{
  "abstract": "Atopic dermatitis (AD) is characterized by a self-reinforcing loop between epidermal barrier dysfunction and type 2-skewed inflammation; yet the most perturbed keratinocyte states and actionable epidermal targets remain incompletely defined. We integrated pharmacogenomic target mining, complementary machine-learning feature selection (LASSO and SVM-RFE), single-cell state–resolved perturbation analyses (Augur and scDist), and structure-based molecular modeling (molecular docking, MD simulation, and MM-PBSA free energy calculation) to prioritize candidate targets of baicalin in AD. CA2 emerged as a convergent epidermal candidate; scRNA-seq analyses localized CA2-associated transcriptional differences to keratinocytes, with the keratinocyte compartment exhibiting the disease-associated strongest separability and transcriptomic distance, accompanied by enrichment of metabolic reprogramming, epithelial junction and barrier remodeling, and proliferative quiescence gene programs. Structure-based evaluation supported a computationally plausible baicalin–CA2 interaction, with an estimated MM-PBSA binding free energy of −22.082 kcal/mol. Collectively, these findings nominate a computationally supported “baicalin–CA2–Kcs9” axis as a hypothesis-generating framework for epidermal stratification and experimental prioritization in AD.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Dermatology, The First People’s Hospital of Yibin. No. 16 Puhe East Road, Yibin, Sichuan, PR China",
    "Department of Dermatology, The Fourth People’s Hospital of Sichuan Province, No. 12, Chengshou East Street, Jinjiang District, Chengdu, Sichuan, PR China"
  ],
  "article_type": "Research Article",
  "arxiv": null,
  "author_total": 3,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Boyan Yang"
    },
    {
      "affs": [
        1
      ],
      "name": "Guilin Zhou"
    },
    {
      "name": "Jun Dai"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/527c0bw06kg7r.bib",
  "cited_by": 0,
  "doi": "10.1371/journal.pone.0356174",
  "fulltext_url": null,
  "id": "527c0bw06kg7r",
  "issns": [
    "1932-6203"
  ],
  "landing_url": "https://doi.org/10.1371/journal.pone.0356174",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "2708|Dermatology and Skin Diseases"
    ],
    [
      "plos",
      "/Biology and life sciences/Anatomy/Biological tissue/Epithelium/Epithelial cells/Keratinocytes"
    ],
    [
      "plos",
      "/Biology and life sciences/Biochemistry/Biochemical simulations"
    ],
    [
      "plos",
      "/Biology and life sciences/Cell biology/Cellular types/Animal cells/Epithelial cells/Keratinocytes"
    ],
    [
      "plos",
      "/Biology and life sciences/Computational biology/Biochemical simulations"
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    [
      "plos",
      "/Biology and life sciences/Computational biology/Genome analysis/Transcriptome analysis"
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      "plos",
      "/Biology and life sciences/Genetics/Genomics/Genome analysis/Transcriptome analysis"
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    [
      "plos",
      "/Computer and information sciences/Artificial intelligence/Machine learning/Machine learning algorithms"
    ],
    [
      "plos",
      "/Medicine and health sciences/Anatomy/Biological tissue/Epithelium/Epithelial cells/Keratinocytes"
    ],
    [
      "plos",
      "/Medicine and health sciences/Medical conditions/Inflammatory diseases"
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      "/Physical sciences/Chemistry/Computational chemistry/Molecular dynamics"
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      "/Physical sciences/Mathematics/Applied mathematics/Algorithms/Machine learning algorithms"
    ],
    [
      "plos",
      "/Physical sciences/Physics/Thermodynamics/Free energy"
    ],
    [
      "plos",
      "/Research and analysis methods/Chemical characterization/Binding analysis/Cell binding assay"
    ],
    [
      "plos",
      "/Research and analysis methods/Simulation and modeling/Algorithms/Machine learning algorithms"
    ]
  ],
  "openalex": "W7212229800",
  "pdf_url": "https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0356174&type=printable",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-11",
  "publisher": "Public Library of Science",
  "ris": "https://research.draconitic.com/paper/527c0bw06kg7r.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:56Z",
      "native_id": "10.1371/journal.pone.0356174",
      "source": "plos",
      "url": "https://doi.org/10.1371/journal.pone.0356174"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "plos:computational biology",
      "subfield": "bioinformatics"
    },
    {
      "field": "biology",
      "origin": "plos:cell biology",
      "subfield": "cell-biology"
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    {
      "field": "medicine",
      "origin": "plos:anatomy",
      "subfield": "physiology"
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  "title": "A bioinformatic single-cell and structure-informed framework identifies a baicalin–CA2–keratinocyte state axis in atopic dermatitis",
  "title_text": "A bioinformatic single-cell and structure-informed framework identifies a baicalin–CA2–keratinocyte state axis in atopic dermatitis",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:17:56Z",
  "url": "https://research.draconitic.com/paper/527c0bw06kg7r",
  "venue": "PLOS One",
  "versions": []
}