Publications by authors named "S A Pedersen"

Background: The Assessment of SpondyloArthritis international Society Health Index (ASAS HI) is a novel questionnaire of global functioning for patients with axial spondyloarthritis (SpA).

Objective: The objective was to assess the construct validity, discriminatory ability and responsiveness of ASAS HI in relation to patient-reported outcome measures (PROMs), MRI and radiography.

Methods: Data from two longitudinal studies with tumour necrosis factor inhibitor (TNFi) initiation (novel MRI And biomarkers in Golimumab-treated patients with axial spondyloarthritis (MANGO): n=45) respectively tapering (Dose adjustment of Biological treatment in patients with SpA (DOBIS): n=106) were used.

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Aims: This analysis evaluated whether gastrointestinal (GI) adverse events (AEs) including nausea, vomiting, diarrhoea (N/V/D) and dyspepsia were associated with weight reduction with tirzepatide across the SURMOUNT-1 to -4 trials.

Materials And Methods: SURMOUNT-1 to -4 were global Phase 3 clinical trials evaluating the safety and efficacy of tirzepatide among participants with obesity or overweight with or without type 2 diabetes (T2D). Participants were randomly assigned to receive once weekly subcutaneous tirzepatide or placebo.

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GATA binding protein 3 (GATA3), a member of the GATA family transcription factors, is a key player in various physiological and pathological conditions. It is known for its ability to bind to the DNA sequence "GATA", which enables its key role in critical processes in multiple tissues and organs including the immune system, endocrine system, and nervous system. GATA3 also modulates cell differentiation, proliferation, and apoptosis via controlling gene expression.

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Article Synopsis
  • Small cell lung cancer (SCLC) is a highly aggressive cancer with poor survival rates, and current diagnostic methods are invasive and limited.
  • This study introduces a new machine learning technique that uses metabolomics data to distinguish between SCLC, non-small cell lung cancer (NSCLC), and healthy individuals, achieving high accuracy in classification.
  • Key metabolites were identified as important predictors, and the stacking ensemble model effectively combines different classifiers, providing a promising non-invasive alternative for early lung cancer detection.
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