| 1. |
- Unemo, Magnus, 1970-, et al.
(författare)
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Sexually transmitted infections : challenges ahead
- 2017
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Ingår i: The Lancet - Infectious diseases. - 1473-3099 .- 1474-4457. ; 17:8, s. e235-e279
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Forskningsöversikt (refereegranskat)abstract
- WHO estimated that nearly 1 million people become infected every day with any of four curable sexually transmitted infections (STIs): chlamydia, gonorrhoea, syphilis, and trichomoniasis. Despite their high global incidence, STIs remain a neglected area of research. In this Commission, we have prioritised five areas that represent particular challenges in STI treatment and control. Chlamydia remains the most commonly diagnosed bacterial STI in high-income countries despite widespread testing recommendations, sensitive and specific non-invasive testing techniques, and cheap effective therapy. We discuss the challenges for chlamydia control and evidence to support a shift from the current focus on infection-based screening to improved management of diagnosed cases and of chlamydial morbidity, such as pelvic inflammatory disease. The emergence and spread of antimicrobial resistance in Neisseria gonorrhoeae is globally recognised. We review current and potential future control and treatment strategies, with a focus on novel antimicrobials. Bacterial vaginosis is the most common vaginal disorder in women, but current treatments are associated with frequent recurrence. Recurrence after treatment might relate to evidence that suggests sexual transmission is integral to the pathogenesis of bacterial vaginosis, which has substantial implications for the development of effective management approaches. STIs disproportionately affect low-income and middle-income countries. We review strategies for case management, focusing on point-of-care tests that hold considerable potential for improving STI control. Lastly, STIs in men who have sex with men have increased since the late 1990s. We discuss the contribution of new biomedical HIV prevention strategies and risk compensation. Overall, this Commission aims to enhance the understanding of some of the key challenges facing the field of STIs, and outlines new approaches to improve the clinical management of STIs and public health.
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| 2. |
- Koslicki, David, et al.
(författare)
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ARK : Aggregation of Reads by K-Means for Estimation of Bacterial Community Composition
- 2015
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Ingår i: PLOS ONE. - : PUBLIC LIBRARY SCIENCE. - 1932-6203. ; 10:10
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Tidskriftsartikel (refereegranskat)abstract
- Motivation Estimation of bacterial community composition from high-throughput sequenced 16S rRNA gene amplicons is a key task in microbial ecology. Since the sequence data from each sample typically consist of a large number of reads and are adversely impacted by different levels of biological and technical noise, accurate analysis of such large datasets is challenging. Results There has been a recent surge of interest in using compressed sensing inspired and convex-optimization based methods to solve the estimation problem for bacterial community composition. These methods typically rely on summarizing the sequence data by frequencies of low-order k-mers and matching this information statistically with a taxonomically structured database. Here we show that the accuracy of the resulting community composition estimates can be substantially improved by aggregating the reads from a sample with an unsupervised machine learning approach prior to the estimation phase. The aggregation of reads is a pre-processing approach where we use a standard K-means clustering algorithm that partitions a large set of reads into subsets with reasonable computational cost to provide several vectors of first order statistics instead of only single statistical summarization in terms of k-mer frequencies. The output of the clustering is then processed further to obtain the final estimate for each sample. The resulting method is called Aggregation of Reads by K-means (ARK), and it is based on a statistical argument via mixture density formulation. ARK is found to improve the fidelity and robustness of several recently introduced methods, with only a modest increase in computational complexity. Availability An open source, platform-independent implementation of the method in the Julia programming language is freely available at https://github.com/dkoslicki/ARK. A Matlab implementation is available at http://www.ee.kth.se/ctsoftware.
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| 3. |
- Queckboerner, Suzanna, et al.
(författare)
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Stromal Heterogeneity in the Human Proliferative Endometrium-A Single-Cell RNA Sequencing Study
- 2021
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Ingår i: Journal of Personalized Medicine. - : MDPI. - 2075-4426. ; 11:6
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Tidskriftsartikel (refereegranskat)abstract
- The endometrium undergoes regular regeneration and stromal proliferation as part of the normal menstrual cycle. To better understand cellular interactions driving the mechanisms in endometrial regeneration we employed single-cell RNA sequencing. Endometrial biopsies were obtained during the proliferative phase of the menstrual cycle from healthy fertile women and processed to single-cell suspensions which were submitted for sequencing. In addition to known endometrial cell types, bioinformatic analysis revealed multiple stromal populations suggestive of specific stromal niches with the ability to control inflammation and extracellular matrix composition. Ten different stromal cells and two pericyte subsets were identified. Applying different R packages (Seurat, SingleR, Velocyto) we established cell cluster diversity and cell lineage/trajectory, while using external data to validate our findings. By understanding healthy regeneration in the described stromal compartments, we aim to identify points of further investigation and possible targets for novel therapy development for benign gynecological disorders affecting endometrial regeneration and proliferation such as endometriosis and Asherman's syndrome.
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