OSCR

Assessing the importance of sex and disease-specific anatomy in electrophysiology and mechanical simulations with a newly developed public virtual cohort of four-chamber heart models.

Code ↔ Paper

11 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 11 matches
  1. [1] § 3. Methods › 3.2. Image to mesh stage › 3.2.1 Multi-stage segmentation. ↔ cemrg_heartbuilder/meshing/ModelCreationParameters.py, lines 1–29 · score 0.94 · venae cavae, pulmonary artery, pulmonary veins, blood pools, right atrial, left atrial
  2. [2] § 3. Methods › 3.2. Image to mesh stage › 3.2.1 Multi-stage segmentation. ↔ cemrg_heartbuilder/segmentation/process_handler.py, lines 273–319 · score 0.83 · pulmonary artery, right atrium, right ventricle, ventricle myocardium, right atrial, left atrial
  3. [3] § 3. Methods › 3.2. Image to mesh stage › 3.2.2. Conversion to mesh. ↔ cemrg_heartbuilder/meshing/MeshingParameters.py, lines 5–119 · score 0.75 · cell_size, facet_distance, facet_size, edge, smooth, segmentation
  4. [4] § 3. Methods › 3.5. Statistical tests and comparison of volume to clinical literature › 3.5.1. Effect sizes and statistical significance. ↔ examples/comprehensive_analysis.py, lines 262–330 · score 0.71 · post hoc, way ANOVA, pairwise, marginal, Tukey, Cohen
  5. [5] § 3. Methods › 3.3. Mesh processing and model creation ↔ cemrg_heartbuilder/simulation/toolbox.py, lines 248–325 · score 0.70 · Bachmann bundle, left atrium, right ventricular, fast, FEC, electrophysiology
  6. [6] § 3. Methods › 3.3. Mesh processing and model creation ↔ cemrg_heartbuilder/meshing/ModelCreationParameters.py, lines 1–29 · score 0.63 · vena cava, pulmonary veins, creation, Ventricular, tags, mesh
  7. [7] § 4. Results › 4.1. Geometric characterization and correlation with simulation outputs ↔ examples/comprehensive_analysis.py, lines 209–260 · score 0.63 · FDR correction, post hoc, way ANOVA, Cohen
  8. [8] § 3. Methods › 3.2. Image to mesh stage › 3.2.2. Conversion to mesh. ↔ cemrg_heartbuilder/meshing/scripts/get_meshing_parfile.py, lines 38–60 · score 0.59 · cell_size, facet_distance, facet_size, edge, segmentation, mesh
  9. [9] § 3. Methods › 3.5. Statistical tests and comparison of volume to clinical literature › 3.5.2. Left atrial and left ventricular volume. ↔ examples/plot_cardiac_distributions.py, lines 23–53 · score 0.55 · LV volume, right ventricles, females, atrial, cardiac, HF
  10. [10] § 2. Study population ↔ examples/plot_cardiac_distributions.py, lines 23–53 · score 0.52 · narrow QRS, wide QRS, Female, cardiac, HF
  11. [11] § 3. Methods › 3.3. Mesh processing and model creation ↔ cemrg_heartbuilder/simulation/scripts/basic_extract_ep_output.py, lines 23–34 · score 0.50 · Bachmann bundle, fast, EP, FEC, electrophysiology, atria

Paper

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The authors' code

Python · 340 lines · 9.1 KB · no license · 2 matches

  1. import os
  2. import json
  4. UNUSED_TAG = 200
  5. DEFAULT_ETAGS = {
  6. 'T_LV': 1,
  7. 'T_RV': 2,
  8. 'T_UNUSED' : UNUSED_TAG, # unused tag
  9. 'T_LA' : UNUSED_TAG, # left atrial wall
  10. 'T_LABP' : UNUSED_TAG, # left atrial blood pool
  11. 'T_LINFPULMVEINCUT' : UNUSED_TAG, # left inferior pulmonary vein (cut)
  12. 'T_LSUPPULMVEINCUT' : UNUSED_TAG, # left superior pulmonary vein (cut)
  13. 'T_RINFPULMVEINCUT' : UNUSED_TAG, # right inferior pulmonary vein (cut)
  14. 'T_RSUPPULMVEINCUT' : UNUSED_TAG, # right superior pulmonary vein (cut)
  15. 'T_RA' : UNUSED_TAG, # right atrial wall
  16. 'T_RABP' : UNUSED_TAG, # right atrial blood pool
  17. 'T_LVBP' : UNUSED_TAG, # left ventricular blood pool
  18. 'T_AORTA' : UNUSED_TAG, # aorta
  19. 'T_AORTABP' : UNUSED_TAG, # aortic blood pool
  20. 'T_MITRALVV' : UNUSED_TAG, # mitral valve
  21. 'T_AORTICVV' : UNUSED_TAG, # aortic valve
  22. 'T_RVBP' : UNUSED_TAG, # right ventricular blood pool
  23. 'T_VCINF' : UNUSED_TAG, # vena cava inferior
  24. 'T_VCSUP' : UNUSED_TAG, # vena cava superior
  25. 'T_PULMARTERY' : UNUSED_TAG, # pulmonary artery
  26. 'T_PULMARTERYBP' : UNUSED_TAG, # pulmonary artery blood pool
  27. 'T_TRICUSPVV' : UNUSED_TAG, # tricuspic valve
  28. 'T_PULMVV' : UNUSED_TAG # pulmonic valve
  29. }
  31. class ETagsParameters:
  32. def __init__(self, type='base') -> None:
  33. types = ['base', 'la', 'ra']
  34. if type not in types :
  35. raise ValueError(f'Invalid type for ETagsParameters. Must be one of {types}')
  37. self.type = type
  38. self.tags = DEFAULT_ETAGS.copy()
  39. self.update_tags()
  41. def update_type(self, type) :
  42. self.type = type
  43. self.update_tags()
  45. def update_tags(self) :
  46. tag_names = list(self.tags.keys())
  47. if self.type == 'base' :
  48. self.tags['T_LV'] = 1
  49. self.tags['T_RV'] = 2
  50. tag_names.remove('T_LV')
  51. tag_names.remove('T_RV')
  53. elif self.type == 'la' :
  54. self.tags['T_LV'] = 3
  55. tag_names.remove('T_LV')
  57. elif self.type == 'ra' :
  58. self.tags['T_LV'] = 4
  59. tag_names.remove('T_LV')
  61. for tag in tag_names :
  62. self.tags[tag] = UNUSED_TAG
  64. def save_to_file(self, filename) :
  65. filename += '.sh' if not filename.endswith('.sh') else ''
  66. # list tags different to UNUSED_TAG
  67. tags_used = {k:v for k,v in self.tags.items() if v != UNUSED_TAG}
  68. tags_unused = {k:v for k,v in self.tags.items() if v == UNUSED_TAG}
  70. with open(filename, 'w') as f :
  71. f.write('#!/bin/bash\n')
  72. f.write('\n')
  73. if self.type != 'base' :
  74. f.write(f'## CHANGE ONLY THIS LABEL SO THAT THE T_LV = THE LABELOF YOUR {self.type.upper()}')
  75. else :
  76. f.write('## ONLY CHANGE THESE LABELS TO MATCH YOUR MESH LABELS')
  77. f.write('\n\n')
  79. for k,v in tags_used.items() :
  80. f.write(f'{k}={v}\n')
  82. f.write('\n')
  84. for k,v in tags_unused.items() :
  85. f.write(f'{k}={v}\n')
  88. DEFAULT_ATRIA_MAP = {
  89. "la": {
  90. "phi_min_aorta_side": -3.15,
  91. "begin_interp_aorta_side": -2.09,
  92. "end_interp_aorta_side": -1.54,
  93. "phi_max": 0,
  94. "begin_interp_not_aorta": -0.1,
  95. "end_interp_not_aorta": 0.7,
  96. "phi_min_not_aorta": 3.15
  97. },
  99. "ra": {
  100. "phi_min_aorta_side": -3.15,
  101. "begin_interp_aorta_side": -1.0,
  102. "end_interp_aorta_side": -0.5,
  103. "phi_max": 0,
  104. "begin_interp_not_aorta": 0.5,
  105. "end_interp_not_aorta": 2,
  106. "phi_min_not_aorta": 3.15
  107. },
  109. "Iz": {
  110. "Iz_0": 0,
  111. "Iz_1": 0.4,
  112. "Iz_2": 0.7,
  113. "Iz_3": 1.0
  114. }
  115. }
  116. class AtriaMapSettings:
  117. def __init__(self) -> None:
  118. self.settings = DEFAULT_ATRIA_MAP.copy()
  120. def save_to_file(self, filename) :
  121. filename += '.json' if not filename.endswith('.json') else ''
  122. with open(filename, 'w') as f :
  123. json.dump(self.settings, f, indent=4)
  125. def load_from_file(self, filename) :
  126. filename += '.json' if not filename.endswith('.json') else ''
  127. with open(filename, 'r') as f :
  128. self.settings = json.load(f)
  130. def update_settings(self, settings) :
  131. self.settings = settings
  133. def change(self, atria, key, value) :
  134. if atria not in self.settings :
  135. raise ValueError(f'{atria} not found in the settings')
  137. if key not in self.settings[atria] :
  138. raise ValueError(f'{key} not found in the settings')
  140. self.settings[atria][key] = value
  142. DEFAULT_BACHMANN_BUNDLE = {
  143. "FEC_height": 0.8,
  144. "LA": {
  145. "phi_min": -0.54,
  146. "phi_max": 2,
  147. "z_min": 0.0,
  148. "z_max": 0.71
  149. },
  150. "RA": {
  151. "phi_min": -1.85,
  152. "phi_max": 1.4,
  153. "z_min": 0.0,
  154. "z_max": 0.46
  155. }
  156. }
  157. class BachmannBundleSettings:
  158. def __init__(self) -> None:
  159. self.settings = DEFAULT_BACHMANN_BUNDLE.copy()
  161. def save_to_file(self, filename) :
  162. filename += '.json' if not filename.endswith('.json') else ''
  163. with open(filename, 'w') as f :
  164. json.dump(self.settings, f, indent=4)
  166. def load_from_file(self, filename) :
  167. filename += '.json' if not filename.endswith('.json') else ''
  168. with open(filename, 'r') as f :
  169. self.settings = json.load(f)
  171. def update_settings(self, settings) :
  172. self.settings = settings
  174. def change(self, atria, key, value) :
  175. if atria not in self.settings :
  176. raise ValueError(f'{atria} not found in the settings')
  178. if key not in self.settings[atria] :
  179. raise ValueError(f'{key} not found in the settings')
  181. self.settings[atria][key] = value
  183. def change_la(self, key, value) :
  184. self.change('LA', key, value)
  186. def change_ra(self, key, value) :
  187. self.change('RA', key, value)
  189. def change_fec(self, value) :
  190. self.settings['FEC_height'] = value
  192. DEFAULT_MESH_TAGS = {
  193. "LV": 1,
  194. "RV": 2,
  195. "LA": 3,
  196. "RA": 4,
  197. "Ao": 5,
  198. "PArt": 6,
  199. "MV": 7,
  200. "TV": 8,
  201. "AV": 9,
  202. "PV": 10,
  203. "LSPV": 11,
  204. "LIPV": 12,
  205. "RSPV": 13,
  206. "RIPV": 14,
  207. "LAA": 15,
  208. "SVC": 16,
  209. "IVC": 17,
  210. "LAA_ring": 18,
  211. "SVC_ring": 19,
  212. "IVC_ring": 20,
  213. "LSPV_ring": 21,
  214. "LIPV_ring": 22,
  215. "RSPV_ring": 23,
  216. "RIPV_ring": 24,
  217. "FEC_LV": 25,
  218. "FEC": 25,
  219. "BB": 26,
  220. "AV_plane": 27,
  221. "FEC_RV": 28,
  222. "FEC_SV": 29
  223. }
  226. DEFAULT_FASCICLES_SETTINGS = {
  227. "LVsept" :{
  228. "z" : 0.61,
  229. "phi" : 0.73,
  230. "rho" : 0.0,
  231. "v" : -1.0,
  232. "radius": 2000.0,
  233. "radius_phi": 0.05,
  234. "radius_rho": 0.05
  235. },
  236. "LVpost" :{
  237. "z" : 0.47,
  238. "phi" : -1.36,
  239. "rho" : 0.0,
  240. "v" : -1.0,
  241. "radius": 2000.0,
  242. "radius_phi": 0.05,
  243. "radius_rho": 0.05
  244. },
  245. "LVant" :{
  246. "z" : 0.82,
  247. "phi" : 1.94,
  248. "rho" : 0.0,
  249. "v" : -1.0,
  250. "radius": 2000.0,
  251. "radius_phi": 0.05,
  252. "radius_rho": 0.05
  253. },
  254. "RVsept" :{
  255. "z" : 0.73,
  256. "phi" : -0.04,
  257. "rho" : 1.0,
  258. "v" : -1.0,
  259. "radius": 2000.0,
  260. "radius_phi": 0.05,
  261. "radius_rho": 0.05
  262. },
  263. "RVmod" :{
  264. "z" : 0.63,
  265. "phi" : 0.21,
  266. "rho" : 0,
  267. "v" : 1.0,
  268. "radius": 2000.0,
  269. "radius_phi": 0.05,
  270. "radius_rho": 0.05
  271. },
  272. "SAN" :{
  273. "radius": 2000.0
  274. }
  275. }
  277. ## EP SIMS
  278. DEFAULT_EP_TAGS = {
  279. "LV": 1,
  280. "RV": 2,
  281. "LA": 3,
  282. "RA": 4,
  283. "atria": [3,4],
  284. "FEC_LV": 25,
  285. "FEC_RV": 28,
  286. "FEC_SV": 29,
  287. "fast_endo": [25,28,29],
  288. "BB": 26,
  289. "AV_plane": [27],
  290. "aorta": [5],
  291. "pulmonary_artery": [6],
  292. "vein_rings": [18,19,20,21,22,23,24],
  293. "valve_planes": [7,8,9,10,11,12,13,14,15,16,17]
  294. }
  296. ##
  297. CV_F_V_SHARED = 0.407284
  298. CV_F_A_SHARED = 0.353259
  299. ANI_RATIO_V_SHARED = 0.272427
  300. ANI_RATIO_A_SHARED = 0.340049
  301. K_FEC_SHARED = 1.55631
  302. K_BB_SHARED = 1.65963
  304. DEFAULT_EP_VELOCITIES = {
  305. "EP": {
  306. "CV_f_v": CV_F_V_SHARED,
  307. "CV_ventricles": CV_F_V_SHARED,
  308. "ani_ratio_v": ANI_RATIO_V_SHARED,
  309. "k_ventricles": ANI_RATIO_V_SHARED,
  310. "k_FEC": K_FEC_SHARED,
  311. "CV_f_a": CV_F_A_SHARED,
  312. "CV_atria": CV_F_A_SHARED,
  313. "ani_ratio_a": ANI_RATIO_A_SHARED,
  314. "k_atria": ANI_RATIO_A_SHARED,
  315. "k_BB": K_BB_SHARED
  316. }
  317. }
  319. LAPLACE_FILE=[["experiment\t= 2",
  320. "bidomain\t= 1"],
  321. ["num_gregions\t= 1",
  322. "gregion[0].g_et\t= 1",
  323. "gregion[0].g_el\t= 1",
  324. "gregion[0].g_en\t= 1",
  325. "gregion[0].g_il\t= 1",
  326. "gregion[0].g_it\t= 1",
  327. "gregion[0].g_in\t= 1"],
  328. ["num_stim\t= 2",
  329. "stimulus[0].stimtype\t= 3",
  330. "stimulus[1].duration\t= 1",
  331. "stimulus[1].strength\t= 1",
  332. "stimulus[1].stimtype\t= 2"]]
  334. def write_laplace_carp_par(filename):
  336. with open(filename, 'w') as f :
  337. for i in range(len(LAPLACE_FILE)):
  338. for j in range(len(LAPLACE_FILE[i])):
  339. f.write(LAPLACE_FILE[i][j]+"\n")
  340. f.write("\n")

ModelCreationParameters.py at commit 9976818, no license · at the source

Overview

Authors: José Alonso Solís-Lemus1, Rosie K Barrows1, Cristobal Rodero1, Marina Strocchi1,2, Natalie Montarello3, Nishant Lahoti3, Cesare Corrado1, Abdul Qayyum1, Shahrokh Rahmani1, Caroline Roney4, Gernot Plank5,6, Christoph Augustin5,6, Hao Xu3, Alistair Young3, Pras Pathmanathan7, Ronak Rajani3, Steven A Niederer1,8
ORCID iDs: José Alonso Solís-Lemus, Rosie K Barrows, Cristobal Rodero, Gernot Plank, Christoph Augustin, Alistair Young
  1. National Heart and Lung Institute, Imperial College London, London, United Kingdom
  2. Cardiac Rhythm Management, Medtronic, London, United Kingdom
  3. School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
  4. School of Engineering and Materials Science, Queen Mary University of London, London, United Kingdom
  5. Gottfried Schatz Research Center, Division of Medical Physics and Biophysics, Medical University of Graz, Graz, Austria
  6. BioTechMed-Graz, Graz, Austria
  7. Food and Drug Administration, Silver Spring, Maryland, United States of America
  8. Alan Turing Institute, London, United Kingdom
Institutions: Imperial College London (United Kingdom); Medtronic (United Kingdom) (United Kingdom); King's College London (United Kingdom); Queen Mary University of London (United Kingdom); Medical University of Graz (Austria); BioTechMed-Graz (Austria); United States Food and Drug Administration (United States); The Alan Turing Institute (United Kingdom)
Journal: PLoS computational biology, volume 22, issue 6, article e1014325
Dates: received 18 November 2025; accepted 11 May 2026; published online 2 June 2026
Type: Research article · Language: English
License: none stated
Identifiers: DOI 10.1371/journal.pcbi.1014325 · PMID 42228749 · PMCID PMC13252842 · OpenAlex W7163202000
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: computational modeling (no new data) (modality), human (organism), computational (subfield)
Methods: Connectivity, Statistics, Evoked potentials
MeSH: Heart*, Models, Anatomic*, Models, Cardiovascular*, Cohort Studies, Computational Biology, Computer Simulation, Female, Heart Failure, Humans, Male, Middle Aged, Sex Factors (* major topic)
Topic: Cardiac electrophysiology and arrhythmias (Cardiology and Cardiovascular Medicine, Medicine), according to OpenAlex
Funding: British Heart Foundation (PG/15/91/31812, SP/18/6/33805, RG/20/4/34803, PG/13/37/30280); European Research Council (864055)
Citations: not cited yet (Europe PMC); 71 references in the paper

Abstract

The abstract is not reproduced here: the paper's license (none stated) does not allow it. Read it in the paper, at the publisher or on Europe PMC.

Repositories

Its files are read in the Code ↔ Paper reader above, with 11 matches between paragraphs and lines of code.

OpenHeartDevelopers/cemrg-heartbuilder

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 9976818fe7e3baa573ddf3b28236057d6c081794, 28 August 2026
Languages: Python (96), Shell (28)
Size: 145 files, 124 scripts
Software Heritage: not archived
Found in: “Data Availability”
Holds: README, license file, environment (pyproject.toml, requirements.txt, docker/Dockerfile), tests
Not found: CITATION.cff, continuous integration, documentation
Tools: NumPy (42 files), pandas (13 files), SimpleITK (8 files), Matplotlib (6 files), SciPy (5 files), seaborn (5 files), statsmodels (2 files), h5py (1 file), imageio (1 file), pydicom (1 file), scikit-image (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
126 files
At the source: github.com/OpenHeartDevelopers/cemrg-heartbuilder

Zenodo 4593739

License: CC-BY-4.0
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Size: 41 files
Software Heritage: not checked
Found in: the references
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)
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Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

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Read it in the paper: doi.org/10.1371/journal.pcbi.1014325.

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Recorded: type, language, journal, volume, issue, pages, dates, 17 authors, 12 MeSH terms, 2 funders, 63 references.

Cite

This paper

Solís-Lemus, J. A., Barrows, R. K., Rodero, C., Strocchi, M., Montarello, N., Lahoti, N., Corrado, C., Qayyum, A., Rahmani, S., Roney, C., Plank, G., Augustin, C., Xu, H., Young, A., Pathmanathan, P., Rajani, R., & Niederer, S. A. (2026). Assessing the importance of sex and disease-specific anatomy in electrophysiology and mechanical simulations with a newly developed public virtual cohort of four-chamber heart models. PLoS computational biology, 22(6), e1014325. https://doi.org/10.1371/journal.pcbi.1014325

BibTeX

@article{solislemus2026assessing,
author = {Solís-Lemus, José Alonso and Barrows, Rosie K and Rodero, Cristobal and Strocchi, Marina and Montarello, Natalie and Lahoti, Nishant and Corrado, Cesare and Qayyum, Abdul and Rahmani, Shahrokh and Roney, Caroline and Plank, Gernot and Augustin, Christoph and Xu, Hao and Young, Alistair and Pathmanathan, Pras and Rajani, Ronak and Niederer, Steven A},
title = {{Assessing the importance of sex and disease-specific anatomy in electrophysiology and mechanical simulations with a newly developed public virtual cohort of four-chamber heart models}},
journal = {PLoS computational biology},
year = {2026},
month = jun,
volume = {22},
number = {6},
pages = {e1014325},
publisher = {PLOS},
issn = {1553-734X},
doi = {10.1371/journal.pcbi.1014325},
url = {https://doi.org/10.1371/journal.pcbi.1014325},
pmid = {42228749},
pmcid = {PMC13252842}
}

RIS

TY - JOUR
AU - Solís-Lemus, José Alonso
AU - Barrows, Rosie K
AU - Rodero, Cristobal
AU - Strocchi, Marina
AU - Montarello, Natalie
AU - Lahoti, Nishant
AU - Corrado, Cesare
AU - Qayyum, Abdul
AU - Rahmani, Shahrokh
AU - Roney, Caroline
AU - Plank, Gernot
AU - Augustin, Christoph
AU - Xu, Hao
AU - Young, Alistair
AU - Pathmanathan, Pras
AU - Rajani, Ronak
AU - Niederer, Steven A
TI - Assessing the importance of sex and disease-specific anatomy in electrophysiology and mechanical simulations with a newly developed public virtual cohort of four-chamber heart models
T2 - PLoS computational biology
J2 - PLoS Comput Biol
PY - 2026
DA - 2026/06/02
VL - 22
IS - 6
SP - e1014325
SN - 1553-734X
PB - PLOS
DO - 10.1371/journal.pcbi.1014325
UR - https://doi.org/10.1371/journal.pcbi.1014325
LA - en
ER -

CSL-JSON

{
"id": "10.1371/journal.pcbi.1014325",
"type": "article-journal",
"title": "Assessing the importance of sex and disease-specific anatomy in electrophysiology and mechanical simulations with a newly developed public virtual cohort of four-chamber heart models",
"container-title": "PLoS computational biology",
"author": [
{
"family": "Solís-Lemus",
"given": "José Alonso"
},
{
"family": "Barrows",
"given": "Rosie K"
},
{
"family": "Rodero",
"given": "Cristobal"
},
{
"family": "Strocchi",
"given": "Marina"
},
{
"family": "Montarello",
"given": "Natalie"
},
{
"family": "Lahoti",
"given": "Nishant"
},
{
"family": "Corrado",
"given": "Cesare"
},
{
"family": "Qayyum",
"given": "Abdul"
},
{
"family": "Rahmani",
"given": "Shahrokh"
},
{
"family": "Roney",
"given": "Caroline"
},
{
"family": "Plank",
"given": "Gernot"
},
{
"family": "Augustin",
"given": "Christoph"
},
{
"family": "Xu",
"given": "Hao"
},
{
"family": "Young",
"given": "Alistair"
},
{
"family": "Pathmanathan",
"given": "Pras"
},
{
"family": "Rajani",
"given": "Ronak"
},
{
"family": "Niederer",
"given": "Steven A"
}
],
"container-title-short": "PLoS Comput Biol",
"volume": "22",
"issue": "6",
"page": "e1014325",
"DOI": "10.1371/journal.pcbi.1014325",
"PMID": "42228749",
"PMCID": "PMC13252842",
"ISSN": "1553-734X",
"publisher": "PLOS",
"URL": "https://doi.org/10.1371/journal.pcbi.1014325",
"language": "en",
"issued": {
"date-parts": [
[
2026,
6,
2
]
]
}
}

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