| 1. |
- Weinstein, John N., et al.
(author)
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The cancer genome atlas pan-cancer analysis project
- 2013
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In: Nature Genetics. - : Springer Science and Business Media LLC. - 1061-4036 .- 1546-1718. ; 45:10, s. 1113-1120
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Research review (peer-reviewed)abstract
- The Cancer Genome Atlas (TCGA) Research Network has profiled and analyzed large numbers of human tumors to discover molecular aberrations at the DNA, RNA, protein and epigenetic levels. The resulting rich data provide a major opportunity to develop an integrated picture of commonalities, differences and emergent themes across tumor lineages. The Pan-Cancer initiative compares the first 12 tumor types profiled by TCGA. Analysis of the molecular aberrations and their functional roles across tumor types will teach us how to extend therapies effective in one cancer type to others with a similar genomic profile. © 2013 Nature America, Inc. All rights reserved.
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| 2. |
- Hillier, Ladeana W, et al.
(author)
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Sequence and comparative analysis of the chicken genome provide unique perspectives on vertebrate evolution
- 2004
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In: Nature. - 0028-0836 .- 1476-4687. ; 432:7018, s. 695-716
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Journal article (peer-reviewed)abstract
- We present here a draft genome sequence of the red jungle fowl, Gallus gallus. Because the chicken is a modern descendant of the dinosaurs and the first non-mammalian amniote to have its genome sequenced, the draft sequence of its genome--composed of approximately one billion base pairs of sequence and an estimated 20,000-23,000 genes--provides a new perspective on vertebrate genome evolution, while also improving the annotation of mammalian genomes. For example, the evolutionary distance between chicken and human provides high specificity in detecting functional elements, both non-coding and coding. Notably, many conserved non-coding sequences are far from genes and cannot be assigned to defined functional classes. In coding regions the evolutionary dynamics of protein domains and orthologous groups illustrate processes that distinguish the lineages leading to birds and mammals. The distinctive properties of avian microchromosomes, together with the inferred patterns of conserved synteny, provide additional insights into vertebrate chromosome architecture.
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| 3. |
- Knevel, R, et al.
(author)
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Rheumatic?-A Digital Diagnostic Decision Support Tool for Individuals Suspecting Rheumatic Diseases: A Multicenter Pilot Validation Study
- 2022
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In: Frontiers in medicine. - : Frontiers Media SA. - 2296-858X. ; 9, s. 774945-
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Journal article (peer-reviewed)abstract
- Digital diagnostic decision support tools promise to accelerate diagnosis and increase health care efficiency in rheumatology. Rheumatic? is an online tool developed by specialists in rheumatology and general medicine together with patients and patient organizations. It calculates a risk score for several rheumatic diseases. We ran a pilot study retrospectively testing Rheumatic? for its ability to differentiate symptoms from existing or emerging immune-mediated rheumatic diseases from other rheumatic and musculoskeletal complaints and disorders in patients visiting rheumatology clinics.Materials and MethodsThe performance of Rheumatic? was tested using in three university rheumatology centers: (A) patients at Risk for RA (Karolinska Institutet, n = 50 individuals with musculoskeletal complaints and anti-citrullinated protein antibody positivity) (B) patients with early joint swelling [dataset B (Erlangen) n = 52]. (C) Patients with early arthritis where the clinician considered it likely to be of auto-immune origin [dataset C (Leiden) n = 73]. In dataset A we tested whether Rheumatic? could predict the development of arthritis. In dataset B and C we tested whether Rheumatic? could predict the development of an immune-mediated rheumatic diseases. We examined the discriminative power of the total score with the Wilcoxon rank test and the area-under-the-receiver-operating-characteristic curve (AUC-ROC). Next, we calculated the test characteristics for these patients passing the first or second expert-based Rheumatic? scoring threshold.ResultsThe total test scores differentiated between: (A) Individuals developing arthritis or not, median 245 vs. 163, P < 0.0001, AUC-ROC = 75.3; (B) patients with an immune-mediated arthritic disease or not median 191 vs. 107, P < 0.0001, AUC-ROC = 79.0; but less patients with an immune-mediated arthritic disease or not amongst those where the clinician already considered an immune mediated disease most likely (median 262 vs. 212, P < 0.0001, AUC-ROC = 53.6). Threshold-1 (advising to visit primary care doctor) was highly specific in dataset A and B (0.72, 0.87, and 0.23, respectively) and sensitive (0.67, 0.61, and 0.67). Threshold-2 (advising to visit rheumatologic care) was very specific in all three centers but not very sensitive: specificity of 1.0, 0.96, and 0.91, sensitivity 0.05, 0.07, 0.14 in dataset A, B, and C, respectively.ConclusionRheumatic? is a web-based patient-centered multilingual diagnostic tool capable of differentiating immune-mediated rheumatic conditions from other musculoskeletal problems. The current scoring system needs to be further optimized.
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| 4. |
- Knevel, R, et al.
(author)
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Rheumatic?-A Digital Diagnostic Decision Support Tool for Individuals Suspecting Rheumatic Diseases: A Multicenter Pilot Validation Study
- 2022
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In: Frontiers in medicine. - : Frontiers Media SA. - 2296-858X. ; 9, s. 774945-
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Journal article (peer-reviewed)abstract
- Digital diagnostic decision support tools promise to accelerate diagnosis and increase health care efficiency in rheumatology. Rheumatic? is an online tool developed by specialists in rheumatology and general medicine together with patients and patient organizations. It calculates a risk score for several rheumatic diseases. We ran a pilot study retrospectively testing Rheumatic? for its ability to differentiate symptoms from existing or emerging immune-mediated rheumatic diseases from other rheumatic and musculoskeletal complaints and disorders in patients visiting rheumatology clinics.Materials and MethodsThe performance of Rheumatic? was tested using in three university rheumatology centers: (A) patients at Risk for RA (Karolinska Institutet, n = 50 individuals with musculoskeletal complaints and anti-citrullinated protein antibody positivity) (B) patients with early joint swelling [dataset B (Erlangen) n = 52]. (C) Patients with early arthritis where the clinician considered it likely to be of auto-immune origin [dataset C (Leiden) n = 73]. In dataset A we tested whether Rheumatic? could predict the development of arthritis. In dataset B and C we tested whether Rheumatic? could predict the development of an immune-mediated rheumatic diseases. We examined the discriminative power of the total score with the Wilcoxon rank test and the area-under-the-receiver-operating-characteristic curve (AUC-ROC). Next, we calculated the test characteristics for these patients passing the first or second expert-based Rheumatic? scoring threshold.ResultsThe total test scores differentiated between: (A) Individuals developing arthritis or not, median 245 vs. 163, P < 0.0001, AUC-ROC = 75.3; (B) patients with an immune-mediated arthritic disease or not median 191 vs. 107, P < 0.0001, AUC-ROC = 79.0; but less patients with an immune-mediated arthritic disease or not amongst those where the clinician already considered an immune mediated disease most likely (median 262 vs. 212, P < 0.0001, AUC-ROC = 53.6). Threshold-1 (advising to visit primary care doctor) was highly specific in dataset A and B (0.72, 0.87, and 0.23, respectively) and sensitive (0.67, 0.61, and 0.67). Threshold-2 (advising to visit rheumatologic care) was very specific in all three centers but not very sensitive: specificity of 1.0, 0.96, and 0.91, sensitivity 0.05, 0.07, 0.14 in dataset A, B, and C, respectively.ConclusionRheumatic? is a web-based patient-centered multilingual diagnostic tool capable of differentiating immune-mediated rheumatic conditions from other musculoskeletal problems. The current scoring system needs to be further optimized.
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| 5. |
- Knevel, R, et al.
(author)
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RHEUMATIC? - A DIGITAL DIAGNOSTIC DECISION SUPPORT TOOL FOR INDIVIDUALS SUSPECTING RHEUMATIC DISEASES: A MULTICENTER VALIDATION STUDY
- 2021
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In: ANNALS OF THE RHEUMATIC DISEASES. - : BMJ. - 0003-4967 .- 1468-2060. ; 80, s. 87-88
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Conference paper (other academic/artistic)abstract
- Digital diagnostic decision support tools promise to accelerate diagnosis and increase health care efficiency in rheumatology. Rheumatic? is an online tool developed by specialists in rheumatology and general medicine together with patients and patient organizations for individuals suspecting a rheumatic disease.1,2 The tool can be used by people suspicious for rheumatic diseases resulting in individual advise on eventually seeking further health care.Objectives:We tested Rheumatic? for its ability to differentiate symptoms from immune-mediated diseases from other rheumatic and musculoskeletal complaints and disorders in patients visiting rheumatology clinics.Methods:The performance of Rheumatic? was tested using data from 175 patients from three university rheumatology centers covering two different settings:A.Risk-RA phase setting. Here, we tested whether Rheumatic? could predict the development of arthritis in 50 at risk-individuals with musculoskeletal complaints and anti-citrullinated protein antibody positivity from the KI (Karolinska Institutet)B.Early arthritis setting. Here, we tested whether Rheumatic? could predict the development of an immune-mediated rheumatic disease in i) EUMC (Erlangen) n=52 patients and ii) LUMC (Leiden) n=73 patients.In each setting, we examined the discriminative power of the total score with the Wilcoxon rank test and the area-under-the-receiver-operating-characteristic curve (AUC-ROC).Results:In setting A, the total test score clearly differentiated between individuals developing arthritis or not, median 245 versus 163, P < 0.0001, AUC-ROC = 75.3 (Figure 1). Also within patients with arthritis the Rheumatic? total score was significantly higher in patients developing an immune-mediated arthritic disease versus those who did not: median score EUMC 191 versus 107, P < 0.0001, AUC-ROC = 79.0, and LUMC 262 versus 212, P < 0.0001, AUC-ROC = 53.6.Figure 1.(Area under) the receiver operating curve for the total Rheumatic? scoreConclusion:Rheumatic? is a web-based patient-centered multilingual diagnostic tool capable of differentiating immune-mediated rheumatic conditions from other musculoskeletal problems. A following subject of research is how the tool performs in a population-wide setting.References:[1]Knitza J. et al. Mobile Health in Rheumatology: A Patient Survey Study Exploring Usage, Preferences, Barriers and eHealth Literacy. JMIR mHealth and uHealth. 2020.[2]https://rheumatic.elsa.science/en/Acknowledgements:This project has received funding from EIT Health. EIT Health is supported by the European Institute of Innovation and Technology (EIT), a body of the European Union that receives support from the European Union’s Horizon 2020 Research and Innovation program.This project has received funding from the Innovative Medicines Initiative 2 Joint Undertaking under grant agreement No 777357, RTCure.Disclosure of Interests:Rachel Knevel: None declared, Johannes Knitza: None declared, Aase Hensvold: None declared, Alexandra Circiumaru: None declared, Tor Bruce Employee of: Ocean Observations, Sebastian Evans Employee of: Elsa Science, Tjardo Maarseveen: None declared, Marc Maurits: None declared, Liesbeth Beaart- van de Voorde: None declared, David Simon: None declared, Arnd Kleyer: None declared, Martina Johannesson: None declared, Georg Schett: None declared, Thomas Huizinga: None declared, Sofia Svanteson Employee of: Elsa Science, Alexandra Lindfors Employee of: Ocean Observations, Lars Klareskog: None declared, Anca Catrina: None declared
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| 10. |
- Circiumaru, A, et al.
(author)
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SPECIFIC ACPA REACTIVITIES AND INFLAMMATORY BIOMARKERS ALONG WITH ULTRASOUND TENOSYNOVITIS ARE ASSOCIATED WITH ARTHRITIS ONSET IN A POPULATION AT RISK FOR RHEUMATOID ARTHRITIS
- 2020
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In: ANNALS OF THE RHEUMATIC DISEASES. - : BMJ. - 0003-4967 .- 1468-2060. ; 79, s. 1247-1247
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Conference paper (other academic/artistic)abstract
- Anti-citrullinated protein antibodies (ACPA) are characteristic markers for rheumatoid arthritis (RA), developing years before disease onset. Early clinical and biological biomarkers could provide useful information on the onset of RA in predisposed individuals.Objectives:The aim of the study was to investigate whether ACPA along with inflammatory markers and musculoskeletal ultrasound changes could predict arthritis development in individuals at risk for RA.Methods:ACPA-positive individuals with musculoskeletal complaints were referred from primary care to a rheumatology clinic, recruited in the Risk-RA research program and followed-up for up to 3 years, between April 2014 and October 2019. All individuals lacked arthritis both at clinical examination by a trained rheumatologist and ultrasound assessment of hands and feet and any other symptomatic joints (according to EULAR-OMERACT definition). Blood samples were collected at inclusion and were analyzed for 15 ACPA fine specificities (by custom made peptide array), 92 inflammation-associated protein biomarkers (by multiplex immunoassay with Olink extension technology) and HLA-SE (DR low resolution kit). Statistical analysis used univariate and multivariate models with backwards selection and cox regression.Results:268 individuals with a median age of 48 (36-58) were recruited, out of which 212 (79%) were females. 75 (28%) developed arthritis within 11 months of follow-up while the median follow-up for those not developing arthritis was 21 months (14-28). Increased ACPA levels, shorter symptom duration and RF positivity were the main differences between individuals developing arthritis and those who did not. In univariate models, the presence of HLA-SE, specific ACPA reactivities, certain inflammatory markers and ultrasound-detected tenosynovitis were associated with arthritis development. In multivariate analysis the presence of anti-cit-fillagrin (HR 2.1 (95% CI 1.2-3.7, p 0.01), IL6 levels (HR 1.4 (95% CI 1.2-1.7, p 0.0001) and tenosynovitis (HR 2.9 (95% CI 1.7-5.0, p 0.0001) remained significant predictors for arthritis onset.Conclusion:Certain ACPA reactivities together with inflammatory markers and ultrasound-detected tenosynovitis predict arthritis development in predisposed individuals for developing RA.Disclosure of Interests:None declared
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