Publications by authors named "M C Bongaerts"
Background: In 2021, a novel group of Chlamydia strains in wild birds was classified as avian Chlamydia abortus, with unknown zoonotic potential. We report relevant features of avian C abortus infections from a Dutch family cluster and unrelated historical cases using clinical, epidemiological, and microbiological data.
Methods: An outbreak of avian C abortus started in the Netherlands in December, 2022.
Epstein-Barr virus (EBV) contributes to oncogenesis and immune evasion in nasopharyngeal carcinoma (NPC). At present, an aggregated, higher-level view on the impact of EBV genes toward the immune microenvironment of NPC is lacking. To this end, we have interrogated tumor-derived RNA sequences of 106 treatment-naive NPC patients for 98 EBV transcripts, and captured the presence of 10 different immune cell populations as well as 23 different modes of T-cell evasion.
View Article and Find Full Text PDFUveal melanomas (UM) are detected earlier. Consequently, tumors are smaller, allowing for novel eye-preserving treatments. This reduces tumor tissue available for genomic profiling.
View Article and Find Full Text PDFOligosaccharidoses, sphingolipidoses and mucolipidoses are lysosomal storage disorders (LSDs) in which defective breakdown of glycan-side chains of glycosylated proteins and glycolipids leads to the accumulation of incompletely degraded oligosaccharides within lysosomes. In metabolic laboratories, these disorders are commonly diagnosed by thin-layer chromatography (TLC) but more recently also mass spectrometry-based approaches have been published. To expand the possibilities to screen for these diseases, we developed an ultra-high-performance liquid chromatography (UHPLC) with a high-resolution accurate mass (HRAM) mass spectrometry (MS) screening platform, together with an open-source iterative bioinformatics pipeline.
View Article and Find Full Text PDFArticle Synopsis
- Untargeted metabolomics (UM) is being used to screen for inborn errors of metabolism (IEM), and this study analyzed various outlier detection methods for identifying patient profiles.
- Different outlier detection methods showed varying levels of effectiveness in detecting IEM, with some methods performing consistently well across datasets, while others excelled in more balanced sample conditions.
- The study highlights the importance of using PCA transformations to enhance method performance and notes that while some methods succeeded in detecting 90% of IEM patients without false positives, further refinements are needed for reliable clinical application.