Publications by authors named "Cristina Bax"
Article Synopsis
- The study outlines a new method combining methylation analysis with Machine Learning to classify patients with Facio-Scapulo-Humeral Dystrophy (FSHD) based on their DNA.
- The research involved two groups: a training set of 133 FSHD patients and 150 healthy controls, and a testing set of 27 FSHD patients and 25 controls, revealing that FSHD patients had significantly lower methylation levels.
- A Machine Learning model identified four key CpG sites for distinguishing FSHD patients, achieving high accuracy (0.94), sensitivity (0.93), and specificity (0.96), indicating its potential as a reliable biomarker for diagnosing the disease.
Objectives: The present study compared the performance of the Lumipulse G Sars-CoV-2 Ag kit with the TaqPath COVID-19 RT-PCR CE IVD kit.
Methods: The study was conducted on 4266 naso-oropharyngeal swabs. Samples were subjected to antigen RT-PCR tests for the detection of Sars-CoV-2 and related variants.
In this study, we investigated the sequence of (Structural Maintenance of Chromosomes flexible Hinge Domain containing 1) SMCHD1 gene in a cohort of clinically defined FSHD (facioscapulohumeral muscular dystrophy) patients in order to assess the distribution of SMCHD1 variants, considering the D4Z4 fragment size in terms of repeated units (RUs; short fragment: 1-7 RU, borderline: 8-10RU and normal fragment: >11RU). The analysis of SMCHD1 revealed the presence of 82 variants scattered throughout the introns, exons and 3'untranslated region (3'UTR) of the gene. Among them, 64 were classified as benign polymorphisms and 6 as VUS (variants of uncertain significance).
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