{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T06:09:35Z","timestamp":1784182175152,"version":"3.55.0"},"reference-count":53,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T00:00:00Z","timestamp":1784160000000},"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. Microbiol."],"abstract":"<jats:p>\n                    Respiratory viral detection in large metropolitan clinical settings is shaped by age, seasonality, testing behavior, and environmental context, but these dimensions are rarely evaluated together using multiplex molecular data. Istanbul's transcontinental position, dense urban structure, and heterogeneous climatic and air-quality conditions provide a relevant setting for integrated analysis of respiratory viral detection. This retrospective sample-level study analyzed 21,151 clinician-requested respiratory specimens tested by multiplex reverse transcription polymerase chain reaction in Istanbul, T\u00fcrkiye, during 2022\u20132025. The study aimed to characterize age-stratified respiratory viral detection patterns and to explore short-term associations with meteorological and pollutant-specific air-quality exposures. Detection patterns were classified as single-target, dual-target, or \u22653-target positivity. ERA5-derived meteorological variables and station-based pollutant data were linked to specimen collection dates using a predefined incubation-informed 1\u20139-day pre-sampling exposure window. Environmental analyses were exploratory and interpreted using effect estimates, 95% confidence intervals, nominal\n                    <jats:italic>p<\/jats:italic>\n                    -values, and false-discovery-rate-adjusted q values where applicable; SARS-CoV-2-specific analyses were restricted to 2024\u20132025. After excluding SARS-CoV-2, at least one evaluated respiratory viral target was detected in 4,836 specimens (22.9%). Positive specimens were obtained from younger patients than target-negative specimens [median age, 6 years (interquartile range, 1\u201332) vs. 19 years [interquartile range, 4\u201359];\n                    <jats:italic>p<\/jats:italic>\n                    = 7.58 \u00d7 10\n                    <jats:sup>\u2212146<\/jats:sup>\n                    ]. Children aged 1\u20135 years accounted for 30.1% of single-target, 44.1% of dual-target, and 48.2% of \u22653-target detections. Enterovirus\/rhinovirus was the most frequently detected viral target, identified in 1,604 of 4,836 positive specimens (33.2%). Seasonal patterns varied by age group and viral target. Exploratory exposure analyses showed crude signals linking low temperature and selected pollutant-specific exposures with dual+\u22653-target positivity, with attenuation of most estimates after year\u2013month temporal-control sensitivity analyses. Overall, respiratory viral detection in this tertiary-care hospital testing cohort varied mainly by age, viral target, and season, while the environmental analyses provided temporally controlled contextual signals rather than confirmatory causal evidence. These findings support cautious, context-aware interpretation of multiplex respiratory viral testing data in large urban clinical settings.\n                  <\/jats:p>","DOI":"10.3389\/fmicb.2026.1893118","type":"journal-article","created":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T05:37:35Z","timestamp":1784180255000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Age-stratified respiratory viral detection patterns and exploratory environmental associations in a tertiary-care hospital testing cohort in Istanbul, T\u00fcrkiye"],"prefix":"10.3389","volume":"17","author":[{"given":"Murat","family":"Yaman","sequence":"first","affiliation":[{"name":"Medical Microbiology, Marmara University Pendik Training and Research Hospital","place":["Istanbul, T\u00fcrkiye"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Arzu","family":"Ilki","sequence":"additional","affiliation":[{"name":"Medical Microbiology, Marmara University Pendik Training and Research Hospital","place":["Istanbul, T\u00fcrkiye"]},{"name":"Department of Medical Microbiology, Faculty of Medicine, Marmara University","place":["Istanbul, T\u00fcrkiye"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,7,16]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"e0330716","DOI":"10.1371\/journal.pone.0330716","article-title":"Machine learning framework for forecasting air pollution: evaluating seasonal and climatic influences in Istanbul, Turkey","volume":"20","author":"Al-Rousan","year":"2025","journal-title":"PLoS ONE"},{"key":"B2","doi-asserted-by":"publisher","first-page":"289","DOI":"10.1111\/j.2517-6161.1995.tb02031.x","article-title":"Controlling the false discovery rate: a practical and powerful approach to multiple testing","volume":"57","author":"Benjamini","year":"1995","journal-title":"J. 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