Riddelliine is a representative naturally occurring genotoxic pyrrolizidine alkaloid. We have studied the mechanism by which riddelliine induces hepatocellular tumors in vivo. Metabolism of riddelliine by liver microsomes of F344 female rats generated riddelliine N-oxide and dehydroretronecine (DHR) as major metabolites. Metabolism was enhanced when liver microsomes from phenobarbital-treated rats were used. Metabolism in the presence of calf thymus DNA resulted in eight DNA adducts that were identical to those obtained from the reaction of DHR with calf thymus DNA. Two of these adducts were identified as DHR-modified 7-deoxyguanosin-N(2)-yl epimers (DHR-3'-dGMP); the other six were DHR-derived DNA adducts, but their structures were not characterized. A similar DNA adduct profile was detected in the livers of female F344 rats fed riddelliine, and a dose-response relationship was obtained for the level of the total (eight) DHR-derived DNA adducts and the level of the DHR-3'-dGMP adducts. These results suggest that riddelliine induces liver tumors in rats through a genotoxic mechanism and the eight DHR-derived DNA adducts are likely to contribute to liver tumor development.

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Pyrrolizidine alkaloids are naturally occurring genotoxic chemicals produced by a large number of plants. Metabolism of pyrrolizidine alkaloids in vivo and in vitro generates dehydroretronecine (DHR) as a common reactive metabolite. In this study, we report the development of a (32)P-postlabeling/HPLC method for detection of (i) two DHR-3'-dGMP and four DHR-3'-dAMP adducts and (ii) a set of eight DHR-derived DNA adducts in vitro and in vivo. The approach involves (1) synthesis of DHR-3'-dGMP, DHR-3'-dAMP, and DHR-3',5'-dG-bisphosphate standards and characterization of their structures by mass and (1)H NMR spectral analyses, (2) development of optimal conditions for enzymatic DNA digestion, adduct enrichment, and (32)P-postlabeling, and (3) development of optimal HPLC conditions. Using this methodology, we have detected eight DHR-derived DNA adducts, including the two epimeric DHR-3',5'-dG-bisphosphate adducts both in vitro and in vivo.

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Herbal remedies containing aristolochic acid (AA) have been designated to be a strong carcinogen. This review summarizes major epidemiologic evidence to argue for the causal association between AA exposure and urothelial carcinoma as well as nephropathy. The exposure scenarios include the following: Belgian women taking slimming pills containing single material Guang Fang Ji, consumptions of mixtures of Chinese herbal products in the general population and patients with chronic renal failure in Taiwan, occupational exposure in Chinese herbalists, and food contamination in farming villages in valleys of the Danube River. Such an association is corroborated by detecting specific DNA adducts in the tumor tissue removed from affected patients. Preventive actions of banning such use and education to the healthcare professionals and public are necessary for the safety of herbal remedies.

Background: Ventilator-associated pneumonia (VAP) is a significant cause of mortality in the intensive care unit. Early diagnosis of VAP is important to provide appropriate treatment and reduce mortality. Developing a noninvasive and highly accurate diagnostic method is important. The invention of electronic sensors has been applied to analyze the volatile organic compounds in breath to detect VAP using a machine learning technique. However, the process of building an algorithm is usually unclear and prevents physicians from applying the artificial intelligence technique in clinical practice. Clear processes of model building and assessing accuracy are warranted. The objective of this study was to develop a breath test for VAP with a standardized protocol for a machine learning technique. Methods: We conducted a case-control study. This study enrolled subjects in an intensive care unit of a hospital in southern Taiwan from February 2017 to June 2019. We recruited patients with VAP as the case group and ventilated patients without pneumonia as the control group. We collected exhaled breath and analyzed the electric resistance changes of 32 sensor arrays of an electronic nose. We split the data into a set for training algorithms and a set for testing. We applied eight machine learning algorithms to build prediction models, improving model performance and providing an estimated diagnostic accuracy. Results: A total of 33 cases and 26 controls were used in the final analysis. Using eight machine learning algorithms, the mean accuracy in the testing set was 0.81 ± 0.04, the sensitivity was 0.79 ± 0.08, the specificity was 0.83 ± 0.00, the positive predictive value was 0.85 ± 0.02, the negative predictive value was 0.77 ± 0.06, and the area under the receiver operator characteristic curves was 0.85 ± 0.04. The mean kappa value in the testing set was 0.62 ± 0.08, which suggested good agreement. Conclusions: There was good accuracy in detecting VAP by sensor array and machine learning techniques. Artificial intelligence has the potential to assist the physician in making a clinical diagnosis. Clear protocols for data processing and the modeling procedure needed to increase generalizability.