The Toxicology Investigators Consortium 2024 Annual Report - Kim Aldy; Shao Li; Mari Costantini; Alyssa Falise; Rachel Culbreth; Paul M. Wax; Jeffery Brent; Michael Abesamis; Faiz Ahmed; Salman Ahsan; Peter Akpunonu; Adam Algren; et al. (Journal of Medical Toxicology, 2026)

Benchmarking CO₂ storage simulations: Results from the 11th Society of Petroleum Engineers Comparative Solution Project - Jan M. Nordbotten; Martin A. Fernø; Bernd Flemisch; Anthony R. Kovscek; Knut-Andreas Lie; Jakub W. Both; Olav Møyner; Tor Harald Sandve; Etienne Ahusborde; Sebastian Bauer; Zhangxing Chen; Holger Class; et al. (International Journal of Greenhouse Gas Control, 2025)

Nutrients monitoring on the Bui multipurpose dam project in the Savannah region of Ghana - Jacob Asomaning; Cynthia Laar; Crentsil Kofi Bempah; Godfred Ayanu; John Senu; Ruby Eshun; George Crabbe; Eric Ofosu Antwi (Environmental Nanotechnology, Monitoring & Management, 2024)

Combination and summary of ATLAS dark matter searches interpreted in a 2HDM with a pseudo-scalar mediator using 139 fb−1 of s = 13 TeV pp collision data - G. Aad; B. Abbott; K. Abeling; N.J. Abicht; S.H. Abidi; A. Aboulhorma; H. Abramowicz; H. Abreu; Y. Abulaiti; A.C. Abusleme Hoffman; B.S. Acharya; C. Adam Bourdarios; et al. (Science Bulletin, 2024)

The Toxicology Investigators Consortium 2023 Annual Report - Adrienne Hughes; Alexandra Amaducci; Sharan L. Campleman; Shao Li; Mari Costantini; Meghan B. Spyres; Hannah Spungen; Jessica Kent; Alyssa Falise; Rachel Culbreth; Paul M. Wax; Jeffery Brent; et al. (Journal of Medical Toxicology, 2024)

Applications and advancements of nanoparticle-based drug delivery in alleviating lung cancer and chronic obstructive pulmonary disease - Gabriele De Rubis; Keshav Raj Paudel; Leander Corrie; Samir Mehndiratta; Vyoma K. Patel; Popat S. Kumbhar; Arehalli Sidramappa Manjappa; John Disouza; Vandana Patravale; Gaurav Gupta; Bikash Manandhar; Rashi Rajput; et al. (Naunyn-Schmiedeberg's Archives of Pharmacology, 2024)

The Toxicology Investigators Consortium 2022 Annual Report - Alexandra M. Amaducci; Sharan L. Campleman; Shao Li; Dana L. Karshenas; Meghan B. Spyres; Lynn A. Farrugia; A. Min Kang; Rachel E. Culbreth; Paul M. Wax; Jeffrey Brent; Kim Aldy; Jennifer Acciani; et al. (Journal of Medical Toxicology, 2023)

The population structure, sex ratio and reproductive potential of limpets (Patella spp.) on natural shores and artificial structures in the Irish Sea - Hannah S. Earp; Ruby George; Paul R. Brooks; Veronica Farrugia Drakard; Bryan J. Thompson; Benjamin Fisher; Róisín Hayden; Tasman P. Crowe; Pippa J. Moore (Marine Environmental Research, 2023)

The Toxicology Investigators Consortium Case Registry—the 2021 Annual Report - Jennifer S. Love; Dana L. Karshenas; Meghan B. Spyres; Lynn A. Farrugia; A. Min Kang; HoanVu Nguyen; Sharan L. Campleman; Shao Li; Paul M. Wax; Jeffery Brent; Kim Aldy; Michael Abesamis; et al. (Journal of Medical Toxicology, 2022)

Inter-laboratory automation of the in vitro micronucleus assay using imaging flow cytometry and deep learning - John W. Wills; Jatin R. Verma; Benjamin J. Rees; Danielle S. G. Harte; Qiellor Haxhiraj; Claire M. Barnes; Rachel Barnes; Matthew A. Rodrigues; Minh Doan; Andrew Filby; Rachel E. Hewitt; Catherine A. Thornton; et al. (Archives of Toxicology, 2021)
The in vitro micronucleus assay is a globally significant method for DNA damage quantification used for regulatory compound safety testing in addition to inter-individual monitoring of environmental, lifestyle and occupational factors. However, it relies on time-consuming and user-subjective manual scoring. Here we show that imaging flow cytometry and deep learning image classification represents a capable platform for automated, inter-laboratory operation. Images were captured for the cytokinesis-block micronucleus (CBMN) assay across three laboratories using methyl methanesulphonate (1.25–5.0 μg/mL) and/or carbendazim (0.8–1.6 μg/mL) exposures to TK6 cells. Human-scored image sets were assembled and used to train and test the classification abilities of the “DeepFlow” neural network in both intra- and inter-laboratory contexts. Harnessing image diversity across laboratories yielded a network able to score unseen data from an entirely new laboratory without any user configuration. Image classification accuracies of 98%, 95%, 82% and 85% were achieved for ‘mononucleates’, ‘binucleates’, ‘mononucleates with MN’ and ‘binucleates with MN’, respectively. Successful classifications of ‘trinucleates’ (90%) and ‘tetranucleates’ (88%) in addition to ‘other or unscorable’ phenotypes (96%) were also achieved. Attempts to classify extremely rare, tri- and tetranucleated cells with micronuclei into their own categories were less successful (≤ 57%). Benchmark dose analyses of human or automatically scored micronucleus frequency data yielded quantitation of the same equipotent concentration regardless of scoring method. We conclude that this automated approach offers significant potential to broaden the practical utility of the CBMN method across industry, research and clinical domains. We share our strategy using openly-accessible frameworks.

Poster abstracts - J. Ferrie; M. Shipley; F. Cappuccio; E. Brunner; M. Miller; M. Kumari; M. Marmot; A. Coenen; J. L. Castillo; F. Araya; G. Bustamante; L. Montecino; et al. (Sleep and Biological Rhythms, 2007)

Inhibition of spermatogenesis in rainbow trout during chronic cyanide poisoning - Sylvia M. Ruby; D. George Dixon; Grard Leduc (Archives of Environmental Contamination and Toxicology, 1979)