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- ODonnell, Michael, et al.
(författare)
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Registered Replication Report: Dijksterhuis and van Knippenberg (1998)
- 2018
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Ingår i: Perspectives on Psychological Science. - : SAGE PUBLICATIONS LTD. - 1745-6916 .- 1745-6924. ; 13:2, s. 268-294
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Tidskriftsartikel (refereegranskat)abstract
- Dijksterhuis and van Knippenberg (1998) reported that participants primed with a category associated with intelligence (professor) subsequently performed 13% better on a trivia test than participants primed with a category associated with a lack of intelligence (soccer hooligans). In two unpublished replications of this study designed to verify the appropriate testing procedures, Dijksterhuis, van Knippenberg, and Holland observed a smaller difference between conditions (2%-3%) as well as a gender difference: Men showed the effect (9.3% and 7.6%), but women did not (0.3% and -0.3%). The procedure used in those replications served as the basis for this multilab Registered Replication Report. A total of 40 laboratories collected data for this project, and 23 of these laboratories met all inclusion criteria. Here we report the meta-analytic results for those 23 direct replications (total N = 4,493), which tested whether performance on a 30-item general-knowledge trivia task differed between these two priming conditions (results of supplementary analyses of the data from all 40 labs, N = 6,454, are also reported). We observed no overall difference in trivia performance between participants primed with the professor category and those primed with the hooligan category (0.14%) and no moderation by gender.
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| 2. |
- Swiderska-Chadaj, Zaneta, et al.
(författare)
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Learning to detect lymphocytes in immunohistochemistry with deep learning
- 2019
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Ingår i: Medical Image Analysis. - : ELSEVIER. - 1361-8415 .- 1361-8423. ; 58
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Tidskriftsartikel (refereegranskat)abstract
- The immune system is of critical importance in the development of cancer. The evasion of destruction by the immune system is one of the emerging hallmarks of cancer. We have built a dataset of 171,166 manually annotated CD3(+) and CD8(+) cells, which we used to train deep learning algorithms for automatic detection of lymphocytes in histopathology images to better quantify immune response. Moreover, we investigate the effectiveness of four deep learning based methods when different subcompartments of the whole-slide image are considered: normal tissue areas, areas with immune cell clusters, and areas containing artifacts. We have compared the proposed methods in breast, colon and prostate cancer tissue slides collected from nine different medical centers. Finally, we report the results of an observer study on lymphocyte quantification, which involved four pathologists from different medical centers, and compare their performance with the automatic detection. The results give insights on the applicability of the proposed methods for clinical use. U-Net obtained the highest performance with an F1-score of 0.78 and the highest agreement with manual evaluation (kappa = 0.72), whereas the average pathologists agreement with reference standard was kappa = 0.64. The test set and the automatic evaluation procedure are publicly available at lyon19.grand-challenge.org. (C) 2019 Elsevier B.V. All rights reserved.
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| 3. |
- Trabert, Britton, et al.
(författare)
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Antibodies Against Chlamydia trachomatis and Ovarian Cancer Risk in Two Independent Populations
- 2019
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Ingår i: Journal of the National Cancer Institute. - : Oxford University Press. - 0027-8874 .- 1460-2105. ; 111:2, s. 129-136
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Tidskriftsartikel (refereegranskat)abstract
- Background: Pelvic inflammatory disease (PID) has been associated with ovarian cancer risk. To clarify the role of Chlamydia trachomatis and other infectious agents in the development of ovarian cancer, we evaluated the association of serologic markers with incident ovarian cancer using a staged approach in two independent populations.Methods: Studies included: 1) a case-control study in Poland (244 ovarian cancers/556 control subjects) and 2) a prospective nested case-control study in the PLCO Cancer Screening Trial (160 ovarian cancers/159 control subjects). Associations of serologic marker levels with ovarian cancer risk at diagnostic as well as higher thresholds, identified in Poland and independently evaluated in PLCO, were estimated using multivariable adjusted logistic regression.Results: In the Polish study, antibodies (based on laboratory cut-point) against the chlamydia plasmid-encoded Pgp3 protein (serological gold standard) were associated with increased ovarian cancer risk (adjusted odds ratio [OR] = 1.63, 95% confidence interval [CI] = 1.20 to 2.22); when a positive result was redefined at higher levels, ovarian cancer risk was increased (cut-point 2: OR = 2.00, 95% CI = 1.38 to 2.89; cut-point 3 [max OR]: OR = 2.19, 95% CI = 1.29 to 3.73). In the prospective PLCO study, Pgp3 antibodies were associated with elevated risk at the laboratory cut-point (OR = 1.43, 95% CI = 0.78 to 2.63) and more stringent cut-points (cut-point 2: OR = 2.25, 95% CI = 1.07 to 4.71); cut-point 3: OR = 2.53, 95% CI = 0.63 to 10.08). In both studies, antibodies against other infectious agents measured were not associated with risk.Conclusions: In two independent populations, antibodies against prior/current C. trachomatis (Pgp3) were associated with a doubling in ovarian cancer risk, whereas markers of other infectious agents were unrelated. These findings lend support for an association between PID and ovarian cancer.
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