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Precision symptom phenotyping identifies early clinical and proteomic predictors of distinct COVID-19 sequelae.

Nusrat J EpsiJosh G ChenowethPaul W BlairDavid A LindholmAnuradha GanesanTahaniyat LalaniAlfred SmithRupal M ModyMilissa U JonesRhonda E ColomboChristopher J ColomboChristina SchofieldEvan C EwersDerek T LarsonCatherine M BerjohnRyan C MavesAnthony C FriesDavid ChangAndrew WyattAnn I ScherCelia ByrneJennifer RusieckiDavid L SaundersJeffrey LivezeyAllison MalloySamantha BazanCarlos MaldonadoMargaret Sanchez EdwardsKatrin MendeMark P SimonsRobert J O'ConnellDavid R TribbleBrian K AganTimothy H BurgessSimon D PollettStephanie A Richard
Published in: The Journal of infectious diseases (2024)
We identified three distinct symptom-based PCC phenotypes with specific clinical risk factors and early post-infection inflammatory predictors. With further validation and characterization, this framework may allow more precise classification of PCC cases and potentially improve the diagnosis, prognostication, and treatment of PCC.
Keyphrases
  • risk factors
  • coronavirus disease
  • sars cov
  • machine learning
  • high throughput
  • deep learning
  • genome wide
  • oxidative stress
  • dna methylation
  • smoking cessation