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Rapid FTIR Spectral Fingerprinting of Kidney Allograft Perfusion Fluids Distinguishes DCD from DBD Donors

2025-11Artigo científico Acesso aberto
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dc.contributor.authorRamalhete, Luis
dc.contributor.authorRamalhete, Luís
dc.contributor.authorAraújo, Rúben
dc.contributor.authorAraújo, Rúben
dc.contributor.authorVieira, Miguel Bigotte
dc.contributor.authorBigotte Vieira, Miguel
dc.contributor.authorVigia, Emanuel
dc.contributor.authorVigia, Emanuel
dc.contributor.authorPena, Ana
dc.contributor.authorCarrelha, Sofia
dc.contributor.authorFerreira, Anibal
dc.contributor.authorFerreira, Anibal
dc.contributor.authorCalado, Cecília R.C.
dc.contributor.institutionNOVA Medical School|Faculdade de Ciências Médicas (NMS|FCM)
dc.contributor.pblMDPI - Multidisciplinary Digital Publishing Institute
dc.date.accessioned2025-12-05T21:16:47Z
dc.date.available2025-12-05T21:16:47Z
dc.date.issued2025-11
dc.descriptionFunding Information: This research was funded by Centro Cl\u00EDnico Acad\u00E9mico de Lisboa, grant number FF-CCAL.05.2025. Publisher Copyright: © 2025 by the authors.
dc.description.abstractBackground/Objectives: Rapid, objective phenotyping of donor kidneys is needed to support peri-implant decisions. Label-free Fourier-transform infrared (FTIR) spectroscopy of static cold-storage Celsior® perfusion fluid can discriminate kidneys recovered from donation after circulatory death (DCD) versus donation after brain death (DBD). Methods: Preservation solution from isolated kidney allografts (n = 10; 5 DCD/5 DBD) matched on demographics was analyzed in the Amide I and fingerprint regions. Several spectral preprocessing steps were applied, and feature extraction was based on the Fast Correlation-Based Filter. Support vector machines and Naïve Bayes were evaluated. Unsupervised structure was assessed based on cosine distance, multidimensional scaling, and hierarchical clustering. Two-dimensional correlation spectroscopy (2D-COS) was used to examine band co-variation. Results: Donor cohorts were well balanced, except for higher terminal serum creatinine in DCD. Quality metrics were comparable, indicating no systematic technical bias. In Amide I, derivatives improved classification, but performance remained modest (e.g., second derivative with feature selection yielded an area under the curve (AUC) of 0.88 and an accuracy of 0.90 for support vector machines; Naïve Bayes reached an AUC of 0.92 with an accuracy of 0.70). The fingerprint window was most informative. Naïve Bayes with second derivative plus feature selection identified bands at ~1202, ~1203, ~1342, and ~1413 cm−1 and achieved an AUC of 1.00 and an accuracy of 1.00. Unsupervised analyses showed coherent grouping in the fingerprint region, and 2D correlation maps indicated coordinated multi-band changes. Conclusions: Performance in this 10-sample pilot should be interpreted cautiously, as perfect leave-one-out cross-validation (LOOCV) estimates are vulnerable to overfitting. The findings are preliminary and hypothesis-generating, and they require confirmation in larger, multicenter cohorts with a pre-registered analysis pipeline and external validation.en
dc.description.versionpublishersversion
dc.description.versionpublished
dc.format.extent2661339
dc.identifier.doi10.3390/metabo15110702
dc.identifier.issn2218-1989
dc.identifier.otherPURE: 136843592
dc.identifier.otherPURE UUID: 440130ee-168a-4c52-98af-cced0251e5e4
dc.identifier.otherScopus: 105022878485
dc.identifier.urihttp://hdl.handle.net/10362/191576
dc.identifier.urlhttps://www.scopus.com/pages/publications/105022878485
dc.language.isoeng
dc.peerreviewedyes
dc.subjectDCD vs. DBD
dc.subjectFTIR spectroscopy
dc.subjectkidney transplantation
dc.subjectmachine learning
dc.subjectperfusion fluid
dc.subjectEndocrinology, Diabetes and Metabolism
dc.subjectBiochemistry
dc.subjectMolecular Biology
dc.subjectSDG 3 - Good Health and Well-being
dc.titleRapid FTIR Spectral Fingerprinting of Kidney Allograft Perfusion Fluids Distinguishes DCD from DBD Donorsen
dc.title.subtitleA Pilot Machine Learning Studyen
dc.typejournal article
degois.publication.issue11
degois.publication.titleMetabolites
degois.publication.volume15
dspace.entity.typePublication
person.familyNameRamalhete
person.familyNameAraújo
person.familyNameBigotte Vieira
person.familyNameVigia
person.familyNameFerreira
person.givenNameLuís
person.givenNameRúben
person.givenNameMiguel
person.givenNameEmanuel
person.givenNameAnibal
person.identifier2296066
person.identifier591378
person.identifier1068794
person.identifier.ciencia-idDF19-022D-AA10
person.identifier.ciencia-id9A18-BFDC-ED95
person.identifier.ciencia-idFF17-2C76-E2CC
person.identifier.ciencia-idE918-DF3E-1A17
person.identifier.ciencia-id2E10-C387-4982
person.identifier.orcid0000-0002-8911-3380
person.identifier.orcid0000-0002-9369-6486
person.identifier.orcid0000-0003-0528-2716
person.identifier.orcid0000-0002-4525-9062
person.identifier.orcid0000-0002-3300-6033
person.identifier.ridV-4629-2018
person.identifier.ridW-7385-2018
person.identifier.scopus-author-id57208672678
person.identifier.scopus-author-id57192837006
person.identifier.scopus-author-id7402999494
rcaap.rightsopenAccess
relation.isAuthorOfPublicationfe01f4ed-bfba-4de1-bc4e-cc8ddcca231f
relation.isAuthorOfPublication59fa57e8-0b38-403e-9cf2-af94a56f0bfd
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relation.isAuthorOfPublicationf22579ea-365b-421d-abfe-aaa271b7062b
relation.isAuthorOfPublication.latestForDiscoveryf22579ea-365b-421d-abfe-aaa271b7062b

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