High-resolution genome-wide scan of genes, gene-networks and cellular systems impacting the yeast ionome

Peer reviewedPublisher PD

Prioritising references for systematic reviews with RobotAnalyst: A user study

Screening references is a time-consuming step necessary for systematic reviews and guideline development. Previous studies have shown that human effort can be reduced by using machine learning software to prioritise large reference collections such that most of the relevant references are identified before screening is completed. We describe and evaluate RobotAnalyst, a Web-based software system that combines text-mining and machine learning algorithms for organising references by their content and actively prioritising them based on a relevancy classification model trained and updated throughout the process. We report an evaluation over 22 reference collections (most are related to public health topics) screened using RobotAnalyst with a total of 43 610 abstract-level decisions. The number of references that needed to be screened to identify 95% of the abstract-level inclusions for the evidence review was reduced on 19 of the 22 collections. Significant gains over random sampling were achieved for all reviews conducted with active prioritisation, as compared with only two of five when prioritisation was not used. RobotAnalyst's descriptive clustering and topic modelling functionalities were also evaluated by public health analysts. Descriptive clustering provided more coherent organisation than topic modelling, and the content of the clusters was apparent to the users across a varying number of clusters. This is the first large-scale study using technology-assisted screening to perform new reviews, and the positive results provide empirical evidence that RobotAnalyst can accelerate the identification of relevant studies. The results also highlight the issue of user complacency and the need for a stopping criterion to realise the work savings

A systematic review of contamination (aerosol, splatter and droplet generation) associated with oral surgery and its relevance to COVID-19

IntroductionThe current COVID-19 pandemic caused by the SARS-CoV-2 virus has impacted the delivery of dental care globally and has led to re-evaluation of infection control standards. However, lack of clarity around what is known and unknown regarding droplet and aerosol generation in dentistry (including oral surgery and extractions), and their relative risk to patients and the dental team, necessitates a review of evidence relating to specific dental procedures. This review is part of a wider body of research exploring the evidence on bioaerosols in dentistry and involves detailed consideration of the risk of contamination in relation to oral surgery.MethodsA comprehensive search of Medline (OVID), Embase (OVID), Cochrane Central Register of Controlled Trials, Scopus, Web of Science, LILACS and ClinicalTrials.Gov was conducted using key terms and MeSH (Medical Subject Headings) words relating to the review questions. Methodological quality including sensitivity was assessed using a schema developed to measure quality aspects of studies using a traffic light system to allow inter- and intra-study overview and comparison. A narrative synthesis was conducted for assessment of the included studies and for the synthesis of results.ResultsEleven studies on oral surgery (including extractions) were included in the review. They explored microbiological (bacterial and fungal) and blood (visible and/or imperceptible) contamination at the person level (patients, operators and assistants) and/or at a wider environmental level, using settle plates, chemiluminescence reagents or air samplers; all within 1 m of the surgical site. Studies were of generally low to medium quality and highlighted an overall risk of contaminated aerosol, droplet and splatter generation during oral surgery procedures, most notably during removal of impacted teeth using rotatory handpieces. Risk of contamination and spread was increased by factors, including proximity to the operatory site, longer duration of treatment, higher procedural complexity, non-use of an extraoral evacuator and areas involving more frequent contact during treatment.ConclusionA risk of contamination (microbiological, visible and imperceptible blood) to patients, dental team members and the clinical environment is present during oral surgery procedures, including routine extractions. However, the extent of contamination has not been explored fully in relation to time and distance. Variability across studies with regards to the analysis methods used and outcome measures makes it difficult to draw robust conclusions. Further studies with improved methodologies, including higher test sensitivity and consideration of viruses, are required to validate these findings

A Systematic Review of Argumentation Related to the Engineering-Designed World

Background Across academic disciplines, researchers have found that argumentation‐based pedagogies increase learners\u27 achievement and engagement. Engineering educational researchers and teachers of engineering may benefit from knowledge regarding how argumentation related to engineering has been practiced and studied. Purpose/Hypothesis Drawing from terms and concepts used in national standards for K‐12 education and accreditation requirements for undergraduate engineering education, this study was designed to identify how arguments and argumentation related to the engineering‐designed world were operationalized in relevant literature. Methodology Specified search terms and inclusion criteria were used to identify 117 empirical studies related to engineering argumentation and educational research. A qualitative content analysis was used to identify trends across these studies. Findings Overall, engineering‐related argumentation was associated with a variety of positive learner outcomes. Across many studies, arguments were operationalized in practice as statements regarding whether an existing technology should be adopted in a given context, usually with a limited number of supports (e.g., costs and ethics) provided for each claim. Relatively few studies mentioned empirical practices, such as tests. Most studies did not name the race/ethnicity of participants nor report engineering‐specific outcomes. Conclusions Engineering educators in K‐12 and undergraduate settings can create learning environments in which learners use a range of epistemic practices, including empirical practices, to support a range of claims. Researchers can study engineering‐specific outcomes while specifying relevant demographics of their research participants

Multidrug-resistant tuberculosis treatment adherence in migrants: a systematic review and meta-analysis.

BACKGROUND: Multidrug-resistant tuberculosis (MDR-TB) is a growing concern in meeting global targets for TB control. In high-income low-TB-incidence countries, a disproportionate number of MDR-TB cases occur in migrant (foreign-born) populations, with concerns about low adherence rates in these patients compared to the host non-migrant population. Tackling MDR-TB in this context may, therefore, require unique approaches. We conducted a systematic review and meta-analysis to identify and synthesise data on MDR-TB treatment adherence in migrant patients to inform evidence-based strategies to improve care pathways and health outcomes in this group. METHODS: This systematic review and meta-analysis was conducted in line with PRISMA guidelines (PROSPERO 42017070756). The databases Embase, MEDLINE, Global Health and PubMed were searched to 24 May 2017 for primary research reporting MDR-TB treatment adherence and outcomes in migrant populations, with no restrictions on dates or language. A meta-analysis was conducted using random-effects models. RESULTS: From 413 papers identified in the database search, 15 studies reporting on MDR-TB treatment outcomes for 258 migrants and 174 non-migrants were included in the systematic review and meta-analysis. The estimated rate of adherence to MDR-TB treatment across migrant patients was 71% [95% confidence interval (CI) = 58-84%], with non-adherence reported among 20% (95% CI = 4-37%) of migrant patients. A key finding was that there were no differences in estimated rates of adherence [risk ratio (RR) = 1.05; 95% CI = 0.82-1.34] or non-adherence (RR = 0.97; 95% CI = 0.79-1.36) between migrants and non-migrants. CONCLUSIONS: MDR-TB treatment adherence rates among migrants in high-income low-TB-incidence countries are approaching global targets for treatment success (75%), and are comparable to rates in non-migrants. The findings highlight that only just over 70% of migrant and non-migrant patients adhere to MDR-TB treatment. The results point to the importance of increasing adherence in all patient groups, including migrants, with an emphasis on tailoring care based on social risk factors for poor adherence. We believe that MDR-TB treatment targets are not ambitious enough

Syndromic surveillance: STL for modeling, visualizing, and monitoring disease counts

<p>Abstract</p> <p>Background</p> <p>Public health surveillance is the monitoring of data to detect and quantify unusual health events. Monitoring pre-diagnostic data, such as emergency department (ED) patient chief complaints, enables rapid detection of disease outbreaks. There are many sources of variation in such data; statistical methods need to accurately model them as a basis for timely and accurate disease outbreak methods.</p> <p>Methods</p> <p>Our new methods for modeling daily chief complaint counts are based on a seasonal-trend decomposition procedure based on loess (STL) and were developed using data from the 76 EDs of the Indiana surveillance program from 2004 to 2008. Square root counts are decomposed into inter-annual, yearly-seasonal, day-of-the-week, and random-error components. Using this decomposition method, we develop a new synoptic-scale (days to weeks) outbreak detection method and carry out a simulation study to compare detection performance to four well-known methods for nine outbreak scenarios.</p> <p>Result</p> <p>The components of the STL decomposition reveal insights into the variability of the Indiana ED data. Day-of-the-week components tend to peak Sunday or Monday, fall steadily to a minimum Thursday or Friday, and then rise to the peak. Yearly-seasonal components show seasonal influenza, some with bimodal peaks.</p> <p>Some inter-annual components increase slightly due to increasing patient populations. A new outbreak detection method based on the decomposition modeling performs well with 90 days or more of data. Control limits were set empirically so that all methods had a specificity of 97%. STL had the largest sensitivity in all nine outbreak scenarios. The STL method also exhibited a well-behaved false positive rate when run on the data with no outbreaks injected.</p> <p>Conclusion</p> <p>The STL decomposition method for chief complaint counts leads to a rapid and accurate detection method for disease outbreaks, and requires only 90 days of historical data to be put into operation. The visualization tools that accompany the decomposition and outbreak methods provide much insight into patterns in the data, which is useful for surveillance operations.</p