88 research outputs found

    Risk factors for oesophageal, lung, oral and laryngeal cancers in black South Africans

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    The authors used data collected from 1995 to 1999, from an on-going cancer case–control study in greater Johannesburg, to estimate the importance of tobacco and alcohol consumption and other suspected risk factors with respect to cancer of the oesophagus (267 men and 138 women), lung (105 men and 41 women), oral cavity (87 men and 37 women), and larynx (51 men). Cancers not associated with tobacco or alcohol consumption were used as controls (804 men and 1370 women). Tobacco smoking was found to be the major risk factor for all of these cancers with odds ratios ranging from 2.6 (95% CI 1.5–4.5) for oesophageal cancer in female ex-smokers to 50.9 (95% CI 12.6–204.6) for lung cancer in women, and 23.9 (95% CI 9.5–60.3) for lung cancer and 23.6 (95% CI 4.6–121.2) for laryngeal cancer in men who smoked 15 or more grams of tobacco a day. This is the first time an association between smoking and oral and laryngeal cancers has been shown in sub-Saharan Africa. Long-term residence in the Transkei region in the southeast of the country continues to be a risk factor for oesophageal cancer, especially in women (odds ratio=14.7, 95% CI 4.7–46.0), possibly due to nutritional factors. There was a slight increase in lung cancer (odds ratio=2.9, 95% CI 1.1–7.5) in men working in ‘potentially noxious’ industries. ‘Frequent’ alcohol consumption, on its own, caused a marginally elevated risk for oesophageal cancer (odds ratio=1.7, 95% CI 1.0–2.9, for women and odds ratio=1.8, 95% CI 1.2–2.8, for men). The risks for oesophageal cancer in relation to alcohol consumption increased significantly in male and female smokers (odds ratio=4.7, 95% CI=2.8–7.9 in males and odds ratio=4.8, 95% CI 3.2–6.1 in females). The above results are broadly in line with international findings

    Cognitive function during early abstinence from opioid dependence: a comparison to age, gender, and verbal intelligence matched controls

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    BACKGROUND: Individuals with opioid dependence have cognitive deficits during abuse period in attention, working memory, episodic memory, and executive function. After protracted abstinence consistent cognitive deficit has been found only in executive function. However, few studies have explored cognitive function during first weeks of abstinence. The purpose of this study was to study cognitive function of individuals with opioid dependence during early abstinence. It was hypothesized that cognitive deficits are pronounced immediately after peak withdrawal symptoms have passed and then partially recover. METHODS: Fifteen patients with opioid dependence and fifteen controls matched for, age, gender, and verbal intelligence were tested with a cognitive test battery When patients performed worse than controls correlations between cognitive performance and days of withdrawal, duration of opioid abuse, duration of any substance abuse, or opioid withdrawal symptom inventory score (Short Opiate Withdrawal Scale) were analyzed. RESULTS: Early abstinent opioid dependent patients performed statistically significantly worse than controls in tests measuring complex working memory, executive function, and fluid intelligence. Their complex working memory and fluid intelligence performances correlated statistically significantly with days of withdrawal. CONCLUSION: The results indicate a rather general neurocognitive deficit in higher order cognition. It is suggested that cognitive deficit during early abstinence from opioid dependence is related to withdrawal induced neural dysregulation in the prefrontal cortex and is partly transient

    Local and global regulation of transcription initiation in bacteria

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    Ten years of Nature Reviews Neuroscience: insights from the highly cited

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    Searches for the Z gamma decay mode of the Higgs boson and for new high-mass resonances in pp collisions at root s=13 TeV with the ATLAS detector

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    This article presents searches for the Zγ decay of the Higgs boson and for narrow high-mass resonances decaying to Zγ, exploiting Z boson decays to pairs of electrons or muons. The data analysis uses 36.1 fb−1 of pp collisions at s√=13s=13 recorded by the ATLAS detector at the CERN Large Hadron Collider. The data are found to be consistent with the expected Standard Model background. The observed (expected — assuming Standard Model pp → H → Zγ production and decay) upper limit on the production cross section times the branching ratio for pp → H → Zγ is 6.6. (5.2) times the Standard Model prediction at the 95% confidence level for a Higgs boson mass of 125.09 GeV. In addition, upper limits are set on the production cross section times the branching ratio as a function of the mass of a narrow resonance between 250 GeV and 2.4 TeV, assuming spin-0 resonances produced via gluon-gluon fusion, and spin-2 resonances produced via gluon-gluon or quark-antiquark initial states. For high-mass spin-0 resonances, the observed (expected) limits vary between 88 fb (61 fb) and 2.8 fb (2.7 fb) for the mass range from 250 GeV to 2.4 TeV at the 95% confidence level

    Image-based promoter prediction: a promoter prediction method based on evolutionarily generated patterns

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    Abstract Prediction of promoter regions is crucial for studying gene function and regulation. The well-accepted position weight matrix method for this purpose relies on predefined motifs, which would hinder application across different species. Here, we introduce image-based promoter prediction (IBPP) as a method that creates an “image” from training promoter sequences using an evolutionary approach and predicts promoters by matching with the “image”. We used Escherichia coli σ70 promoter sequences to test the performance of IBPP and the combination of IBPP and a support vector machine algorithm (IBPP-SVM). The “images” generated with IBPP could effectively distinguish promoter from non-promoter sequences. Compared with IBPP, IBPP-SVM showed a substantial improvement in sensitivity. Furthermore, both methods showed good performance for sequences of up to 2,000 nt in length. The performances of IBPP and IBPP-SVM were largely affected by the threshold and dimension of vectors, respectively. The source code and documentation are freely available at https://github.com/hahatcdg/IBPP
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