{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,25]],"date-time":"2026-07-25T01:56:33Z","timestamp":1784944593604,"version":"3.55.0"},"reference-count":90,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2023,10,26]],"date-time":"2023-10-26T00:00:00Z","timestamp":1698278400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Immunol."],"abstract":"<jats:sec><jats:title>Background<\/jats:title><jats:p>The surge in the number of publications on psoriasis has posed significant challenges for researchers in effectively managing the vast amount of information. However, due to the lack of tools to process metadata, no comprehensive bibliometric analysis has been conducted.<\/jats:p><\/jats:sec><jats:sec><jats:title>Objectives<\/jats:title><jats:p>This study is to evaluate the trends and current hotspots of psoriatic research from a macroscopic perspective through a bibliometric analysis assisted by machine learning based semantic analysis.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>Publications indexed under the Medical Subject Headings (MeSH) term \u201cPsoriasis\u201d from 2003 to 2022 were extracted from PubMed. The generative statistical algorithm latent Dirichlet allocation (LDA) was applied to identify specific topics and trends based on abstracts. The unsupervised Louvain algorithm was used to establish a network identifying relationships between topics.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>A total of 28,178 publications were identified. The publications were derived from 176 countries, with United States, China, and Italy being the top three countries. For the term \u201cpsoriasis\u201d, 9,183 MeSH terms appeared 337,545 times. Among them, MeSH term \u201cSeverity of illness index\u201d, \u201cTreatment outcome\u201d, \u201cDermatologic agents\u201d occur most frequently. A total of 21,928 publications were included in LDA algorithm, which identified three main areas and 50 branched topics, with \u201cMolecular pathogenesis\u201d, \u201cClinical trials\u201d, and \u201cSkin inflammation\u201d being the most increased topics. LDA networks identified \u201cSkin inflammation\u201d was tightly associated with \u201cMolecular pathogenesis\u201d and \u201cBiological agents\u201d. \u201cNail psoriasis\u201d and \u201cEpidemiological study\u201d have presented as new research hotspots, and attention on topics of comorbidities, including \u201cCardiovascular comorbidities\u201d, \u201cPsoriatic arthritis\u201d, \u201cObesity\u201d and \u201cPsychological disorders\u201d have increased gradually.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>Research on psoriasis is flourishing, with molecular pathogenesis, skin inflammation, and clinical trials being the current hotspots. The strong association between skin inflammation and biologic agents indicated the effective translation between basic research and clinical application in psoriasis. Besides, nail psoriasis, epidemiological study and comorbidities of psoriasis also draw increased attention.<\/jats:p><\/jats:sec>","DOI":"10.3389\/fimmu.2023.1272080","type":"journal-article","created":{"date-parts":[[2023,10,26]],"date-time":"2023-10-26T14:38:42Z","timestamp":1698331122000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":9,"title":["A comprehensive overview of psoriatic research over the past 20 years: machine learning-based bibliometric 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