Application of CFD in designing a drug delivery mixing chamber: an experimental and computational study

The purpose of this novel research was to understand the flow behaviour and improve the efficiency of the Volumatic™ spacer, using a combination of engineering tools such as CFD, Laser Doppler Anemometry (LDA) and Row visualization techniques. The lack of information on the Volumatic /A/ spacer meant that, initial understanding had to be gained into the flow behaviour within the spacer. This was initially performed by injecting air carrying a tracer concentration to represent t li<^drug portion of the medicine. The efficiency (volume of drug collected at the mouth piece) was found to be about 6.5% which was in the same order as the figure quoted in the literature Chuffart A series of parametric studies were carried out to discover the effects of various parameters on The overall efficiency of the spacer. In the initial part a series of jet profiles were studied at the inlet, these were in the shape of straight, cone shape and spray jet profiles. It was concluded that the jet with a cone angle of 5° increased the efficiency of the spacer from G.5% to 9.4%. The next stage of parametric study involved reducing the length of the spacer from 0.24 m to 0.12 m and varying the inlet velocity from 40 m/s down to 10 m/s. The findings concluded that t in1 efficiency of the spacer could be increased to 23%, using a velocity of 40 m/s at inlet. The length was reduced from 0.12 m to 0.06 m and a similar study as described above was carried out. This time it was concluded that reducing the velocity to 30 m/s increased the efficiency to 30%. The other interesting feature to come out of this study was that the whole of tIk1 spacer volume was used, hence the drug was mixing better than in the original Volumatic /A/ spacer, where about one third of the spacer volume remained completely empty of the drug. The studies carried out so far had shown that the additional increase in drug delivery efficiency in the case of the Volumatic 7 A/ spacer, was not substantial enough to justify the considerable manufacturing costs which have to be met, if the Volumatic 7 A/ spacer was to be remanufactured in its improved design. The way forward seemed to be in the development of a new design. The new design had to be small enough, so that it could be carried around easily by patients, who do not use1 the current spacer due to its size. The new design had to be economical in terms of manufacture, simple to use and easy to clean. The reasons mentioned above and the current trend towards the tube type spacer designs, implied the logical approach would be to base the design on a similar geometry. A tube type spacer was modelled with two holes drilled directly opposite each other, a distance of 10 mm away from the pMDI's nozzle. The holes introduced a pressure difference, hence directing the drug towards the patient's airway system. The new spacer had a length of 0.1 m. The computational results showed that the efficiency had increased to 71% for this particular design. The CFD results obtained from the initial study on the Volumatic 7 M spacer were validated using LDA measurements. The velocities along four different locations were measured. At each location the velocities were measured at increments of 5 mm for a distance of 50 mm inside the spacer. The LDA results showed very good agreements with those obtained from CFD. The volume of data sampled experimentally at each point was 25,000 data points. This large volume of data eliminated any random sources of error, and as the CF D simulations were carried out some six months prior to LDA results, it was safe to assume that the drug had been modelled accurately. The same experimental set up was used to measure velocity values for the tube spacer, but in this instance, velocity measurements were made only along two planes, due to limited time and availability of the drug source. Finally laser light sheeting was used to illuminate the Volumatic T spacer and a high speed KODAK camera capable of capturing 4500 frames per second was used. The visualization study proved that there was a portion of the Volumatie /A/ spacer which at times was free of any drug. The originality of the work has been described in the following paragraph: Prior to this research there was no comprehensive study available combining engineering tools such as Computational Fluid Dynamics (CFD), Laser Doppler Anemoinetry (LDA) and High Speed Photography to study the (low pattern within the current Volumatic /A/ spacer design and hence analysing its efficiency. The studies carried out were of the impaction type. The results of this study have confirmed that there are several parameters contributing to the efficiency of the Volumatic' A/ spacer. This knowledge was not available in the open literature previously. The initial part of this study has provided a scientific approach to analysing the flow patterns, hence obtaining an accurate value for the efficiency of the current device. This part of the study alone is a valuable tool for industry, because it has given industry data which has not been previously available. The results from this study have indicated that, the Aero Chamber Spacer type design has an efficiency of 71% compared to the current 10% efficiency of the Volumatic 7 A/ spacer. The efficiencies discussed are measured in terms of the percentage of the drug delivered to the mouth piece. The benefit to industry would be saving at a conservative estimate in terms of millions of Pounds annually. This can be calculated from industry's own figures that, 1 out of every 5 new born baby suffers from asthma in various degrees. The drug is the most expensive component of the device, hence a more efficient device would use a lesser quantity of the drug. Finally the combination of techniques used, and the number of data samples taken for example in the case of LDA measurements some 25000 data samples were taken and averaged at each point, has ensured a high degree of accuracy and confidence in the results presented

MuSERA: Multiple sample enriched region assessment

Enriched region (ER) identification is a fundamental step in several next-generation sequencing (NGS) experiment types. Yet, although NGS experimental protocols recommend producing replicate samples for each evaluated condition and their consistency is usually assessed, typically pipelines for ER identification do not consider available NGS replicates. This may alter genome-wide descriptions of ERs, hinder significance of subsequent analyses on detected ERs and eventually preclude biological discoveries that evidence in replicate could support. MuSERA is a broadly useful stand-alone tool for both interactive and batch analysis of combined evidence from ERs in multiple ChIP-seq or DNase-seq replicates. Besides rigorously combining sample replicates to increase statistical significance of detected ERs, it also provides quantitative evaluations and graphical features to assess the biological relevance of each determined ER set within its genomic context; they include genomic annotation of determined ERs, nearest ER distance distribution, global correlation assessment of ERs and an integrated genome browser.We review MuSERA rationale and implementation, and illustrate how sets of significant ERs are expanded by applying MuSERA on replicates for several types of NGS data, including ChIP-seq of transcription factors or histone marks and DNase-seq hypersensitive sites. We show that MuSERA can determine a new, enhanced set of ERs for each sample by locally combining evidence on replicates, and prove how the easy-to-use interactive graphical displays and quantitative evaluations that MuSERA provides effectively support thorough inspection of obtained results and evaluation of their biological content, facilitating their understanding and biological interpretations. MuSERA is freely available at http://www.bioinformatics.deib.polimi.it/MuSERA/

Using combined evidence from replicates to evaluate ChIP-seq peaks

Motivation: Chromatin Immunoprecipitation followed by sequencing (ChIP-seq) detects genome-wide DNA–protein interactions and chromatin modifications, returning enriched regions (ERs), usually associated with a significance score. Moderately significant interactions can correspond to true, weak interactions, or to false positives; replicates of a ChIP-seq experiment can provide co-localised evidence to decide between the two cases. We designed a general methodological framework to rigorously combine the evidence of ERs in ChIP-seq replicates, with the option to set a significance threshold on the repeated evidence and a minimum number of samples bearing this evidence. Results: We applied our method to Myc transcription factor ChIP-seq datasets in K562 cells available in the ENCODE project. Using replicates, we could extend up to 3 times the ER number with respect to single-sample analysis with equivalent significance threshold. We validated the ‘rescued’ ERs by checking for the overlap with open chromatin regions and for the enrichment of the motif that Myc binds with strongest affinity; we compared our results with alternative methods (IDR and jMOSAiCS), obtaining more validated peaks than the former and less peaks than latter, but with a better validation. Availability and implementation: An implementation of the proposed method and its source code under GPLv3 license are freely available at http://www.bioinformatics.deib.polimi.it/MSPC/ and http://mspc.codeplex.com/, respectively. Contact: [email protected] Supplementary information: Supplementary Material are available at Bioinformatics online

Next Generation Indexing for Genomic Intervals

Di4 (1D intervals incremental inverted index) is a multi-resolution, single-dimension indexing framework for efficient, scalable, and extensible computation of genomic interval expressions. The framework has a tri-layer architecture: the semantic layer provides orthogonal and generic means (including the support of user-defined function) of sense-making and higher-lever reasoning from region-based datasets; the logical layer provides building blocks for region calculus and topological relations between intervals; the physical layer abstracts from persistence technology and makes the model adaptable to variety of persistence technologies, spanning from small-scale (e.g., B+tree) to large-scale (e.g., LevelDB). The extensibility of Di4 to application scenarios is shown with an example of comparative evaluation of ChIP-seq and DNase-Seq replicates. Performance of Di4 is benchmarked for small and large scale scenarios under common bioinformatics application scenarios. Di4 is freely available from https://genometric.github.io/Di4

Explorative visual analytics on interval-based genomic data and their metadata

Background: With the wide-spreading of public repositories of NGS processed data, the availability of user-friendly and effective tools for data exploration, analysis and visualization is becoming very relevant. These tools enable interactive analytics, an exploratory approach for the seamless "sense-making" of data through on-the-fly integration of analysis and visualization phases, suggested not only for evaluating processing results, but also for designing and adapting NGS data analysis pipelines. Results: This paper presents abstractions for supporting the early analysis of NGS processed data and their implementation in an associated tool, named GenoMetric Space Explorer (GeMSE). This tool serves the needs of the GenoMetric Query Language, an innovative cloud-based system for computing complex queries over heterogeneous processed data. It can also be used starting from any text files in standard BED, BroadPeak, NarrowPeak, GTF, or general tab-delimited format, containing numerical features of genomic regions; metadata can be provided as text files in tab-delimited attribute-value format. GeMSE allows interactive analytics, consisting of on-the-fly cycling among steps of data exploration, analysis and visualization that help biologists and bioinformaticians in making sense of heterogeneous genomic datasets. By means of an explorative interaction support, users can trace past activities and quickly recover their results, seamlessly going backward and forward in the analysis steps and comparative visualizations of heatmaps. Conclusions: GeMSE effective application and practical usefulness is demonstrated through significant use cases of biological interest. GeMSE is available at http://www.bioinformatics.deib.polimi.it/GeMSE/ , and its source code is available at https://github.com/Genometric/GeMSEunder GPLv3 open-source license

The Galaxy platform for accessible, reproducible and collaborative biomedical analyses: 2018 update

Galaxy (homepage: https://galaxyproject.org, main public server: https://usegalaxy.org) is a web-based scientific analysis platform used by tens of thousands of scientists across the world to analyze large biomedical datasets such as those found in genomics, proteomics, metabolomics and imaging. Started in 2005, Galaxy continues to focus on three key challenges of data-driven biomedical science: making analyses accessible to all researchers, ensuring analyses are completely reproducible, and making it simple to communicate analyses so that they can be reused and extended. During the last two years, the Galaxy team and the open-source community around Galaxy have made substantial improvements to Galaxy's core framework, user interface, tools, and training materials. Framework and user interface improvements now enable Galaxy to be used for analyzing tens of thousands of datasets, and >5500 tools are now available from the Galaxy ToolShed. The Galaxy community has led an effort to create numerous high-quality tutorials focused on common types of genomic analyses. The Galaxy developer and user communities continue to grow and be integral to Galaxy's development. The number of Galaxy public servers, developers contributing to the Galaxy framework and its tools, and users of the main Galaxy server have all increased substantially

The global burden of cancer attributable to risk factors, 2010-19 : a systematic analysis for the Global Burden of Disease Study 2019

Background Understanding the magnitude of cancer burden attributable to potentially modifiable risk factors is crucial for development of effective prevention and mitigation strategies. We analysed results from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019 to inform cancer control planning efforts globally. Methods The GBD 2019 comparative risk assessment framework was used to estimate cancer burden attributable to behavioural, environmental and occupational, and metabolic risk factors. A total of 82 risk-outcome pairs were included on the basis of the World Cancer Research Fund criteria. Estimated cancer deaths and disability-adjusted life-years (DALYs) in 2019 and change in these measures between 2010 and 2019 are presented. Findings Globally, in 2019, the risk factors included in this analysis accounted for 4.45 million (95% uncertainty interval 4.01-4.94) deaths and 105 million (95.0-116) DALYs for both sexes combined, representing 44.4% (41.3-48.4) of all cancer deaths and 42.0% (39.1-45.6) of all DALYs. There were 2.88 million (2.60-3.18) risk-attributable cancer deaths in males (50.6% [47.8-54.1] of all male cancer deaths) and 1.58 million (1.36-1.84) risk-attributable cancer deaths in females (36.3% [32.5-41.3] of all female cancer deaths). The leading risk factors at the most detailed level globally for risk-attributable cancer deaths and DALYs in 2019 for both sexes combined were smoking, followed by alcohol use and high BMI. Risk-attributable cancer burden varied by world region and Socio-demographic Index (SDI), with smoking, unsafe sex, and alcohol use being the three leading risk factors for risk-attributable cancer DALYs in low SDI locations in 2019, whereas DALYs in high SDI locations mirrored the top three global risk factor rankings. From 2010 to 2019, global risk-attributable cancer deaths increased by 20.4% (12.6-28.4) and DALYs by 16.8% (8.8-25.0), with the greatest percentage increase in metabolic risks (34.7% [27.9-42.8] and 33.3% [25.8-42.0]). Interpretation The leading risk factors contributing to global cancer burden in 2019 were behavioural, whereas metabolic risk factors saw the largest increases between 2010 and 2019. Reducing exposure to these modifiable risk factors would decrease cancer mortality and DALY rates worldwide, and policies should be tailored appropriately to local cancer risk factor burden. Copyright (C) 2022 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license.Peer reviewe