789 research outputs found

    The thoughtful self

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    The relationship between a concept in the external world (e.g., the self), and its representation in cognition

    A Self-Reference False Memory Effect in the DRM Paradigm: Evidence from Eastern and Western Samples

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    It is well established that processing information in relation to oneself (i.e., selfreferencing) leads to better memory for that information than processing that same information in relation to others (i.e., other-referencing). However, it is unknown whether self-referencing also leads to more false memories than other-referencing. In the current two experiments with European and East Asian samples, we presented participants the Deese-Roediger/McDermott (DRM) lists together with their own name or other people’s name (i.e., β€œTrump” in Experiment 1 and β€œLi Ming” in Experiment 2). We found consistent results across the two experiments; that is, in the self-reference condition, participants had higher true and false memory rates compared to those in the other-reference condition. Moreover, we found that selfreferencing did not exhibit superior mnemonic advantage in terms of net accuracy compared to other-referencing and neutral conditions. These findings are discussed in terms of theoretical frameworks such as spreading activation theories and the fuzzytrace theory. We propose that our results reflect the adaptive nature of memory in the sense that cognitive processes that increase mnemonic efficiency may also increase susceptibility to associative false memories

    Views of addiction neuroscientists and clinicians on the clinical impact of a β€˜Brain Disease Model of Addiction’

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    Addiction is increasingly described as a "chronic and relapsing brain disease". The potential impact of the brain disease model on the treatment of addiction or addicted individuals' treatment behaviour remains uncertain. We conducted a qualitative study to examine: (i) the extent to which leading Australian addiction neuroscientists and clinicians accept the brain disease view of addiction; and (ii) their views on the likely impacts of this view on addicted individuals' beliefs and behaviour. Thirty-one Australian addiction neuroscientists and clinicians (10 females and 21 males; 16 with clinical experience and 15 with no clinical experience) took part in 1 h semi-structured interviews. Most addiction neuroscientists and clinicians did not uncritically support the use of brain disease model of addiction. Most were cautious about the potential for adverse impacts on individuals' recovery and motivation to enter treatment. While some recognised the possibility that the brain disease model of addiction may provide a rationale for addicted persons to seek treatment and motivate behaviour change, Australian addiction neuroscientist and clinicians do not assume that messages about "diseased brains" will always lead to increased treatment-seeking and reduced drug use. Research is needed on how neuroscience research could be used in ways that optimise positive outcomes for addicted persons

    Topoisomerase I but not thymidylate synthase is associated with improved outcome in patients with resected colorectal cancer treated with irinotecan containing adjuvant chemotherapy

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    <p>Abstract</p> <p>Background</p> <p>Thymidylate synthase (TS) and Topoisomerase I (Topo I) are significant biomarkers in colorectal cancer (CRC). We aimed to study the expression of TS and Topo I in patients with resected CRC who received adjuvant chemotherapy and correlated it with clinical outcome.</p> <p>Methods</p> <p>All patients diagnosed with CRC between 1989 and 2007 and treated with adjuvant chemotherapy within Hellenic Cooperative Oncology Group's (HeCOG) protocols, were identified. Archival paraffin-embedded tumor tissues were used for immunohistochemical detection of TS and Topo I. Immunohistochemistry was performed on tissue microarray slides using monoclonal antibodies against TS and Topo I. The results were correlated with survival (OS) and disease free survival (DFS).</p> <p>Results</p> <p>A cohort of 498 patients with a median age of 61 years and Dukes' stage B (49%) and C (51%) fulfilled the criteria of the study. All patients received adjuvant 5-FU-based chemotherapy, 38% irinotecan-containing. Positive TS and Topo I expression was found in 43% and 48% of cases, respectively. Five-year OS was 74% and DFS was 68%. In univariate analysis no association of TS and Topo I expression with OS and DFS was identified. In multivariate analysis however, Topo I expression was associated with a reduced risk of death (HR = 0.61, 95% CI 0.42-0.88, p = 0.009). In the irinotecan-treated subgroup, those patients who expressed Topo I had a better OS (HR = 0.47, 95% CI 0.23-0.94, p = 0.033).</p> <p>Conclusion</p> <p>Patients with resected CRC expressing Topo I seem to benefit from irinotecan-containing adjuvant chemotherapy. However randomised prospective trials are needed to confirm these results.</p

    A phase II study to determine the ability of gefitinib to reverse fluoropyrimidine resistance in metastatic colorectal cancer (the INFORM study)

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    There are data suggesting that inhibition of epidermal growth factor receptor (EGFR) tyrosine kinase signalling may reverse resistance to fluoropyrimidine treatment. To investigate this further, the INFORM study was an open-label, non-comparative phase II study of gefitinib (Iressa, ZD1839; AstraZeneca, Wilmington, DE, USA) 250 mg daily in combination with 5-fluorouracil (5-FU administered as an intravenous 400 mg mβˆ’2 bolus injection followed by 2800 mg mβˆ’2 infusion over 46 h and folinic acid administered as a 350 mg infusion over 2 h) every 2 weeks for up to 12 cycles in 24 patients with metastatic colorectal cancer refractory to previous fluoropyrimidine treatment. There were no objective responses. The stable disease rate was 37.5% (95% CI: 18.80, 59.41), median progression-free survival measured 116 days and overall survival was 226 days. Quality of life was unchanged compared to baseline values, and the commonest toxicities were diarrhoea, rash and fatigue with 7 out of 24 (29%) patients having a grade 3 or 4 toxicity. Gefitinib does not sensitise patients with fluoropyrimidine refractory metastatic colorectal cancer to 5-FU chemotherapy

    Disparities and risks of sexually transmissible infections among men who have sex with men in China: a meta-analysis and data synthesis.

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    BACKGROUND: Sexually transmitted infections (STIs), including Hepatitis B and C virus, are emerging public health risks in China, especially among men who have sex with men (MSM). This study aims to assess the magnitude and risks of STIs among Chinese MSM. METHODS: Chinese and English peer-reviewed articles were searched in five electronic databases from January 2000 to February 2013. Pooled prevalence estimates for each STI infection were calculated using meta-analysis. Infection risks of STIs in MSM, HIV-positive MSM and male sex workers (MSW) were obtained. This review followed the PRISMA guidelines and was registered in PROSPERO. RESULTS: Eighty-eight articles (11 in English and 77 in Chinese) investigating 35,203 MSM in 28 provinces were included in this review. The prevalence levels of STIs among MSM were 6.3% (95% CI: 3.5-11.0%) for chlamydia, 1.5% (0.7-2.9%) for genital wart, 1.9% (1.3-2.7%) for gonorrhoea, 8.9% (7.8-10.2%) for hepatitis B (HBV), 1.2% (1.0-1.6%) for hepatitis C (HCV), 66.3% (57.4-74.1%) for human papillomavirus (HPV), 10.6% (6.2-17.6%) for herpes simplex virus (HSV-2) and 4.3% (3.2-5.8%) for Ureaplasma urealyticum. HIV-positive MSM have consistently higher odds of all these infections than the broader MSM population. As a subgroup of MSM, MSW were 2.5 (1.4-4.7), 5.7 (2.7-12.3), and 2.2 (1.4-3.7) times more likely to be infected with chlamydia, gonorrhoea and HCV than the broader MSM population, respectively. CONCLUSION: Prevalence levels of STIs among MSW were significantly higher than the broader MSM population. Co-infection of HIV and STIs were prevalent among Chinese MSM. Integration of HIV and STIs healthcare and surveillance systems is essential in providing effective HIV/STIs preventive measures and treatments. TRIAL REGISTRATION: PROSPERO NO: CRD42013003721

    Prediction of hot spot residues at protein-protein interfaces by combining machine learning and energy-based methods

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    Background: Alanine scanning mutagenesis is a powerful experimental methodology for investigating the structural and energetic characteristics of protein complexes. Individual aminoacids are systematically mutated to alanine and changes in free energy of binding (Delta Delta G) measured. Several experiments have shown that protein-protein interactions are critically dependent on just a few residues ("hot spots") at the interface. Hot spots make a dominant contribution to the free energy of binding and if mutated they can disrupt the interaction. As mutagenesis studies require significant experimental efforts, there is a need for accurate and reliable computational methods. Such methods would also add to our understanding of the determinants of affinity and specificity in protein-protein recognition.Results: We present a novel computational strategy to identify hot spot residues, given the structure of a complex. We consider the basic energetic terms that contribute to hot spot interactions, i.e. van der Waals potentials, solvation energy, hydrogen bonds and Coulomb electrostatics. We treat them as input features and use machine learning algorithms such as Support Vector Machines and Gaussian Processes to optimally combine and integrate them, based on a set of training examples of alanine mutations. We show that our approach is effective in predicting hot spots and it compares favourably to other available methods. In particular we find the best performances using Transductive Support Vector Machines, a semi-supervised learning scheme. When hot spots are defined as those residues for which Delta Delta G >= 2 kcal/mol, our method achieves a precision and a recall respectively of 56% and 65%.Conclusion: We have developed an hybrid scheme in which energy terms are used as input features of machine learning models. This strategy combines the strengths of machine learning and energy-based methods. Although so far these two types of approaches have mainly been applied separately to biomolecular problems, the results of our investigation indicate that there are substantial benefits to be gained by their integration
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