P17-08 Integrating motility tracking and high-content morphological image analysis for predictive immunotoxicology - A. Martin; R. Mahendran; E. Hoffman; M. Silva; K. Schaffer; A. Jötten; P. Paulitschke; V. Hutter (Toxicology Letters, 2025)

P17-07 In vitro approaches for inhalation safety assessment of poorly soluble and insoluble materials - L. Scott; J. Kelsall; E. Tha; L. Ma-Hock; R. Landsiedel; V. Hutter (Toxicology Letters, 2025)

Temporal trends and spatial variations of chlorinated paraffins and olefins in sewage sludge from eight Swiss wastewater treatment plants from 1993 to 2020 - Jules Hutter; Oscar Mendo Diaz; Marco Knobloch; Markus Zennegg; Jean Claude Vogel; Edith Durisch; Urs Stalder; Laurent Bigler; Susanne Kern; Andreas M. Buser; Norbert V. Heeb (Chemosphere, 2025)

P15-17 In vitro immune off-target toxicity assessment using morph_ONE for parenterally administered antibody-drug conjugate test item - B. Ghiot; E. Hoffman; J. Kelsall; A. Saeed; R. Mahendran; L. Scott; V. Hutter (Toxicology Letters, 2024)

Evolution of chlorinated paraffin and olefin fingerprints in sewage sludge from 1993 to 2020 of a Swiss municipal wastewater treatment plant - Marco C. Knobloch; Jules Hutter; Oscar Mendo Diaz; Markus Zennegg; Jean Claude Vogel; Edith Durisch; Urs Stalder; Laurent Bigler; Susanne Kern; Davide Bleiner; Norbert V. Heeb (Chemosphere, 2024)

High content analysis of in vitro alveolar macrophage responses can provide mechanistic insight for inhaled product safety assessment - V. Hutter; S. Hopper; J. Skamarauskas; E. Hoffman (Toxicology in Vitro, 2023)

Common, low-frequency, rare, and ultra-rare coding variants contribute to COVID-19 severity - Chiara Fallerini; Nicola Picchiotti; Margherita Baldassarri; Kristina Zguro; Sergio Daga; Francesca Fava; Elisa Benetti; Sara Amitrano; Mirella Bruttini; Maria Palmieri; Susanna Croci; Mirjam Lista; et al. (Human Genetics, 2022)
The combined impact of common and rare exonic variants in COVID-19 host genetics is currently insufficiently understood. Here, common and rare variants from whole-exome sequencing data of about 4000 SARS-CoV-2-positive individuals were used to define an interpretable machine-learning model for predicting COVID-19 severity. First, variants were converted into separate sets of Boolean features, depending on the absence or the presence of variants in each gene. An ensemble of LASSO logistic regression models was used to identify the most informative Boolean features with respect to the genetic bases of severity. The Boolean features selected by these logistic models were combined into an Integrated PolyGenic Score that offers a synthetic and interpretable index for describing the contribution of host genetics in COVID-19 severity, as demonstrated through testing in several independent cohorts. Selected features belong to ultra-rare, rare, low-frequency, and common variants, including those in linkage disequilibrium with known GWAS loci. Noteworthily, around one quarter of the selected genes are sex-specific. Pathway analysis of the selected genes associated with COVID-19 severity reflected the multi-organ nature of the disease. The proposed model might provide useful information for developing diagnostics and therapeutics, while also being able to guide bedside disease management.

A genome-wide association study of breast cancer in women of African ancestry - Fang Chen; Gary K. Chen; Daniel O. Stram; Robert C. Millikan; Christine B. Ambrosone; Esther M. John; Leslie Bernstein; Wei Zheng; Julie R. Palmer; Jennifer J. Hu; Tim R. Rebbeck; Regina G. Ziegler; et al. (Human Genetics, 2013)

Meta-analysis of new genome-wide association studies of colorectal cancer risk - Ulrike Peters; Carolyn M. Hutter; Li Hsu; Fredrick R. Schumacher; David V. Conti; Christopher S. Carlson; Christopher K. Edlund; Robert W. Haile; Steven Gallinger; Brent W. Zanke; Mathieu Lemire; Jagadish Rangrej; et al. (Human Genetics, 2012)