OSCR

Multimodal age-dependent diffusion-MRI analysis of the neocortex in a rat model of cortical dysplasia.

Code ↔ Paper

5 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 5 matches
  1. [1] § Methods › Diffusion-MRI analysis ↔ displasia_tckfixelsample_CSD.m, lines 83–190 · score 0.68 · absolute dot product, perpendicular tensor, segment, CSD, slice
  2. [2] § Methods › Diffusion-MRI analysis ↔ displasia_tckfixelsample.m, lines 94–201 · score 0.66 · absolute dot product, perpendicular tensor, segment, slice
  3. [3] § Methods › Diffusion-MRI analysis ↔ displasia_anat_preproc_and_register.sh, lines 40–127 · score 0.57 · pre processed, bias field, FSL
  4. [4] § Methods › Ex vivo assessment › Image processing and structure tensor analysis ↔ assessment/orientationj_python_port/distribution.ipynb, lines 1–53 · score 0.50 · dominant orientation, structure tensor, Gaussian, plugin, background, coherency
  5. [5] § Methods › Statistical analysis ↔ Data_visualization.R, lines 167–216 · score 0.50 · post hoc, predictor, LMM, fit, age, vertex

Paper

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The authors' code

MATLAB · 324 lines · 12 KB · no license · 1 match

  1. function VALUES = displasia_tckfixelsample(f_tck, f_PDD, f_nComp, ff_values_in, f_prefix)
  2. % VALUES = displasia_tckfixelsample(f_tck, f_PDD, f_nComp, ff_values_in, f_prefix)
  3. %
  4. % f_tck : Filename for the streamlines tck
  5. % f_PDD : Filename for the Principal Diffusion Directions file (MRDS, 4D).
  6. % f_nComp : Filename for the number of components (MRDS, 3D).
  7. % ff_values_in : Cell array of filenames of MRDS metrics to sample.
  8. % Each file should be MRDS, 4D.
  9. % f_prefix : Prefix for the output file names.
  10. %
  11. % Consider:
  12. % addpath('/home/lconcha/software/mrtrix_matlab/matlab');
  13. % addpath(genpath('/home/lconcha/software/dicm2nii-master'))
  14. % addpath /home/lconcha/software/Displasias/
  15. %
  16. % __________________________________________________________________________________
  17. % EXAMPLE:
  18. % f_tck = 'dwi/15/tck/dwi_l_out_resampled_native.tck';
  19. % f_PDD = 'dwi/dwi_MRDS_Diff_BIC_PDDs_CARTESIAN.nii.gz';
  20. % f_MRDS_ncomp = 'dwi/dwi_MRDS_Diff_BIC_NUM_COMP.nii.gz';
  21. % f_MRDS_FA = 'dwi/dwi_MRDS_Diff_BIC_FA.nii.gz';
  22. % f_MRDS_MD = 'dwi/dwi_MRDS_Diff_BIC_MD.nii.gz';
  23. % ff_values = {f_MRDS_FA, f_MRDS_MD};
  24. % f_prefix = '/tmp/prefix';
  25. %
  26. % VALUES = displasia_tckfixelsample(f_tck, f_PDD, f_MRDS_ncomp, ff_values, f_prefix);
  27. % __________________________________________________________________________________
  28. %
  29. % LU15 (0N(H4
  30. % INB-UNAM
  31. % Feb 2023
  32. % [email hidden]
  38. %% Load tck
  39. % tck_world = read_mrtrix_tracks(f_tck);
  40. % tmptck = [tempname '.tck'];
  41. % fprintf(1, '[INFO] Converting tck to voxel coordinates.\n')
  42. % systemcommand = ['export LD_LIBRARY_PATH="";tckconvert -scanner2voxel ' f_nComp ' ' f_tck ' ' tmptck ' -force -quiet'];
  43. % fprintf(1,' executing: %s\n',systemcommand);
  44. % fprintf('Loading %s\n',f_tck);
  45. % [status,result] = system(systemcommand);
  46. % tck = read_mrtrix_tracks(tmptck);
  47. % [status,result] = system(['rm -f ' tmptck]);
  48. [tck,tck_world] = displasia_load_tck_voxelcoords(f_tck,f_nComp);
  52. %% Load voxel data for PDD and nComp
  53. fprintf('Loading %s\n',f_PDD);
  54. [info,PDD] = displasia_load_nii(f_PDD);
  55. if ndims(PDD) ~= 4
  56. fprintf(1,'ERROR. %s does not have 4 dimensions. It should be a 4D volume with nvolumes = 3, 6, 9 or 12. Bye.\n',f_PDD);
  57. VALUES = [];
  58. return
  59. end
  60. fprintf('Loading %s\n',f_nComp);
  61. [info,nComp] = displasia_load_nii(f_nComp);
  62. if ndims(nComp) ~= 3
  63. fprintf(1,'ERROR. %s should have three dimensions Bye.\n',f_nComp);
  64. VALUES = [];
  65. return
  66. end
  70. %% Prepare tsfs
  71. %nFixels = size(PDD,4) ./ 3;
  72. nFixels = 3; % forcing 3 pixels
  74. tsf_par = tck_world;
  75. tsf_perp = tck_world;
  76. tsf_index_par = tck_world;
  77. tsf_ncomp = tck_world;
  78. tsf_dot_parallel2streamline = tck_world;
  79. tsf_dot_perp2slicenormal = tck_world;
  83. %% Identify parallel/perpendicular
  84. fprintf(1,'Identifying par/perp... ')
  85. for s = 1 : length(tck.data)
  86. if mod(s,10) == 0
  87. fprintf (1,'%d ',length(tck.data)-s);
  88. end
  89. this_streamline = tck.data{s};
  90. this_index_par = zeros(size(this_streamline,1),1);
  91. this_index_perp = zeros(size(this_streamline,1),1);
  92. this_nComp = zeros(size(this_streamline,1),1);
  93. this_dot_parallel2streamline = zeros(size(this_streamline,1),1);
  94. this_dot_perp2slicenormal = zeros(size(this_streamline,1),1);
  96. Rxyz1 = this_streamline(1,:);
  97. origin = [0 0 0];
  98. Rxyz3 = this_streamline(end,:);
  100. PLANE = createPlane(normalizeVector3d(Rxyz1), origin ,normalizeVector3d(Rxyz3)); % create a plane centered at origin
  101. NORMAL = planeNormal(PLANE);
  104. for p = 1 : size(this_streamline,1);
  105. Axyz = this_streamline(p,:);
  106. if p == size(this_streamline,1)
  107. Bxyz = this_streamline(p-1,:);
  108. else
  109. Bxyz = this_streamline(p+1,:);
  110. end
  112. normSegment = (Axyz-Bxyz) ./ norm(Axyz-Bxyz);
  113. mindices = Axyz +1; % matlab offset
  115. PDD1(1) = interp3(PDD(:,:,:,1),mindices(2), mindices(1), mindices(3)); % I cannot get interpn to work, so I do this stupid thing.
  116. PDD1(2) = interp3(PDD(:,:,:,2),mindices(2), mindices(1), mindices(3));
  117. PDD1(3) = interp3(PDD(:,:,:,3),mindices(2), mindices(1), mindices(3));
  119. PDD2(1) = interp3(PDD(:,:,:,4),mindices(2), mindices(1), mindices(3));
  120. PDD2(2) = interp3(PDD(:,:,:,5),mindices(2), mindices(1), mindices(3));
  121. PDD2(3) = interp3(PDD(:,:,:,6),mindices(2), mindices(1), mindices(3));
  123. PDD3(1) = interp3(PDD(:,:,:,7),mindices(2), mindices(1), mindices(3));
  124. PDD3(2) = interp3(PDD(:,:,:,8),mindices(2), mindices(1), mindices(3));
  125. PDD3(3) = interp3(PDD(:,:,:,9),mindices(2), mindices(1), mindices(3));
  127. normPDD1= PDD1./norm(PDD1);
  128. normPDD2= PDD2./norm(PDD2);
  129. normPDD3= PDD3./norm(PDD3);
  130. normPDDs = [normPDD1;normPDD2;normPDD3];
  132. dots(1) = dot(normSegment,normPDD1);
  133. dots(2) = dot(normSegment,normPDD2);
  134. dots(3) = dot(normSegment,normPDD3);
  137. thisnComp = interp3(nComp,mindices(2), mindices(1), mindices(3), 'nearest');
  138. if s==50 & p==4
  139. fprintf('Checking time... ncomp for streamline %d, point %d, is %d\n',s,p,thisnComp);
  140. end
  143. if thisnComp < 3
  144. dots(thisnComp+1:end) = NaN; % Remove PDDs if nCom does not support them.
  145. end
  147. if thisnComp > 1
  148. [themax,indexpar] = max(abs(dots));
  149. [themin,indexperp] = min(abs(dots));
  150. else
  151. [themax,indexpar] = max(abs(dots));
  152. themin = NaN;
  153. indexperp = 3;
  154. end
  159. this_index_par(p,1) = indexpar;
  160. this_index_perp(p,1) = indexperp;
  161. this_nComp(p,1) = thisnComp;
  163. % calculate the absolute dot products between:
  164. % Streamline to parallel tensor
  165. this_dot_parallel2streamline(p,1) = abs(dots(indexpar));
  166. % Slice normal to perpendicular tensor
  167. if thisnComp > 1
  168. this_dot_perp2slicenormal(p,1) = abs(dot(normPDDs(indexperp,:),NORMAL));
  169. else
  170. this_dot_perp2slicenormal(p,1) = -999; % cannot calculate this value if we only found one tensor. -999 is a placeholder for trash.
  171. end
  176. end
  177. try
  178. tsf_index_par.data{s} = this_index_par;
  179. tsf_index_perp.data{s} = this_index_perp;
  180. tsf_ncomp.data{s} = this_nComp;
  181. tsf_dot_parallel2streamline.data{s} = this_dot_parallel2streamline;
  182. tsf_dot_perp2slicenormal.data{s} = this_dot_perp2slicenormal;
  183. VALUES.dot_parallel2streamline(s,:) = this_dot_parallel2streamline;
  184. VALUES.dot_perp2slicenormal(s,:) = this_dot_perp2slicenormal;
  185. VALUES.ncomp{s} = this_nComp;
  186. catch
  187. fprintf(1,'Hey!')
  188. end
  189. end
  190. fprintf (1,'\nFinished identifying par/perp\n',s);
  194. %% Do the sampling
  195. for i = 1 : length(ff_values_in)
  196. f_values_in = ff_values_in{i};
  197. fprintf('Loading %s ... ',f_values_in);
  198. %V = niftiread(f_values_in);
  199. [info,V] = displasia_load_nii(f_values_in);
  200. fprintf(1,'\n');
  201. info = niftiinfo(f_values_in);
  202. [fold,fname,ext] = fileparts(info.Filename);
  203. varName = strrep(fname,'.nii','');
  204. if ndims(V) ~= 4
  205. fprintf(1,'ERROR. %s does not have 4 dimensions. This script can only handle 4D. Bye.\n',f_values_in);
  206. VALUES = [];
  207. return
  208. end
  209. fprintf(1,'[INFO] Sampling %s \n', f_values_in)
  210. for s = 1 : length(tck.data)
  211. if mod(s,10) == 0
  212. fprintf (1,'%d ',length(tck.data)-s);
  213. end
  214. this_streamline = tck.data{s};
  215. this_data_par = zeros(size(this_streamline,1),1);
  216. this_data_perp = zeros(size(this_streamline,1),1);
  217. for p = 1 : size(this_streamline,1);
  218. xyz = this_streamline(p,:);
  220. % vox_indices = [xyz 1] * inv(info.Transform.T);
  221. % vox_indices = vox_indices(1:3);
  222. % mindices = vox_indices + 1;
  223. % matlab_indices = uint8(vox_indices + 1);
  224. mindices = xyz +1;
  227. %thisnComp = interp3(nComp,mindices(2), mindices(1), mindices(3), 'nearest');
  228. thisnComp = tsf_ncomp.data{s}(p);
  229. indexpar = tsf_index_par.data{s}(p);
  230. indexperp = tsf_index_perp.data{s}(p);
  232. vals(1) = interp3(V(:,:,:,1),mindices(2), mindices(1), mindices(3));
  233. vals(2) = interp3(V(:,:,:,2),mindices(2), mindices(1), mindices(3));
  234. vals(3) = interp3(V(:,:,:,3),mindices(2), mindices(1), mindices(3));
  236. if thisnComp < 3
  237. vals(thisnComp+1:end) = -1; % remove values if nComp does not support them.
  238. end
  240. if max(vals) < 0 && thisnComp > 0
  241. fprintf(1,'WTF? All values are invalid!')
  242. fprintf(1,'Streamline %d, point %d\n',s,p);
  243. disp(vals)
  244. end
  246. val_par = vals(indexpar);
  247. val_perp = vals(indexperp);
  249. this_data_par(p,1) = val_par;
  250. this_data_perp(p,1) = val_perp;
  252. if s==3 & p==1
  253. fprintf(1,'checking time\n');
  254. fprintf(1,' Streamline %d, point %d. Parallel value is %1.3g, Perp value is %1.3g\n',s,p,val_par,val_perp);
  256. end
  257. end
  259. tsf_par.data{s} = this_data_par;
  260. tsf_perp.data{s} = this_data_perp;
  262. end
  263. fprintf (1,'\nFinished sampling %s\n',fname);
  265. %%%%% write per-value tsf files
  266. f_tsf_par_out = [f_prefix '_' varName '_par.tsf'];
  267. f_tsf_perp_out = [f_prefix '_' varName '_perp.tsf'];
  269. fprintf(1,' [INFO] Writing tsf_par: %s\n',f_tsf_par_out);
  270. write_mrtrix_tsf(tsf_par,f_tsf_par_out);
  271. fprintf(1,' [INFO] Writing tsf_perp: %s\n',f_tsf_perp_out);
  272. write_mrtrix_tsf(tsf_perp,f_tsf_perp_out);
  276. if regexp(varName,'^[0-9]')
  277. varName = ['x_' varName];
  278. end
  279. VALUES.par.(varName) = tsf_par.data;
  280. VALUES.perp.(varName) = tsf_perp.data;
  281. end
  284. %%%%% writer overall tsf files
  285. fprintf(1,'[INFO] Writing tsf files\n');
  286. f_tsf_dot_parallel2streamline = [f_prefix '_dot_parallel2streamline.tsf'];
  287. f_tsf_dot_perp2slicenormal = [f_prefix '_dot_perp2slicenormal.tsf'];
  288. f_tsf_ncomp = [f_prefix '_ncomp.tsf'];
  289. fprintf(1,' [INFO] Writing tsf_dot_parallel2streamline: %s\n',f_tsf_dot_parallel2streamline);
  290. write_mrtrix_tsf(tsf_dot_parallel2streamline,f_tsf_dot_parallel2streamline);
  291. fprintf(1,' [INFO] Writing tsf_dot_perp2slicenormal: %s\n',f_tsf_dot_perp2slicenormal);
  292. write_mrtrix_tsf(tsf_dot_perp2slicenormal,f_tsf_dot_perp2slicenormal);
  293. fprintf(1,' [INFO] Writing tsf_ncomp: %s\n',f_tsf_ncomp);
  294. write_mrtrix_tsf(tsf_ncomp,f_tsf_ncomp);
  295. f_tsf_par_index_out = [f_prefix '_par_index.tsf'];
  296. fprintf(1,' [INFO] Writing tsf_index_par: %s\n',f_tsf_par_index_out);
  297. write_mrtrix_tsf(tsf_index_par,f_tsf_par_index_out)
  299. fprintf(1,'[INFO] Writing text files\n');
  300. varNames = fieldnames(VALUES.par);
  301. for n = 1 : length(varNames)
  302. thisVarName = varNames{n};
  303. f_txt = [f_prefix '_' thisVarName '_par.txt'];
  304. thismat = cell2mat(VALUES.par.(thisVarName));
  305. fprintf(1,' [INFO] Writing %s\n',f_txt);
  306. save(f_txt,'thismat','-ascii');
  307. f_txt = [f_prefix '_' thisVarName '_perp.txt'];
  308. thismat = cell2mat(VALUES.perp.(thisVarName));
  309. fprintf(1,' [INFO] Writing %s\n',f_txt);
  310. save(f_txt,'thismat','-ascii');
  311. end
  312. f_txt = [f_prefix '_dot_parallel2streamline.txt'];
  313. thismat = VALUES.dot_parallel2streamline;
  314. fprintf(1,' [INFO] Writing %s\n',f_txt);
  315. save(f_txt,'thismat','-ascii');
  316. f_txt = [f_prefix '_dot_perp2slicenormal.txt'];
  317. thismat = VALUES.dot_perp2slicenormal;
  318. fprintf(1,' [INFO] Writing %s\n',f_txt);
  319. save(f_txt,'thismat','-ascii');
  320. f_txt = [f_prefix '_nComp.txt'];
  321. thismat = cell2mat(VALUES.ncomp);
  322. fprintf(1,' [INFO] Writing %s\n',f_txt);
  323. save(f_txt,'thismat','-ascii');

displasia_tckfixelsample_CSD.m at commit 243c6a9, no license · at the source

Overview

Authors: Paulina J Villaseñor1,2, Hiram Luna-Munguía1, Alonso Ramirez-Manzanares3, Ricardo Coronado-Leija4, Luis Concha1
ORCID iDs: Paulina J Villaseñor, Hiram Luna-Munguía, Alonso Ramirez-Manzanares, Ricardo Coronado-Leija, Luis Concha
  1. Instituto de Neurobiología, Universidad Nacional Autónoma de México Campus Juriquilla, Querétaro, Querétaro Mexico
  2. Division of Neuroscience, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK
  3. Centro de Investigación en Matemáticas, A.C., Guanajuato, Mexico
  4. Bernard and Irene Schwartz Center for Biomedical Imaging, New York University School of Medicine, New York, USA
Institutions: University of Manchester (United Kingdom); Universidad Nacional Autónoma de México (Mexico); Mathematics Research Center (Mexico); New York University (United States)
Journal: Scientific reports, volume 16, issue 1, article 25650
Dates: received 3 February 2026; accepted 28 April 2026; published online 5 June 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1038/s41598-026-51531-w · PMID 42243246 · PMCID PMC13478594 · OpenAlex W7163544105
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: structural MRI / diffusion (modality), rat (organism)
Methods: Statistics, Physiology & signal measures, fMRI & imaging
Keywords: Anatomy, Biological techniques, Engineering, Neuroscience
MeSH: Diffusion Magnetic Resonance Imaging*, Malformations of Cortical Development*, Neocortex*, Aging, Animals, Disease Models, Animal, Female, Rats (* major topic)
Topic: Advanced Neuroimaging Techniques and Applications (Radiology, Nuclear Medicine and Imaging, Medicine), according to OpenAlex
Funding: Dirección General de Asuntos del Personal Académico, Universidad Nacional Autónoma de México (IN213423, IN2044720, IA200621, IN211326); Secretaría de Ciencia, Humanidades, Tecnología e Innovación (798166, CF-218-2023)
Citations: not cited yet (Europe PMC); 92 references in the paper

Abstract

The abstract is not reproduced here: the paper's license (CC BY-NC-ND) does not allow it. Read it in the paper, at the publisher or on Europe PMC.

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Biomedical-Imaging-Group/OrientationJ

License: GPL-3.0
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 91fb42704f9eca1358fd84531970c61add099d68, 27 September 2026
Languages: Java (104), Jupyter (10), Python (7), JavaScript (3)
Size: 285 files, 124 scripts
Software Heritage: not archived
Found in: the text, “Image processing and structure tensor analysis”
Holds: README, license file, environment (docs/requirements-docs.txt), continuous integration, documentation, 10 notebooks
Not found: CITATION.cff, tests
Tools: NumPy (10 files), tifffile (9 files), Matplotlib (8 files), pandas (5 files), SciPy (3 files), scikit-image (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
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At the source: github.com/Biomedical-Imaging-Group/OrientationJ

paulinajv/Vertex-wise-LMM-analysis

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Evidence: files inventoried
Commit: b84c02b30b302fbec7630d402c4d4d7fc303c36a, 24 March 2026
Languages: R (6)
Size: 15 files, 6 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: tidyverse (6 files), emmeans (3 files), lme4 (3 files), ggplot2 (2 files), caret (1 file), easystats (1 file), lmerTest (1 file)
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At the source: github.com/paulinajv/Vertex-wise-LMM-analysis

lconcha/Displasias

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 243c6a9d4a00b53166ef945ce656eb2000fd5a42, 11 March 2026
Languages: MATLAB (79), Shell (38), Python (6), R (2)
Size: 149 files, 125 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README, environment (environment.yml)
Not found: license file, CITATION.cff, tests, continuous integration, documentation
Tools: MRtrix3 (25 files), Statistics and Machine Learning Toolbox (16 files), Image Processing Toolbox (13 files), NumPy (5 files), NiBabel (4 files), FSL (3 files), SciPy (3 files), DIPY (2 files), Matplotlib (1 file), NetworkX (1 file), reticulate (1 file), scikit-image (1 file), scikit-learn (1 file), shadedErrorBar (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
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126 files
At the source: github.com/lconcha/Displasias

The paper's code and data availability statement is in the Data section.

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Data

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Read it in the paper: doi.org/10.1038/s41598-026-51531-w.

Versions

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Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 5 authors, 4 keywords, 8 MeSH terms, 2 funders, 89 references.

Cite

This paper

Villaseñor, P. J., Luna-Munguía, H., Ramirez-Manzanares, A., Coronado-Leija, R., & Concha, L. (2026). Multimodal age-dependent diffusion-MRI analysis of the neocortex in a rat model of cortical dysplasia. Scientific reports, 16(1), 25650. https://doi.org/10.1038/s41598-026-51531-w

BibTeX

@article{villasenor2026multimodal,
author = {Villaseñor, Paulina J and Luna-Munguía, Hiram and Ramirez-Manzanares, Alonso and Coronado-Leija, Ricardo and Concha, Luis},
title = {{Multimodal age-dependent diffusion-MRI analysis of the neocortex in a rat model of cortical dysplasia}},
journal = {Scientific reports},
year = {2026},
month = jun,
volume = {16},
number = {1},
pages = {25650},
publisher = {Nature Publishing Group},
issn = {2045-2322},
doi = {10.1038/s41598-026-51531-w},
url = {https://doi.org/10.1038/s41598-026-51531-w},
pmid = {42243246},
pmcid = {PMC13478594}
}

RIS

TY - JOUR
AU - Villaseñor, Paulina J
AU - Luna-Munguía, Hiram
AU - Ramirez-Manzanares, Alonso
AU - Coronado-Leija, Ricardo
AU - Concha, Luis
TI - Multimodal age-dependent diffusion-MRI analysis of the neocortex in a rat model of cortical dysplasia
T2 - Scientific reports
J2 - Sci Rep
PY - 2026
DA - 2026/06/05
VL - 16
IS - 1
SP - 25650
SN - 2045-2322
PB - Nature Publishing Group
DO - 10.1038/s41598-026-51531-w
UR - https://doi.org/10.1038/s41598-026-51531-w
LA - en
ER -

CSL-JSON

{
"id": "10.1038/s41598-026-51531-w",
"type": "article-journal",
"title": "Multimodal age-dependent diffusion-MRI analysis of the neocortex in a rat model of cortical dysplasia",
"container-title": "Scientific reports",
"author": [
{
"family": "Villaseñor",
"given": "Paulina J"
},
{
"family": "Luna-Munguía",
"given": "Hiram"
},
{
"family": "Ramirez-Manzanares",
"given": "Alonso"
},
{
"family": "Coronado-Leija",
"given": "Ricardo"
},
{
"family": "Concha",
"given": "Luis"
}
],
"container-title-short": "Sci Rep",
"volume": "16",
"issue": "1",
"page": "25650",
"DOI": "10.1038/s41598-026-51531-w",
"PMID": "42243246",
"PMCID": "PMC13478594",
"ISSN": "2045-2322",
"publisher": "Nature Publishing Group",
"URL": "https://doi.org/10.1038/s41598-026-51531-w",
"language": "en",
"issued": {
"date-parts": [
[
2026,
6,
5
]
]
}
}

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