METHODS: Systematic review of literature on GDM in SEA countries was performed using the Ovid MEDLINE®, Scopus, and WPRIM databases between 1975 and 2020. All published studies on GDM conducted in or published by authors from any SEA country were included in our analysis. Bibliometric information was obtained from Scopus and bibliometrics diagrams were created using VOSviewer software.
RESULTS: A total of 322 articles were obtained in this study. The number of publications showed an upward trend starting 2011. The country with the greatest number of publications was Malaysia while The National University of Singapore was the most productive institution in GDM research in SEA. The focus of GDM research in SEA were on the prevalence, prevention, diagnosis, and pregnancy outcomes. GDP, research expenditure, and researchers per million people were positively correlated with research productivity and impact in GDM research in SEA.
CONCLUSIONS: This is the first bibliometric analysis on GDM in SEA countries. GDM research in SEA continued to increase in the past years but still lagged behind that of other regions. The SEA countries should consider increasing support for research to produce substantial research that can serve as basis for evidence-based and locally applicable GDM interventions.
METHODS: Data was retrieved from the Scopus database, and a bibliometric analysis was performed using VOSviewer software.
RESULTS: Following a screening process, a total of 121 articles were identified, with S. aromaticum yielding a higher number compared to C. canephora. A detailed exploration of each plant revealed active components such as eugenol, β-caryophyllene, α-humulene, caffeine, mangiferin, and chlorogenic acids, each exhibiting stimulatory effects alongside antioxidant and anti-inflammatory properties. The neuroprotective effects were attributed to the reduction of oxidative stress and inflammation, coupled with the stimulation of neurotransmitters and hormones like dopamine, serotonin, cortisol, and adrenaline.
CONCLUSIONS: The review showed that these plants positively affect mood and cognition by influencing the brain's pleasure system. This suggests the need for further research to combine these plant extracts for developing 'Tenang tea', a potential herbal blend for managing stress and anxiety.
METHODS: We searched for publications that contained specific words regarding methanol poisoning in Scopus database.
RESULTS: A total of 912 articles, with 8,317 citations and with an average of 9.1 citations per document, were retrieved on methanol poisoning, and the bulk of the articles were published from the USA (20.9%), followed by Spain (4.4%), Canada (4.3%), India (3.1%), and France (3.0%). The articles were published belonging to 57 countries. No data related to methanol poisoning were published from 155 (73.1%) out of 212 countries. Twenty-one documents (2.3%) were published in Clinical Toxicology, whereas 18 (2.0%) were published in The Lancet.
CONCLUSIONS: Scientific production related to methanol poisoning is increasing. articles have been published in a wide range of journals with a variety of subject areas, most notably clinical toxicology; and the country with the greatest production was the USA.
MATERIALS AND METHODS: The search was performed without any restriction on the study design, publication year, or language using the Web of Science (WoS) group of Clarivate Analytics enabling the search through "All Databases." Based on the citation count as available in WoS, the articles were sorted in a descending manner. Information regarding each article was then extracted, which included its authorship, counts of citation (in other databases), citation density, current citation index (2019), publication year, country of publication, journal of article, evidence level based on study design, and keywords description.
RESULTS: The count of citation for each article varied in each database, that is, 175 to 2,003 in WoS, 89 to 1,981 in Scopus, and 126 to 3,492 when searched in Google Scholar. The highest number of articles (n = 10) related to dental caries were published in 2004. A total of 301 authors made valuable contributions to this field, out of which J.D. Featherstone had coauthored 6 articles. A significant negative correlation (p < 0.01) was found between the age of the article and the citation density (r =-0.545). However, a nonsignificant correlation (p = 0.952) occurred between the age of publication and the citation count (r = 0.006).
CONCLUSION: The results of this systematic review provide a critical appraisal of the context underpinning scientific developments in the field of dental caries and also highlighted trends in clinical management and research.
OBJECTIVE: This paper aims to introduce a GAN technology for the diagnosis of eye disorders, particularly glaucoma. This paper illustrates deep adversarial learning as a potential diagnostic tool and the challenges involved in its implementation. This study describes and analyzes many of the pitfalls and problems that researchers will need to overcome to implement this kind of technology.
METHODS: To organize this review comprehensively, articles and reviews were collected using the following keywords: ("Glaucoma," "optic disc," "blood vessels") and ("receptive field," "loss function," "GAN," "Generative Adversarial Network," "Deep learning," "CNN," "convolutional neural network" OR encoder). The records were identified from 5 highly reputed databases: IEEE Xplore, Web of Science, Scopus, ScienceDirect, and PubMed. These libraries broadly cover the technical and medical literature. Publications within the last 5 years, specifically 2015-2020, were included because the target GAN technique was invented only in 2014 and the publishing date of the collected papers was not earlier than 2016. Duplicate records were removed, and irrelevant titles and abstracts were excluded. In addition, we excluded papers that used optical coherence tomography and visual field images, except for those with 2D images. A large-scale systematic analysis was performed, and then a summarized taxonomy was generated. Furthermore, the results of the collected articles were summarized and a visual representation of the results was presented on a T-shaped matrix diagram. This study was conducted between March 2020 and November 2020.
RESULTS: We found 59 articles after conducting a comprehensive survey of the literature. Among the 59 articles, 30 present actual attempts to synthesize images and provide accurate segmentation/classification using single/multiple landmarks or share certain experiences. The other 29 articles discuss the recent advances in GANs, do practical experiments, and contain analytical studies of retinal disease.
CONCLUSIONS: Recent deep learning techniques, namely GANs, have shown encouraging performance in retinal disease detection. Although this methodology involves an extensive computing budget and optimization process, it saturates the greedy nature of deep learning techniques by synthesizing images and solves major medical issues. This paper contributes to this research field by offering a thorough analysis of existing works, highlighting current limitations, and suggesting alternatives to support other researchers and participants in further improving and strengthening future work. Finally, new directions for this research have been identified.