{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,22]],"date-time":"2026-09-22T20:13:25Z","timestamp":1790108005706,"version":"4.0.1"},"reference-count":68,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2026,6,27]],"date-time":"2026-06-27T00:00:00Z","timestamp":1782518400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Medical Sciences"],"abstract":"<jats:p>Background: Rejection remains a major barrier to long-term kidney allograft survival, and pre-transplant risk stratification remains incomplete. This study evaluated whether pre-transplant serum Fourier-transform infrared (FTIR) spectra, analyzed using machine learning methods, could identify kidney transplant recipients at increased risk of subsequent biopsy-proven rejection. Methods: In this retrospective single-center study, 80 pre-transplant serum samples collected on the day of transplantation were initially evaluated; after spectral quality control, 79 samples were retained for analysis. FTIR spectra were acquired in transmission mode and analyzed in the 600\u20131900 cm\u22121 and 2800\u20133400 cm\u22121 regions. Multiple preprocessing strategies were assessed, including Rubber Band baseline correction, vector normalization, and first- and second-derivative transformation, with and without normalization. Na\u00efve Bayes classifiers with Leave-One-Out Cross-Validation and Fast Correlation-Based Filter feature selection were applied. Results: Exploratory analysis showed broad overlap between groups, indicating a subtle multivariate spectral signal. In the initial exploratory workflow, classifier performance depended strongly on preprocessing and feature selection. Because non-nested feature selection may produce optimistic estimates, the main supervised analysis was repeated using FCBF nested within each LOOCV training fold. The best-performing nested model was obtained using second derivative transformation followed by normalization in the combined 600\u20131900 and 2800\u20133400 cm\u22121 regions, achieving an AUC of 0.837, accuracy of 0.747, sensitivity of 0.675, specificity of 0.821, balanced accuracy of 0.748, and F1-score of 0.730. Permutation testing with 1000 label-randomized repetitions supported performance above chance expectation, with no permuted model reaching the observed AUC (empirical p = 0.000999). Conclusions: Pre-transplant serum FTIR spectroscopy combined with leakage-aware nested machine learning analysis identified an internally validated spectral signal associated with subsequent biopsy-proven rejection. These findings support FTIR as a promising complementary and hypothesis-generating approach for pre-transplant biochemical risk phenotyping, requiring external multicenter validation before clinical application.<\/jats:p>","DOI":"10.3390\/medsci14030353","type":"journal-article","created":{"date-parts":[[2026,6,29]],"date-time":"2026-06-29T05:49:33Z","timestamp":1782712173000},"page":"353","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Risk Phenotyping Before Graft Implantation: FTIR Spectroscopy and Machine Learning for Complementary Risk Stratification in Kidney Transplantation"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8911-3380","authenticated-orcid":false,"given":"Luis","family":"Ramalhete","sequence":"first","affiliation":[{"name":"Blood and Transplantation Center of Lisbon, Instituto Portugu\u00eas do Sangue e da Transplanta\u00e7\u00e3o, Alameda das Linhas de Torres, No. 117, 1769-001 Lisbon, Portugal"},{"name":"NOVA Medical School, Faculdade de Ci\u00eancias M\u00e9dicas, Universidade NOVA de Lisboa, 1169-056 Lisbon, Portugal"},{"name":"iNOVA4Health\u2014Advancing Precision Medicine, N\u00facleo de Investiga\u00e7\u00e3o em Doen\u00e7as Renais, NOVA Medical School, Faculdade de Ci\u00eancias M\u00e9dicas, Universidade NOVA de Lisboa, 1169-056 Lisbon, Portugal"},{"name":"LBS\u2014Lisbon Business & Government School, Rua de S\u00e3o Bernardo, 34-A, 1200-427 Lisbon, Portugal"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9369-6486","authenticated-orcid":false,"given":"R\u00faben","family":"Ara\u00fajo","sequence":"additional","affiliation":[{"name":"NOVA Medical School, Faculdade de Ci\u00eancias M\u00e9dicas, Universidade NOVA de Lisboa, 1169-056 Lisbon, Portugal"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4525-9062","authenticated-orcid":false,"given":"Emanuel","family":"Vigia","sequence":"additional","affiliation":[{"name":"NOVA Medical School, Faculdade de Ci\u00eancias M\u00e9dicas, Universidade NOVA de Lisboa, 1169-056 Lisbon, Portugal"},{"name":"iNOVA4Health\u2014Advancing Precision Medicine, N\u00facleo de Investiga\u00e7\u00e3o em Doen\u00e7as Renais, NOVA Medical School, Faculdade de Ci\u00eancias M\u00e9dicas, Universidade NOVA de Lisboa, 1169-056 Lisbon, Portugal"},{"name":"LBS\u2014Lisbon Business & Government School, Rua de S\u00e3o Bernardo, 34-A, 1200-427 Lisbon, Portugal"},{"name":"Hepatobiliopancreatic and Transplantation Center, Curry Cabral Hospital, Unidade Local de Sa\u00fade de S\u00e3o Jos\u00e9, R. da Benefic\u00eancia 8, 1050-099 Lisbon, Portugal"},{"name":"Centro Cl\u00ednico Acad\u00e9mico de Lisboa, Faculdade de Ci\u00eancias M\u00e9dicas, Universidade NOVA de Lisboa, 1169-056 Lisbon, Portugal"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0528-2716","authenticated-orcid":false,"given":"Miguel Bigotte","family":"Vieira","sequence":"additional","affiliation":[{"name":"NOVA Medical School, Faculdade de Ci\u00eancias M\u00e9dicas, Universidade NOVA de Lisboa, 1169-056 Lisbon, Portugal"},{"name":"iNOVA4Health\u2014Advancing Precision Medicine, N\u00facleo de Investiga\u00e7\u00e3o em Doen\u00e7as Renais, NOVA Medical School, Faculdade de Ci\u00eancias M\u00e9dicas, Universidade NOVA de Lisboa, 1169-056 Lisbon, Portugal"},{"name":"Centro Cl\u00ednico Acad\u00e9mico de Lisboa, Faculdade de Ci\u00eancias M\u00e9dicas, Universidade NOVA de Lisboa, 1169-056 Lisbon, Portugal"},{"name":"Nephrology, Hospital Curry Cabral, Unidade Local de Sa\u00fade de S\u00e3o Jos\u00e9, R. da Benefic\u00eancia 8, 1050-099 Lisbon, Portugal"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3300-6033","authenticated-orcid":false,"given":"Anibal","family":"Ferreira","sequence":"additional","affiliation":[{"name":"NOVA Medical School, Faculdade de Ci\u00eancias M\u00e9dicas, Universidade NOVA de Lisboa, 1169-056 Lisbon, Portugal"},{"name":"iNOVA4Health\u2014Advancing Precision Medicine, N\u00facleo de Investiga\u00e7\u00e3o em Doen\u00e7as Renais, NOVA Medical School, Faculdade de Ci\u00eancias M\u00e9dicas, Universidade NOVA de Lisboa, 1169-056 Lisbon, Portugal"},{"name":"Centro Cl\u00ednico Acad\u00e9mico de Lisboa, Faculdade de Ci\u00eancias M\u00e9dicas, Universidade NOVA de Lisboa, 1169-056 Lisbon, Portugal"},{"name":"Nephrology, Hospital Curry Cabral, Unidade Local de Sa\u00fade de S\u00e3o Jos\u00e9, R. da Benefic\u00eancia 8, 1050-099 Lisbon, Portugal"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5264-9755","authenticated-orcid":false,"given":"Cecilia R. C.","family":"Calado","sequence":"additional","affiliation":[{"name":"ISEL\u2014Instituto Superior de Engenharia de Lisboa, Instituto Polit\u00e9cnico de Lisboa, Rua Conselheiro Em\u00eddio Navarro 1, 1959-007 Lisbon, Portugal"},{"name":"Institute for Bioengineering and Biosciences (iBB), The Associate Laboratory Institute for Health and Bioeconomy-i4HB, Instituto Superior T\u00e9cnico (IST), Universidade de Lisboa (UL), Av. Rovisco Pais, 1049-001 Lisbon, Portugal"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,6,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"338","DOI":"10.1016\/j.ajt.2023.10.016","article-title":"The Banff 2022 Kidney Meeting Report: Reappraisal of Microvascular Inflammation and the Role of Biopsy-Based Transcript Diagnostics","volume":"24","author":"Naesens","year":"2024","journal-title":"Am. J. Transplant."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1097\/TP.0000000000004624","article-title":"The Molecular Phenotype of Kidney Transplants: Insights from the MMDx Project","volume":"108","author":"Halloran","year":"2024","journal-title":"Transplantation"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"sfaf196","DOI":"10.1093\/ckj\/sfaf196","article-title":"Major Complications of Percutaneous Native and Transplant Kidney Biopsy: A Complete 10-Year National Prospective Cohort Study","volume":"18","author":"Geddes","year":"2025","journal-title":"Clin. Kidney J."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"364","DOI":"10.1053\/j.ajkd.2024.07.018","article-title":"Biomarkers of Rejection in Kidney Transplantation","volume":"85","author":"Westphal","year":"2025","journal-title":"Am. J. Kidney Dis."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Ramalhete, L., Ara\u00fajo, R., Vieira, M.B., Vigia, E., Calado, C.R.C., and Ferreira, A. (2026). Rejection-Focused Precision Medicine in Kidney Transplantation: Biology, Biomarkers, and Artificial Intelligence. Life, 16.","DOI":"10.3390\/life16040674"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Strader, M., and Kant, S. (2025). Novel Biomarkers for Rejection in Kidney Transplantation: A Comprehensive Review. J. Clin. Med., 14.","DOI":"10.3390\/jcm14155489"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Furian, L., Bestard, O., Budde, K., Cozzi, E., Diekmann, F., Mamode, N., Naesens, M., Pengel, L.H.M., Schwartz Sorensen, S., and Vistoli, F. (2024). European Consensus on the Management of Sensitized Kidney Transplant Recipients: A Delphi Study. Transpl. Int., 37.","DOI":"10.3389\/ti.2024.12475"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Kang, Z.-Y., Han, X.-Y., Liu, C., Liu, W., and Li, D.-H. (2025). Association of Donor-Specific Antibodies with Adverse Outcomes in Solid Organ Transplantation: A Systematic Review and Meta-Analysis of 69 Studies. Front. Immunol., 16.","DOI":"10.3389\/fimmu.2025.1633853"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Betjes, M.G.H., Kal-van Gestel, J.A., Roelen, D., Kho, M.M.L., Heidt, S., de Weerd, A.E., and van de Wetering, J. (2025). Increasing Donor Kidney Age Significantly Aggravates the Negative Effect of Pretransplant Donor-Specific Anti-HLA Antibodies on Kidney Graft Survival. Front. Immunol., 16.","DOI":"10.3389\/fimmu.2025.1574324"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Deng, Y., Frischnknecht, L., Wehmeier, C., de Rougemont, O., Villard, J., Ferrari-Lacraz, S., Golshayan, D., Gannag\u00e9, M., Binet, I., and Wirthmueller, U. (2024). Pre-Transplant Donor Specific Antibodies in ABO Incompatible Kidney Transplantation\u2014Data from the Swiss Transplant Cohort Study. Front. Immunol., 15.","DOI":"10.3389\/fimmu.2024.1355128"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Mattoo, A., Jaffe, I.S., Keating, B., Montgomery, R.A., and Mangiola, M. (2024). Improving Long-Term Kidney Allograft Survival by Rethinking HLA Compatibility: From Molecular Matching to Non-HLA Genes. Front. Genet., 15.","DOI":"10.3389\/fgene.2024.1442018"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"795","DOI":"10.1016\/j.kint.2024.07.030","article-title":"Transfusion-Induced HLA Sensitization in Wait-List Patients and Kidney Transplant Recipients","volume":"106","author":"Willicombe","year":"2024","journal-title":"Kidney Int."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Frischknecht, L., Deng, Y., Wehmeier, C., de Rougemont, O., Villard, J., Ferrari-Lacraz, S., Golshayan, D., Gannag\u00e9, M., Binet, I., and Wirthmueller, U. (2022). The Impact of Pre-Transplant Donor Specific Antibodies on the Outcome of Kidney Transplantation\u2014Data from the Swiss Transplant Cohort Study. Front. Immunol., 13.","DOI":"10.3389\/fimmu.2022.1005790"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"2755","DOI":"10.1111\/tri.14119","article-title":"Pre-Transplant Donor-Specific HLA Antibodies and Risk for Poor First-Year Renal Transplant Outcomes: Results from the Swiss Transplant Cohort Study","volume":"34","author":"Wehmeier","year":"2021","journal-title":"Transpl. Int."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"de Rougemont, O., Deng, Y., Frischknecht, L., Wehmeier, C., Villard, J., Ferrari-Lacraz, S., Golshayan, D., Gannag\u00e9, M., Binet, I., and Wirthmueller, U. (2023). Donation Type and the Effect of Pre-Transplant Donor Specific Antibodies\u2014Data from the Swiss Transplant Cohort Study. Front. Immunol., 14.","DOI":"10.3389\/fimmu.2023.1104371"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Tramper, T., Roelen, D.L., Brand-Schaaf, S.H., Kal-van Gestel, J.A., Kho, M.M.L., Reinders, M.E.J., Roodnat, J.I., van de Wetering, J., Betjes, M.G.H., and de Weerd, A.E. (2023). The Detrimental Effect of Donor-Specific Antibodies Is Irrespective of Its Level in Highly-Immunized Living Donor Kidney Transplant Recipients: A Case-Control Series. Front. Immunol., 13.","DOI":"10.3389\/fimmu.2022.1093359"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Senev, A., Ray, B., Lerut, E., Hariharan, J., Heylen, C., Kuypers, D., Sprangers, B., Emonds, M.-P., and Naesens, M. (2022). The Pre-Transplant Non-HLA Antibody Burden Associates with the Development of Histology of Antibody-Mediated Rejection After Kidney Transplantation. Front. Immunol., 13.","DOI":"10.3389\/fimmu.2022.809059"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"2320","DOI":"10.1038\/s41591-024-03087-3","article-title":"Cell-Free DNA for the Detection of Kidney Allograft Rejection","volume":"30","author":"Aubert","year":"2024","journal-title":"Nat. Med."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"943","DOI":"10.1016\/j.kint.2024.07.027","article-title":"Evaluation of Non-Invasive Biomarkers of Kidney Allograft Rejection in a Prospective Multicenter Unselected Cohort Study (EU-TRAIN)","volume":"106","author":"Goutaudier","year":"2024","journal-title":"Kidney Int."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Punukollu, R., Parajuli, S., Chaudhari, H., and Mour, G. (2025). Genomic and Biomarker Innovations in Predicting Kidney Transplant Rejection. J. Clin. Med., 14.","DOI":"10.3390\/jcm14113642"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Lazarou, C., Moysidou, E., Christodoulou, M., Lioulios, G., Sampani, E., Dimitriadis, C., Fylaktou, A., and Stangou, M. (2025). Non-Invasive Biomarkers for Early Diagnosis of Kidney Allograft Dysfunction: Current and Future Applications in the Era of Precision Medicine. Medicina, 61.","DOI":"10.20944\/preprints202501.0181.v1"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Zielinski, D., Goutaudier, V., Sablik, M., Divard, G., Aubert, O., Piedrafita, A., Mezine, F., Dagobert, J., Certain, A., and Robin, B. (Am. J. Transplant., 2026). Corrigendum to \u201cMolecular Diagnosis of Kidney Allograft Rejection Based on the Banff Human Organ Transplant Gene Panel: A Multicenter International Study\u201d [American Journal of Transplantation 25 (2025) 1631\u20131642], Am. J. Transplant., in press.","DOI":"10.1016\/j.ajt.2026.01.012"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1534","DOI":"10.34067\/KID.0000000000000549","article-title":"Clinical Value of Peripheral Blood Gene Expression Profile and Donor-Derived Cellfree DNA for Identifying Persistent Rejection","volume":"5","author":"Heilman","year":"2024","journal-title":"Kidney360"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"20357","DOI":"10.1038\/s41598-024-70390-x","article-title":"Transforming Kidney Transplant Monitoring with Urine CXCL9 and CXCL10: Practical Clinical Implementation","volume":"14","author":"Tinel","year":"2024","journal-title":"Sci. Rep."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"e15314","DOI":"10.1111\/ctr.15314","article-title":"Extracellular Vesicles as Potential Diagnostic Markers for Kidney Allograft Rejection","volume":"38","author":"Thongwitokomarn","year":"2024","journal-title":"Clin. Transplant."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.pathol.2023.10.004","article-title":"Exosomes and Microvesicles in Kidney Transplantation: The Long Road from Trash to Gold","volume":"56","author":"Ramalhete","year":"2024","journal-title":"Pathology"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1007\/s40472-025-00501-8","article-title":"Non-Invasive Biomarkers for Kidney Transplant Monitoring: A Comprehensive Review","volume":"13","author":"Damas","year":"2026","journal-title":"Curr. Transplant. Rep."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Ramalhete, L.M., Ara\u00fajo, R., Ferreira, A., and Calado, C.R.C. (2022). Proteomics for Biomarker Discovery for Diagnosis and Prognosis of Kidney Transplantation Rejection. Proteomes, 10.","DOI":"10.3390\/proteomes10030024"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Sebastian, A., Silvy, M., Coiffard, B., Reynaud-Gaubert, M., Magdinier, F., Chiaroni, J., Picard, C., and Pedini, P. (2024). A Review of Cell-Free DNA and Epigenetics for Non-Invasive Diagnosis in Solid Organ Transplantation. Front. Transplant., 3.","DOI":"10.3389\/frtra.2024.1474920"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Tharmaraj, D., Mulley, W.R., and Dendle, C. (2024). Current and Emerging Tools for Simultaneous Assessment of Infection and Rejection Risk in Transplantation. Front. Immunol., 15.","DOI":"10.3389\/fimmu.2024.1490472"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"122","DOI":"10.1016\/j.canlet.2020.02.020","article-title":"Biofluid Diagnostics by FTIR Spectroscopy: A Platform Technology for Cancer Detection","volume":"477","author":"Sala","year":"2020","journal-title":"Cancer Lett."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"e202000025","DOI":"10.1002\/tbio.202000025","article-title":"Fourier-Transform Infrared Spectroscopy of Biofluids: A Practical Approach","volume":"3","author":"Theakstone","year":"2021","journal-title":"Transl. Biophotonics"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Kowal, A., Jakubczyk, P., Bal, W., Piasecka, Z., Szuler, K., \u0141ach, K., Sopel, K., Cebulski, J., and Chaber, R. (2025). Serum Fourier-Transform Infrared Spectroscopy with Machine Learning for Screening of Pediatric Acute Lymphoblastic Leukemia: A Proof-of-Concept Study. Cancers, 17.","DOI":"10.3390\/cancers17213548"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"e70267","DOI":"10.1002\/jbio.70267","article-title":"Urine-Based FTIR Spectroscopy and Machine Learning Enable Non-Invasive Kidney Cancer Detection","volume":"19","author":"Mitura","year":"2026","journal-title":"J. Biophotonics"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"237","DOI":"10.1016\/j.pdpdt.2018.09.008","article-title":"Vibration Spectroscopy and Body Biofluids: Literature Review for Clinical Applications","volume":"24","author":"Leal","year":"2018","journal-title":"Photodiagn. Photodyn. Ther."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"103529","DOI":"10.1016\/j.vibspec.2023.103529","article-title":"Label-Free Discrimination of T and B Lymphocyte Activation Based on Vibrational Spectroscopy\u2014A Machine Learning Approach","volume":"126","author":"Ramalhete","year":"2023","journal-title":"Vib. Spectrosc."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"McHardy, R.G., Cameron, J.M., Eustace, D.A., Baker, M.J., and Palmer, D.S. (2025). From Lab to Clinic: Artificial Intelligence with Spectroscopic Liquid Biopsies. Diagnostics, 15.","DOI":"10.3390\/diagnostics15202589"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"112759","DOI":"10.1016\/j.isci.2025.112759","article-title":"A Review on Spectral Data Preprocessing Techniques for Machine Learning and Quantitative Analysis","volume":"28","author":"Yan","year":"2025","journal-title":"iScience"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1080\/05704928.2020.1738453","article-title":"ATR-FTIR Spectroscopy as the Future of Diagnostics: A Systematic Review of the Approach Using Bio-Fluids","volume":"56","author":"Naseer","year":"2021","journal-title":"Appl. Spectrosc. Rev."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"804","DOI":"10.1080\/05704928.2021.1946076","article-title":"Clinical Applications of Infrared and Raman Spectroscopy in the Fields of Cancer and Infectious Diseases","volume":"56","author":"Paraskevaidi","year":"2021","journal-title":"Appl. Spectrosc. Rev."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1561","DOI":"10.1080\/10408347.2022.2036941","article-title":"Raman, Infrared and Brillouin Spectroscopies of Biofluids for Medical Diagnostics and for Detection of Biomarkers","volume":"53","author":"Aitekenov","year":"2023","journal-title":"Crit. Rev. Anal. Chem."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"28991","DOI":"10.1038\/s41598-025-09912-0","article-title":"Biospectroscopy Screening and Molecular Fingerprinting of Gastric Cancer Cases from Biofluids by Vibrational Spectroscopy Allied with Chemometrics","volume":"15","author":"Tugrul","year":"2025","journal-title":"Sci. Rep."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Ramalhete, L., Vieira, M.B., Ara\u00fajo, R., Vigia, E., Aires, I., Ferreira, A., and Calado, C.R.C. (2024). Predicting Cellular Rejection of Renal Allograft Based on the Serum Proteomic Fingerprint. Int. J. Mol. Sci., 25.","DOI":"10.3390\/ijms25073844"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Ramalhete, L., Ara\u00fajo, R., Vieira, M.B., Vigia, E., Aires, I., Ferreira, A., and Calado, C.R.C. (2025). Integration of FTIR Spectroscopy and Machine Learning for Kidney Allograft Rejection: A Complementary Diagnostic Tool. J. Clin. Med., 14.","DOI":"10.3390\/jcm14030846"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Ramalhete, L., Ara\u00fajo, R., Vieira, M.B., Vigia, E., Pena, A., Carrelha, S., Teixeira, C., Ferreira, A., and Calado, C.R.C. (2026). Machine Learning-Assisted FTIR Spectroscopy Analysis of Kidney Preservation Fluids for Delayed Graft Function Risk Stratification. J. Clin. Med., 15.","DOI":"10.3390\/jcm15072762"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"673","DOI":"10.3390\/biomedinformatics4010037","article-title":"Revolutionizing Kidney Transplantation: Connecting Machine Learning and Artificial Intelligence with Next-Generation Healthcare\u2014From Algorithms to Allografts","volume":"4","author":"Ramalhete","year":"2024","journal-title":"BioMedInformatics"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Ramalhete, L., Ara\u00fajo, R., Vieira, M.B., Vigia, E., Pena, A., Carrelha, S., Ferreira, A., and Calado, C.R.C. (2025). Rapid FTIR Spectral Fingerprinting of Kidney Allograft Perfusion Fluids Distinguishes DCD from DBD Donors: A Pilot Machine Learning Study. Metabolites, 15.","DOI":"10.3390\/metabo15110702"},{"key":"ref_48","first-page":"12","article-title":"Risk Factors of Acute T-Cell Mediated and Borderline Rejection Diagnosed in Early Protocol Kidney Transplant Biopsies","volume":"38","author":"Vida","year":"2024","journal-title":"Port. Kidney J."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"1427","DOI":"10.1039\/D4AN01459F","article-title":"Exploring the Potential of Fourier Transform-Infrared Spectroscopy of Urine for Non-Invasive Monitoring of Inflammation Associated with a Kidney Transplant","volume":"150","author":"Sarkees","year":"2025","journal-title":"Analyst"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"1488","DOI":"10.1016\/j.transproceed.2019.03.012","article-title":"Clinical Significance of Revised Banff Criteria in the Diagnosis of Antibody-Mediated Rejection","volume":"51","author":"Jeong","year":"2019","journal-title":"Transplant. Proc."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Aravamudhan, A., Krug, K., Stoffel, M., and Muluhngwi, P. (2025). Evaluating the Diagnostic Utility of Dd-CfDNA in Renal Allograft Surveillance: A Single-Center Perspective. Genes, 16.","DOI":"10.3390\/genes16070724"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1056\/NEJMoa1215555","article-title":"Urinary-Cell MRNA Profile and Acute Cellular Rejection in Kidney Allografts","volume":"369","author":"Suthanthiran","year":"2013","journal-title":"N. Engl. J. Med."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"138","DOI":"10.1097\/MOT.0000000000001135","article-title":"Urine CXCL10 as a Biomarker in Kidney Transplantation","volume":"29","author":"Schaub","year":"2024","journal-title":"Curr. Opin. Organ Transplant."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1097\/TP.0000000000004339","article-title":"Diagnostic Potential of Minimally Invasive Biomarkers: A Biopsy-Centered Viewpoint from the Banff Minimally Invasive Diagnostics Working Group","volume":"107","author":"Huang","year":"2023","journal-title":"Transplantation"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"387","DOI":"10.1681\/ASN.2021091191","article-title":"The Trifecta Study: Comparing Plasma Levels of Donor-Derived Cell-Free DNA with the Molecular Phenotype of Kidney Transplant Biopsies","volume":"33","author":"Halloran","year":"2022","journal-title":"J. Am. Soc. Nephrol."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"634","DOI":"10.1007\/s11368-022-03337-2","article-title":"Evaluation of Data Pre-Processing and Regression Models for Precise Estimation of Soil Organic Carbon Using Vis\u2013NIR Spectroscopy","volume":"23","author":"Wang","year":"2023","journal-title":"J. Soils Sediments"},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Starshinova, A.A., Sabirova, A., Koroteeva, O., Kudryavtsev, I., Rubinstein, A., Aquino, A., Trulioff, A.S., Belyaeva, E., Kulpina, A., and Sharipov, R.A. (2026). Detection for New Biomarkers of Tuberculosis Infection Activity Using Machine Learning Methods. Diseases, 14.","DOI":"10.3390\/diseases14020066"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"e70221","DOI":"10.1002\/ejp.70221","article-title":"Resolving Interpretation Challenges in Machine Learning Feature Selection with an Iterative Approach in Biomedical Pain Data","volume":"30","author":"Himmelspach","year":"2026","journal-title":"Eur. J. Pain"},{"key":"ref_59","first-page":"1419","article-title":"The Effect of Feature Selection on Machine Learning Classification","volume":"9","author":"Pardede","year":"2025","journal-title":"JOIV Int. J. Inform. Vis."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"1509","DOI":"10.1118\/1.599017","article-title":"Feature Selection and Classifier Performance in Computer-Aided Diagnosis: The Effect of Finite Sample Size","volume":"27","author":"Sahiner","year":"2000","journal-title":"Med. Phys."},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Huang, K., Thomas, N., Gooley, P.R., and Armstrong, C.W. (2022). Systematic Review of NMR-Based Metabolomics Practices in Human Disease Research. Metabolites, 12.","DOI":"10.3390\/metabo12100963"},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Bratton, T.K., Fischer, A.G., Burns, C.T., and Valdes, R. (2026). Laboratory Medicine Approaches for Assessing Delayed Graft Function in Renal Transplant. J. Appl. Lab. Med., jfag048.","DOI":"10.1093\/jalm\/jfag048"},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Sancho, A., Gavela, E., Kanter, J., Beltr\u00e1n, S., Castro, C., Escudero, V., Pantoja, J., Molina, P., Vizca\u00edno, B., and Gonz\u00e1lez, M. (2022). Graft Survival Differences in Kidney Transplants Related to Recipient Sex and Age. Front. Med., 9.","DOI":"10.3389\/fmed.2022.962094"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"e2237580","DOI":"10.1001\/jamanetworkopen.2022.37580","article-title":"Association Between Pretransplant Dialysis Modality and Kidney Transplant Outcomes: A Systematic Review and Meta-Analysis","volume":"5","author":"Ngamvichchukorn","year":"2022","journal-title":"JAMA Netw. Open"},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Ramalhete, L., Ara\u00fajo, R., Teixeira, C., Pedro, I., Silva, I., and Ferreira, A. (2025). Mapping Anti-HLA Class I Cross-Reactivity for Transplantation Using Interpretable Embedding and Clustering of SAB MFI. AI Med., 1.","DOI":"10.3390\/aimed1010001"},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"e152","DOI":"10.1097\/TXD.0000000000000664","article-title":"The Risk of Transplant Failure with HLA Mismatch in First Adult Kidney Allografts 2: Living Donors, Summary, Guide","volume":"3","author":"Williams","year":"2017","journal-title":"Transplant. Direct"},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"3114","DOI":"10.1097\/CCM.0b013e3181bc7bd5","article-title":"Why We Should Be Wary of Single-Center Trials","volume":"37","author":"Bellomo","year":"2009","journal-title":"Crit. Care Med."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"1235","DOI":"10.2147\/JHC.S456093","article-title":"Limited Generalizability of Retrospective Single-Center Cohort Study in Comparison to Multicenter Cohort Study on Prognosis of Hepatocellular Carcinoma","volume":"11","author":"Kim","year":"2024","journal-title":"J. Hepatocell. Carcinoma"}],"container-title":["Medical Sciences"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2076-3271\/14\/3\/353\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,29]],"date-time":"2026-06-29T06:51:29Z","timestamp":1782715889000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2076-3271\/14\/3\/353"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6,27]]},"references-count":68,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2026,7]]}},"alternative-id":["medsci14030353"],"URL":"https:\/\/doi.org\/10.3390\/medsci14030353","relation":{},"ISSN":["2076-3271"],"issn-type":[{"value":"2076-3271","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,6,27]]}}}