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- Abdellah, Tebani, et al.
(author)
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Integration of molecular profiles in a longitudinal wellness profiling cohort.
- 2020
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In: Nature communications. - : Springer Science and Business Media LLC. - 2041-1723. ; 11:1
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Journal article (peer-reviewed)abstract
- An important aspect of precision medicine is to probe the stability in molecular profiles among healthy individuals over time. Here, we sample a longitudinal wellness cohort with 100 healthy individuals and analyze blood molecular profiles including proteomics, transcriptomics, lipidomics, metabolomics, autoantibodies andimmune cell profiling, complementedwith gut microbiota composition and routine clinical chemistry. Overall, our results show high variation between individuals across different molecular readouts, while the intra-individual baseline variation is low. The analyses show that each individual has a unique and stable plasma protein profile throughout the study period and that many individuals also show distinct profiles with regards to the other omics datasets, with strong underlying connections between the blood proteome and the clinical chemistry parameters. In conclusion, the results support an individual-based definition of health and show that comprehensive omics profiling in a longitudinal manner is a path forward for precision medicine.
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- Andersson, Matilda, et al.
(author)
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Augmentation Strategies for Self-Supervised Representation Learning from Electrocardiograms
- 2023
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In: 31st European Signal Processing Conference, EUSIPCO 2023 - Proceedings. - 2219-5491. - 9789464593600 ; , s. 1075-1079
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Conference paper (peer-reviewed)abstract
- In this paper, we investigate the effects of different augmentation strategies in self-supervised representation learning from electrocardiograms. Our study examines the impact of random resized crop and time out on downstream performance. We also consider the importance of the signal length. Furthermore, instead of using two augmented copies of the sample as a positive pair, we suggest augmenting only one. The second signal is kept as the original signal. These different augmentation strategies are investigated in the context of pre-training and fine-tuning, following the different self-supervised learning frameworks BYOL, SimCLR, and VICReg. We formulate the downstream task as a multi-label classification task using a public dataset containing ECG recordings and annotations. In our experiments, we demonstrate that self-supervised learning can consistently outperform classical supervised learning when configured correctly. These findings are of particular importance in the medical domain, as the medical labeling process is particularly expensive, and clinical ground truth is often difficult to define. We are hopeful that our findings will be a catalyst for further research into augmentation strategies in self-supervised learning to improve performance in the detection of cardiovascular disease.
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- Andersson, Pontus, et al.
(author)
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FLIP: A Difference Evaluator for Alternating Images
- 2020
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In: Proceedings of the ACM in Computer Graphics and Interactive Techniques. - : Association for Computing Machinery (ACM). - 2577-6193. ; 3:2, s. 1-23
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Journal article (peer-reviewed)abstract
- Image quality measures are becoming increasingly important in the field of computer graphics. For example, there is currently a major focus on generating photorealistic images in real time by combining path tracing with denoising, for which such quality assessment is integral. We present FLIP, which is a difference evaluator with a particular focus on the differences between rendered images and corresponding ground truths. Our algorithm produces a map that approximates the difference perceived by humans when alternating between two images. FLIP is a combination of modified existing building blocks, and the net result is surprisingly powerful. We have compared our work against a wide range of existing image difference algorithms and we have visually inspected over a thousand image pairs that were either retrieved from image databases or generated in-house. We also present results of a user study which indicate that our method performs substantially better, on average, than the other algorithms. To facilitate the use of FLIP, we provide source code in C++, MATLAB, NumPy/SciPy, and PyTorch.
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- Dillner, Joakim, et al.
(author)
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Antibodies to SARS-CoV-2 and risk of past or future sick leave
- 2021
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In: Scientific Reports. - : Springer Nature. - 2045-2322. ; 11:1
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Journal article (peer-reviewed)abstract
- The extent that antibodies to SARS-CoV-2 may protect against future virus-associated disease is unknown. We invited all employees (n=15,300) at work at the Karolinska University Hospital, Stockholm, Sweden to participate in a study examining SARS-Cov-2 antibodies in relation to registered sick leave. For consenting 12,928 healthy hospital employees antibodies to SARS-CoV-2 could be determined and compared to participant sick leave records. Subjects with viral serum antibodies were not at excess risk for future sick leave (adjusted odds ratio (OR) controlling for age and sex: 0.85 [95% confidence interval (CI) (0.85 (0.43-1.68)]. By contrast, subjects with antibodies had an excess risk for sick leave in the weeks prior to testing [adjusted OR in multivariate analysis: 3.34 (2.98-3.74)]. Thus, presence of viral antibodies marks past disease and protection against excess risk of future disease. Knowledge of whether exposed subjects have had disease in the past or are at risk for future disease is essential for planning of control measures.Trial registration: First registered on 02/06/20, ClinicalTrials.gov NCT04411576.
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- Elfstrom, K. Miriam, et al.
(author)
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Differences in risk for SARS-CoV-2 infection among healthcare workers
- 2021
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In: Preventive Medicine Reports. - : Elsevier BV. - 2211-3355. ; 24
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Journal article (peer-reviewed)abstract
- Healthcare workers (HCWs) are a risk group for SARS-CoV-2 infection, but which healthcare work that conveys risk and to what extent such risk can be prevented is not clear. Starting on April 24th, 2020, all employees at work (n = 15,300) at the Karolinska University Hospital, Stockholm, Sweden were invited and 92% consented to participate in a SARS-CoV-2 cohort study. Complete SARS-CoV-2 serology was available for n = 12,928 employees and seroprevalences were analyzed by age, sex, profession, patient contact, and hospital department. Relative risks were estimated to examine the association between type of hospital department as a proxy for different working environment exposure and risk for seropositivity, adjusting for age, sex, sampling week, and profession. Wards that were primarily responsible for COVID-19 patients were at increased risk (adjusted OR 1.95 (95% CI 1.65-2.32) with the notable exception of the infectious diseases and intensive care units (adjusted OR 0.86 (95% CI 0.66-1.13)), that were not at increased risk despite being highly exposed. Several units with similar types of work varied greatly in seroprevalences. Among the professions examined, nurse assistants had the highest risk (adjusted OR 1.62 (95% CI 1.38-1.90)). Although healthcare workers, in particular nurse assistants, who attend to COVID-19 patients are a risk group for SARS-CoV-2 infection, several units caring for COVID-19 patients had no excess risk. Large variations in seroprevalences among similar units suggest that healthcare work-related risk of SARS-CoV-2 infection may be preventable.
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| 10. |
- Evangelou, Evangelos, et al.
(author)
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Meta-analysis of genome-wide association studies confirms a susceptibility locus for knee osteoarthritis on chromosome 7q22
- 2011
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In: Annals of the Rheumatic Diseases. - : BMJ. - 1468-2060 .- 0003-4967. ; 70:2, s. 349-355
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Journal article (peer-reviewed)abstract
- Objectives Osteoarthritis (OA) is the most prevalent form of arthritis and accounts for substantial morbidity and disability, particularly in older people. It is characterised by changes in joint structure, including degeneration of the articular cartilage, and its aetiology is multifactorial with a strong postulated genetic component. Methods A meta-analysis was performed of four genome-wide association (GWA) studies of 2371 cases of knee OA and 35 909 controls in Caucasian populations. Replication of the top hits was attempted with data from 10 additional replication datasets. Results With a cumulative sample size of 6709 cases and 44 439 controls, one genome-wide significant locus was identified on chromosome 7q22 for knee OA (rs4730250, p = 9.2 x 10(-9)), thereby confirming its role as a susceptibility locus for OA. Conclusion The associated signal is located within a large (500 kb) linkage disequilibrium block that contains six genes: PRKAR2B (protein kinase, cAMP-dependent, regulatory, type II, beta), HPB1 (HMG-box transcription factor 1), COG5 (component of oligomeric golgi complex 5), GPR22 (G protein-coupled receptor 22), DUS4L (dihydrouridine synthase 4-like) and BCAP29 (B cell receptor-associated protein 29). Gene expression analyses of the (six) genes in primary cells derived from different joint tissues confirmed expression of all the genes in the joint environment.
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