| 1. |
- Bouget, D., et al.
(författare)
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Preoperative Brain Tumor Imaging: Models and Software for Segmentation and Standardized Reporting
- 2022
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Ingår i: Frontiers in Neurology. - : Frontiers Media SA. - 1664-2295. ; 13
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Tidskriftsartikel (refereegranskat)abstract
- For patients suffering from brain tumor, prognosis estimation and treatment decisions are made by a multidisciplinary team based on a set of preoperative MR scans. Currently, the lack of standardized and automatic methods for tumor detection and generation of clinical reports, incorporating a wide range of tumor characteristics, represents a major hurdle. In this study, we investigate the most occurring brain tumor types: glioblastomas, lower grade gliomas, meningiomas, and metastases, through four cohorts of up to 4,000 patients. Tumor segmentation models were trained using the AGU-Net architecture with different preprocessing steps and protocols. Segmentation performances were assessed in-depth using a wide-range of voxel and patient-wise metrics covering volume, distance, and probabilistic aspects. Finally, two software solutions have been developed, enabling an easy use of the trained models and standardized generation of clinical reports: Raidionics and Raidionics-Slicer. Segmentation performances were quite homogeneous across the four different brain tumor types, with an average true positive Dice ranging between 80 and 90%, patient-wise recall between 88 and 98%, and patient-wise precision around 95%. In conjunction to Dice, the identified most relevant other metrics were the relative absolute volume difference, the variation of information, and the Hausdorff, Mahalanobis, and object average symmetric surface distances. With our Raidionics software, running on a desktop computer with CPU support, tumor segmentation can be performed in 16-54 s depending on the dimensions of the MRI volume. For the generation of a standardized clinical report, including the tumor segmentation and features computation, 5-15 min are necessary. All trained models have been made open-access together with the source code for both software solutions and validation metrics computation. In the future, a method to convert results from a set of metrics into a final single score would be highly desirable for easier ranking across trained models. In addition, an automatic classification of the brain tumor type would be necessary to replace manual user input. Finally, the inclusion of post-operative segmentation in both software solutions will be key for generating complete post-operative standardized clinical reports.
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| 2. |
- Deschasaux, M., et al.
(författare)
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Depicting the composition of gut microbiota in a population with varied ethnic origins but shared geography
- 2018
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Ingår i: Nature Medicine. - : Springer Science and Business Media LLC. - 1078-8956 .- 1546-170X. ; 24:10, s. 1526-31
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Tidskriftsartikel (refereegranskat)abstract
- Trillions of microorganisms inhabit the human gut and are regarded as potential key factors for health(1,2). Characteristics such as diet, lifestyle, or genetics can shape the composition of the gut microbiota(2-6) and are usually shared by individuals from comparable ethnic origin. So far, most studies assessing how ethnicity relates to the intestinal microbiota compared small groups living at separate geographical locations(7-10). Using fecal 16S ribosomal RNA gene sequencing in 2,084 participants of the Healthy Life in an Urban Setting (HELIUS) study(11,12), we show that individuals living in the same city tend to share similar gut microbiota characteristics with others of their ethnic background. Ethnicity contributed to explain the interindividual dissimilarities in gut microbiota composition, with three main poles primarily characterized by operational taxonomic units (OTUs) classified as Prevotella (Moroccans, Turks, Ghanaians), Bacteroides (African Surinamese, South-Asian Surinamese), and Clostridiales (Dutch). The Dutch exhibited the greatest gut microbiota alpha-diversity and the South-Asian Surinamese the smallest, with corresponding enrichment or depletion in numerous OTUs. Ethnic differences in alpha-diversity and interindividual dissimilarities were independent of metabolic health and only partly explained by ethnic-related characteristics including sociodemographic, lifestyle, or diet factors. Hence, the ethnic origin of individuals may be an important factor to consider in microbiome research and its potential future applications in ethnic-diverse societies.
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| 3. |
- Verhaar, B. J. H., et al.
(författare)
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Associations between gutmicrobiota, faecal short-chain fatty acids, and blood pressure across ethnic groups: the HELIUS study
- 2020
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Ingår i: European Heart Journal. - : Oxford University Press (OUP). - 0195-668X .- 1522-9645. ; 41:44, s. 4259-4267
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Tidskriftsartikel (refereegranskat)abstract
- Aims Preliminary evidence from animal and human studies shows that gut microbiota composition and levels of microbiota-derived metabolites, including short-chain fatty acids (SCFAs), are associated with blood pressure (BP). We hypothesized that faecal microbiota composition and derived metabolites may be differently associated with BP across ethnic groups Methods and results We included 4672 subjects (mean age 49.8 +/- 11.7 years, 52% women) from six different ethnic groups participating in the HEalthy Life In an Urban Setting (HELIUS) study. The gut microbiota was profiled using 16S rRNA gene amplicon sequencing. Associations between microbiota composition and office BP were assessed using machine learning prediction models. In the subgroups with the largest associations, faecal SCFA levels were compared in 200 subjects with lower or higher systolic BP. Faecal microbiota composition explained 4.4% of the total systolic BP variance. Best predictors for systolic BP included Roseburia spp., Clostridium spp., Romboutsia spp., and Ruminococcaceae spp. Explained variance of the microbiota composition was highest in Dutch subjects (4.8%), but very low in South-Asian Surinamese, African Surinamese, Ghanaian, Moroccan and Turkish descent groups (explained variance <0.8%). Faecal SCFA levels, including acetate (P < 0.05) and propionate (P < 0.01), were lower in young Dutch participants with low systolic BP Conclusions Faecal microbiota composition is associated with BP, but with strongly divergent associations between ethnic groups. Intriguingly, while Dutch participants with lower BP had higher abundances of several SCFA-producing microbes, they had lower faecal SCFA levels. Intervention studies with SCFAs could provide more insight in the effects of these metabolites on BP.
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| 4. |
- Krzywicka, K., et al.
(författare)
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Age-Stratified Risk of Cerebral Venous Sinus Thrombosis After SARS-CoV-2 Vaccination
- 2022
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Ingår i: Neurology. - : Ovid Technologies (Wolters Kluwer Health). - 0028-3878 .- 1526-632X. ; 98:7, s. E759-E768
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Tidskriftsartikel (refereegranskat)abstract
- Background and Objectives Cerebral venous sinus thrombosis (CVST) as a part of the thrombosis and thrombocytopenia syndrome is a rare adverse drug reaction of severe acute respiratory syndrome coronavirus disease 2 (SARS-CoV-2) vaccination. Estimated background rate of CVST with thrombocytopenia is 0.1 per million per month. We assessed the age-stratified risk of CVST with and without thrombocytopenia after SARS-CoV-2 vaccination. Methods We estimated the absolute risk of CVST with and without thrombocytopenia within 28 days of a first dose of 4 SARS-CoV-2 vaccinations using data from the European Medicines Agency's EudraVigilance database (until June 13, 2021). As a denominator, we used data on vaccine delivery from 31 European countries. For 22.8 million adults from 25 countries, we estimated the absolute risk of CVST after the first dose of ChAdOx1 nCov-19 per age category. Results The absolute risk of CVST within 28 days of first-dose vaccination was 7.5 (95% confidence interval [CI] 6.9-8.3), 0.7 (95% CI 0.2-2.4), 0.6 (95% CI 0.5-0.7), and 0.6 (95% CI 0.3-1.1) per million of first doses of ChAdOx1 nCov-19, Ad26.COV2.S, BNT162b2, and mRNA-1273, respectively. The absolute risk of CVST with thrombocytopenia within 28 days of first dose vaccination was 4.4 (95% CI 3.9-4.9), 0.7 (95% CI 0.2-2.4), 0.0 (95% CI 0.0-0.1), and 0.0 (95% CI 0.0-0.2) per million of first doses of ChAdOx1 nCov-19, Ad26.COV2.S, BNT162b2, and mRNA-1273, respectively. In recipients of ChAdOx1 nCov-19, the absolute risk of CVST, both with and without thrombocytopenia, was the highest in the 18- to 24-year-old group (7.3 per million, 95% CI 2.8-18.8 and 3.7 per million, 95% CI 1.0-13.3, respectively). The risk of CVST with thrombocytopenia in ChAdOx1 nCov-19 recipients was the lowest in the age group >= 70 years (0.2, 95% CI 0.0-1.3). Age <60 years compared to >= 60 years was a predictor for CVST with thrombocytopenia (incidence rate ratio 5.79, 95% CI 2.98-11.24, p < 0.001). Discussion The risk of CVST with thrombocytopenia within 28 days of first-dose vaccination with ChAdOx1 nCov-19 was higher in younger age groups. The risk of CVST with thrombocytopenia was slightly increased in patients receiving Ad26.COV2.S compared with the estimated background risk. The risk of CVST with thrombocytopenia was not increased in recipients of SARS-CoV-2 mRNA vaccines.
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| 5. |
- Bel Lassen, P., et al.
(författare)
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Protein intake, metabolic status and the gut microbiota in different ethnicities: Results from two independent cohorts
- 2021
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Ingår i: Nutrients. - : MDPI AG. - 2072-6643. ; 13:9
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Tidskriftsartikel (refereegranskat)abstract
- Background: Protein intake has been associated with the development of pre-diabetes (pre-T2D) and type 2 diabetes (T2D). The gut microbiota has the capacity to produce harmful metabolites derived from dietary protein. Furthermore, both the gut microbiota composition and metabolic status (e.g., insulin resistance) can be modulated by diet and ethnicity. However, to date most studies have predominantly focused on carbohydrate and fiber intake with regards to metabolic status and gut microbiota composition. Objectives: To determine the associations between dietary protein intake, gut microbiota composition, and metabolic status in different ethnicities. Methods: Separate cross-sectional analysis of two European cohorts (MetaCardis, n = 1759; HELIUS, n = 1528) including controls, patients with pre-T2D, and patients with T2D of Caucasian/non-Caucasian origin with nutritional data obtained from Food Frequency Questionnaires and gut microbiota composition. Results: In both cohorts, animal (but not plant) protein intake was associated with pre-T2D status and T2D status after adjustment for confounders. There was no significant association between protein intake (total, animal, or plant) with either gut microbiota alpha diversity or beta diversity, regardless of ethnicity. At the species level, we identified taxonomical signatures associated with animal protein intake that overlapped in both cohorts with different abundances according to metabolic status and ethnicity. Conclusions: Animal protein intake is associated with pre-T2D and T2D status but not with gut microbiota beta or alpha diversity, regardless of ethnicity. Gut microbial taxonomical signatures were identified, which could function as potential modulators in the association between dietary protein intake and metabolic status. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.
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| 6. |
- Prodan, A., et al.
(författare)
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Comparing bioinformatic pipelines for microbial 16S rRNA amplicon sequencing
- 2020
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Ingår i: Plos One. - : Public Library of Science (PLoS). - 1932-6203. ; 15:1
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Tidskriftsartikel (refereegranskat)abstract
- Microbial amplicon sequencing studies are an important tool in biological and biomedical research. Widespread 16S rRNA gene microbial surveys have shed light on the structure of many ecosystems inhabited by bacteria, including the human body. However, specialized software and algorithms are needed to convert raw sequencing data into biologically meaningful information (i.e. tables of bacterial counts). While different bioinformatic pipelines are available in a rapidly changing and improving field, users are often unaware of limitations and biases associated with individual pipelines and there is a lack of agreement regarding best practices. Here, we compared six bioinformatic pipelines for the analysis of amplicon sequence data: three OTU-level flows (QIIME-uclust, MOTHUR, and USEARCH-UPARSE) and three ASV-level (DADA2, Qiime2-Deblur, and USEARCH-UNOISE3). We tested workflows with different quality control options, clustering algorithms, and cutoff parameters on a mock community as well as on a large (N = 2170) recently published fecal sample dataset from the multi-ethnic HELIUS study. We assessed the sensitivity, specificity, and degree of consensus of the different outputs. DADA2 offered the best sensitivity, at the expense of decreased specificity compared to USEARCH-UNOISE3 and Qiime2-Deblur. USEARCH-UNOISE3 showed the best balance between resolution and specificity. OTU-level USEARCH-UPARSE and MOTHUR performed well, but with lower specificity than ASV-devel pipelines. QIIME-uclust produced large number of spurious OTUs as well as inflated alpha-diversity measures and should be avoided in future studies. This study provides guidance for researchers using amplicon sequencing to gain biological insights.
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| 7. |
- Helland, Ragnhild Holden, et al.
(författare)
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Segmentation of glioblastomas in early post-operative multi-modal MRI with deep neural networks.
- 2023
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Ingår i: Scientific reports. - 2045-2322. ; 13:1
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Tidskriftsartikel (refereegranskat)abstract
- Extent of resection after surgery is one of the main prognostic factors for patients diagnosed with glioblastoma. To achieve this, accurate segmentation and classification of residual tumor from post-operative MR images is essential. The current standard method for estimating it is subject to high inter- and intra-rater variability, and an automated method for segmentation of residual tumor in early post-operative MRI could lead to a more accurate estimation of extent of resection. In this study, two state-of-the-art neural network architectures for pre-operative segmentation were trained for the task. The models were extensively validated on a multicenter dataset with nearly 1000 patients, from 12 hospitals in Europe and the United States. The best performance achieved was a 61% Dice score, and the best classification performance was about 80% balanced accuracy, with a demonstrated ability to generalize across hospitals. In addition, the segmentation performance of the best models was on par with human expert raters. The predicted segmentations can be used to accurately classify the patients into those with residual tumor, and those with gross total resection.
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