Latin America: the next region for haematopoietic transplant progress

Haematopoietic cell transplant activity in the 28 countries comprising Latin America is poorly defined. We conducted a voluntary survey of members of the Latin American Bone Marrow Transplantation Group regarding transplant activity 2009–2012. Collated responses were compared with data of transplant rates from the Worldwide Network for Blood and Marrow Transplantation for other geographic regions. Several socio-economic variables were analysed to determine correlations with transplant rates. In total, 94 teams from 12 countries reported 11519 transplants including 7033 autotransplants and 4486 allotransplants. Annual activity increased from 2517 transplants in 2009 to 3263 in 2012, a 30% increase. Median transplants rate (transplant per million inhabitants) in 2012 was 64 (autotransplants, median 40; allotransplants, median 24). This rate is substantially lower than that in North America and European regions (482 and 378) but higher than that in the Eastern Mediterranean and Asia Pacific regions (30 and 45). However, the Latin America transplant rate is 5–8-fold lower than that in America and Europe, suggesting a need to increase transplant availability. Transplant team density in Latin America (teams per million population; 1.8) is 3–4-fold lower than that in North America (6.2) or Europe (7.6). Within Latin America, there is substantial diversity in transplant rates by country partially explained by diverse socio-economic variables including per capita gross national income, health expenditure and physician density. These data should help inform future health-care policy in Latin America

Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Abstract Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries

Paral·lelització del pipeline Bcbio-nextgen per al tractament de dades genòmiques

In the recent years, the costs of obtaining biological data have been drastically reduced. This has lead into an exponential growth of the available data. Having such growth of data to analyze sometimes results in very platform-dependent and difficult to scale software solutions. This final project tries to provide a solution to those problems in a real bioinformatics core facility in the Science For Life Laboratory. Science For Life Laboratory is a center for large-scale biosciences with the focus in health and environmental research. It is located in Stockholm, Sweden. This laboratory has 15 next generation sequencing instruments at present, with a combined capacity for DNA sequencing equal to several hundreds of complete human genomes per year. This implies a massive amount of data to be managed and analyzed. This data is analyzed using bcbio-nextgen. bcbio-nextgen is an in-house maintained genomics pipeline, originally developed by Brad Chapman at Harvard School of Public Health [Rom12]. The first goal of this project is to automate the installation, deployment and testing of the aforementioned pipeline. On the other hand, the alignment1 step of the analysis will be modified to use Seal, a Hadoop based aligner. This will allow us to check that all automations are working properly, as the pipeline will have to be installed and tested in several nodes

Paral·lelització del pipeline Bcbio-nextgen per al tractament de dades genòmiques

In the recent years, the costs of obtaining biological data have been drastically reduced. This has lead into an exponential growth of the available data. Having such growth of data to analyze sometimes results in very platform-dependent and difficult to scale software solutions. This final project tries to provide a solution to those problems in a real bioinformatics core facility in the Science For Life Laboratory. Science For Life Laboratory is a center for large-scale biosciences with the focus in health and environmental research. It is located in Stockholm, Sweden. This laboratory has 15 next generation sequencing instruments at present, with a combined capacity for DNA sequencing equal to several hundreds of complete human genomes per year. This implies a massive amount of data to be managed and analyzed. This data is analyzed using bcbio-nextgen. bcbio-nextgen is an in-house maintained genomics pipeline, originally developed by Brad Chapman at Harvard School of Public Health [Rom12]. The first goal of this project is to automate the installation, deployment and testing of the aforementioned pipeline. On the other hand, the alignment1 step of the analysis will be modified to use Seal, a Hadoop based aligner. This will allow us to check that all automations are working properly, as the pipeline will have to be installed and tested in several nodes

Risk of diabetes in patients with sleep apnea: comparison of surgery versus CPAP in a long-term follow-up study

Abstract This study aimed to obtain a comprehensive view of the risk of developing diabetes in patients with obstructive sleep apnea (OSA) and to compare this risk between patients receiving continuous positive airway pressure (CPAP) therapy versus upper airway surgery (UAS). We used local and the global-scale federated data research network TriNetX to obtain access to electronic medical records, including those for patients diagnosed with OSA, from health-care organizations (HCOs) worldwide. Using propensity score matching and the score-matched analyses of data for 5 years of follow-up, we found that patients who had undergone UAS had a lower risk of developing diabetes than those who used CPAP (risk ratio 0.415, 95% confidence interval (CI) 0.349–0.493). The risk for newly diagnosed diabetes patients showed a similar pattern (hazard ratio 0.382; 95% CI 0.317–0.459). Both therapies seem to protect against diabetes (Risk 0.081 after UAS vs. 0.195 after CPAP). Analysis of the large data sets collected from HCOs in Europe and globally lead us to conclude that, in patients with OSA, UAS can prevent the development of diabetes better than CPAP. Graphical Abstrac

Alert Classification for the ALeRCE Broker System: The Anomaly Detector

Astronomical broker systems, such as Automatic Learning for the Rapid Classification of Events (ALeRCE), are currently analyzing hundreds of thousands of alerts per night, opening up an opportunity to automatically detect anomalous unknown sources. In this work, we present the ALeRCE anomaly detector, composed of three outlier detection algorithms that aim to find transient, periodic, and stochastic anomalous sources within the Zwicky Transient Facility data stream. Our experimental framework consists of cross-validating six anomaly detection algorithms for each of these three classes using the ALeRCE light-curve features. Following the ALeRCE taxonomy, we consider four transient subclasses, five stochastic subclasses, and six periodic subclasses. We evaluate each algorithm by considering each subclass as the anomaly class. For transient and periodic sources the best performance is obtained by a modified version of the deep support vector data description neural network, while for stochastic sources the best results are obtained by calculating the reconstruction error of an autoencoder neural network. Including a visual inspection step for the 10 most promising candidates for each of the 15 ALeRCE subclasses, we detect 31 bogus candidates (i.e., those with photometry or processing issues) and seven potential astrophysical outliers that require follow-up observations for further analysis

Common Workflow Language, draft 3

The Common Workflow Language (CWL) is an informal, multi-vendor working group consisting of various organizations and individuals that have an interest in portability of data analysis workflows. Our goal is to create specifications that enable data scientists to describe analysis tools and workflows that are powerful, easy to use, portable, and support reproducibility.CWL builds on technologies such as JSON-LD and Avro for data modeling and Docker for portable runtime environments. CWL is designed to express workflows for data-intensive science, such as Bioinformatics, Medical Imaging, Chemistry, Physics, and Astronomy.This is draft 3 of the CWL tool and workflow specification, released on 2016-02-05.The specification, in HTML format, is in the draft-3/docs folder

Common Workflow Language, draft 3

The Common Workflow Language (CWL) is an informal, multi-vendor working group consisting of various organizations and individuals that have an interest in portability of data analysis workflows. Our goal is to create specifications that enable data scientists to describe analysis tools and workflows that are powerful, easy to use, portable, and support reproducibility.CWL builds on technologies such as JSON-LD and Avro for data modeling and Docker for portable runtime environments. CWL is designed to express workflows for data-intensive science, such as Bioinformatics, Medical Imaging, Chemistry, Physics, and Astronomy.This is draft 3 of the CWL tool and workflow specification, released on 2016-02-05.The specification, in HTML format, is in the draft-3/docs folder

Risk of diabetes in patients with sleep apnea: comparison of surgery versus CPAP in a long-term follow-up study

This study aimed to obtain a comprehensive view of the risk of developing diabetes in patients with obstructive sleep apnea (OSA) and to compare this risk between patients receiving continuous positive airway pressure (CPAP) therapy versus upper airway surgery (UAS). We used local and the global-scale federated data research network TriNetX to obtain access to electronic medical records, including those for patients diagnosed with OSA, from health-care organizations (HCOs) worldwide. Using propensity score matching and the score-matched analyses of data for 5 years of follow-up, we found that patients who had undergone UAS had a lower risk of developing diabetes than those who used CPAP (risk ratio 0.415, 95% confidence interval (CI) 0.349-0.493). The risk for newly diagnosed diabetes patients showed a similar pattern (hazard ratio 0.382; 95% CI 0.317-0.459). Both therapies seem to protect against diabetes (Risk 0.081 after UAS vs. 0.195 after CPAP). Analysis of the large data sets collected from HCOs in Europe and globally lead us to conclude that, in patients with OSA, UAS can prevent the development of diabetes better than CPAP