Nervous System Sampling for General Toxicity and Neurotoxicity Studies in the Laboratory Minipig With Emphasis on the Göttingen Minipig.

The use of minipigs as an alternative nonclinical species has increased in the last 20 years. The Society of Toxicologic Pathology (STP) has produced generic "best practice" recommendations for nervous system sampling in nonrodents during general toxicity studies ( 41[7]: 1028-1048, 2013), but their adaptation to the minipig has not been attempted. Here, we describe 2 trimming schemes suitable for evaluating the unique neuroanatomic features of the minipig brain in nonclinical toxicity studies.

Transformation of microbiology data into a standardised data representation using OpenEHR.

The spread of multidrug resistant organisms (MDRO) is a global healthcare challenge. Nosocomial outbreaks caused by MDRO are an important contributor to this threat. Computer-based applications facilitating outbreak detection can be essential to address this issue.

Proposal to Eliminate Urethane-Treated Positive Control Dose Groups in 26-Week Tg.rasH2 Carcinogenicity Studies.

Short-term (26 weeks) Tg.rasH2 mouse carcinogenicity studies have been conducted as an alternative model to the conventional 2-year mouse carcinogenicity studies, using urethane as a positive control material. In these studies, urethane was used at a dose of 1,000 mg/kg/dose, administered intraperitoneally on days 1, 3, and 5.

Using Deep Learning Artificial Intelligence Algorithms to Verify N-Nitroso-N-Methylurea and Urethane Positive Control Proliferative Changes in Tg-RasH2 Mouse Carcinogenicity Studies.

In Tg-rasH2 carcinogenicity mouse models, a positive control group is treated with a carcinogen such as urethane or N-nitroso-N-methylurea to test study validity based on the presence of the expected proliferative lesions in the transgenic mice. We hypothesized that artificial intelligence-based deep learning (DL) could provide decision support for the toxicologic pathologist by screening for the proliferative changes, verifying the expected pattern for the positive control groups. Whole slide images (WSIs) of the lungs, thymus, and stomach from positive control groups were used for supervised training of a convolutional neural network (CNN).