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The aim of this review was to establish the frequency with which trials take into account missingness, and to discover what methods trialists use for adjustment in randomised controlled trials with longitudinal measurements. Failing to address the problems that can arise from missing outcome data can result in misleading conclusions. Missing data should be addressed as a means of a sensitivity analysis of the complete case analysis results. One hundred publications of randomised controlled trials with longitudinal measurements were selected randomly from trial publications from the years 2005 to 2012. Information was extracted from these trials, including whether reasons for dropout were reported, what methods were used for handing the missing data, whether there was any explanation of the methods for missing data handling, and whether a statistician was involved in the analysis. The main focus of the review was on missing data post dropout rather than missing interim data. Of all the papers in the study, 9 (9%) had no missing data. More than half of the papers included in the study failed to make any attempt to explain the reasons for their choice of missing data handling method. Of the papers with clear missing data handling methods, 44 papers (50%) used adequate methods of missing data handling, whereas 30 (34%) of the papers used missing data methods which may not have been appropriate. In the remaining 17 papers (19%), it was difficult to assess the validity of the methods used. An imputation method was used in 18 papers (20%) to address the problems caused by missingness. Multiple imputation methods were introduced in 1987 and are an efficient way of accounting for missing data in general, and yet only 4 papers used these methods. All these 4 papers were published in the past four years. Out of the 18 papers which used imputation, only 7 displayed the results as a sensitivity analysis of the complete case analysis results. 61% of the papers that used an imputation explained the reasons for their chosen method. Just under a third of the papers made no reference to reasons for missing outcome data. There was little consistency in reporting of missing data within longitudinal trials; some papers provided great detail about the missing data, while others made no reference to it. [ABSTRACT FROM AUTHOR]

Background: In northern Nigeria, UNICEF has supported introduction of a short message service (SMS) system for data transmission in the Community-based Management of Acute Malnutrition (CMAM) programme. The SMS system operates in parallel to the traditional paper-based system, and weekly data are transmitted directly from the health facilities to the federal level. For the paper system, monthly data summaries are passed through all levels of government. We assessed the data quality and performance of both CMAM information systems. Methods: We undertook a contextualised study in one state in north-west Nigeria, with additional analysis of secondary data from five states. Fieldwork methods included: observation of the data system in nine selected facilities in three local government areas; recounting of data for admissions, exits, and ready-to-use therapeutic food (RUTF) utilisation; and interviews with health workers and government officials. Results: While the small number of facilities does not enable robust generalisation of the quantitative findings, the strengths and weaknesses detected pertain to the whole programme, as they relate to how the system was designed and is operated. We found that the accuracy and reliability of CMAM data were deficient to a similar extent in the paper-based and SMS systems. For the audited month, we found large discrepancies between recounted data and paper records in regard to admissions, exits and RUTF cartons consumed in the majority of facilities visited. There was also a large discrepancy in the reported percentage of "deaths or defaulters" (6.5%) compared to 22% based on a recount of outpatient cards. Errors are mainly introduced during data collection and when completing tallies at facility level. Conclusion: Our findings indicate the need for improvements in the design of the monitoring system, training and supervision of data management, and communication of results; as well as clear evidence on how measures to improve data quality may affect performance of individual CMAM clinics. The CMAM default and death rates currently reported in Nigeria are likely to be under-estimates, and therefore provide a misleadingly good impression of CMAM programme performance. [ABSTRACT FROM AUTHOR]

Background: This paper discusses how collective intelligence (CI) methods can be implemented to improve government data infrastructures, not only to support understanding and primary use of complex national data but also to increase the dissemination and secondary impact of research based on these data. The case study uses the Northern Ireland Longitudinal Study (NILS), a member of the UK family of census/administrative data longitudinal studies (UKLS). Methods: A stakeholder-engaged CI approach was applied to inform the transformation of the NILS Research Support Unit (RSU) infrastructure to support researchers in their use of government data, including collaborative decision-making and better dissemination of research outputs. Results: We provide an overview of NILS RSU infrastructure design changes that have been implemented to date, focusing on a website redesign to meet user information requirements and the formation of better working partnerships between data users and providers within the Northern Ireland data landscape. We also discuss the key challenges faced by the design team during this project of transformation. Conclusion: Our primary objective to improve government data infrastructure and to increase dissemination and the impact of research based on data was a complex and multifaceted challenge due to the number of stakeholders involved and their often conflicting perspectives. Results from this CI approach have been pivotal in highlighting how NILS RSU can work collaboratively with users to maximize the potential of this data, in terms of forming multidisciplinary networks to ensure the research is utilized in policy and in the literature and providing academic support and resources to attract new researchers. [ABSTRACT FROM AUTHOR]

Background: While modern electronic data collection methods (e.g., computer touch-screen or web-based) hold much promise, most current studies continue to make use of more traditional data collection techniques, including paper-and-pencil administration and telephone interviews. The present randomized trial investigated the measurement properties of the EORTC QLQ-C30 under three different modes of administration (MOA's). Methods: A heterogeneous sample of 314 cancer patients undergoing treatment at a specialized treatment center in Amsterdam were randomized to one of three MOA's for the QLQ-C30: paper-and-pencil at home via the mail, telephone interview, and paper-and-pencil at the hospital clinic. Group differences in internal consistency reliabilities (Cronbach's alpha coefficient) for the scale scores were compared. Differences in mean scale scores were also compared by means of ANOVA, with adjustment for potential confounders. Results: Only one statistically significant, yet minor, difference in Cronbach's alpha between the MOA groups was observed for the Role Functioning scale (all 3 alphas >0.80). Significant differences in group means -after adjustment- were found for the Emotional Functioning (EF) scale. Patients completing the written questionnaire at home had significantly lower levels of EF as compared to those interviewed via the telephone; EF scores of those completing the questionnaire at the clinic fell in-between those of the other two groups. These differences, however, were small in magnitude. Conclusions: MOA had little effect on the reliability or the mean scores of the EORTC QLQ-C30, with the possible exception of the EF scale. [ABSTRACT FROM AUTHOR]

Background: The health sector aims to improve health outcomes and access to healthcare. At the same time, the sector relies on unsustainable environmental practices that are increasingly recognised to be catastrophic threats to human health and health inequities. As such, a moral imperative exists for the sector to address these practices. While strides are currently underway to mitigate the environmental impacts of healthcare, less is known about how health researchers are addressing these issues, if at all. Methods: This paper uses an interview methodology to explore the attitudes of UK health researchers using data-intensive methodologies about the adverse environmental impacts of their practices, and how they view the importance of these considerations within wider health goals. Results: Interviews with 26 researchers showed that participants wanted to address the environmental and related health harms associated with their research and they reflected on how they could do so in alignment with their own research goals. However, when tensions emerged, their own research was prioritised. This was related to their own desires as researchers and driven by the broader socio-political context of their research endeavours. Conclusion: To help mitigate the environmental and health harms associated with data-intensive health research, the socio-political context of research culture must be addressed. [ABSTRACT FROM AUTHOR]

Background: The COVID-19 pandemic has presented unique challenges for rapidly designing, initiating, and delivering therapeutic clinical trials. PRINCIPLE (Platform Randomised Trial of Treatments in the Community for Epidemic and Pandemic Illnesses) is the UK national platform investigating repurposed therapies for COVID-19 treatment of older people in the community at high risk of complications. Standard methods of patient recruitment were failing to meet the required pace and scale of enrolment. This paper describes the development and appraisal of a near real-time, data-driven, ethical approach for enhancing recruitment in community care by contacting people with a recent COVID-19 positive test result from the central NHS Test and Trace service within approximately 24-48 h of their test result.Methods: A multi-disciplinary team was formed to solve the technical, ethical, public perception, logistical and information governance issues required to provide a near-real time (approximately within 24-48 h of receiving a positive test) feed of potential trial participants from test result data to the research team. PRINCIPLE was also given unique access to the Summary Care Record (SCR) to ensure safe prescribing, and to enable the trial team to quickly and safely bring consented patients into the trial. A survey of the public was used to understand public perceptions of the use of test data for this proposed methodology.Results: Prior to establishing the data service, PRINCIPLE registered on average 87 participants per week. This increased by up to 87 additional people registered per week from the test data, contributing to an increase from 1013 recruits to PRINCIPLE at the start of October 2020 to 2802 recruits by 20 December 2020. Whilst procedural caveats were identified by the public consultation, out of 2639 people contacted by PRINCIPLE following a positive test result, no one raised a concern about being approached.Conclusions: This paper describes a novel approach to using near-real time NHS operational data to recruit community-based patients within a few days of presentation with acute illness. This approach increased recruitment and reduced time between positive test and randomisation, allowing more rapid evaluation of treatments and increased safety for participants. End-to-end public and patient involvement in the design of the approach provided evidence to inform information governance decisions.Trial Registration: PRINCIPLE is funded by UK Research and Innovation and the Department of Health and Social Care through the National Institute for Health Research. EudraCT number: 2020-001209-22 . 26/03/2020 ISRCTN registry: ISRCTN86534580 . 20/03/2020 REC number: 20/SC/058 IRAS number: 281958. [ABSTRACT FROM AUTHOR]

Background: Realist synthesis is an increasingly popular approach to the review and synthesis of evidence, which focuses on understanding the mechanisms by which an intervention works (or not). There are few published examples of realist synthesis. This paper therefore fills a gap by describing, in detail, the process used for a realist review and synthesis to answer the question 'what interventions and strategies are effective in enabling evidence-informed healthcare?' The strengths and challenges of conducting realist review are also considered. Methods: The realist approach involves identifying underlying causal mechanisms and exploring how they work under what conditions. The stages of this review included: defining the scope of the review (concept mining and framework formulation); searching for and scrutinising the evidence; extracting and synthesising the evidence; and developing the narrative, including hypotheses. Results: Based on key terms and concepts related to various interventions to promote evidence-informed healthcare, we developed an outcome-focused theoretical framework. Questions were tailored for each of four theory/intervention areas within the theoretical framework and were used to guide development of a review and data extraction process. The search for literature within our first theory area, change agency, was executed and the screening procedure resulted in inclusion of 52 papers. Using the questions relevant to this theory area, data were extracted by one reviewer and validated by a second reviewer. Synthesis involved organisation of extracted data into evidence tables, theming and formulation of chains of inference, linking between the chains of inference, and hypothesis formulation. The narrative was developed around the hypotheses generated within the change agency theory area. Conclusions: Realist synthesis lends itself to the review of complex interventions because it accounts for context as well as outcomes in the process of systematically and transparently synthesising relevant literature. While realist synthesis demands flexible thinking and the ability to deal with complexity, the rewards include the potential for more pragmatic conclusions than alternative approaches to systematic reviewing. A separate publication will report the findings of the review. [ABSTRACT FROM AUTHOR]

An amendment to this paper has been published and can be accessed via the original article. [ABSTRACT FROM AUTHOR]

Background: Sharing de-identified individual-level health research data is widely promoted and has many potential benefits. However there are also some potential harms, such as misuse of data and breach of participant confidentiality. One way to promote the benefits of sharing while ameliorating its potential harms is through the adoption of a managed access approach where data requests are channeled through a Data Access Committee (DAC), rather than making data openly available without restrictions. A DAC, whether a formal or informal group of individuals, has the responsibility of reviewing and assessing data access requests. Many individual groups, consortiums, institutional and independent DACs have been established but there is currently no widely accepted framework for their organization and function.Main Text: We propose that DACs, should have the role of both promotion of data sharing and protection of data subjects, their communities, data producers, their institutions and the scientific enterprise. We suggest that data access should be granted by DACs as long as the data reuse has potential social value and provided there is low risk of foreseeable harms. To promote data sharing and to motivate data producers, DACs should encourage secondary uses that are consistent with the interests of data producers and their own institutions. Given the suggested roles of DACs, there should be transparent, simple and clear application procedures for data access. The approach to review of applications should be proportionate to the potential risks involved. DACs should be established within institutional and legal frameworks with clear lines of accountability, terms of reference and membership. We suggest that DACs should not be modelled after research ethics committees (RECs) because their functions and goals of review are different from those of RECs. DAC reviews should be guided by the principles of public health ethics instead of research ethics.Conclusions: In this paper we have suggested a framework under which DACs should operate, how they should be organised, and how to constitute them. [ABSTRACT FROM AUTHOR]

Background: Stepped wedge randomised trial designs involve sequential roll-out of an intervention to participants (individuals or clusters) over a number of time periods. By the end of the study, all participants will have received the intervention, although the order in which participants receive the intervention is determined at random. The design is particularly relevant where it is predicted that the intervention will do more good than harm (making a parallel design, in which certain participants do not receive the intervention unethical) and/or where, for logistical, practical or financial reasons, it is impossible to deliver the intervention simultaneously to all participants. Stepped wedge designs offer a number of opportunities for data analysis, particularly for modelling the effect of time on the effectiveness of an intervention. This paper presents a review of 12 studies (or protocols) that use (or plan to use) a stepped wedge design. One aim of the review is to highlight the potential for the stepped wedge design, given its infrequent use to date. Methods: Comprehensive literature review of studies or protocols using a stepped wedge design. Data were extracted from the studies in three categories for subsequent consideration: study information (epidemiology, intervention, number of participants), reasons for using a stepped wedge design and methods of data analysis. Results: The 12 studies included in this review describe evaluations of a wide range of interventions, across different diseases in different settings. However the stepped wedge design appears to have found a niche for evaluating interventions in developing countries, specifically those concerned with HIV. There were few consistent motivations for employing a stepped wedge design or methods of data analysis across studies. The methodological descriptions of stepped wedge studies, including methods of randomisation, sample size calculations and methods of analysis, are not always complete. Conclusion: While the stepped wedge design offers a number of opportunities for use in future evaluations, a more consistent approach to reporting and data analysis is required. [ABSTRACT FROM AUTHOR]

Background: Metabolomics datasets are often high-dimensional though only a limited number of variables are expected to be informative given a specific research question. The important task of selecting informative variables can therefore become complex. In this paper we look at discriminating between two groups. Two tasks need to be performed: (i) finding variables which differ between the two groups; and (ii) determining how the selected variables can be used to classify new subjects. We introduce an approach using minimum classification error rates as test statistics to find discriminatory and therefore informative variables. The thresholds resulting in the minimum error rates can be used to classify new subjects. This approach transforms error rates into p-values and is referred to as ERp. Results: We show that non-parametric hypothesis testing, based on minimum classification error rates as test statistics, can find statistically significantly shifted variables. The discriminatory ability of variables becomes more apparent when error rates are evaluated based on their corresponding p-values, as relatively high error rates can still be statistically significant. ERp can handle unequal and small group sizes, as well as account for the cost of misclassification. ERp retains (if known) or reveals (if unknown) the shift direction, aiding in biological interpretation. The threshold resulting in the minimum error rate can immediately be used to classify new subjects. We use NMR generated metabolomics data to illustrate how ERp is able to discriminate subjects diagnosed with Mycobacterium tuberculosis infected meningitis from a control group. The list of discriminatory variables produced by ERp contains all biologically relevant variables with appropriate shift directions discussed in the original paper from which this data is taken. Conclusions: ERp performs variable selection and classification, is non-parametric and aids biological interpretation while handling unequal group sizes and misclassification costs. All this is achieved by a single approach which is easy to perform and interpret. ERp has the potential to address many other characteristics of metabolomics data. Future research aims to extend ERp to account for a large proportion of observations below the detection limit, as well as expand on interactions between variables. [ABSTRACT FROM AUTHOR]

Background Levels of haemoglobin A1c (HbA1c) and blood lipids are important determinants of risk in patients with diabetes. Standard analysis methods based upon venous blood samples can be logistically challenging in resource-poor settings where much of the diabetes epidemic is occurring. Dried blood spots (DBS) provide a simple alternative method for sample collection but the comparability of data from analyses based on DBS is not well established. Methods We conducted a systematic review and meta-analysis to define the association of findings for HbA1c and blood lipids for analyses based upon standard methods compared to DBS. The Cochrane, Embase and Medline databases were searched for relevant reports and summary regression lines were estimated. Results 705 abstracts were found by the initial electronic search with 6 further reports identified by manual review of the full papers. 16 studies provided data for one or more outcomes of interest. There was a close agreement between the results for HbA1c assays based on venous and DBS samples (DBS = 0.9858venous + 0.3809), except for assays based upon affinity chromatography. Significant adjustment was required for assays of total cholesterol (DBS = 0.6807venous + 1.151) but results for triglycerides (DBS = 0.9557venous + 0.1427) were directly comparable. Conclusions For HbA1c and selected blood lipids, assays based on DBS samples are clearly associated with assays based on standard venous samples. There are, however, significant uncertainties about the nature of these associations and there is a need for standardisation of the sample collection, transportation, storage and analysis methods before the technique can be considered mainstream. This should be a research priority because better elucidation of metabolic risks in resource poor settings, where venous sampling is infeasible, will be key to addressing the global epidemic of cardiovascular diseases. [ABSTRACT FROM AUTHOR]

Background: Evidence-based health workforce policies are essential to ensure the provision of high-quality health services and to support the attainment of universal health coverage (UHC). This paper describes the main characteristics of available health workforce data for 74 of the 75 countries identified under the 'Countdown to 2015' initiative as accounting for more than 95% of the world's maternal, newborn and child deaths. It also discusses best practices in the development of health workforce metrics post-2015.Methods: Using available health workforce data from the Global Health Workforce Statistics database from the Global Health Observatory, we generated descriptive statistics to explore the current status, recent trends in the number of skilled health professionals (SHPs: physicians, nurses, midwives) per 10 000 population, and future requirements to achieve adequate levels of health care in the 74 countries. A rapid literature review was conducted to obtain an overview of the types of methods and the types of data sources used in human resources for health (HRH) studies.Results: There are large intercountry and interregional differences in the density of SHPs to progress towards UHC in Countdown countries: a median of 10.2 per 10 000 population with range 1.6 to 142 per 10 000. Substantial efforts have been made in some countries to increase the availability of SHPs as shown by a positive average exponential growth rate (AEGR) in SHPs in 51% of Countdown countries for which there are data. Many of these countries will require large investments to achieve levels of workforce availability commensurate with UHC and the health-related sustainable development goals (SDGs). The availability, quality and comparability of global health workforce metrics remain limited. Most published workforce studies are descriptive, but more sophisticated needs-based workforce planning methods are being developed.Conclusions: There is a need for high-quality, comprehensive, interoperable sources of HRH data to support all policies towards UHC and the health-related SDGs. The recent WHO-led initiative of supporting countries in the development of National Health Workforce Accounts is a very promising move towards purposive health workforce metrics post-2015. Such data will allow more countries to apply the latest methods for health workforce planning. [ABSTRACT FROM AUTHOR]

Background: To understand the roles they play in complex diseases, genes need to be investigated in the networks they are involved in. Integration of gene expression and network data is a promising approach to prioritize disease-associated genes. Some methods have been developed in this field, but the problem is still far from being solved. Results: In this paper, we developed a method, Networked Gene Prioritizer (NGP), to prioritize cancer-associated genes. Applications on several breast cancer and lung cancer datasets demonstrated that NGP performs better than the existing methods. It provides stable top ranking genes between independent datasets. The top-ranked genes by NGP are enriched in the cancer-associated pathways. The top-ranked genes by NGP-PLK1, MCM2, MCM3, MCM7, MCM10 and SKP2 might coordinate to promote cell cycle related processes in cancer but not normal cells. Conclusions: In this paper, we have developed a method named NGP, to prioritize cancer-associated genes. Our results demonstrated that NGP performs better than the existing methods. [ABSTRACT FROM AUTHOR]

Background: Data related to forensic postmortems or autopsies are still mainly captured in hard copy format and archived. This paper-based practice impacts on the practitioner's ability to report on incidence, prevalence, and statistical trends related to cases that are commonly seen in mortuaries in forensic medicine. An autopsy can be used to inform and provide evidence-based knowledge for further research about important issues, including social development and assist in providing statistics and data for public health initiatives for implementation and monitoring. Currently, in forensic medicine and pathology research developments are largely hampered by the inefficient data capturing system which only allows access to basic information while pertinent information is largely recorded manually and is therefore difficult to obtain. There is thus a need to improve the efficiency of the data capturing system in forensic pathology, and this review is intended to inform the choice and decisions of appropriate data capture practices and is being conducted to identify nationally and internationally the current data mining and storage systems in place. Methods: The methodology for this scoping review will be guided by the methodological framework for scoping review. The search strategy was developed by the authors, and we will conduct a search from 1 January 2008 of electronic databases (Cochrane Library, Scopus, Web of Science, and Science Direct) and search through WorldCat and PubMed for citations and literature using both keywords and the Medical Subject Headings (MeSH).The electronic search will be supplemented by hand searching references of the included studies and references in journals and websites. All articles will be assessed for eligibility by two reviewers (the primary and secondary authors) and uploaded into EndNote Excel spreadsheet, and duplicates will be identified and removed. The two reviewers (primary and secondary authors) will screen the eligible abstracts and articles against the inclusion criteria, and selection will be on a minimum percentage agreement of 50%. The selection process will be documented by following and using a PRISMA flow diagram. The extracted data will be analyzed and reported in the form of a narrative review with descriptive analysis and text analysis once the data is summarized for description and characterization. Discussion: The results of this review will identify and describe data capturing, management, and storage practices for use in forensic medicine. It will also review the efficiency of the different data systems and report where possible on the uses of the data system within the forensic medicine and pathology field. Ethics and dissemination: Although research ethics approval is not required for this scoping review because the study will not include human or animal participants, the study was submitted for approval to the University of Kwazulu Natal Biomedical Research Ethics Committee and obtained provisional approval. Data will be sourced only from published literature and gray literature. The results will be presented at relevant national and international conferences and published in a peer-reviewed journal. All search results including excluded studies will be added into an addendum in the article and made available for public perusal to therefore ensure transparency and reproducibility. [ABSTRACT FROM AUTHOR]

Background: With the advent of high throughput biotechnology data acquisition platforms such as micro arrays, SNP chips and mass spectrometers, data sets with many more variables than observations are now routinely being collected. Finding relationships between response variables of interest and variables in such data sets is an important problem akin to finding needles in a haystack. Whilst methods for a number of response types have been developed a general approach has been lacking. Results: The major contribution of this paper is to present a unified methodology which allows many common (statistical) response models to be fitted to such data sets. The class of models includes virtually any model with a linear predictor in it, for example (but not limited to), multiclass logistic regression (classification), generalised linear models (regression) and survival models. A fast algorithm for finding sparse well fitting models is presented. The ideas are illustrated on real data sets with numbers of variables ranging from thousands to millions. R code implementing the ideas is available for download. Conclusion: The method described in this paper enables existing work on response models when there are less variables than observations to be leveraged to the situation when there are many more variables than observations. It is a powerful approach to finding parsimonious models for such datasets. The method is capable of handling problems with millions of variables and a large variety of response types within the one framework. The method compares favourably to existing methods such as support vector machines and random forests, but has the advantage of not requiring separate variable selection steps. It is also works for data types which these methods were not designed to handle. The method usually produces very sparse models which make biological interpretation simpler and more focused. [ABSTRACT FROM AUTHOR]

Background: Although electronic health information systems (EHIS) with immunization components exist in Kenya, questions and concerns remain about their use and alignment with the Kenya Ministry of Health's (MOH) National Vaccine and Immunization Program (NVIP). This article reports on the findings of an assessment of select EHIS with immunization components in Kenya, specifically related to system design, development, and implementation.Methods: We conducted a rapid assessment of select EHIS with immunization components in Kenya from January to May 2017 to understand the design, development, implementation of the EHIS including the lessons learned from their use. We also assessed how the data elements in the EHIS compared to the data elements in the Maternal and Child Health Booklet used in the existing paper based system in Kenya.Results: The EHIS reviewed varied in purpose, content, and population covered. Only one system was built to focus specifically on immunization data. Substantial differences in system functionality and immunization-related data elements included in the EHIS were identified. None of the EHIS had all the data elements necessary to fully replace or operate independently from the standardized paper-based system for recording immunization data in Kenya.Conclusions: Overall, the findings of this assessment highlighted substantial variation in the EHIS with immunization components. The findings provide insights and lessons learned for the Kenya MOH NVIP, immunization partners, vendors of EHIS, and users of EHIS to consider as Kenya transitions from paper-based to electronic immunization information systems. [ABSTRACT FROM AUTHOR]

Background: Snakebite envenomation (SBE) and scorpion sting envenomation (SSE) are significant neglected tropical diseases that primarily affect impoverished communities in rural areas of developing nations. A lack of understanding about snake and scorpion species and their distribution exacerbates the disabilities and fatalities caused by SBE and SSE. In Sudan, particularly in regions affected by ongoing conflicts where healthcare resources are scarce, social media platforms offer a cost-effective approach to addressing public health challenges. Our aim in this study is to highlight the benefits of using social media for data collection and health promotion in such environments. Methods: We present a cost-effective communication and data collection strategy implemented at the Toxic Organisms Research Centre (TORC) of the University of Khartoum, focusing on a Facebook group, "Scorpions and Snakes of Sudan", as our primary social media platform. Additionally, we discuss the lessons learned and the initial impact of this strategy on enhancing population health literacy. Results: The group community is composed of ~ 5000 members from 14 countries. During the period from January 2023 to January 2024, we received 417 enquiries about snakes and scorpions belonging to 11 families and composed of 55 species. In addition, 53 other enquiries covered a range of organisms and their tracks (e.g., spiders, skinks, chameleons, foxes, sun spiders, centipedes, lizards, moth larvae, and insect tracks). The first photographic evidence of Malpolon monspessulanus in Sudan was via the group activities. The rare species Telescopus gezirae, the Blue Nile cat snake, is also documented via the group member's queries. Recognizing the evolving nature of social media use in public health, we also address the current limitations and evidence gaps that need to be addressed to effectively translate best practices into policy. Conclusion: In conclusion, utilizing Facebook as an institutional platform to share scientific information in simple Arabic language underscores the proactive roles that citizens, scientists, and public health stakeholders can play in leveraging social media for eHealth, eAwareness, and public health initiatives. This approach highlights the potential for collaborative efforts, particularly during crises, to maximize the benefits of social media in advancing public health. Author Summary: Snakebite envenomation (SBE) and scorpion sting envenomation (SSE) are significant public health issues worldwide, particularly affecting vulnerable populations such as children and agricultural workers. SBE alone affects millions of people annually, resulting in thousands of deaths and permanent disabilities. SSE is also a serious concern, with thousands of deaths reported annually but with a dangerous lack of information and in registered incidences, especially in countries such as Sudan. In Sudan, both SBE and SSE are prevalent, with specific regions experiencing higher incidences. To address these challenges, the Toxic Organisms Research Centre (TORC) at the University of Khartoum established a Facebook group, "Scorpions and Snakes of Sudan", to raise community awareness and collect data on venomous and nonvenomous organisms. The group has attracted nearly 5000 participants, mostly from Sudan, and has received enquiries from nine countries. The data collected through this platform provides valuable insights into the distribution and diversity of snakes and scorpions, aiding in public health interventions and education efforts. [ABSTRACT FROM AUTHOR]

Health inequalities amplified by the COVID-19 pandemic have disproportionately affected racialized and equity-deserving communities across Canada. In the Municipality of Peel, existing data, while limited, illustrates that individuals from racialized and equity-deserving communities continue to suffer, receive delayed care, and die prematurely. In response to these troubling statistics, grassroots community advocacy has called on health systems leaders in Peel to work with community and non-profit organizations to address the critical data and infrastructure gaps that hinder addressing the social determinants of health in the region. To support these advocacy efforts, we used a community-based participatory research approach to understand how we might build a data collection ecosystem across sectors, alongside community residents and service providers, to accurately capture the data about the social determinants of health. This approach involved developing a community engagement council, defining the problem with the community, mapping what data is actively collected and what is excluded, and understanding experiences of sociodemographic data collection from community members and service providers. Guided by community voices, our study focused on sociodemographic data collection in the primary care context and identified which service providers use and collect these data, how data are used in their work, the facilitators and barriers to data use and collection. Additionally, we gained insight into how sociodemographic data collection could be respectful, safe, and properly governed from the perspectives of community members. From this study, we identify a set of eight recommendations for sociodemographic data collection and highlight limitations. This foundational community-based work will inform future research in establishing data governance in partnership with diverse and equity-deserving communities. [ABSTRACT FROM AUTHOR]

Background: This paper suggests a method to assess the extent to which ultra-short Heart Rate Variability (HRV) features (less than 5 min) can be considered as valid surrogates of short HRV features (nominally 5 min). Short term HRV analysis has been widely investigated for mental stress assessment, whereas the validity of ultra-short HRV features remains unclear. Therefore, this study proposes a method to explore the extent to which HRV excerpts can be shortened without losing their ability to automatically detect mental stress.Methods: ECGs were acquired from 42 healthy subjects during a university examination and resting condition. 23 features were extracted from HRV excerpts of different lengths (i.e., 30 s, 1 min, 2 min, 3 min, and 5 min). Significant differences between rest and stress phases were investigated using non-parametric statistical tests at different time-scales. Features extracted from each ultra-short length were compared with the standard short HRV features, assumed as the benchmark, via Spearman's rank correlation analysis and Bland-Altman plots during rest and stress phases. Using data-driven machine learning approaches, a model aiming to detect mental stress was trained, validated and tested using short HRV features, and assessed on the ultra-short HRV features.Results: Six out of 23 ultra-short HRV features (MeanNN, StdNN, MeanHR, StdHR, HF, and SD2) displayed consistency across all of the excerpt lengths (i.e., from 5 to 1 min) and 3 out of those 6 ultra-short HRV features (MeanNN, StdHR, and HF) achieved good performance (accuracy above 88%) when employed in a well-dimensioned automatic classifier.Conclusion: This study concluded that 6 ultra-short HRV features are valid surrogates of short HRV features for mental stress investigation. [ABSTRACT FROM AUTHOR]

Background: Cancer is a complex, multiscale dynamical system, with interactions between tumor cells and non-cancerous host systems. Therapies act on this combined cancer-host system, sometimes with unexpected results. Systematic investigation of mechanistic computational models can augment traditional laboratory and clinical studies, helping identify the factors driving a treatment's success or failure. However, given the uncertainties regarding the underlying biology, these multiscale computational models can take many potential forms, in addition to encompassing high-dimensional parameter spaces. Therefore, the exploration of these models is computationally challenging. We propose that integrating two existing technologies—one to aid the construction of multiscale agent-based models, the other developed to enhance model exploration and optimization—can provide a computational means for high-throughput hypothesis testing, and eventually, optimization. Results: In this paper, we introduce a high throughput computing (HTC) framework that integrates a mechanistic 3-D multicellular simulator (PhysiCell) with an extreme-scale model exploration platform (EMEWS) to investigate high-dimensional parameter spaces. We show early results in applying PhysiCell-EMEWS to 3-D cancer immunotherapy and show insights on therapeutic failure. We describe a generalized PhysiCell-EMEWS workflow for high-throughput cancer hypothesis testing, where hundreds or thousands of mechanistic simulations are compared against data-driven error metrics to perform hypothesis optimization. Conclusions: While key notational and computational challenges remain, mechanistic agent-based models and high-throughput model exploration environments can be combined to systematically and rapidly explore key problems in cancer. These high-throughput computational experiments can improve our understanding of the underlying biology, drive future experiments, and ultimately inform clinical practice. [ABSTRACT FROM AUTHOR]

Background: Visualization plays an important role in epidemic time series analysis and forecasting. Viewing time series data plotted on a graph can help researchers identify anomalies and unexpected trends that could be overlooked if the data were reviewed in tabular form; these details can influence a researcher's recommended course of action or choice of simulation models. However, there are challenges in reviewing data sets from multiple data sources – data can be aggregated in different ways (e.g., incidence vs. cumulative), measure different criteria (e.g., infection counts, hospitalizations, and deaths), or represent different geographical scales (e.g., nation, HHS Regions, or states), which can make a direct comparison between time series difficult. In the face of an emerging epidemic, the ability to visualize time series from various sources and organizations and to reconcile these datasets based on different criteria could be key in developing accurate forecasts and identifying effective interventions. Many tools have been developed for visualizing temporal data; however, none yet supports all the functionality needed for easy collaborative visualization and analysis of epidemic data. Results: In this paper, we present EpiViewer, a time series exploration dashboard where users can upload epidemiological time series data from a variety of sources and compare, organize, and track how data evolves as an epidemic progresses. EpiViewer provides an easy-to-use web interface for visualizing temporal datasets either as line charts or bar charts. The application provides enhanced features for visual analysis, such as hierarchical categorization, zooming, and filtering, to enable detailed inspection and comparison of multiple time series on a single canvas. Finally, EpiViewer provides several built-in statistical Epi-features to help users interpret the epidemiological curves. Conclusion: EpiViewer is a single page web application that provides a framework for exploring, comparing, and organizing temporal datasets. It offers a variety of features for convenient filtering and analysis of epicurves based on meta-attribute tagging. EpiViewer also provides a platform for sharing data between groups for better comparison and analysis. Our user study demonstrated that EpiViewer is easy to use and fills a particular niche in the toolspace for visualization and exploration of epidemiological data. [ABSTRACT FROM AUTHOR]

Background: For over 20 years, the National Health Service in England has run a system of national planning for highly specialised healthcare services. The aim is to ensure that very rare diseases are treated, and very complex procedures performed, in only a few centres, each of which maintains a volume high enough to maintain excellent outcomes. The commissioning strategy for the provision of these national services in England is strongly centralising. Centralising does however create a duty to ensure that patients distant from the treatment centres are not thereby disadvantaged. The commissioning process ensures sufficient capacity to treat the entire national caseload of clinically eligible patients. The aim of this paper is to apply the Systematic Component of Variation (SCV) to study access to services commissioned by the National Specialised Commissioning Team (NSCT) in England. The discussion focuses on the potential explanations for a high level of systematic variation between areas and on the use of the SCV to support the monitoring and development of these nationally commissioned services. Method: Data from nationally commissioned services for the year ending 2011 were received from treating hospital. Mid year age and sex appropriate population estimates were then obtained to provide denominator data. Data were analysed at the geographic level of strategic health authority. Results: 30 services met all requirements for analysis. There is no apparent relationship between SCV and number of locations from which the service is provided. On inspection high SCV is more common among recently commissioned services. Discussion: The importance of the SCV lies in its ability to support the development of highly specialised services. Once the random variation has been accounted for, the reasons for a systematic component can be explored. While no absolute cut- off exists, the SCV can be used to gauge and explore services that are potentially not covering the national caseload. The reason for a high SCV may not be immediately apparent; thus the SCV can aid those responsible for commissioning the service to seek potential explanations and identify improvements. Conclusion: We have reviewed spatial variation in access to a set of highly specialised services in England. On inspecting our results, we believe that they suggest that equity of access can usually be achieved at about five years after establishing a service, and this is not dependent, within the geography of England, on the number of centres designated. [ABSTRACT FROM AUTHOR]

Background: It is widely agreed that interventions to change professionals' practice need to be clearly specified. This involves (1) selecting and defining the intervention techniques, (2) operationalising the techniques and deciding their delivery, and (3) formulating hypotheses about the mechanisms through which the techniques are thought to result in change. Descriptions of methods to achieve these objectives are limited. This paper reports methods and illustrates outputs from a study to meet these objectives, specifically from the Good Goals study to improve occupational therapists' caseload management practice. Methods: (1) Behaviour change techniques were identified and selected from an existing matrix that maps techniques to determinants. An existing coding manual was used to define the techniques. (2) A team of occupational therapists generated context-relevant, acceptable modes of delivery for the techniques; these data were compared and contrasted with previously collected data, literature on caseload management, and the aims of the intervention. (3) Hypotheses about the mechanisms of change were formulated by drawing on the matrix and on theories of behaviour change. Results: (1) Eight behaviour change techniques were selected: goal specified; self-monitoring; contract; graded tasks; increasing skills (problem solving, decision making, goal setting); coping skills; rehearsal of relevant skills; social processes of encouragement, support, and pressure; demonstration by others; and feedback. (2) A range of modes of delivery were generated (e.g., graded tasks' consisting of series of clinical cases and situations that become increasingly difficult). Conditions for acceptable delivery were identified (e.g., 'self-monitoring' was acceptable only if delivered at team level). The modes of delivery were specified as face-to-face training, task sheets, group tasks, DVDs, and team-based weekly meetings. (3) The eight techniques were hypothesized to target caseload management practice through eleven mediating variables. Three domains were hypothesized to be most likely to change: beliefs about capabilities, motivation and goals, and behavioural regulation. Conclusions: The project provides an exemplar of a systematic and reportable development of a quality-improvement intervention, with its methods likely to be applicable to other projects. A subsequent study of the intervention has provided early indication that use of systematic methods to specify interventions may help to maximize acceptability and effectiveness. [ABSTRACT FROM AUTHOR]

Background: It is recognised as good practice to use qualitative methods to elicit users' views of internet-delivered health-care interventions during their development. This paper seeks to illustrate the advantages of combining usability testing with 'theoretical modelling', i.e. analyses that relate the findings of qualitative studies during intervention development to social science theory, in order to gain deeper insights into the reasons and context for how people respond to the intervention. This paper illustrates how usability testing may be enriched by theoretical modelling by means of two qualitative studies of users' views of the delivery of information in an internet-delivered intervention to help users decide whether they needed to seek medical care for their cold or flu symptoms. Methods: In Study 1, 21 participants recruited from a city in southern England were asked to 'think aloud' while viewing draft web-pages presented in paper format. In Study 2, views of our prototype website were elicited, again using think aloud methods, in a sample of 26 participants purposively sampled for diversity in education levels. Both data-sets were analysed by thematic analysis. Results: Study 1 revealed that although the information provided by the draft web-pages had many of the intended empowering benefits, users often felt overwhelmed by the quantity of information. Relating these findings to theory and research on factors influencing preferences for information-seeking we hypothesised that to meet the needs of different users (especially those with lower literacy levels) our website should be designed to provide only essential personalised advice, but with options to access further information. Study 2 showed that our website design did prove accessible to users with different literacy levels. However, some users seemed to want still greater control over how information was accessed. Conclusions: Educational level need not be an insuperable barrier to appreciating web-based access to detailed health-related information, provided that users feel they can quickly gain access to the specific information they seek. [ABSTRACT FROM AUTHOR]

Background: This is the seventh in a series of papers reporting Sustainability in Health care by Allocating Resources Effectively (SHARE) in a local healthcare setting. The SHARE Program was a systematic, integrated, evidence-based program for resource allocation within a large Australian health service. It aimed to facilitate proactive use of evidence from research and local data; evidence-based decision-making for resource allocation including disinvestment; and development, implementation and evaluation of disinvestment projects. From the literature and responses of local stakeholders it was clear that provision of expertise and education, training and support of health service staff would be required to achieve these aims. Four support services were proposed. This paper is a detailed case report of the development, implementation and evaluation of a Data Service, Capacity Building Service and Project Support Service. An Evidence Service is reported separately.Methods: Literature reviews, surveys, interviews, consultation and workshops were used to capture and process the relevant information. Existing theoretical frameworks were adapted for evaluation and explication of processes and outcomes.Results: Surveys and interviews identified current practice in use of evidence in decision-making, implementation and evaluation; staff needs for evidence-based practice; nature, type and availability of local health service data; and preferred formats for education and training. The Capacity Building and Project Support Services were successful in achieving short term objectives; but long term outcomes were not evaluated due to reduced funding. The Data Service was not implemented at all. Factors influencing the processes and outcomes are discussed.Conclusion: Health service staff need access to education, training, expertise and support to enable evidence-based decision-making and to implement and evaluate the changes arising from those decisions. Three support services were proposed based on research evidence and local findings. Local factors, some unanticipated and some unavoidable, were the main barriers to successful implementation. All three proposed support services hold promise as facilitators of EBP in the local healthcare setting. The findings from this study will inform further exploration. [ABSTRACT FROM AUTHOR]

Background: Adequate performance assessment benefits from the use of disaggregated data to allow a proper evaluation of health systems. Since routinely collected data are usually not disaggregated enough to allow stratified analyses of healthcare needs, utilisation, cost and quality across different sectors, international research projects could fill this gap by exploring means to data collection or even providing individual-level data. The aim of this paper is therefore to (1) study the availability and accessibility of relevant European-funded health projects, and (2) to analyse their contents and methodologies.Methods: The European Commission Public Health Projects Database and CORDIS were searched for eligible projects, which were then analysed by information openly available online.Results: Overall, only a few of the 39 identified projects produced data useful for proper performance assessment, due to, for example, lacking available or accessible data, or poor linkage of health status to costs and patient experiences. Other problems were insufficient databases to identify projects and poor communication of project contents and results.Conclusions: A new approach is necessary to improve accessibility to and coverage of data on outcomes, quality and costs of health systems enabling decision-makers and health professionals to properly assess performance. [ABSTRACT FROM AUTHOR]

Background: Health-related research funders, regulators and journals expect that de-identified individual-level health data be shared widely, with as few restrictions as possible; yet, in reality, the volume of shared data remains low.Main Body: Health researchers and other data producers are reluctant to share their data unless they are confident that their datasets are of high quality and reliable, and that they are used in accordance with the values and aims of their institutions. We argue that having an institutional, departmental or group data management and sharing policy is the first step towards encouraging researchers and healthcare professionals to share their data more widely. Our paper outlines the elements of a data management and sharing policy, which should include aims consistent with those of the institution as well as with data management procedures, models of data sharing, request procedures, consent models and cost recovery mechanisms. A policy would help an institution, department or group maximise the use of its data and protect the interests of the institution and its members. We base our recommendations on our experience collecting and curating data for large clinical trials conducted in low- and middle-income countries, facilitating the sharing of datasets with secondary users, whilst teaching data management and conducting empirical research on data sharing. Although the fundamentals of a policy are general, the paper is focused on the low- and middle-income country context.Conclusion: We argue that having an institutional, departmental or group data management and sharing policy is the first step in promoting data sharing. [ABSTRACT FROM AUTHOR]

Leveraging artificial intelligence (AI) approaches in animal health (AH) makes it possible to address highly complex issues such as those encountered in quantitative and predictive epidemiology, animal/human precision-based medicine, or to study host × pathogen interactions. AI may contribute (i) to diagnosis and disease case detection, (ii) to more reliable predictions and reduced errors, (iii) to representing more realistically complex biological systems and rendering computing codes more readable to non-computer scientists, (iv) to speeding-up decisions and improving accuracy in risk analyses, and (v) to better targeted interventions and anticipated negative effects. In turn, challenges in AH may stimulate AI research due to specificity of AH systems, data, constraints, and analytical objectives. Based on a literature review of scientific papers at the interface between AI and AH covering the period 2009–2019, and interviews with French researchers positioned at this interface, the present study explains the main AH areas where various AI approaches are currently mobilised, how it may contribute to renew AH research issues and remove methodological or conceptual barriers. After presenting the possible obstacles and levers, we propose several recommendations to better grasp the challenge represented by the AH/AI interface. With the development of several recent concepts promoting a global and multisectoral perspective in the field of health, AI should contribute to defract the different disciplines in AH towards more transversal and integrative research. [ABSTRACT FROM AUTHOR]

Background: Scaffold proteins are known for being crucial regulators of various cellular functions by assembling multiple proteins involved in signaling and metabolic pathways. Identification of scaffold proteins and the study of their molecular mechanisms can open a new aspect of cellular systemic regulation and the results can be applied in the field of medicine and engineering. Despite being highlighted as the regulatory roles of dozens of scaffold proteins, there was only one known computational approach carried out so far to find scaffold proteins from interactomes. However, there were limitations in finding diverse types of scaffold proteins because their criteria were restricted to the classical scaffold proteins. In this paper, we will suggest a systematic approach to predict massive scaffold proteins from interactomes and to characterize the roles of scaffold proteins comprehensively. Results: From a total of 10,419 basic scaffold protein candidates in protein interactomes, we classified them into three classes according to the structural evidences for scaffolding, such as domain architectures, domain interactions and protein complexes. Finally, we could define 2716 highly reliable scaffold protein candidates and their characterized functional features. To assess the accuracy of our prediction, the gold standard positive and negative data sets were constructed. We prepared 158 gold standard positive data and 844 gold standard negative data based on the functional information from Gene Ontology consortium. The precision, sensitivity and specificity of our testing was 80.3, 51.0, and 98.5 % respectively. Through the function enrichment analysis of highly reliable scaffold proteins, we could confirm the significantly enriched functions that are related to scaffold protein binding. We also identified functional association between scaffold proteins and their recruited proteins. Furthermore, we checked that the disease association of scaffold proteins is higher than kinases. Conclusions: In conclusion, we could predict larger volume of scaffold proteins and analyzed their functional characteristics. Deeper understandings about the roles of scaffold proteins from this study will provide a higher opportunity to find therapeutic or engineering applications of scaffold proteins using their functional characteristics. [ABSTRACT FROM AUTHOR]

Background: Poor oral health is a chronic condition that can be extremely costly to manage. In Australia, publicly funded dental services are provided to community members deemed to be eligible--those who are socio-economically disadvantaged or determined to be at higher risk of dental disease. Historically public dental services have nominally been allocated based on the size of the eligible population in a geographic area. This approach has been largely inadequate for reducing disparities in dental disease, primarily because the approach is treatment-focused, and oral health is influenced by a variety of interacting factors. This paper describes the developmental process of a multi-dimensional community-level risk assessment model, to profile a community's risk of poor oral health. Methods: A search of the evidence base was conducted to identify robust frameworks for conceptualisation of risk factors and associated performance indicators. Government and other agency websites were also searched to identify publicly available data assets with items relevant to oral diseases. Data quality and analysis considerations were assessed for the use of mixed data sources. Results: Several frameworks and associated indicator sets (twelve national and eight state-wide data collections with relevant indicators) were identified. Determination of the system inputs for the Model were primarily informed by the World Health Organisation's (WHO) operational model for an Integrated Oral Health-Chronic Disease Prevention System, and Australia's National Oral Health Plan 2004-2013. Data quality and access informed the final selection of indicators. Conclusions: Despite limitations in the quality and regularity of data collections, there are numerous data sources available that provide the required data inputs for community-level risk assessment for oral health. Assessing risk in this way will enhance our ability to deliver appropriate public oral health care services and address the uneven distribution of oral disease across the social gradient. [ABSTRACT FROM AUTHOR]

Background: China has become a global player in the field of biosamples research and analysis of genetic data. The Beijing Genomics Institute is a genetics factory where enormous amounts of biosamples/data from all over the world are being analyzed. Most of the global bioethics discussions focused on research conducted by scientists from industrialized countries with subjects from poorer countries. Today, however, samples from industrialized nations are being analyzed in China on an unprecedented scale. This means that one should not just focus on bioethics developments in western countries, but also should pay attention to the situation in China. Under this era of rapid advancement in genomics, reassessing the conventionally accepted bioethical principles is strongly needed. Discussion: In this paper, we will analyze the case of BGI in the context of the Chinese regulatory system in order to identify methods to regulate genetic research more effectively and to strengthen BGI's role in international collaborative research projects. Three main issues concerning sample collection and samples/data management are addressed. Firstly, an ambiguous definition of research, which does not specifically include biosamples/data, when applied to genetic research, may cause confusion and leave loopholes in governance. Secondly, the current regulations do not provide sufficient guidelines on the details of what information to present to prospective subjects, and how to combine informed consent with strategies of re-consent, withdrawal and feedback from research. Finally, the existing regulations do not adequately address issues of genetic privacy and data protection. Summary: Bioethical issues related to genetic research in China may be partially due to the nature of genetic research and partially stems from the strategy of simply adopting general international guidelines into the Chinese context without detailed considerations of the local needs. However, there are no perfect readymade ethical solutions for everyone; every country faces different open questions and challenges behind what appears to be unified guidelines. Given the importance of China in international genetic research, other countries ought to be concerned about the bioethical developments in China. China should also have a substantive discussion with the international community on bioethics issues. [ABSTRACT FROM AUTHOR]

An amendment to this paper has been published and can be accessed via the original article. [ABSTRACT FROM AUTHOR]

Background: Research in paramedicine faces challenges in developing research capacity, including access to high-quality data. A variety of unique factors in the paramedic work environment influence data quality. In other fields of healthcare, data quality assessment (DQA) frameworks provide common methods of quality assessment as well as standards of transparent reporting. No similar DQA frameworks exist for paramedicine, and practices related to DQA are sporadically reported. This scoping review aims to describe the range, extent, and nature of DQA practices within research in paramedicine. Methods: This review followed a registered and published protocol. In consultation with a professional librarian, a search strategy was developed and applied to MEDLINE (National Library of Medicine), EMBASE (Elsevier), Scopus (Elsevier), and CINAHL (EBSCO) to identify studies published from 2011 through 2021 that assess paramedic data quality as a stated goal. Studies that reported quantitative results of DQA using data that relate primarily to the paramedic practice environment were included. Protocols, commentaries, and similar study types were excluded. Title/abstract screening was conducted by two reviewers; full-text screening was conducted by two, with a third participating to resolve disagreements. Data were extracted using a piloted data-charting form. Results: Searching yielded 10,105 unique articles. After title and abstract screening, 199 remained for full-text review; 97 were included in the analysis. Included studies varied widely in many characteristics. Majorities were conducted in the United States (51%), assessed data containing between 100 and 9,999 records (61%), or assessed one of three topic areas: data, trauma, or out-of-hospital cardiac arrest (61%). All data-quality domains assessed could be grouped under 5 summary domains: completeness, linkage, accuracy, reliability, and representativeness. Conclusions: There are few common standards in terms of variables, domains, methods, or quality thresholds for DQA in paramedic research. Terminology used to describe quality domains varied among included studies and frequently overlapped. The included studies showed no evidence of assessing some domains and emerging topics seen in other areas of healthcare. Research in paramedicine would benefit from a standardized framework for DQA that allows for local variation while establishing common methods, terminology, and reporting standards. [ABSTRACT FROM AUTHOR]

Background Protein complexes are basic cellular entities that carry out the functions of their components. It can be found that in databases of protein complexes of yeast like CYC2008, the major type of known protein complexes is heterodimeric complexes. Although a number of methods for trying to predict sets of proteins that form arbitrary types of protein complexes simultaneously have been proposed, it can be found that they often fail to predict heterodimeric complexes. Results In this paper, we have designed several features characterizing heterodimeric protein complexes based on genomic data sets, and proposed a supervised-learning method for the prediction of heterodimeric protein complexes. This method learns the parameters of the features, which are embedded in the naïve Bayes classifier. The log-likelihood ratio derived from the naïve Bayes classifier with the parameter values obtained by maximum likelihood estimation gives the score of a given pair of proteins to predict whether the pair is a heterodimeric complex or not. A five-fold cross-validation shows good performance on yeast. The trained classifiers also show higher predictability than various existing algorithms on yeast data sets with approximate and exact matching criteria. Conclusions Heterodimeric protein complex prediction is a rather harder problem than heteromeric protein complex prediction because heterodimeric protein complex is topologically simpler. However, it turns out that by designing features specialized for heterodimeric protein complexes, predictability of them can be improved. Thus, the design of more sophisticate features for heterodimeric protein complexes as well as the accumulation of more accurate and useful genome-wide data sets will lead to higher predictability of heterodimeric protein complexes. [ABSTRACT FROM AUTHOR]

Background: Markov state models have been widely used to study conformational changes of biological macromolecules. These models are built from short timescale simulations and then propagated to extract long timescale dynamics. However, the solvent information in molecular simulations are often ignored in current methods, because of the large number of solvent molecules in a system and the indistinguishability of solvent molecules upon their exchange. Methods: We present a solvent signature that compactly summarizes the solvent distribution in the highdimensional data, and then define a distance metric between different configurations using this signature. We next incorporate the solvent information into the construction of Markov state models and present a fast geometric clustering algorithm which combines both the solute-based and solvent-based distances. Results: We have tested our method on several different molecular dynamical systems, including alanine dipeptide, carbon nanotube, and benzene rings. With the new solvent-based signatures, we are able to identify different solvent distributions near the solute. Furthermore, when the solute has a concave shape, we can also capture the water number inside the solute structure. Finally we have compared the performances of different Markov state models. The experiment results show that our approach improves the existing methods both in the computational running time and the metastability. Conclusions: In this paper we have initiated an study to build Markov state models for molecular dynamical systems with solvent degrees of freedom. The methods we described should also be broadly applicable to a wide range of biomolecular simulation analyses. [ABSTRACT FROM AUTHOR]

Background: A growing corpus of research focuses on assessing the quality of the local built environment and also examining the relationship between the built environment and health outcomes and indicators in communities. However, there is a lack of research presenting a highly resolved, systematic, and comprehensive spatial approach to assessing the built environment over a large geographic extent. In this paper, we contribute to the built environment literature by describing a tool used to assess the residential built environment at the tax parcel-level, as well as a methodology for summarizing the data into meaningful indices for linkages with health data. Methods: A database containing residential built environment variables was constructed using the existing body of literature, as well as input from local community partners. During the summer of 2008, a team of trained assessors conducted an on-foot, curb-side assessment of approximately 17,000 tax parcels in Durham, North Carolina, evaluating the built environment on over 80 variables using handheld Global Positioning System (GPS) devices. The exercise was repeated again in the summer of 2011 over a larger geographic area that included roughly 30,700 tax parcels; summary data presented here are from the 2008 assessment. Results: Built environment data were combined with Durham crime data and tax assessor data in order to construct seven built environment indices. These indices were aggregated to US Census blocks, as well as to primary adjacency communities (PACs) and secondary adjacency communities (SACs) which better described the larger neighborhood context experienced by local residents. Results were disseminated to community members, public health professionals, and government officials. Conclusions: The assessment tool described is both easily-replicable and comprehensive in design. Furthermore, our construction of PACs and SACs introduces a novel concept to approximate varying scales of community and describe the built environment at those scales. Our collaboration with community partners at all stages of the tool development, data collection, and dissemination of results provides a model for engaging the community in an active research program. [ABSTRACT FROM AUTHOR]

Individual differences in response to pharmacologic treatment limits the usefulness of mean data obtained from randomized controlled trials. These individual differences exist even in genetically uniform inbred mouse strains. While stratification can be of value in large studies, the individual patient history is the most effective currently available guide for personalized medicine in psychopharmacology. [ABSTRACT FROM AUTHOR]

Background: Biology is rapidly becoming a data intensive, data-driven science. It is essential that data is represented and connected in ways that best represent its full conceptual content and allows both automated integration and data driven decision-making. Recent advancements in distributed multi-relational directed graphs, implemented in the form of the Semantic Web make it possible to deal with complicated heterogeneous data in new and interesting ways. Results: This paper presents a new approach, scenario driven data modelling (SDDM), that integrates multirelational directed graphs with data streams. SDDM can be applied to virtually any data integration challenge with widely divergent types of data and data streams. In this work, we explored integrating genetics data with reports from traditional media. SDDM was applied to the New Delhi metallo-beta-lactamase gene (NDM-1), an emerging global health threat. The SDDM process constructed a scenario, created a RDF multi-relational directed graph that linked diverse types of data to the Semantic Web, implemented RDF conversion tools (RDFizers) to bring content into the Sematic Web, identified data streams and analytical routines to analyse those streams, and identified user requirements and graph traversals to meet end-user requirements. Conclusions: We provided an example where SDDM was applied to a complex data integration challenge. The process created a model of the emerging NDM-1 health threat, identified and filled gaps in that model, and constructed reliable software that monitored data streams based on the scenario derived multi-relational directed graph. The SDDM process significantly reduced the software requirements phase by letting the scenario and resulting multi-relational directed graph define what is possible and then set the scope of the user requirements. Approaches like SDDM will be critical to the future of data intensive, data-driven science because they automate the process of converting massive data streams into usable knowledge. [ABSTRACT FROM AUTHOR]

Background: Detection of genomic DNA copy number variations (CNVs) can provide a complete and more comprehensive view of human disease. It is interesting to identify and represent relevant CNVs from a genomewide data due to high data volume and the complexity of interactions. Results: In this paper, we incorporate the DNA copy number variation data derived from SNP arrays into a computational shrunken model and formalize the detection of copy number variations as a case-control classification problem. More than 80% accuracy can be obtained using our classification model and by shrinkage, the number of relevant CNVs to disease can be determined. In order to understand relevant CNVs, we study their corresponding SNPs in the genome and a statistical software PLINK is employed to compute the pair-wise SNPSNP interactions, and identify SNP networks based on their P-values. Our selected SNP networks are statistically significant compared with random SNP networks and play a role in the biological process. For the unique genes that those SNPs are located in, a gene-gene similarity value is computed using GOSemSim and gene pairs that have similarity values being greater than a threshold are selected to construct gene networks. A gene enrichment analysis show that our gene networks are functionally important. Experimental results demonstrate that our selected SNP and gene networks based on the selected CNVs contain some functional relationships directly or indirectly to disease study. Conclusions: Two datasets are given to demonstrate the effectiveness of the introduced method. Some statistical and biological analysis show that this shrunken classification model is effective in identifying CNVs from genomewide data and our proposed framework has a potential to become a useful analysis tool for SNP data sets. [ABSTRACT FROM AUTHOR]

The article presents a research paper that discusses various grounding issues related to the working of any biomedical multiscale model (MSM). It is mentioned that grounding can be considered as a collection of units, dimensions or objects to which refers a model constituent or a variable. Also, grounding decisions plays an important role in affecting model flexibility, adaptability and reusability. It is suggested that grounding choices should depend upon system information and validation data.