Publicação
Prediction of chronic kidney disease based on simulated serum analysis by vibrational spectroscopy
| datacite.subject.fos | Engenharia e Tecnologia::Engenharia Química | |
| datacite.subject.sdg | 03:Saúde de Qualidade | |
| dc.contributor.author | Serrano, Diogo | |
| dc.contributor.author | Zoio, Paulo | |
| dc.contributor.author | Fonseca, Luís P. | |
| dc.contributor.author | Calado, Cecília | |
| dc.date.accessioned | 2026-10-20T10:43:49Z | |
| dc.date.available | 2026-10-20T10:43:49Z | |
| dc.date.issued | 2026-06-21 | |
| dc.description.abstract | The development of new technologies enabling rapid, frequent, and reagent-free moni toring of kidney function is recognized as being of paramount importance. In this work, mid-(MIR) and near-infrared (NIR) spectroscopy were compared for the prediction of key renal biomarkers—creatinine, urea and albumin—using 54 serum solutions mimicking the biochemical profiles of five stages of chronic kidney disease (CKD). MIR spectra were acquired in a high-throughput microplate platform after a simple dehydration step, while the NIR spectra were obtained directly from liquid serum using a fiber optic probe. After evaluating several spectral pre-processing methods and targeted spectral regions, excellent regression models (R2 > 0.9 for the best models) were obtained for the three biomarkers. MIR provided highly accurate urea predictions, whereas optimized NIR sub-regions en abled excellent estimation of creatinine and albumin. Both MIR and NIR, associated with supervised classification methods, enabled us to successfully distinguish healthy from dis eased profiles and to identify the diseases state with AUC > 0.93. These findings highlight the complementary value of MIR and NIR spectroscopy for kidney disease assessment and their potential integration into point-of-care diagnostic systems. | eng |
| dc.identifier.citation | Serrano, D., Zoio, P., Fonseca, L. P., & Calado, C. R. C. (2026). Prediction of Chronic Kidney Disease Based on Simulated Serum Analysis by Vibrational Spectroscopy. Biosensors, 16(6), 347. https://doi.org/10.3390/bios16060347 | |
| dc.identifier.doi | 10.3390/bios16060347 | |
| dc.identifier.eissn | 2079-6374 | |
| dc.identifier.uri | http://hdl.handle.net/10400.21/23233 | |
| dc.language.iso | eng | |
| dc.peerreviewed | yes | |
| dc.publisher | MDPI | |
| dc.relation.hasversion | https://www.mdpi.com/2079-6374/16/6/347 | |
| dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | |
| dc.subject | Kidney diseases | |
| dc.subject | IR spectroscopy | |
| dc.subject | Biomarkers | |
| dc.subject | Monitoring | |
| dc.title | Prediction of chronic kidney disease based on simulated serum analysis by vibrational spectroscopy | eng |
| dc.type | research article | |
| dspace.entity.type | Publication | |
| oaire.citation.issue | 6 | |
| oaire.citation.title | Biosensors | |
| oaire.citation.volume | 16 | |
| oaire.version | http://purl.org/coar/version/c_970fb48d4fbd8a85 | |
| person.familyName | Calado | |
| person.givenName | Cecília | |
| person.identifier | 130332 | |
| person.identifier.ciencia-id | 9418-E320-3177 | |
| person.identifier.orcid | 0000-0002-5264-9755 | |
| person.identifier.rid | E-2102-2014 | |
| person.identifier.scopus-author-id | 6603163260 | |
| relation.isAuthorOfPublication | e8577257-c64c-4481-9b2b-940fedb360cc | |
| relation.isAuthorOfPublication.latestForDiscovery | e8577257-c64c-4481-9b2b-940fedb360cc |