{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,28]],"date-time":"2026-09-28T21:11:35Z","timestamp":1790629895136,"version":"4.1.0"},"reference-count":33,"publisher":"Wiley","issue":"4","license":[{"start":{"date-parts":[[2024,1,3]],"date-time":"2024-01-03T00:00:00Z","timestamp":1704240000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Pediatric Pulmonology"],"published-print":{"date-parts":[[2024,4]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Background<\/jats:title><jats:p>International guidelines disagree on how best to diagnose primary ciliary dyskinesia (PCD), not least because many tests rely on pattern recognition. We hypothesized that quantitative distribution of ciliary ultrastructural and motion abnormalities would detect most frequent PCD\u2010causing groups of genes by soft computing analysis.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>Archived data on transmission electron microscopy and high\u2010speed video analysis from 212 PCD patients were re\u2010examined to quantitate distribution of ultrastructural (10 parameters) and functional ciliary features (4 beat pattern and 2 frequency parameters). The correlation between ultrastructural and motion features was evaluated by blinded clustering analysis of the first two principal components, obtained from ultrastructural variables for each patient. Soft computing was applied to ultrastructure to predict ciliary beat frequency (CBF) and motion patterns by a regression model. Another model classified the patients into the five most frequent PCD\u2010causing gene groups, from their ultrastructure, CBF and beat patterns.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The patients were subdivided into six clusters with similar values to homologous ultrastructural phenotype, motion patterns, and CBF, except for clusters 1 and 4, attributable to normal ultrastructure. The regression model confirmed the ability to predict functional ciliary features from ultrastructural parameters. The genetic classification model identified most of the different groups of genes, starting from all quantitative parameters.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>Applying soft computing methodologies to PCD diagnostic tests optimizes their value by moving from pattern recognition to quantification. The approach may also be useful to evaluate atypical PCD, and novel genetic abnormalities of unclear disease\u2010producing potential in the future.<\/jats:p><\/jats:sec>","DOI":"10.1002\/ppul.26842","type":"journal-article","created":{"date-parts":[[2024,1,3]],"date-time":"2024-01-03T14:34:09Z","timestamp":1704292449000},"page":"891-898","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["The genetic framework of primary ciliary dyskinesia assessed by soft computing analysis"],"prefix":"10.1002","volume":"59","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8266-1923","authenticated-orcid":false,"given":"Massimo","family":"Pifferi","sequence":"first","affiliation":[{"name":"Department of Pediatrics University Hospital of Pisa Pisa Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Attilio L.","family":"Boner","sequence":"additional","affiliation":[{"name":"Pediatric Unit, Department of Surgical 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