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- Cruz, Raquel, et al.
(författare)
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Novel genes and sex differences in COVID-19 severity
- 2022
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Ingår i: Human Molecular Genetics. - : Oxford University Press. - 0964-6906 .- 1460-2083. ; 31:22, s. 3789-3806
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Tidskriftsartikel (refereegranskat)abstract
- Here, we describe the results of a genome-wide study conducted in 11 939 coronavirus disease 2019 (COVID-19) positive cases with an extensive clinical information that were recruited from 34 hospitals across Spain (SCOURGE consortium). In sex-disaggregated genome-wide association studies for COVID-19 hospitalization, genome-wide significance (P < 5 × 10−8) was crossed for variants in 3p21.31 and 21q22.11 loci only among males (P = 1.3 × 10−22 and P = 8.1 × 10−12, respectively), and for variants in 9q21.32 near TLE1 only among females (P = 4.4 × 10−8). In a second phase, results were combined with an independent Spanish cohort (1598 COVID-19 cases and 1068 population controls), revealing in the overall analysis two novel risk loci in 9p13.3 and 19q13.12, with fine-mapping prioritized variants functionally associated with AQP3 (P = 2.7 × 10−8) and ARHGAP33 (P = 1.3 × 10−8), respectively. The meta-analysis of both phases with four European studies stratified by sex from the Host Genetics Initiative (HGI) confirmed the association of the 3p21.31 and 21q22.11 loci predominantly in males and replicated a recently reported variant in 11p13 (ELF5, P = 4.1 × 10−8). Six of the COVID-19 HGI discovered loci were replicated and an HGI-based genetic risk score predicted the severity strata in SCOURGE. We also found more SNP-heritability and larger heritability differences by age (<60 or ≥60 years) among males than among females. Parallel genome-wide screening of inbreeding depression in SCOURGE also showed an effect of homozygosity in COVID-19 hospitalization and severity and this effect was stronger among older males. In summary, new candidate genes for COVID-19 severity and evidence supporting genetic disparities among sexes are provided.
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| 2. |
- Saravia, Marta Estela, et al.
(författare)
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Recovery of mutans streptococci on MSB, SB-20 and SB-20M agar media
- 2013
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Ingår i: Archives of Oral Biology. - : Elsevier BV. - 0003-9969. ; 58, s. 311-316
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Tidskriftsartikel (refereegranskat)abstract
- Objective: The recovery of mutans streptococci in saliva and dental biofilm samples depends, in part, on the culture medium used. In this study, we compared (i) the culture media Sucrose-Bacitracin agar (SB-20), Modified SB-20 (SB-20M) and Mitis Salivarius Bacitracin agar (MSB) in the count of colony forming units (cfu) of mutans streptococci and (ii) in the morphological and biochemical differentiation between Streptococcus mutans and Streptococcus sobrinus. Design: Samples of non-stimulated saliva from 20 children were plated on SB-20, SB-20M and MSB, and incubated in microaerophilia at 37 °C for 72 h. Identification of microorganisms was based on analysis of colony morphology under stereomicroscopy. The biochemical identification of colonies was done by biochemical tests using sugar fermentation, resistance to bacitracin and hydrogen peroxide production. Results: There was no significant difference (p > 0.05) in the number of cfu of mutans streptococci recovered on SB-20 and SB-20M agar. Comparing the media, SB-20 and SB-20M yielded a larger number of mutans streptococci colonies (p < 0.05) and were more effective than MSB in the identification of S. sobrinus (p < 0.05), but not of S. mutans (p > 0.05). Conclusion: There was no significant difference between SB-20 and SB-20M culture media in the count of mutans streptococci, demonstrating that the replacement of sucrose by coarse granular cane sugar did not alter the efficacy of the medium. Compared with MSB, SB-20 and SB-20M allowed counting a larger number of mutans streptococci colonies and a more effective morphological identification of S. sobrinus. © 2012 Elsevier Ltd.
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| 3. |
- Wu, Ona, et al.
(författare)
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Big Data Approaches to Phenotyping Acute Ischemic Stroke Using Automated Lesion Segmentation of Multi-Center Magnetic Resonance Imaging Data
- 2019
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Ingår i: Stroke. - 1524-4628. ; 50:7, s. 1734-1741
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Tidskriftsartikel (refereegranskat)abstract
- Background and Purpose- We evaluated deep learning algorithms' segmentation of acute ischemic lesions on heterogeneous multi-center clinical diffusion-weighted magnetic resonance imaging (MRI) data sets and explored the potential role of this tool for phenotyping acute ischemic stroke. Methods- Ischemic stroke data sets from the MRI-GENIE (MRI-Genetics Interface Exploration) repository consisting of 12 international genetic research centers were retrospectively analyzed using an automated deep learning segmentation algorithm consisting of an ensemble of 3-dimensional convolutional neural networks. Three ensembles were trained using data from the following: (1) 267 patients from an independent single-center cohort, (2) 267 patients from MRI-GENIE, and (3) mixture of (1) and (2). The algorithms' performances were compared against manual outlines from a separate 383 patient subset from MRI-GENIE. Univariable and multivariable logistic regression with respect to demographics, stroke subtypes, and vascular risk factors were performed to identify phenotypes associated with large acute diffusion-weighted MRI volumes and greater stroke severity in 2770 MRI-GENIE patients. Stroke topography was investigated. Results- The ensemble consisting of a mixture of MRI-GENIE and single-center convolutional neural networks performed best. Subset analysis comparing automated and manual lesion volumes in 383 patients found excellent correlation (ρ=0.92; P<0.0001). Median (interquartile range) diffusion-weighted MRI lesion volumes from 2770 patients were 3.7 cm3 (0.9-16.6 cm3). Patients with small artery occlusion stroke subtype had smaller lesion volumes ( P<0.0001) and different topography compared with other stroke subtypes. Conclusions- Automated accurate clinical diffusion-weighted MRI lesion segmentation using deep learning algorithms trained with multi-center and diverse data is feasible. Both lesion volume and topography can provide insight into stroke subtypes with sufficient sample size from big heterogeneous multi-center clinical imaging phenotype data sets.
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