{
  "abstract": "Discriminating glycated human serum albumin from unmodified albumin and aptamer-bound complexes at the single-molecule level is a sensing challenge unmet by conventional bulk analytical methods, which cannot resolve individual protein modification states. Here, we present a proof-of-concept solid-state nanopore platform combined with aptamer recognition and supervised machine learning for single-event discrimination of human serum albumin (HSA), glycated HSA (GHSA), and aptamer-bound GHSA (Apt+GHSA). Using a 14 nm silicon nitride nanopore, synthetic HSA, GHSA, and Apt+GHSA produced distinguishable ionic current blockade signatures, while dwell times remained similar. Serum-derived HSA showed broader, heterogeneous blockade distributions consistent with mixed albumin proteoforms; aptamer treatment generated a higher-blockade population consistent with Apt+GHSA-like events. A modified fluorescence aptamer assay estimated serum glycated albumin content at approximately 30%. To resolve overlapping signatures, five signal-derived features were extracted from individual translocation events and used to train supervised machine learning classifiers. Random Forest and Gradient Boosting achieved the highest three-class test accuracy of 94.50% for HSA, Apt+GHSA, and serum-derived HSA; Gradient Boosting achieved the highest macroaverage AUC of 0.9915. Applied to aptamer-treated serum-derived HSA, Gradient Boosting classified 54.6% of events as Apt+GHSA-like, reflecting aptamer-responsive nanopore signatures rather than a direct quantification of the glycated albumin percentage. This study establishes a nanopore–aptamer–machine learning framework for resolving albumin modification and binding states in biologically derived samples.",
  "abstract_shown": true,
  "affiliations": [
    "National Science and Technology Development Agency (NSTDA) , , Thailand Science Park , ,",
    "University of Strathclyde, Technology and Innovation Centre , , 99 George Street , ,",
    "Vidyasirimedhi Institute of Science and Technology (VISTEC) , , ,",
    "Mahidol University , , ,"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 16,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Harit Pitakjakpipop",
      "orcid": "0000-0002-3873-9076"
    },
    {
      "affs": [
        0
      ],
      "name": "Ibrar Alam"
    },
    {
      "affs": [
        0,
        1
      ],
      "name": "Kiatnida Treerattrakoon"
    },
    {
      "affs": [
        0
      ],
      "name": "Warittha Thongkham"
    },
    {
      "affs": [
        2
      ],
      "name": "Kawin Loha"
    },
    {
      "affs": [
        0
      ],
      "name": "Thanaya Saeyang"
    },
    {
      "affs": [
        0
      ],
      "name": "Jarunee Vanichtanankul",
      "orcid": "0000-0003-3431-1467"
    },
    {
      "affs": [
        0
      ],
      "name": "Itthi Chatnuntawech"
    },
    {
      "affs": [
        0
      ],
      "name": "Surat Teerapittayanon"
    },
    {
      "affs": [
        0
      ],
      "name": "Rawisara Tuncharoen"
    },
    {
      "affs": [
        0
      ],
      "name": "Phattaraporn Pongsalee"
    },
    {
      "affs": [
        0
      ],
      "name": "Thanapun Thanomputsa"
    },
    {
      "affs": [
        3
      ],
      "name": "Poramin Boonbanjong",
      "orcid": "0000-0003-2703-1176"
    },
    {
      "affs": [
        3
      ],
      "name": "Nuankanya Sathirapongsasuti"
    },
    {
      "affs": [
        0
      ],
      "name": "Thitikorn Boonkoom"
    },
    {
      "affs": [
        0
      ],
      "name": "Deanpen Japrung",
      "orcid": "0000-0003-4206-6641"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/e09xp9trka93p.bib",
  "cited_by": 0,
  "doi": "10.1021/acsomega.6c09079",
  "fulltext_url": null,
  "id": "e09xp9trka93p",
  "issns": [
    "2470-1343"
  ],
  "landing_url": "https://pubs.acs.org/acsodf/article/doi/10.1021/acsomega.6c09079/5460396/Machine-Learning-Enhanced-Solid-State-Nanopore",
  "language": null,
  "licence": "CC-BY-NC-ND-4.0",
  "licence_label": "CC BY-NC-ND 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by-nc-nd/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "2204|Nanopore and Nanochannel Transport Studies"
    ]
  ],
  "openalex": "W7224099781",
  "pdf_url": "https://pubs.acs.org/doi/pdf/10.1021/acsomega.6c09079",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-28",
  "publisher": "American Chemical Society (ACS)",
  "ris": "https://research.draconitic.com/paper/e09xp9trka93p.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:18:40Z",
      "native_id": "10.1021/acsomega.6c09079",
      "source": "crossref",
      "url": "https://pubs.acs.org/acsodf/article/doi/10.1021/acsomega.6c09079/5460396/Machine-Learning-Enhanced-Solid-State-Nanopore"
    }
  ],
  "subjects": [
    {
      "field": "chemistry",
      "origin": "issn:2470-1343",
      "subfield": "general"
    }
  ],
  "title": "Machine Learning-Enhanced Solid-State Nanopore Analysis for Single-Molecule Discrimination of Albumin and Aptamer-Bound Glycated Albumin Complexes",
  "title_text": "Machine Learning-Enhanced Solid-State Nanopore Analysis for Single-Molecule Discrimination of Albumin and Aptamer-Bound Glycated Albumin Complexes",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:18:40Z",
  "url": "https://research.draconitic.com/paper/e09xp9trka93p",
  "venue": "ACS Omega",
  "versions": []
}