| 1. |
- Benhamou, S, et al.
(författare)
-
Meta- and pooled analyses of the effects of glutathione S-transferase M1 polymorphisms and smoking on lung cancer risk
- 2002
-
Ingår i: Carcinogenesis. - : Oxford University Press (OUP). - 0143-3334 .- 1460-2180. ; 23:8, s. 1343-1350
-
Tidskriftsartikel (refereegranskat)abstract
- Susceptibility to lung cancer may in part be attributable to inter-individual variability in metabolic activation or detoxification of tobacco carcinogens. The glutathione S-transferase M1 (GSTM1) genetic polymorphism has been extensively studied in this context; two recent meta-analyses of case-control studies suggested an association between GSTM1 deletion and lung cancer. At least 15 studies have been published after these overviews. We undertook a new meta-analysis to summarize the results of 43 published case-control studies including >18 000 individuals. A slight excess of risk of lung cancer for individuals with the GSTM1 null genotype was found (odds ratio (OR) = 1.17, 95% confidence interval (CI) 1.07-1.27). No evidence of publication bias was found (P = 0.4), however, it is not easy to estimate the extent of such bias and we cannot rule out some degree of publication bias in our results. A pooled analysis of the original data of about 9500 subjects involved in 21 case-control studies from the International Collaborative Study on Genetic Susceptibility to Environmental Carcinogens (GSEC) data set was performed to assess the role of GSTM1 genotype as a modifier of the effect of smoking on lung cancer risk with adequate power. Analyses revealed no evidence of increased risk of lung cancer among carriers of the GSTM1 null genotype (age-, gender- and center-adjusted OR = 1.08, 95% CI 0.98-1.18) and no evidence of interaction between GSTM1 genotype and either smoking status or cumulative tobacco consumption.
|
|
| 2. |
- Garte, S, et al.
(författare)
-
Metabolic gene polymorphism frequencies in control populations
- 2001
-
Ingår i: Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology. - 1055-9965. ; 10:12, s. 1239-1248
-
Tidskriftsartikel (refereegranskat)
|
|
| 3. |
- Kaasinen, E, et al.
(författare)
-
Impact of constitutional TET2 haploinsufficiency on molecular and clinical phenotype in humans
- 2019
-
Ingår i: Nature communications. - : Springer Science and Business Media LLC. - 2041-1723. ; 10:1, s. 1252-
-
Tidskriftsartikel (refereegranskat)abstract
- Clonal hematopoiesis driven by somatic heterozygous TET2 loss is linked to malignant degeneration via consequent aberrant DNA methylation, and possibly to cardiovascular disease via increased cytokine and chemokine expression as reported in mice. Here, we discover a germline TET2 mutation in a lymphoma family. We observe neither unusual predisposition to atherosclerosis nor abnormal pro-inflammatory cytokine or chemokine expression. The latter finding is confirmed in cells from three additional unrelated TET2 germline mutation carriers. The TET2 defect elevates blood DNA methylation levels, especially at active enhancers and cell-type specific regulatory regions with binding sequences of master transcription factors involved in hematopoiesis. The regions display reduced methylation relative to all open chromatin regions in four DNMT3A germline mutation carriers, potentially due to TET2-mediated oxidation. Our findings provide insight into the interplay between epigenetic modulators and transcription factor activity in hematological neoplasia, but do not confirm the putative role of TET2 in atherosclerosis.
|
|
| 4. |
|
|
| 5. |
|
|
| 6. |
|
|
| 7. |
- Smits, KM, et al.
(författare)
-
Association of metabolic gene polymorphisms with tobacco consumption in healthy controls
- 2004
-
Ingår i: International Journal of Cancer. - : Wiley. - 0020-7136 .- 1097-0215. ; 110:2, s. 266-270
-
Tidskriftsartikel (refereegranskat)abstract
- Polymorphisms in genes that encode for metabolic enzymes have been associated with variations in enzyme activity between individuals. Such variations could be associated with differences in individual exposure to carcinogens that are metabolized by these genes. In this study, we examine the association between polymorphisms in several metabolic genes and the consumption of tobacco in a large sample of healthy individuals. The database of the International Collaborative Study on Genetic Susceptibility to Environmental Carcinogens was used. All the individuals who were controls from the case-control studies included in the data set with information on smoking habits and on genetic polymorphisms were selected (n = 20,938). Sufficient information was available on the following genes that are involved in the metabolism of tobacco smoke constituents: CYPIAI, GSTMI, GSTTI, NAT2 and GSTPI. None of the tested genes was clearly associated with smoking behavior. Information on smoking dose, available for a subset of subjects, showed no effect of metabolic gene polymorphisms on the amount of smoking. No association between polymorphisms in the genes studied and tobacco consumption was observed; therefore, no effect of these genes on smoking behavior should be expected.
|
|
| 8. |
|
|
| 9. |
|
|
| 10. |
- Huhtanen, JT, et al.
(författare)
-
Deep learning accurately classifies elbow joint effusion in adult and pediatric radiographs
- 2022
-
Ingår i: Scientific reports. - : Springer Science and Business Media LLC. - 2045-2322. ; 12:1, s. 11803-
-
Tidskriftsartikel (refereegranskat)abstract
- Joint effusion due to elbow fractures are common among adults and children. Radiography is the most commonly used imaging procedure to diagnose elbow injuries. The purpose of the study was to investigate the diagnostic accuracy of deep convolutional neural network algorithms in joint effusion classification in pediatric and adult elbow radiographs. This retrospective study consisted of a total of 4423 radiographs in a 3-year period from 2017 to 2020. Data was randomly separated into training (n = 2672), validation (n = 892) and test set (n = 859). Two models using VGG16 as the base architecture were trained with either only lateral projection or with four projections (AP, LAT and Obliques). Three radiologists evaluated joint effusion separately on the test set. Accuracy, precision, recall, specificity, F1 measure, Cohen’s kappa, and two-sided 95% confidence intervals were calculated. Mean patient age was 34.4 years (1–98) and 47% were male patients. Trained deep learning framework showed an AUC of 0.951 (95% CI 0.946–0.955) and 0.906 (95% CI 0.89–0.91) for the lateral and four projection elbow joint images in the test set, respectively. Adult and pediatric patient groups separately showed an AUC of 0.966 and 0.924, respectively. Radiologists showed an average accuracy, sensitivity, specificity, precision, F1 score, and AUC of 92.8%, 91.7%, 93.6%, 91.07%, 91.4%, and 92.6%. There were no statistically significant differences between AUC's of the deep learning model and the radiologists (p value > 0.05). The model on the lateral dataset resulted in higher AUC compared to the model with four projection datasets. Using deep learning it is possible to achieve expert level diagnostic accuracy in elbow joint effusion classification in pediatric and adult radiographs. Deep learning used in this study can classify joint effusion in radiographs and can be used in image interpretation as an aid for radiologists.
|
|
