Background: Consumption of ultra-processed foods [UPFs] may be associated with negative health outcomes. Limited data exist regarding the potential role of UPFs in the occurrence of allergic diseases. The underlying mechanisms underpinning any such associations are also poorly elucidated.
View Article and Find Full Text PDFWe developed a reusable and open-source machine learning (ML) pipeline that can provide an analytical framework for rigorous biomarker discovery. We implemented the ML pipeline to determine the predictive potential of clinical and immunoproteome antibody data for outcomes associated with Chlamydia trachomatis () infection collected from 222 cis-gender females with high exposure. We compared the predictive performance of 4 ML algorithms (naive Bayes, random forest, extreme gradient boosting with linear booster [xgbLinear], and -nearest neighbors [KNN]), screened from 215 ML methods, in combination with two different feature selection strategies, Boruta and recursive feature elimination.
View Article and Find Full Text PDFBackground: The clinical course of COVID-19 patients ranges from asymptomatic infection, via mild and moderate illness, to severe disease and even fatal outcome. Biomarkers which enable an early prediction of the severity of COVID-19 progression, would be enormously beneficial to guide patient care and early intervention prior to hospitalization.
Methods: Here we describe the identification of plasma protein biomarkers using an antibody microarray-based approach in order to predict a severe cause of a COVID-19 disease already in an early phase of SARS-CoV-2 infection.
Background: Previous research revealed antibodies targeting Chlamydia trachomatis elementary bodies was not associated with reduced endometrial or incident infection in C. trachomatis-exposed women. However, data on the role of C.
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