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Here we sought to identify novel modulators of olfactory function via the examination of nasal mucus biomarkers using an expansive 71-cytokine plex analyzed via machine learning models.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      Olfactory testing was performed via 40-question smell identify test (UPSIT). During endoscopic sinus surgery, sponges were placed in the middle meatus of individuals with CRSwNP (\n                      <jats:italic toggle=\"yes\">n<\/jats:italic>\n                      \u2009=\u200915). Nasal mucus samples were screened by multiplex analysis for 71-cytokine\/chemokines. Results underwent analysis with statistical and machine learning model approaches to assess whether protein concentrations were predictive of olfactory dysfunction.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>In CRSwNP, multiple machine learning models revealed novel cytokines IL-21 and MIP-1\u03b4 as positive predictors of greater olfactory dysfunction. Other cytokines detected by more than one model as predictive of olfactory dysfunction were IL-18, MCP-1, IL-22, and BCA-1. Other cytokines identified to be predictive by at least one model were FLT-3L, LIF, IL-20, SCF, IL-23, and TPO.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Using a 71-cytokine\/chemokine plex analyzed via machine learning, we identified potentially novel roles for MIP-1\u03b4 and IL-21 as modulators of olfactory function in CRSwNP. Use of machine learning for the analysis of nasal mucus cytokines, may serve as powerful tool to analyze complex multiplex immune mediator data.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1177\/19458924261418539","type":"journal-article","created":{"date-parts":[[2026,2,17]],"date-time":"2026-02-17T04:46:21Z","timestamp":1771303581000},"page":"290-300","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":2,"title":["Use of Machine Learning and 71-Plex Immune Mediator Analysis to Identify Nasal Mucus Biomarkers Associated With Olfactory Loss in Patients with CRSwNP"],"prefix":"10.1177","volume":"40","author":[{"given":"Jason Cory","family":"Brunson","sequence":"first","affiliation":[{"name":"University of Florida"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anil","family":"Patel","sequence":"additional","affiliation":[{"name":"University of Florida"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sufiya","family":"Ali","sequence":"additional","affiliation":[{"name":"University of Florida"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Maria","family":"Villanueva","sequence":"additional","affiliation":[{"name":"University of Florida"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4729-028X","authenticated-orcid":false,"given":"Jeb M.","family":"Justice","sequence":"additional","affiliation":[{"name":"University of Florida"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Brian C.","family":"Lobo","sequence":"additional","affiliation":[{"name":"University of Florida"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3744-0451","authenticated-orcid":false,"given":"Nikita","family":"Chapurin","sequence":"additional","affiliation":[{"name":"University of Florida"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Carl","family":"Atkinson","sequence":"additional","affiliation":[{"name":"Northwestern University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6880-6092","authenticated-orcid":false,"given":"Jennifer K.","family":"Mulligan","sequence":"additional","affiliation":[{"name":"University of Florida"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2026,2,16]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1111\/all.13042"},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.14639\/0392-100X-2426"},{"key":"e_1_3_2_4_2","doi-asserted-by":"publisher","DOI":"10.1001\/jamaoto.2016.0927"},{"key":"e_1_3_2_5_2","doi-asserted-by":"publisher","DOI":"10.1002\/alr.22499"},{"key":"e_1_3_2_6_2","doi-asserted-by":"publisher","DOI":"10.1177\/19458924221111830"},{"key":"e_1_3_2_7_2","doi-asserted-by":"publisher","DOI":"10.1002\/alr.23035"},{"key":"e_1_3_2_8_2","doi-asserted-by":"publisher","DOI":"10.1002\/alr.22449"},{"key":"e_1_3_2_9_2","doi-asserted-by":"publisher","DOI":"10.1002\/alr.22160"},{"key":"e_1_3_2_10_2","doi-asserted-by":"publisher","DOI":"10.1002\/lary.24305"},{"key":"e_1_3_2_11_2","doi-asserted-by":"publisher","DOI":"10.1002\/lary.27112"},{"key":"e_1_3_2_12_2","doi-asserted-by":"publisher","DOI":"10.1093\/chemse\/bjab042"},{"key":"e_1_3_2_13_2","doi-asserted-by":"publisher","DOI":"10.1002\/alr.22249"},{"issue":"1","key":"e_1_3_2_14_2","first-page":"1","article-title":"Prediction of phenotypes by secretory biomarkers and machine learning in patients with chronic rhinosinusitis","volume":"29","author":"Becker M","year":"2025","unstructured":"Becker M, Kist AM, Wendler O, et al. 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