Lung function improvement on triple modulators: high-resolution, nationwide data from the Danish Cystic Fibrosis Cohort.

Background: People living with cystic fibrosis in Denmark had early, universal access to triple modulator treatment with elexacaftor/tezacaftor/ivacaftor. Close monitoring allowed us to assess the impact of treatment on lung function and progression of lung disease in an unselected nationwide cystic fibrosis population from 6 years of age.

Methods: Data were analysed using linear mixed-effect models to assess changes in levels and annual rates of change (slopes) in percent predicted (pp) forced expiratory volume in 1 s (FEV), forced vital capacity (FVC) and forced expiratory flow at 25-75% of FVC (ppFEF) between the 12 months pre-treatment and treatment periods.

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Infections in pregnancy are common, and although often not harmful to the woman or her unborn child, some infections may be associated with severe adverse outcomes. Pregnancy causes physiological and immunological adaptations which may make pregnant women more susceptible to infections and at increased risk of more severe illness. Infections in pregnancy represent a challenging area of concern for many specialists and general practitioners.

Real-world data confirm elexacftor/tezacaftor/ivacaftor modulators halves sweat chloride concentration in eligible people with cystic fibrosis.

Sweat chloride concentration, a diagnostic feature in cystic fibrosis (CF), reflects CF transmembrane conductance regulator (CFTR) activity. CFTR modulator therapies, especially elexacaftor/tezacaftor/ivacaftor (ETI), has improved CF outcomes. We report nationwide, real-world data on sweat chloride concentration in people with CF (pwCF) with and without modulator therapies.

Simulated LC-MS Data Set for Assessing the Metabolomics Data Processing Pipeline Implemented into MVAPACK.

Metabolomics commonly relies on using one-dimensional (1D) H NMR spectroscopy or liquid chromatography-mass spectrometry (LC-MS) to derive scientific insights from large collections of biological samples. NMR and MS approaches to metabolomics require, among other issues, a data processing pipeline. Quantitative assessment of the performance of these software platforms is challenged by a lack of standardized data sets with "known" outcomes.