Publications by authors named "M Thiele"

Background: Rigorous assessment of antibody developability is crucial for optimizing lead candidates before progressing to clinical studies. Recent advances in predictive tools for protein structures, surface properties, stability, and immunogenicity have streamlined the development of new biologics. However, accurate prediction of the impact of single amino acid substitutions on antibody structures remains challenging, due to the diversity of complementarity-determining regions (CDRs), particularly CDR3s.

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Background & Aims: Quantifying alcohol intake is crucial for subclassifying participants with steatotic liver disease (SLD) and interpreting clinical trials of alcohol-related liver disease (ALD) and metabolic and alcohol-related liver disease (MetALD). However, the accuracy of self-reported alcohol intake is considered imprecise. We compared the diagnostic and prognostic utility of self-reported alcohol intake with blood-based biomarkers of alcohol intake: phosphatidylethanol (PEth) and carbohydrate-deficient transferrin (CDT).

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Background: Clinically significant liver fibrosis is associated with future adverse events in patients with steatotic liver disease. We designed a software tool for detection of clinically significant liver fibrosis in primary care.

Methods: In this prospective cohort study, we developed and validated LiverPRO using six independent cohorts from Denmark, Germany, and England that included patients from primary and secondary care with steatotic liver disease related to alcohol or metabolic dysfunction.

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This position statement explores the intricate relationship between alcohol intake and metabolic dysfunction in the context of the 2023 nomenclature for steatotic liver disease (SLD). Recent and lifetime alcohol use should be accurately assessed in all patients with SLD to facilitate classification of alcohol use in grams of alcohol per week. Alcohol biomarkers (i.

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The microbiota in individual habitats differ in both relative composition and absolute abundance. While sequencing approaches determine the relative abundances of taxa and genes, they do not provide information on their absolute abundances. Here, we developed a machine-learning approach to predict fecal microbial loads (microbial cells per gram) solely from relative abundance data.

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