{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,11]],"date-time":"2025-12-11T06:40:07Z","timestamp":1765435207927,"version":"3.46.0"},"reference-count":26,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,12,11]],"date-time":"2025-12-11T00:00:00Z","timestamp":1765411200000},"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. Artif. Intell."],"abstract":"\n Background<\/jats:title>\n Surgical site infections (SSI) represent severe complications in adolescent orthopedic surgery. Shanghai\u2019s complex air pollution profile creates a critical context to investigate multi-pollutant impacts on SSI risk in this vulnerable population.<\/jats:p>\n <\/jats:sec>\n \n Methods<\/jats:title>\n We analyzed 32,261 adolescent SSI cases from Shanghai (2019\u20132024) alongside high-resolution pollution\/meteorological data. An evolutionary deep learning model (CNN-BiGRU-Attention optimized by Improved StarFish Algorithm) and generalized additive models (GAMs) assessed lagged effects, age\/gender stratification, and concentration-response relationships.<\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n \n NO\n 2<\/jats:sub>\n and SO\n 2<\/jats:sub>\n showed significantly positive associations with SSI risk at lag0 (concurrent day); O\n 3<\/jats:sub>\n exhibited protective effects (strongest at lag05: \u22122.396% [95% CI: \u22123.349% to \u22121.443%] per 10\u202f\u03bcg\/m\n 3<\/jats:sup>\n increase); Age stratification: 7\u201314 ages groups demonstrated heightened sensitivity to NO\n 2<\/jats:sub>\n \/SO\n 2<\/jats:sub>\n . O\n 3<\/jats:sub>\n effects varied across age groups; Gender differences: O\n 3<\/jats:sub>\n \u2019s negative association was stronger in males; Dose\u2013response: NO\n 2<\/jats:sub>\n \/SO\n 2<\/jats:sub>\n showed monotonic increases with no safety thresholds; O\n 3<\/jats:sub>\n displayed a straight line curve.\n <\/jats:p>\n <\/jats:sec>\n \n Conclusion<\/jats:title>\n \n Multi-pollutant exposure modulates SSI risk in adolescents, with NO\n 2<\/jats:sub>\n \/SO\n 2<\/jats:sub>\n as risk factors and O\n 3<\/jats:sub>\n showing context-dependent protection. Deep learning identified SO\n 2<\/jats:sub>\n \/NO\n 2<\/jats:sub>\n \/O\n 3<\/jats:sub>\n as dominant predictors, supporting perioperative air-quality interventions.\n <\/jats:p>\n <\/jats:sec>","DOI":"10.3389\/frai.2025.1692207","type":"journal-article","created":{"date-parts":[[2025,12,11]],"date-time":"2025-12-11T06:38:13Z","timestamp":1765435093000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Association of Shanghai air pollution with postoperative infection in adolescent orthopedic patients: a study using a deep learning-based evolutionary model"],"prefix":"10.3389","volume":"8","author":[{"given":"Yufeng","family":"Wang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yang","family":"Yuan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yang","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haoqi","family":"Cai","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1965","published-online":{"date-parts":[[2025,12,11]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","first-page":"394","DOI":"10.1080\/01919512.2020.1811636","article-title":"In vitro comparison of antibacterial effect of ozonated water and ozonated gas","volume":"43","author":"Agostini","year":"2021","journal-title":"Ozone Sci. 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