Background: Myasthenia gravis (MG) is a rare chronic autoimmune disease caused by autoantibodies directed against postsynaptic antigens of the neuromuscular junction. Over the last decades, increasing incidence and prevalence rates have been reported. Epidemiological data on prevalence and incidence in Germany are lacking. Furthermore, the MG treatment landscape is rapidly changing due to the continued approval of novel monoclonal antibodies. Method: This is a retrospective study assessing incidence, prevalence, and hospitalization rates of MG as well as treatment patterns in Germany over 10 years based on medical claims data covering 6.1 million insured persons. Results: Between 2011 and 2020, the prevalence rate of MG increased from 15.7 to 28.2 per 100,000 person-years. The age-adjusted incidence rate was 2.8 per 100,000 person-years within the study period (95%-CI, 2.43-3.22) and decreased dramatically in 2020, the year of the COVID-19 pandemic. Similarly, the hospitalization rate fluctuated within the study period but reached an overall low of 8.3% in 2020 (mean hospitalization rate 11.5%). Treatment patterns showed that most MG patients are treated with base therapy. However, crisis intervention is necessary for 2-5% of MG patients, and therapeutic monoclonal antibodies, including rituximab and eculizumab, are increasingly used. Conclusion: This is the first study on MG prevalence and incidence rates in Germany. Data shows an increase in prevalence by 1.8-fold over 10 years. Decreasing incidence and hospitalization rates in 2020 hint at the impact of the COVID-19 pandemic. Treatment patterns in MG are changing with the advent of therapeutic monoclonal antibodies in this indication.
Recent technological advances have resulted in an unprecedented increase in publicly available biomedical data, yet the reuse of the data is often precluded by experimental bias and a lack of annotation depth and consistency. Here we investigate RNA-seq metadata prediction based on gene expression values. We present a deep-learning based domain adaptation algorithm for the automatic annotation of RNA-seq metadata. We show how our algorithm outperforms existing approaches as well as traditional deep learning methods for the prediction of tissue, sample source, and patient sex information across several large data repositories. By using a model architecture similar to siamese networks the algorithm is able to learn biases from datasets with few samples. Our domain adaptation approach achieves metadata annotation accuracies up to 12.3% better than a previously published method. Lastly, we provide a list of more than 10,000 novel tissue and sex label annotations for 8,495 unique SRA samples.
Background Recent technological advances have resulted in an unprecedented increase in publicly available biomedical data, yet the reuse of the data is often precluded by experimental bias and a lack of annotation depth and consistency. Missing annotations makes it impossible for researchers to find datasets specific to their needs. Findings Here, we investigate RNA-sequencing metadata prediction based on gene expression values. We present a deep-learning–based domain adaptation algorithm for the automatic annotation of RNA-sequencing metadata. We show, in multiple experiments, that our model is better at integrating heterogeneous training data compared with existing linear regression–based approaches, resulting in improved tissue type classification. By using a model architecture similar to Siamese networks, the algorithm can learn biases from datasets with few samples. Conclusion Using our novel domain adaptation approach, we achieved metadata annotation accuracies up to 15.7% better than a previously published method. Using the best model, we provide a list of >10,000 novel tissue and sex label annotations for 8,495 unique SRA samples. Our approach has the potential to revive idle datasets by automated annotation making them more searchable.
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