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Background/aim: Excessive fructose intake reportedly leads to the development of nonalcoholic fatty liver disease (NAFLD). In our previous study, we reported that plasma activities of alkaline phosphatase (ALP) isozymes were markedly changed in rats with excessive fructose intake-induced hepatomegaly. In this study, we examined ALP isozyme activity prior to the occurrence of hepatomegaly, and investigated the effect of the timing of sample collection, to explore its potential as a biomarker., Materials and Methods: After 1-week intake of a 63% high-fructose diet (HFrD), blood samples were collected from male rats during sleep or active phases to analyze biochemical parameters., Results: Body and liver weights were similar between the HFrD and control diet groups, indicating that hepatomegaly due to excessive fructose intake had not occurred. The triglyceride levels and glutamate dehydrogenase (GLDH) activity were significantly elevated to similar degrees at both time points. HFrD intake significantly increased liver-type ALP (L-ALP) activity, stimulating it by 12.7% at the sleep phase and by 124.3% at the active phase. HFrD consumption also significantly decreased intestinal-type ALP (I-ALP) at the active phase, but only showed a decreasing trend during the sleep phase., Conclusion: Measurements of plasma ALP isozyme and GLDH activity, and triglyceride levels are effective early biomarkers of impending NAFLD caused by excessive fructose intake. L-ALP and I-ALP activities during the active phase are particularly sensitive for detection of excessive fructose intake before the occurrence of NAFLD., (Copyright © 2023, International Institute of Anticancer Research (Dr. George J. Delinasios), All rights reserved.)

Artificial intelligence (AI)-based image analysis is increasingly being used for preclinical safety-assessment studies in the pharmaceutical industry. In this paper, we present an AI-based solution for preclinical toxicology studies. We trained a set of algorithms to learn and quantify multiple typical histopathological findings in whole slide images (WSIs) of the livers of young Sprague Dawley rats by using a U-Net-based deep learning network. The trained algorithms were validated using 255 liver WSIs to detect, classify, and quantify seven types of histopathological findings (including vacuolation, bile duct hyperplasia, and single-cell necrosis) in the liver. The algorithms showed consistently good performance in detecting abnormal areas. Approximately 75% of all specimens could be classified as true positive or true negative. In general, findings with clear boundaries with the surrounding normal structures, such as vacuolation and single-cell necrosis, were accurately detected with high statistical scores. The results of quantitative analyses and classification of the diagnosis based on the threshold values between "no findings" and "abnormal findings" correlated well with diagnoses made by professional pathologists. However, the scores for findings ambiguous boundaries, such as hepatocellular hypertrophy, were poor. These results suggest that deep learning-based algorithms can detect, classify, and quantify multiple findings simultaneously on rat liver WSIs. Thus, it can be a useful supportive tool for a histopathological evaluation, especially for primary screening in rat toxicity studies., (©2022 The Japanese Society of Toxicologic Pathology.)