nf, 'angles_lines_vs': angles_lines_vs,
  92. 'angles_lines_nf': angles_lines_nf, 'hist_nf': hist_nf, 'hist_vs': hist_vs, 'corr_R': R}
  94. self.data.update(_dict)
  95. self.save_data()
  97. def plot(self):
  98. mosaic = """
  99. abcd
  100. eghi
  101. """
  103. fig, axs = plt.subplot_mosaic(mosaic, figsize=(width_2cols, width_2cols / 2), constrained_layout=True,
  104. dpi=900, per_subplot_kw={'i': {'projection': 'polar'}})
  106. img_vs = tifffile.imread(self.filename_vs)
  107. img_vs = np.clip(img_vs, np.percentile(img_vs, 0.5), np.percentile(img_vs, 99.5))
  108. axs['a'].imshow(img_vs, cmap='gray')
  110. img_nf = tifffile.imread(self.filename_nf)
  111. img_nf = np.clip(img_nf, np.percentile(img_nf, 0.5), np.percentile(img_nf, 99.5))
  112. axs['c'].imshow(img_nf, cmap='gray')
  114. img_mask_vs = tifffile.imread(self.filename_mask_vs) > 255 * 0.5
  115. img_mask_nf = tifffile.imread(self.filename_mask_nf) > 255 * 0.5
  117. axs['b'].imshow(img_mask_vs, cmap='gray')
  118. axs['d'].imshow(img_mask_nf, cmap='gray')
  120. rgb = np.dstack((img_mask_vs * 255, img_mask_nf * 255, np.zeros_like(img_mask_vs))).astype('uint8')
  122. axs['e'].imshow(rgb)
  124. remove_ticks(axs['a'])
  125. remove_ticks(axs['b'])
  126. remove_ticks(axs['c'])
  127. remove_ticks(axs['d'])
  128. remove_ticks(axs['e'])
  129. remove_ticks(axs['g'])
  130. remove_ticks(axs['h'])
  132. axs['a'].set_title('Vasculature', weight='semibold')
  133. axs['b'].set_title('Mask vasculature', weight='semibold')
  135. axs['c'].set_title('Axons', weight='semibold')
  136. axs['d'].set_title('Mask axons', weight='semibold')
  138. axs['e'].set_title('Mask overlay', weight='semibold')
  140. norm = matplotlib.colors.Normalize(vmin=0, vmax=180, clip=True)
  141. mapper = matplotlib.cm.ScalarMappable(norm=norm, cmap='hsv')
  143. axs['g'].imshow(img_mask_vs, cmap='gray')
  145. for line, angle in zip(self.data['lines_vs'], self.data['angles_lines_vs']):
  146. p0, p1 = line
  147. axs['g'].plot((p0[0], p1[0]), (p0[1], p1[1]), c=mapper.to_rgba(angle / np.pi * 180))
  149. plt.colorbar(mapper, ax=axs['g'], label='Orientation vasculature [°]', orientation='horizontal')
  151. axs['h'].imshow(img_mask_nf, cmap='gray')
  153. for line, angle in zip(self.data['lines_nf'], self.data['angles_lines_nf']):
  154. p0, p1 = line
  155. axs['h'].plot((p0[0], p1[0]), (p0[1], p1[1]), c=mapper.to_rgba(angle / np.pi * 180))
  157. plt.colorbar(mapper, ax=axs['h'], label='Orientation axons [°]', orientation='horizontal')
  159. axs['i'].hist([self.data['angles_lines_vs'], self.data['angles_lines_nf']], density=True, bins=20,
  160. label=['VS', 'NF'])
  161. axs['i'].set_thetamax(180)
  163. R = self.data['corr_R']
  164. axs['i'].set_title(f'Orientational correlation (R={R.round(3)})', pad=-20)
  166. axs['i'].legend(loc=5)
  168. for k in axs.keys():
  169. if k != 'i':
  170. axs[k].add_artist(ScaleBar(self.pixelsize, units='µm', frameon=False, color='w', sep=1,
  171. height_fraction=0.03, location='lower right', scale_loc='top',
  172. font_properties={'size': fontsize - 1, 'weight': 'bold'}))
  174. plt.show()
  176. fig.savefig(self.folder + 'result.pdf')
  179. # plot params
  180. fontsize = 8
  181. markersize = 3
  182. labelpad = 1
  183. dpi = 600
  184. save_format = 'png'
  186. mpl.rcParams['pdf.fonttype'] = 42
  187. mpl.rcParams['ps.fonttype'] = 42
  189. width_1cols = 3.5
  190. width_1p5cols = 5
  191. width_2cols = 7.1
  192. plt.rcParams.update({'font.size': fontsize, 'axes.labelpad': labelpad, 'font.family': 'arial'})
  193. plt.style.use('seaborn-v0_8-paper')
  196. def label_all_panels(axs, offset=(-0.1, 1.1), color='k'):
  197. """
  198. Labels all panels in a given dictionary of Axes objects.
  200. Parameters:
  201. axs (dict): A dictionary of Axes objects.
  202. offset (tuple, optional): The x and y offset for the labels. Defaults to (-0.1, 1.1).
  203. color (str, optional): The color of the labels. Defaults to 'k' (black).
  204. """
  205. for key in axs.keys():
  206. label_panel(axs[key], key, offset=offset, color=color)
  209. def label_panel(ax, label, offset=(-0.1, 1.1), color='k'):
  210. """
  211. Labels a single panel with the specified label.
  213. Parameters:
  214. ax (matplotlib.axes.Axes): The Axes object representing the panel to be labeled.
  215. label (str): The label to be displayed.
  216. offset (tuple, optional): The x and y offset for the label. Defaults to (-0.1, 1.1).
  217. color (str, optional): The color of the label. Defaults to 'k' (black).
  218. """
  219. ax.text(offset[0], offset[1], label, transform=ax.transAxes,
  220. fontsize=fontsize + 1, fontweight='bold', va='top', ha='right', color=color)
  223. def remove_all_spines(axs):
  224. """
  225. Removes the spines from all panels in a given dictionary of Axes objects.
  227. Parameters:
  228. axs (dict): A dictionary of Axes objects.
  229. """
  230. for key in axs.keys():
  231. remove_spines(axs[key])
  234. def remove_spines(ax):
  235. """
  236. Removes the spines from a single panel.
  238. Parameters:
  239. ax (matplotlib.axes.Axes): The Axes object representing the panel.
  240. """
  241. ax.spines['right'].set_visible(False)
  242. ax.spines['top'].set_visible(False)
  245. def change_color_spines(ax, c='w'):
  246. """
  247. Changes the color of the spines (borders) of a single panel.
  249. Parameters:
  250. ax (matplotlib.axes.Axes): The Axes object representing the panel.
  251. c (str, optional): The color of the spines. Defaults to 'w' (white).
  252. """
  253. ax.spines['bottom'].set_color(c)
  254. ax.spines['top'].set_color(c)
  255. ax.spines['right'].set_color(c)
  256. ax.spines['left'].set_color(c)
  259. def remove_ticks(ax):
  260. """
  261. Removes the ticks (markings) from both x-axis and y-axis of a single panel.
  263. Parameters:
  264. ax (matplotlib.axes.Axes): The Axes object representing the panel.
  265. """
  266. ax.set_xticks([])
  267. ax.set_yticks([])
  270. def polish_xticks(ax, major, minor, pad=3):
  271. """
  272. Formats and polishes the x-ticks (markings) of a single panel.
  274. Parameters:
  275. ax (matplotlib.axes.Axes): The Axes object representing the panel.
  276. major (float): The major tick spacing.
  277. minor (float): The minor tick spacing.
  278. pad (float, optional): The padding between the x-axis and the tick labels. Defaults to 3.
  279. """
  280. ax.xaxis.set_major_locator(MultipleLocator(major))
  281. ax.xaxis.set_major_formatter(FormatStrFormatter('%g'))
  282. ax.xaxis.set_minor_locator(MultipleLocator(minor))
  283. ax.tick_params(axis='x', pad=pad)
  286. def polish_yticks(ax, major, minor, pad=3):
  287. """
  288. Formats and polishes the y-ticks (markings) of a single panel.
  290. Parameters:
  291. ax (matplotlib.axes.Axes): The Axes object representing the panel.
  292. major (float): The major tick spacing.
  293. minor (float): The minor tick spacing.
  294. pad (float, optional): The padding between the y-axis and the tick labels. Defaults to 3.
  295. """
  296. ax.yaxis.set_major_locator(MultipleLocator(major))
  297. ax.yaxis.set_major_formatter(FormatStrFormatter('%g'))
  298. ax.yaxis.set_minor_locator(MultipleLocator(minor))
  299. ax.tick_params(axis='y', pad=pad)

analysis.py at commit 871cb79, under MIT · at the source

Overview

Authors: Lennart Valentin Schneider1,2,3, Michael Gani Setya1,2,3, Guobin Bao1, Daniel Härtter1,2, Aditi Methi4, Dennis Krüger4, Marie Kristin Schreiber1,3, Ole Jensen5, Elisavet Kanari1, Kea Aline Schmoll1,3, Narasimha Swamy Telugu6, Sadman Sakib4, Eun Seo Kim1, Aminath Luveysa Fahud1, Maham Novin1,2, Aylin Seedorf1,2,3,7, Sebastian Diecke6, André Fischer3,4, Norman Y. Liaw1,2, Wolfram-Hubertus Zimmermann1,2,3,4,8,9,10, Maria-Patapia Zafeiriou1,2,3,9,11,12,13
ORCID iDs: Lennart Valentin Schneider, Michael Gani Setya, Daniel Härtter, Dennis Krüger, Marie Kristin Schreiber, Ole Jensen, Eun Seo Kim, Aminath Luveysa Fahud, Sebastian Diecke, Norman Y. Liaw, Wolfram-Hubertus Zimmermann, Maria-Patapia Zafeiriou
13 affiliations
  1. Institute of Pharmacology and Toxicology, University Medical Center Göttingen,Göttingen, Germany
  2. DZHK (German Center for Cardiovascular Research), partner site Lower Saxony,Göttingen, Germany
  3. MBExC (Multi-Scale Bioimaging Excellence Cluster), Göttingen, Germany
  4. German Center for Neurodegenerative Diseases,Göttingen, Germany
  5. Department of Clinical Pharmacology, University Medical Center Göttingen,Göttingen, Germany
  6. Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), Technology Platform Pluripotent Stem Cells,Berlin, Germany
  7. Department of Cardiology and Pneumology, University Medical Center,Göttingen, Germany
  8. Fraunhofer Institute for Translational Medicine and Pharmacology (ITMP),Göttingen, Germany
  9. German Center for Child and Adolescent Health (DZKJ), partner site Lower Saxony, Göttingen, Germany
  10. Campus-Institute Data Science (CIDAS),Göttingen, Germany
  11. Else Kröner Fresenius Center for Optogenetic Therapies (EKFZ L2T),Göttingen, Germany
  12. Clinic for Pediatric and Adolescent Medicine, University Medical Center Göttingen,Göttingen, Germany
  13. Collaborative Research Center 1690, University of Göttingen,Göttingen, Germany
Institutions: Universitätsmedizin Göttingen (Germany); German Centre for Cardiovascular Research (Germany); German Center for Neurodegenerative Diseases (Germany); Max Delbrück Center (Germany); Fraunhofer Institute for Translational Medicine and Pharmacology (Germany); Campus-Institut Data Science (CIDAS) (Germany); Else Kröner Fresenius Center for Optogenetic Therapies (Germany); University of Göttingen (Germany)
Journal: Nature communications, volume 17, issue 1, article 9404
Dates: received 24 April 2026; accepted 6 August 2026; published online 2 September 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1038/s41467-026-76956-9 · PMID 42686760 · PMCID PMC13538559 · OpenAlex W7205013138
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: human (organism)
Methods: Statistics, Smoothing, state filtering, decompositions, Machine learning, Preprocessing, Connectivity, Single-unit activity, calcium imaging
Keywords: Tissue engineering, Stem-cell biotechnology, Disease model
MeSH: Arrhythmias, Cardiac*, ERG1 Potassium Channel*, Heart*, Myocardium*, Sympathetic Nervous System*, Humans, Myocytes, Cardiac, Neurons, Optogenetics (* major topic)
Topic: Cardiac electrophysiology and arrhythmias (Cardiology and Cardiovascular Medicine, Medicine), according to OpenAlex
Funding: Deutsche Forschungsgemeinschaft (DFG) (496553893); ORCID ID: 0000-0003-1190-4040
Citations: not cited yet (Europe PMC); 101 references in the paper

Abstract

Cardiac autonomic neurons regulate contractility. Autonomic nervous system dysregulation can cause sympathetic overdrive, leading to heart failure, and fatal arrhythmias. Here, we introduce innervated engineered human myocardium (iEHM), a model of neuro-cardiac junctions, constructed by fusing sympathetic neuronal organoids (SNO) and engineered human myocardium (EHM). Projections of sympathetic neurons formed presynaptic terminals in close proximity to cardiomyocytes and to the extensive vascular network co-developing in iEHM. Contractile responses to optogenetic stimulation of the accordingly engineered neuronal component demonstrated functional neuro-cardiac junctions. Modeling long-QT 2 in iEHM revealed sympathetic neuron hyperactivity and after depolarizations, underscoring a central role for sympathetic drive in KCNH2-associated arrhythmias. β-adrenoreceptor blockade was insufficient to rescue the pro-arrhythmic phenotype, while mexiletine targeting both neurons and cardiomyocytes, proved to be more effective. Collectively, our data establishes iEHM as a New Approach Methodology that provides a human-relevant, physiologically integrated model for mechanistic investigations and pharmacological testing.

Reproduced under the paper's license (CC BY), from the paper cited above.

Repositories

Its files are read in the Code ↔ Paper reader above, with 3 matches between paragraphs and lines of code.

danihae/bio-image-unet

License: MIT
State: the link answers, verified on 26 September 2026
Evidence: files inventoried
Commit: ec8a04a93849963e3c76a64f538dc1761ce79b63, 17 August 2026
Languages: Python (59), Jupyter (2)
Size: 69 files, 61 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README, license file, environment (pyproject.toml), tests, continuous integration, 2 notebooks
Not found: CITATION.cff, documentation
Tools: PyTorch (40 files), NumPy (24 files), tifffile (20 files), OpenCV (10 files), Matplotlib (4 files), scikit-image (4 files), SciPy (2 files), napari (1 file), Pillow (1 file)
Availability: 1 check, the latest on 26 September 2026: the link answers
  • 26 September 2026: the link answers
63 files
At the source: github.com/danihae/bio-image-unet

danihae/iEHM-analysis

License: MIT
State: the link answers, verified on 26 September 2026
Evidence: files inventoried
Commit: 871cb79f2c35ee83fc461e0eb2b35f5674582521, 9 July 2026
Languages: Jupyter (4), Python (2)
Size: 11 files, 6 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README, license file, environment (pyproject.toml, uv.lock), 4 notebooks
Not found: CITATION.cff, tests, continuous integration, documentation
Tools: Matplotlib (2 files), NumPy (2 files), tifffile (2 files), scikit-image (1 file), SciPy (1 file)
Availability: 1 check, the latest on 26 September 2026: the link answers
  • 26 September 2026: the link answers
8 files
At the source: github.com/danihae/iEHM-analysis

Code availability

Custom Python scripts for deep learning and image analysis were used. The U-Net implementation is publicly available at https://github.com/danihae/bio-image-unet. The full analysis pipeline, including the iEHM-specific model and processing scripts, can be accessed at https://github.com/danihae/iEHM-analysis. The dataset used in this study is archived at Zenodo (https://zenodo.org/records/21268390) for reproducibility and further analysis.

Reproduced under the paper's license (CC BY), from the paper cited above.

Tracing map

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What the map holds:

  • 2 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 67 scripts, each with its path and the digest of its content;
  • 3 matches between paragraphs of the paper and lines of the code (method lexical-v1);
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Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

Datasets cited

Data availability

The single-nucleus RNA sequencing data generated in this study have been deposited in the NCBI Gene Expression Omnibus database under accession code GSE313106 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE313106). The bulk RNA sequencing data generated in this study have been deposited in the EBI biostudies database under accession code E-MTAB-16232 (https://www.ebi.ac.uk/biostudies/arrayexpress/studies/E-MTAB-16232). Source data are provided with this paper.

Reproduced under the paper's license (CC BY), from the paper cited above.

Versions

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Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 21 authors, 3 keywords, 9 MeSH terms, 2 funders, 100 references.

Cite

This paper

Schneider, L. V., Setya, M. G., Bao, G., Härtter, D., Methi, A., Krüger, D., Schreiber, M. K., Jensen, O., Kanari, E., Schmoll, K. A., Telugu, N. S., Sakib, S., Kim, E. S., Fahud, A. L., Novin, M., Seedorf, A., Diecke, S., Fischer, A., Liaw, N. Y., . . . Zafeiriou, M.-P. (2026). Innervated human cardiac muscle model reveals sympathetic drivers of KCNH2-associated arrhythmias. Nature communications, 17(1), 9404. https://doi.org/10.1038/s41467-026-76956-9

BibTeX

@article{schneider2026innervated,
author = {Schneider, Lennart Valentin and Setya, Michael Gani and Bao, Guobin and Härtter, Daniel and Methi, Aditi and Krüger, Dennis and Schreiber, Marie Kristin and Jensen, Ole and Kanari, Elisavet and Schmoll, Kea Aline and Telugu, Narasimha Swamy and Sakib, Sadman and Kim, Eun Seo and Fahud, Aminath Luveysa and Novin, Maham and Seedorf, Aylin and Diecke, Sebastian and Fischer, André and Liaw, Norman Y. and Zimmermann, Wolfram-Hubertus and Zafeiriou, Maria-Patapia},
title = {{Innervated human cardiac muscle model reveals sympathetic drivers of KCNH2-associated arrhythmias}},
journal = {Nature communications},
year = {2026},
month = sep,
volume = {17},
number = {1},
pages = {9404},
publisher = {Nature Publishing Group},
issn = {2041-1723},
doi = {10.1038/s41467-026-76956-9},
url = {https://doi.org/10.1038/s41467-026-76956-9},
pmid = {42686760},
pmcid = {PMC13538559}
}

RIS

TY - JOUR
AU - Schneider, Lennart Valentin
AU - Setya, Michael Gani
AU - Bao, Guobin
AU - Härtter, Daniel
AU - Methi, Aditi
AU - Krüger, Dennis
AU - Schreiber, Marie Kristin
AU - Jensen, Ole
AU - Kanari, Elisavet
AU - Schmoll, Kea Aline
AU - Telugu, Narasimha Swamy
AU - Sakib, Sadman
AU - Kim, Eun Seo
AU - Fahud, Aminath Luveysa
AU - Novin, Maham
AU - Seedorf, Aylin
AU - Diecke, Sebastian
AU - Fischer, André
AU - Liaw, Norman Y.
AU - Zimmermann, Wolfram-Hubertus
AU - Zafeiriou, Maria-Patapia
TI - Innervated human cardiac muscle model reveals sympathetic drivers of KCNH2-associated arrhythmias
T2 - Nature communications
J2 - Nat Commun
PY - 2026
DA - 2026/09/02
VL - 17
IS - 1
SP - 9404
SN - 2041-1723
PB - Nature Publishing Group
DO - 10.1038/s41467-026-76956-9
UR - https://doi.org/10.1038/s41467-026-76956-9
LA - en
ER -

CSL-JSON

{
"id": "10.1038/s41467-026-76956-9",
"type": "article-journal",
"title": "Innervated human cardiac muscle model reveals sympathetic drivers of KCNH2-associated arrhythmias",
"container-title": "Nature communications",
"author": [
{
"family": "Schneider",
"given": "Lennart Valentin"
},
{
"family": "Setya",
"given": "Michael Gani"
},
{
"family": "Bao",
"given": "Guobin"
},
{
"family": "Härtter",
"given": "Daniel"
},
{
"family": "Methi",
"given": "Aditi"
},
{
"family": "Krüger",
"given": "Dennis"
},
{
"family": "Schreiber",
"given": "Marie Kristin"
},
{
"family": "Jensen",
"given": "Ole"
},
{
"family": "Kanari",
"given": "Elisavet"
},
{
"family": "Schmoll",
"given": "Kea Aline"
},
{
"family": "Telugu",
"given": "Narasimha Swamy"
},
{
"family": "Sakib",
"given": "Sadman"
},
{
"family": "Kim",
"given": "Eun Seo"
},
{
"family": "Fahud",
"given": "Aminath Luveysa"
},
{
"family": "Novin",
"given": "Maham"
},
{
"family": "Seedorf",
"given": "Aylin"
},
{
"family": "Diecke",
"given": "Sebastian"
},
{
"family": "Fischer",
"given": "André"
},
{
"family": "Liaw",
"given": "Norman Y."
},
{
"family": "Zimmermann",
"given": "Wolfram-Hubertus"
},
{
"family": "Zafeiriou",
"given": "Maria-Patapia"
}
],
"container-title-short": "Nat Commun",
"volume": "17",
"issue": "1",
"page": "9404",
"DOI": "10.1038/s41467-026-76956-9",
"PMID": "42686760",
"PMCID": "PMC13538559",
"ISSN": "2041-1723",
"publisher": "Nature Publishing Group",
"URL": "https://doi.org/10.1038/s41467-026-76956-9",
"language": "en",
"issued": {
"date-parts": [
[
2026,
9,
2
]
]
}
}

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