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9/2/2026 6:21:36 AM | Browse: 0 | Download: 0
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Received |
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2026-04-17 01:30 |
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Peer-Review Started |
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2026-04-17 01:30 |
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First Decision by Editorial Office Director |
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2026-06-08 10:12 |
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Return for Revision |
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2026-06-08 10:12 |
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Revised |
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2026-06-21 20:59 |
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Publication Fee Transferred |
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Second Decision by Editor |
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2026-06-29 02:20 |
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Second Decision by Editor-in-Chief |
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Final Decision by Editorial Office Director |
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2026-06-29 08:40 |
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Articles in Press |
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2026-06-29 08:40 |
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Edit the Manuscript by Language Editor |
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Typeset the Manuscript |
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2026-08-27 09:29 |
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Publish the Manuscript Online |
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2026-09-02 06:21 |
| ISSN |
2220-3230 (online) |
| Open Access |
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial (CC BY-NC 4.0) license. No commercial re-use. See Permissions. Published by Baishideng Publishing Group Inc. |
| Copyright |
©Author(s) (or their employer(s)) 2026. No commercial re-use. See Permissions. Published by Baishideng Publishing Group Inc. |
| Article Reprints |
For details, please visit: http://www.wjgnet.com/bpg/gerinfo/247
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| Permissions |
For details, please visit: http://www.wjgnet.com/bpg/gerinfo/207
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| Publisher |
Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA |
| Website |
http://www.wjgnet.com |
| Category |
Transplantation |
| Manuscript Type |
Observational Study |
| Article Title |
Machine perfusion distribution across clinical phenotypes in kidney transplantation: A national cohort study using unsupervised clustering
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| Manuscript Source |
Invited Manuscript |
| All Author List |
Juanita Castellanos De Brigard, Ervandy Rangganata and Vassilios E Papalois |
| ORCID |
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| Funding Agency and Grant Number |
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| Corresponding Author |
Juanita Castellanos De Brigard, MD, Department of Surgery and Cancer, Imperial College London, South Kensington Campus, SW7 2AZ,, London W12 0HS, United Kingdom. j.castellanos-de-brigard24@imperial.ac.uk |
| Key Words |
Kidney transplant; Hypothermic machine perfusion; Normothermic machine perfusion; Machine learning; Clustering; Phenotype |
| Core Tip |
Using unsupervised clustering, we identified latent clinical phenotypes that demonstrated a non-random distribution of machine perfusion (MP) across the population. MP cases were notably concentrated within specific clusters, while the overall cluster structure remained mostly stable when focusing solely on MP recipients. This indicates that MP is integrated within the wider phenotypic landscape rather than creating distinct subgroups. Importantly, these phenotypes continued to hold outcome patterns within the MP cohort, although some varied among certain clusters. These findings suggest that MP interacts with the underlying profiles of recipients and donors, potentially altering outcome trajectories depending on the specific phenotype. |
| Publish Date |
2026-09-02 06:21 |
| Citation |
Castellanos De Brigard J, Rangganata E, Papalois VE. Machine perfusion distribution across clinical phenotypes in kidney transplantation: A national cohort study using unsupervised clustering. World J Transplant 2026; 16(3): 121821
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| URL |
https://www.wjgnet.com/2220-3230/full/v16/i3/121821.htm |
| DOI |
https://doi.org/10.5500/wjt.121821 |
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