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Global Characteristics and Trends in Research on Ferroptosis: A Data-Driven Bibliometric Study.
Dong, X, Tan, Y, Zhuang, D, Hu, T, Zhao, M
Oxidative medicine and cellular longevity. 2022;:8661864
Abstract
Ferroptosis, an iron-dependent form of regulated cell death, has drawn an increasing amount of attention since it was first mentioned in 2012 and is found to play a significant role in the treatment of certain diseases. Our study is aimed at analysing the scientific output of ferroptosis research and at driving future research into novel publications. Publications focused on ferroptosis were retrieved from the SCI-EXPANDED database of the Web of Science Core Collection and were screened according to inclusion criteria. CiteSpace V and Microsoft Excel 2016 were used to evaluate and visualize the results, including generating network maps and analysing annual publications, country, category, references and cocited references, and keywords. As of October 1, 2021, a total of 1690 original articles related to ferroptosis were included, and the overall trend of the number of publications rapidly increased. Among the common categories in the field of ferroptosis, the most common category was biochemistry and molecular biology. Worldwide, China and the United States were the leading countries for research production. The retrieved 1690 publications received 44,650 citations, with an average of 26.42 citations per paper (October 1, 2021). By citation analysis, Scott J Dixon's article in 2012 was the most symbolic reference and the earliest publication in the field of ferroptosis, with the highest citation rate (2709 times). Among the most common keywords, most were related to the mechanisms and regulatory networks of ferroptosis. Furthermore, with accumulating evidence demonstrating the role of ferroptosis in cancers and other diseases, inducing ferroptosis in clinical treatment is becoming a new research focus that should be closely monitored.
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Ethical Challenges in COVID-19 Biospecimen Research: Perspectives From Institutional Review Board Members and Bioethicists.
Lapid, MI, Meagher, KM, Giunta, HC, Clarke, BL, Ouellette, Y, Armbrust, TL, Sharp, RR, Wright, RS
Mayo Clinic proceedings. 2021;(1):165-173
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Abstract
Biospecimen research is a prominent investigative strategy that aims to provide novel insights into coronavirus disease 2019 (COVID-19), inform clinical trials, and develop effective, life-saving treatments. However, COVID-19 biospecimen research raises accompanying ethical concerns and practical challenges for investigators and participants. In this special article, we discuss the ethical issues that are associated with autonomy, beneficence, and justice in COVID-19 biospecimen research and describe strategies to manage the practical challenges, with an emphasis on protecting the rights and welfare of human research participants during a pandemic response. Appropriate institutional review board oversight and bioethics guidance for COVID-19 biospecimen research must maintain their focus on protecting the rights and welfare of research participants, despite the urgent need for more knowledge about the virus and the threat it poses to communities and nations.
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Artificial Intelligence in Nutrients Science Research: A Review.
Sak, J, Suchodolska, M
Nutrients. 2021;(2)
Abstract
Artificial intelligence (AI) as a branch of computer science, the purpose of which is to imitate thought processes, learning abilities and knowledge management, finds more and more applications in experimental and clinical medicine. In recent decades, there has been an expansion of AI applications in biomedical sciences. The possibilities of artificial intelligence in the field of medical diagnostics, risk prediction and support of therapeutic techniques are growing rapidly. The aim of the article is to analyze the current use of AI in nutrients science research. The literature review was conducted in PubMed. A total of 399 records published between 1987 and 2020 were obtained, of which, after analyzing the titles and abstracts, 261 were rejected. In the next stages, the remaining records were analyzed using the full-text versions and, finally, 55 papers were selected. These papers were divided into three areas: AI in biomedical nutrients research (20 studies), AI in clinical nutrients research (22 studies) and AI in nutritional epidemiology (13 studies). It was found that the artificial neural network (ANN) methodology was dominant in the group of research on food composition study and production of nutrients. However, machine learning (ML) algorithms were widely used in studies on the influence of nutrients on the functioning of the human body in health and disease and in studies on the gut microbiota. Deep learning (DL) algorithms prevailed in a group of research works on clinical nutrients intake. The development of dietary systems using AI technology may lead to the creation of a global network that will be able to both actively support and monitor the personalized supply of nutrients.
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A Scoping Review of the Application of Metabolomics in Nutrition Research: The Literature Survey 2000-2019.
Shibutami, E, Takebayashi, T
Nutrients. 2021;(11)
Abstract
Nutrimetabolomics is an emerging field in nutrition research, and it is expected to play a significant role in deciphering the interaction between diet and health. Through the development of omics technology over the last two decades, the definition of food and nutrition has changed from sources of energy and major/micro-nutrients to an essential exposure factor that determines health risks. Furthermore, this new approach has enabled nutrition research to identify dietary biomarkers and to deepen the understanding of metabolic dynamics and the impacts on health risks. However, so far, candidate markers identified by metabolomics have not been clinically applied and more efforts should be made to validate those. To help nutrition researchers better understand the potential of its application, this scoping review outlined the historical transition, recent focuses, and future prospects of the new realm, based on trends in the number of human research articles from the early stage of 2000 to the present of 2019 by searching the Medical Literature Analysis and Retrieval System Online (MEDLINE). Among them, objective dietary assessment, metabolic profiling, and health risk prediction were positioned as three of the principal applications. The continued growth will enable nutrimetabolomics research to contribute to personalized nutrition in the future.
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The Immune System in Human Milk: A Historic Perspective.
Goldman, AS, Chheda, S
Annals of nutrition & metabolism. 2021;(4):189-196
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Abstract
BACKGROUND Human milk contains a remarkable array of immunological agents that evolved over millions of years to protect the recipient human infant. Furthermore, much of the protection persists long after weaning. However, the scientists who first discovered some components of this immune system have rarely been acknowledged. SUMMARY The scientists who made many fundamental immunological discoveries concerning the immune system in human milk include Alfred François Donné, Paul Ehrlich, Lars Å. Hanson, and Jules Bordet. Based upon their discoveries, a wealth of antimicrobial, anti-inflammatory, and immunomodulating agents, and living, activated leukocytes in human milk were later revealed during the last half of the 20th and the first part of the 21st century. Moreover, it was found that human milk enhances the colonization of commensal bacteria that aid to protect the human infant. Key Message: Their discoveries helped to revitalize breastfeeding in industrialized countries during the past several decades.
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Leveraging the urban-rural divide for epigenetic research.
Cronjé, HT, Elliott, HR, Nienaber-Rousseau, C, Pieters, M
Epigenomics. 2020;(12):1071-1081
Abstract
Urbanization coincides with a complex change in environmental exposure and a rapid increase in noncommunicable diseases (NCDs). Epigenetics, including DNA methylation (DNAm), is thought to mediate part of the association between genetic/environmental exposure and NCDs. The urban-rural divide provides a unique opportunity to investigate the effect of the combined presence of multiple forms of environmental exposure on DNAm and the related increase in disease risk. This review evaluates the ability of three epidemiological study designs (migration, income-comparative and urban-rural designs) to investigate the role of DNAm in the association between urbanization and the rise in NCD prevalence. We also discuss the ability of each study design to address the gaps in the current literature, including the complex methylation-mediated risk attributable to the cluster of forms of exposure characterizing urban and rural living, while providing a platform for developing countries to leverage their demographic discrepancies in future research ventures.
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Towards better evidence-informed global action: lessons learnt from the Lancet series and recent developments in physical activity and public health.
Ding, D, Ramirez Varela, A, Bauman, AE, Ekelund, U, Lee, IM, Heath, G, Katzmarzyk, PT, Reis, R, Pratt, M
British journal of sports medicine. 2020;(8):462-468
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In the past few decades, the field of physical activity has grown and evolved in scope, depth, visibility and impact around the world. Global progress has been observed in research and practice in physical activity regarding surveillance, health outcomes, correlates/determinants, interventions, translation and policy. The 2012 and 2016 Lancet series on physical activity provide some of the most comprehensive global analysis on various topics within physical activity. Based on the Lancet series and other key developments in the field, literature searches, and expert group meetings and consultation, we provide a global summary on the progress of, gaps in and future directions for physical activity research in the following areas: (1) surveillance and trends, (2) correlates and determinants, (3) health outcomes and (4) interventions, programmes and policies. Besides lessons learnt within each specific area, several recommendations are shared across areas of research, including improvement in measurement, applying a global perspective with a growing emphasis on low-income and middle-income countries, improving inclusiveness and equity in research, making translation an integral part of research for real-world impact, taking an 'upstream' public health approach, and working across disciplines and sectors to co-design research and co-create solutions. We have summarised lessons learnt and recommendations for future research as 'roadmaps' in progress to encourage moving the field of physical activity towards achieving population-level impact globally.
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Current state and future prospects of artificial intelligence in ophthalmology: a review.
Hogarty, DT, Mackey, DA, Hewitt, AW
Clinical & experimental ophthalmology. 2019;(1):128-139
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Abstract
Artificial intelligence (AI) has emerged as a major frontier in computer science research. Although AI has broad application across many medical fields, it will have particular utility in ophthalmology and will dramatically change the diagnostic and treatment pathways for many eye conditions such as corneal ectasias, glaucoma, age-related macular degeneration and diabetic retinopathy. However, given that AI has primarily been driven as a computer science, its concepts and terminology are unfamiliar to many medical professionals. Important key terms such as machine learning and deep learning are often misunderstood and incorrectly used interchangeably. This article presents an overview of AI and new developments relevant to ophthalmology.
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Research Techniques Made Simple: Mass Spectrometry for Analysis of Proteins in Dermatological Research.
Hammers, CM, Tang, HY, Chen, J, Emtenani, S, Zheng, Q, Stanley, JR
The Journal of investigative dermatology. 2018;(6):1236-1242
Abstract
Identifying previously unknown proteins or detecting the presence of known proteins in research samples is critical to many experiments conducted in life sciences, including dermatology. Sensitive protein detection can help elucidate new intervention targets and mechanisms of disease, such as in autoimmune blistering skin diseases, atopic eczema, or other conditions. Historically, peptides from highly purified single proteins were sequenced, with many limitations, by stepwise degradation from the N-terminus to the C-terminus with subsequent identification by UV absorbance spectroscopy of the released amino acids (i.e., Edman degradation). Recently, however, the availability of comprehensive protein databases from different species (derived from high-throughput next-generation sequencing of those organisms' genomes) and sophisticated bioinformatics analysis tools have facilitated the development and use of mass spectrometry for identification and global analysis of proteins, summarized as mass spectrometry-based proteomics. Mass spectrometry is an analytical technique measuring the mass (m)-to-charge (z) ratio of ionized biological molecules such as peptides. Proteins can be identified by correlating peptide-derived experimental mass spectrometry spectra with theoretical spectra predicted from protein databases. Here we briefly describe how this technique works, how it can be used for identification of proteins, and how this knowledge can be applied in elucidating human biology and disease.
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Can big data solve a big problem? Reporting the obesity data landscape in line with the Foresight obesity system map.
Morris, MA, Wilkins, E, Timmins, KA, Bryant, M, Birkin, M, Griffiths, C
International journal of obesity (2005). 2018;(12):1963-1976
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Abstract
BACKGROUND Obesity research at a population level is multifaceted and complex. This has been characterised in the UK by the Foresight obesity systems map, identifying over 100 variables, across seven domain areas which are thought to influence energy balance, and subsequent obesity. Availability of data to consider the whole obesity system is traditionally lacking. However, in an era of big data, new possibilities are emerging. Understanding what data are available can be the first challenge, followed by an inconsistency in data reporting to enable adequate use in the obesity context. In this study we map data sources against the Foresight obesity system map domains and nodes and develop a framework to report big data for obesity research. Opportunities and challenges associated with this new data approach to whole systems obesity research are discussed. METHODS Expert opinion from the ESRC Strategic Network for Obesity was harnessed in order to develop a data source reporting framework for obesity research. The framework was then tested on a range of data sources. In order to assess availability of data sources relevant to obesity research, a data mapping exercise against the Foresight obesity systems map domains and nodes was carried out. RESULTS A reporting framework was developed to recommend the reporting of key information in line with these headings: Background; Elements; Exemplars; Content; Ownership; Aggregation; Sharing; Temporality (BEE-COAST). The new BEE-COAST framework was successfully applied to eight exemplar data sources from the UK. 80% coverage of the Foresight obesity systems map is possible using a wide range of big data sources. The remaining 20% were primarily biological measurements often captured by more traditional laboratory based research. CONCLUSIONS Big data offer great potential across many domains of obesity research and need to be leveraged in conjunction with traditional data for societal benefit and health promotion.