6 research outputs found

    DNA methylation in rat tissues by a series of homologous aliphatic nitrosamines ranging from N-nitrosodimethylamine to N-nitrosomethyldodecylamine

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    Aliphatic N-nitrosomethylalkylamines exhibit a remarkable organ specificity in rats, the principal targets for tumour induction being liver, oesophagus, urinary bladder and lung. We have determined the extent of DNA methylation in these tissues following a single oral dose (0.1 mmol/kg; 6 h survival) of each of 12 homologues, ranging from N-nitrosodimethylamine (C1) to N-nitrosomethyldodecylamine (C12). Methylpurines (7-and O6-methylguanine) were determined by cation exchange HPLC with fluorescence detection. Highest levels of hepatic DNA methylation were found with N-nitrosodimethylamine (C1) and N-nitrosomethylethylamine (C2), the most potent hepatocarcinogens in this series. Concentrations of methylpurines in liver DNA decreased with increasing chain length for C1-C5. Administration of the higher homologues (C6-C12) caused levels of DNA methylation which by themselves were considered too low to account for their hepatocarcinogenicity. In rat oesophagus, DNA methylation closely paralleled carcinogenicity, the butyl and pentyl derivatives (C4, C5) being most effective. In rat lung, the extent of DNA methylation was generally lower and there was no apparent correlation with carcinogenicity. Methylation of kidney DNA also decreased with increasing chain length and was only detectable for C1-C5. In urinary bladder DNA, methylpurines were below or close to the limit of detection. It is concluded that the initiation of malignant transformation by DNA methylation alone (through hydroxylation at the methylene α-carbon) could be operative for Cl in kidney and lung, for Cl and C2 in liver, and C3-C5 in oesophagus. For the higher homologues, the extent of DNA methylation seems insufficient to explain the complex pattern of tissue specificity, suggesting that DNA modification other than, or in addition to, methylation may be responsibl

    Large expert-curated database for benchmarking document similarity detection in biomedical literature search

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    Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe

    DNA methylation in rat tissues by a series of homologous aliphatic nitrosamines ranging from N-nitrosodimethylamine to N-nitrosomethyldodecylamine

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    Aliphatic N-nitrosomethylalkylamines exhibit a remarkable organ specificity in rats, the principal targets for tumour induction being liver, oesophagus, urinary bladder and lung. We have determined the extent of DNA methylation in these tissues following a single oral dose (0.1 mmol/kg; 6 h survival) of each of 12 homologues, ranging from N-nitrosodimethylamine (C1) to N-nitrosomethyldodecylamine (C12). Methylpurines (7-and O6-methylguanine) were determined by cation exchange HPLC with fluorescence detection. Highest levels of hepatic DNA methylation were found with N-nitrosodimethylamine (C1) and N-nitrosomethylethylamine (C2), the most potent hepatocarcinogens in this series. Concentrations of methylpurines in liver DNA decreased with increasing chain length for C1-C5. Administration of the higher homologues (C6-C12) caused levels of DNA methylation which by themselves were considered too low to account for their hepatocarcinogenicity. In rat oesophagus, DNA methylation closely paralleled carcinogenicity, the butyl and pentyl derivatives (C4, C5) being most effective. In rat lung, the extent of DNA methylation was generally lower and there was no apparent correlation with carcinogenicity. Methylation of kidney DNA also decreased with increasing chain length and was only detectable for C1-C5. In urinary bladder DNA, methylpurines were below or close to the limit of detection. It is concluded that the initiation of malignant transformation by DNA methylation alone (through hydroxylation at the methylene α-carbon) could be operative for Cl in kidney and lung, for Cl and C2 in liver, and C3-C5 in oesophagus. For the higher homologues, the extent of DNA methylation seems insufficient to explain the complex pattern of tissue specificity, suggesting that DNA modification other than, or in addition to, methylation may be responsibl

    Large expert-curated database for benchmarking document similarity detection in biomedical literature search

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    Large expert-curated database for benchmarking document similarity detection in biomedical literature search

    No full text
    Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical science. © The Author(s) 2019. Published by Oxford University Press
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