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Frontiers in Genetics 2023Genetic variants cause a significant portion of developmental disorders and intellectual disabilities (DD/ID), but clinical and genetic heterogeneity makes...
Genetic variants cause a significant portion of developmental disorders and intellectual disabilities (DD/ID), but clinical and genetic heterogeneity makes identification challenging. Compounding the issue is a lack of ethnic diversity in studies into the genetic aetiology of DD/ID, with a dearth of data from Africa. This systematic review aimed to comprehensively describe the current knowledge from the African continent on this topic. Applicable literature published up until July 2021 was retrieved from PubMed, Scopus and Web of Science databases, following PRISMA guidelines, focusing on original research reports on DD/ID where African patients were the focus of the study. The quality of the dataset was assessed using appraisal tools from the Joanna Briggs Institute, whereafter metadata was extracted for analysis. A total of 3,803 publications were extracted and screened. After duplicate removal, title, abstract and full paper screening, 287 publications were deemed appropriate for inclusion. Of the papers analysed, a large disparity was seen between work emanating from North Africa compared to sub-Saharan Africa, with North Africa dominating the publications. Representation of African scientists on publications was poorly balanced, with most research being led by international researchers. There are very few systematic cohort studies, particularly using newer technologies, such as chromosomal microarray and next-generation sequencing. Most of the reports on new technology data were generated outside Africa. This review highlights how the molecular epidemiology of DD/ID in Africa is hampered by significant knowledge gaps. Efforts are needed to produce systematically obtained high quality data that can be used to inform appropriate strategies to implement genomic medicine for DD/ID on the African continent, and to successfully bridge healthcare inequalities.
PubMed: 37234869
DOI: 10.3389/fgene.2023.1137922 -
PeerJ. Computer Science 2022Understanding the complexity of restricted research data is vitally important in the current new era of Open Science. While the FAIR Guiding Principles have been...
Understanding the complexity of restricted research data is vitally important in the current new era of Open Science. While the FAIR Guiding Principles have been introduced to help researchers to make data Findable, Accessible, Interoperable and Reusable, it is still unclear how the notions of FAIR and Openness can be applied in the context of restricted data. Many methods have been proposed in support of the implementation of the principles, but there is yet no consensus among the scientific community as to the suitable mechanisms of making restricted data FAIR. We present here a systematic literature review to identify the methods applied by scientists when researching restricted data in a FAIR-compliant manner in the context of the FAIR principles. Through the employment of a descriptive and iterative study design, we aim to answer the following three questions: (1) What methods have been proposed to apply the FAIR principles to restricted data?, (2) How can the relevant aspects of the methods proposed be categorized?, (3) What is the maturity of the methods proposed in applying the FAIR principles to restricted data?. After analysis of the 40 included publications, we noticed that the methods found, reflect the stages of the Data Life Cycle, and can be divided into the following Classes: Data Collection, Metadata Representation, Data Processing, Anonymization, Data Publication, Data Usage and Post Data Usage. We observed that a large number of publications used 'Access Control' and 'Usage and License Terms' methods, while others such as 'Embargo on Data Release' and the use of 'Synthetic Data' were used in fewer instances. In conclusion, we are presenting the first extensive literature review on the methods applied to confidential data in the context of FAIR, providing a comprehensive conceptual framework for future research on restricted access data.
PubMed: 36091999
DOI: 10.7717/peerj-cs.1038 -
Frontiers in Public Health 2022COVID-19 pandemic is fueling digital health transformation-accelerating innovations of digital health services, surveillance, and interventions, whereas hastening social...
COVID-19 pandemic is fueling digital health transformation-accelerating innovations of digital health services, surveillance, and interventions, whereas hastening social contagion of deliberate infodemic. The USA and many other countries are experiencing a resurgent wave of the COVID-19 pandemic with vaccination rate slowdown, making policymaking fraught with challenges. Political leaders and scientists have publicly warned of a "pandemic of the unvaccinated," reinforcing their calls for citizens to get jabs. However, some scientists accused elites of stigmatizing the unvaccinated people and undermining the moral pillars of public health. Following the PRISMA-ScR guidelines, we first reviewed the nuances of stakeholders involved in the ongoing debates and revealed the potential consequences of divisive pronouncements to provide perspectives to reframe extensible discussions. Then, we employed the convergent cross mapping (CCM) model to reveal the uncharted knock-on effects of the contentious tsunami in a stakeholders-oriented policymaking framework, coupled with rich metadata from the GDELT project and Google Trends. Our experimental findings suggest that current news coverage may shape the mindsets of the vaccines against the unvaccinated, thereby exacerbating the risk of dualistic antagonism in algorithmically infused societies. Finally, we briefly summarized how open debates are conducive to increasing vaccination rates and bolstering the outcomes of impending policies for pandemic preparedness.
Topics: Attitude to Health; COVID-19; COVID-19 Vaccines; Humans; Mass Vaccination; Pandemics; Public Opinion
PubMed: 35493379
DOI: 10.3389/fpubh.2022.830933 -
Journal of Medical Internet Research Jul 2019[This corrects the article DOI: 10.2196/12521.].
[This corrects the article DOI: 10.2196/12521.].
PubMed: 31322126
DOI: 10.2196/14823 -
BMC Medical Research Methodology May 2024Systematic literature reviews (SLRs) are critical for life-science research. However, the manual selection and retrieval of relevant publications can be a time-consuming...
OBJECTIVE
Systematic literature reviews (SLRs) are critical for life-science research. However, the manual selection and retrieval of relevant publications can be a time-consuming process. This study aims to (1) develop two disease-specific annotated corpora, one for human papillomavirus (HPV) associated diseases and the other for pneumococcal-associated pediatric diseases (PAPD), and (2) optimize machine- and deep-learning models to facilitate automation of the SLR abstract screening.
METHODS
This study constructed two disease-specific SLR screening corpora for HPV and PAPD, which contained citation metadata and corresponding abstracts. Performance was evaluated using precision, recall, accuracy, and F1-score of multiple combinations of machine- and deep-learning algorithms and features such as keywords and MeSH terms.
RESULTS AND CONCLUSIONS
The HPV corpus contained 1697 entries, with 538 relevant and 1159 irrelevant articles. The PAPD corpus included 2865 entries, with 711 relevant and 2154 irrelevant articles. Adding additional features beyond title and abstract improved the performance (measured in Accuracy) of machine learning models by 3% for HPV corpus and 2% for PAPD corpus. Transformer-based deep learning models that consistently outperformed conventional machine learning algorithms, highlighting the strength of domain-specific pre-trained language models for SLR abstract screening. This study provides a foundation for the development of more intelligent SLR systems.
Topics: Humans; Machine Learning; Papillomavirus Infections; Economics, Medical; Algorithms; Outcome Assessment, Health Care; Deep Learning; Abstracting and Indexing
PubMed: 38724903
DOI: 10.1186/s12874-024-02224-3 -
Open Research Europe 2021The use of advanced algorithmic techniques is increasingly changing the nature of work for highly trained professionals. In the media industry, one of the technical...
The use of advanced algorithmic techniques is increasingly changing the nature of work for highly trained professionals. In the media industry, one of the technical advancements that often comes under the spotlight is automated journalism, a solution generally understood as the auto generation of journalistic stories through software and algorithms, without any human input except for the initial programming. In order to conduct a systematic review of existing empirical research on automated journalism, I analysed a range of variables that can account for the semantical, chronological and geographical features of a selection of academic articles as well as their research methods, theoretical backgrounds and fields of inquiry. I then engaged with and critically assessed the meta-data that I obtained to provide researchers with a good understanding of the main debates dominating the field. My findings suggest that the expression "automated journalism" should be called into question, that more attention should be devoted to non-English speaking scholarship, that the collective and individual impacts of the technology on media practitioners should be better documented and that well-established sociological theories such as institutionalism and Bourdieu's field theory could constitute two adequate frameworks to study automated journalism practices. This systematic literature therefore provides researchers with an overview of the main challenges and debates that are occurring within the field of automated journalism studies. Future studies should, in particular, make use of institutionalism and field theory to explore how automated journalism is impacting the work of media practitioners, which could help unearth common patterns across media organisations.
PubMed: 37645115
DOI: 10.12688/openreseurope.13096.1 -
Journal of Medical Internet Research Feb 2023In patient care, data are historically generated and stored in heterogeneous databases that are domain specific and often noninteroperable or isolated. As the amount of...
BACKGROUND
In patient care, data are historically generated and stored in heterogeneous databases that are domain specific and often noninteroperable or isolated. As the amount of health data increases, the number of isolated data silos is also expected to grow, limiting the accessibility of the collected data. Medical informatics is developing ways to move from siloed data to a more harmonized arrangement in information architectures. This paradigm shift will allow future research to integrate medical data at various levels and from various sources. Currently, comprehensive requirements engineering is working on data integration projects in both patient care- and research-oriented contexts, and it is significantly contributing to the success of such projects. In addition to various stakeholder-based methods, document-based requirement elicitation is a valid method for improving the scope and quality of requirements.
OBJECTIVE
Our main objective was to provide a general catalog of functional requirements for integrating medical data into knowledge management environments. We aimed to identify where integration projects intersect to derive consistent and representative functional requirements from the literature. On the basis of these findings, we identified which functional requirements for data integration exist in the literature and thus provide a general catalog of requirements.
METHODS
This work began by conducting a literature-based requirement elicitation based on a broad requirement engineering approach. Thus, in the first step, we performed a web-based systematic literature review to identify published articles that dealt with the requirements for medical data integration. We identified and analyzed the available literature by applying the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. In the second step, we screened the results for functional requirements using the requirements engineering method of document analysis and derived the requirements into a uniform requirement syntax. Finally, we classified the elicited requirements into a category scheme that represents the data life cycle.
RESULTS
Our 2-step requirements elicitation approach yielded 821 articles, of which 61 (7.4%) were included in the requirement elicitation process. There, we identified 220 requirements, which were covered by 314 references. We assigned the requirements to different data life cycle categories as follows: 25% (55/220) to data acquisition, 35.9% (79/220) to data processing, 12.7% (28/220) to data storage, 9.1% (20/220) to data analysis, 6.4% (14/220) to metadata management, 2.3% (5/220) to data lineage, 3.2% (7/220) to data traceability, and 5.5% (12/220) to data security.
CONCLUSIONS
The aim of this study was to present a cross-section of functional data integration-related requirements defined in the literature by other researchers. The aim was achieved with 220 distinct requirements from 61 publications. We concluded that scientific publications are, in principle, a reliable source of information for functional requirements with respect to medical data integration. Finally, we provide a broad catalog to support other scientists in the requirement elicitation phase.
Topics: Humans; Knowledge Management; Publications; Data Collection; Systems Analysis; Information Storage and Retrieval
PubMed: 36757764
DOI: 10.2196/41344 -
Cancer Biology & Therapy Sep 2021Scholarly requirements have led to a massive increase of transcriptomic data in the public domain, with millions of samples available for secondary research. We...
Scholarly requirements have led to a massive increase of transcriptomic data in the public domain, with millions of samples available for secondary research. We identified gene-expression datasets representing 10,214 breast-cancer patients in public databases. We focused on datasets that included patient metadata on race and/or immunohistochemistry (IHC) profiling of the ER, PR, and HER-2 proteins. This review provides a summary of these datasets and describes findings from 32 research articles associated with the datasets. These studies have helped to elucidate relationships between IHC, race, and/or treatment options, as well as relationships between IHC status and the breast-cancer intrinsic subtypes. We have also identified broad themes across the analysis methodologies used in these studies, including breast cancer subtyping, deriving predictive biomarkers, identifying differentially expressed genes, and optimizing data processing. Finally, we discuss limitations of prior work and recommend future directions for reusing these datasets in secondary analyses.
Topics: Biomarkers, Tumor; Breast Neoplasms; Female; Humans; Immunohistochemistry; Receptor, ErbB-2; Receptors, Progesterone; Transcriptome
PubMed: 34412551
DOI: 10.1080/15384047.2021.1953902 -
Microbial Cell Factories Oct 2021Recombinant enzyme expression in Escherichia coli is one of the most popular methods to produce bulk concentrations of protein product. However, this method is often...
Recombinant enzyme expression in Escherichia coli is one of the most popular methods to produce bulk concentrations of protein product. However, this method is often limited by the inadvertent formation of inclusion bodies. Our analysis systematically reviews literature from 2010 to 2021 and details the methods and strategies researchers have utilized for expression of difficult to express (DtE), industrially relevant recombinant enzymes in E. coli expression strains. Our review identifies an absence of a coherent strategy with disparate practices being used to promote solubility. We discuss the potential to approach recombinant expression systematically, with the aid of modern bioinformatics, modelling, and 'omics' based systems-level analysis techniques to provide a structured, holistic approach. Our analysis also identifies potential gaps in the methods used to report metadata in publications and the impact on the reproducibility and growth of the research in this field.
Topics: Biotechnology; Escherichia coli; Gene Expression; Inclusion Bodies; Industrial Microbiology; Recombinant Proteins; Research Design; Solubility
PubMed: 34717620
DOI: 10.1186/s12934-021-01698-w -
The Journal of Pediatrics Nov 2020To develop a more comprehensive description of multisystem inflammatory syndrome in children (MIS-C), a novel syndrome linked to severe acute respiratory syndrome...
OBJECTIVE
To develop a more comprehensive description of multisystem inflammatory syndrome in children (MIS-C), a novel syndrome linked to severe acute respiratory syndrome coronavirus 2, by conducting a systematic analysis of studies from different settings that used various inclusion criteria.
STUDY DESIGN
MIS-C studies were identified by searching PubMed and Embase as well as preprint repositories and article references to identify studies of MIS-C cases published from April 25, 2020, through June 29, 2020. MIS-C study metadata were assessed and information on case demographics, clinical symptoms, laboratory measurements, treatments, and outcomes were summarized and contrasted between studies.
RESULTS
Eight studies were identified representing a total of 440 MIS-C cases. Inclusion criteria varied by study: 3 studies selected patients diagnosed with Kawasaki disease, 2 required cardiovascular involvement, and 3 had broader multisystem inclusion criteria. Median age of patients by study ranged from 7.3 to 10 years, and 59% of patients were male. Across all studies, the proportion of patients with positive results for severe acute respiratory syndrome coronavirus 2 reverse transcriptase-polymerase chain reaction tests ranged from 13% to 69% and for serology, from 75% to 100%. Patients with MIS-C had high prevalence of gastrointestinal (87%), dermatologic/mucocutaneous (73%), and cardiovascular (71%) symptoms. Prevalence of cardiovascular, neurologic, and respiratory system involvement significantly differed by study inclusion criteria. All studies reported elevated C-reactive protein, interleukin-6, and fibrinogen levels for at least 75% of patients in each study.
CONCLUSIONS
This systematic review of MIS-C studies assists with understanding this newly identified syndrome and may be useful in developing a refined, universal case definition of MIS-C.
Topics: Biomarkers; COVID-19; COVID-19 Testing; Child; Humans; Systemic Inflammatory Response Syndrome
PubMed: 32768466
DOI: 10.1016/j.jpeds.2020.08.003