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- Mazzaron Barcelos, Gustavo Rafael, et al.
(författare)
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Polymorphisms in glutathione-related genes modify mercury concentrations and antioxidant status in subjects environmentally exposed to methylmercury
- 2013
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Ingår i: Science of the Total Environment. - : Elsevier BV. - 1879-1026 .- 0048-9697. ; 463, s. 319-325
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Tidskriftsartikel (refereegranskat)abstract
- Methylmercury (MeHg) toxicity may vary widely despite similar levels of exposure. This is hypothetically related to genetic differences in enzymes metabolizing MeHg. MeHg causes oxidative stress in experimental models but little is known about its effects on humans. The aims of the present study was to evaluate the effects of polymorphisms in glutathione (GSH)-related genes (GSTM1, GSTT1, GSTP1 and GCLM) on Hg concentrations in blood and hair, as well as MeHg-related effects on catalase (CAT) and glutathione-peroxidase (GPx) activity and GSH concentrations. Study subjects were from an Amazonian population in Brazil chronically exposed to MeHg from fish. Hg in blood and hair were determined by ICP-MS, CAT, GPx and GSH were determined by spectrophotometry, and multiplex PCR (GSTM1 and GSTT1) and TagMan assays (GSTP1 and GCLM) were used for genotyping. Mean Hg concentrations in blood and hair were 48 +/- 36 mu g/L and 14 +/- 10 mu g/g. Persons with the GCLM-588 IT genotype had lower blood and hair Hg than did C-allele carriers (linear regression for Hg in blood beta = -0.32, p = 0.017; and hair beta = -0.33; p = 0.0090; adjusted for fish intake, age and gender). GSTM1*0 homozygous had higher blood (beta = 0.20; p = 0.017) and hair Hg (hair beta = 0.20; p = 0.013). Exposure to MeHg altered antioxidant status (CAT:beta = -0.086; GSH:beta = -0.12; GPx:beta = -0.16; all p < 0.010; adjusted for gender, age and smoking). Persons with GSTM1*0 had higher CAT activity in the blood than those with GSTM1. Our data thus indicate that some GSH-related polymorphisms, such as GSTM1 and GCLM may modify MeHg metabolism and Hg-related antioxidant effects. (C) 2013 Elsevier BY. All rights reserved.
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- Strom, Peter, et al.
(författare)
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Artificial intelligence for diagnosis and grading of prostate cancer in biopsies : a population-based, diagnostic study
- 2020
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Ingår i: The Lancet Oncology. - : Elsevier. - 1470-2045 .- 1474-5488. ; 21:2, s. 222-232
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Tidskriftsartikel (refereegranskat)abstract
- BackgroundAn increasing volume of prostate biopsies and a worldwide shortage of urological pathologists puts a strain on pathology departments. Additionally, the high intra-observer and inter-observer variability in grading can result in overtreatment and undertreatment of prostate cancer. To alleviate these problems, we aimed to develop an artificial intelligence (AI) system with clinically acceptable accuracy for prostate cancer detection, localisation, and Gleason grading.MethodsWe digitised 6682 slides from needle core biopsies from 976 randomly selected participants aged 50–69 in the Swedish prospective and population-based STHLM3 diagnostic study done between May 28, 2012, and Dec 30, 2014 (ISRCTN84445406), and another 271 from 93 men from outside the study. The resulting images were used to train deep neural networks for assessment of prostate biopsies. The networks were evaluated by predicting the presence, extent, and Gleason grade of malignant tissue for an independent test dataset comprising 1631 biopsies from 246 men from STHLM3 and an external validation dataset of 330 biopsies from 73 men. We also evaluated grading performance on 87 biopsies individually graded by 23 experienced urological pathologists from the International Society of Urological Pathology. We assessed discriminatory performance by receiver operating characteristics and tumour extent predictions by correlating predicted cancer length against measurements by the reporting pathologist. We quantified the concordance between grades assigned by the AI system and the expert urological pathologists using Cohen's kappa.FindingsThe AI achieved an area under the receiver operating characteristics curve of 0·997 (95% CI 0·994–0·999) for distinguishing between benign (n=910) and malignant (n=721) biopsy cores on the independent test dataset and 0·986 (0·972–0·996) on the external validation dataset (benign n=108, malignant n=222). The correlation between cancer length predicted by the AI and assigned by the reporting pathologist was 0·96 (95% CI 0·95–0·97) for the independent test dataset and 0·87 (0·84–0·90) for the external validation dataset. For assigning Gleason grades, the AI achieved a mean pairwise kappa of 0·62, which was within the range of the corresponding values for the expert pathologists (0·60–0·73).InterpretationAn AI system can be trained to detect and grade cancer in prostate needle biopsy samples at a ranking comparable to that of international experts in prostate pathology. Clinical application could reduce pathology workload by reducing the assessment of benign biopsies and by automating the task of measuring cancer length in positive biopsy cores. An AI system with expert-level grading performance might contribute a second opinion, aid in standardising grading, and provide pathology expertise in parts of the world where it does not exist.
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- Ström, Peter, et al.
(författare)
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Pathologist-Level Grading of Prostate Biospies with Artificial intelligence
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Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- Background: An increasing volume of prostate biopsies and a world-wide shortage of uro-pathologists puts a strain on pathology departments. Additionally, the high intra- and inter-observer variability in grading can result in over- and undertreatment of prostate cancer. Artificial intelligence (AI) methods may alleviate these problems by assisting pathologists to reduce workload and harmonize grading. Methods: We digitized 6,682 needle biopsies from 976 participants in the population based STHLM3 diagnostic study to train deep neural networks for assessing prostate biopsies. The networks were evaluated by predicting the presence, extent, and Gleason grade of malignant tissue for an independent test set comprising 1,631 biopsies from 245 men. We additionally evaluated grading performance on 87 biopsies individually graded by 23 experienced urological pathologists from the International Society of Urological Pathology. We assessed discriminatory performance by receiver operating characteristics (ROC) and tumor extent predictions by correlating predicted millimeter cancer length against measurements by the reporting pathologist. We quantified the concordance between grades assigned by the AI and the expert urological pathologists using Cohen's kappa. Results: The performance of the AI to detect and grade cancer in prostate needle biopsy samples was comparable to that of international experts in prostate pathology. The AI achieved an area under the ROC curve of 0.997 for distinguishing between benign and malignant biopsy cores, and 0.999 for distinguishing between men with or without prostate cancer. The correlation between millimeter cancer predicted by the AI and assigned by the reporting pathologist was 0.96. For assigning Gleason grades, the AI achieved an average pairwise kappa of 0.62. This was within the range of the corresponding values for the expert pathologists (0.60 to 0.73).
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