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    COVID-19 studies involving machine learning methods: A bibliometric study
    (Lippincott Williams & Wilkins, 2023) Eden, Arzu Baygul; Kayi, Alev Bakir; Erdem, Mustafa Genco; Demirci, Mehmet
    Background:Machine learning (ML) and artificial intelligence (AI) techniques are gaining popularity as effective tools for coronavirus disease of 2019 (COVID-19) research. These strategies can be used in diagnosis, prognosis, therapy, and public health management. Bibliometric analysis quantifies the quality and impact of scholarly publications. ML in COVID-19 research is the focus of this bibliometric analysis.Methods:A comprehensive literature study found ML-based COVID-19 research. Web of Science (WoS) was used for the study. The searches included machine learning, artificial intelligence, and COVID-19. To find all relevant studies, 2 reviewers searched independently. The network visualization was analyzed using VOSviewer 1.6.19.Results:In the WoS Core, the average citation count was 13.6 +/- 41.3. The main research areas were computer science, engineering, and science and technology. According to document count, Tao Huang wrote 14 studies, Fadi Al-Turjman wrote 11, and Imran Ashraf wrote 11. The US, China, and India produced the most studies and citations. The most prolific research institutions were Harvard Medical School, Huazhong University of Science and Technology, and King Abdulaziz University. In contrast, Nankai University, Oxford, and Imperial College London were the most mentioned organizations, reflecting their significant research contributions. First, Covid-19 appeared 1983 times, followed by machine learning and deep learning. The US Department of Health and Human Services funded this topic most heavily. Huang Tao, Feng Kaiyan, and Ashraf Imran pioneered bibliographic coupling.Conclusion:This study provides useful insights for academics and clinicians studying COVID-19 using ML. Through bibliometric data analysis, scholars can learn about highly recognized and productive authors and countries, as well as the publications with the most citations and keywords. New data and methodologies from the pandemic are expected to advance ML and AI modeling. It is crucial to recognize that these studies will pioneer this subject.
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    Oral microbial dysbiosis in patients with oral cavity cancers
    (Springer Heidelberg, 2024) Unlu, Ozge; Demirci, Mehmet; Paksoy, Tugce; Eden, Arzu Baygul; Tansuker, Hasan Deniz; Dalmizrak, Aysegul; Aktan, Cagdas
    Objectives The pathogenesis of oral cavity cancers is complex. We tested the hypothesis that oral microbiota dysbiosis is associated with oral cavity cancer. Materials and methods Patients with primary oral cavity cancer who met the inclusion and exclusion criteria were included in the study. Matching healthy individuals were recruited as controls. Data on socio-demographic and behavioral factors, self-reported periodontal measures and habits, and current dental status were collected using a structured questionnaire and periodontal chartings. In addition to self-reported oral health measures, each participant received a standard and detailed clinical examination. DNA was extracted from saliva samples from patients and healthy controls. Next-generation sequencing was performed by targeting V3-V4 gene regions of the 16 S rRNA with subsequent bioinformatic analyses. Results Patients with oral cavity cancers had a lower quality of oral health than healthy controls. Proteobacteria, Aggregatibacter, Haemophilus, and Neisseria decreased, while Firmicutes, Bacteroidetes, Actinobacteria, Lactobacillus, Gemella, and Fusobacteria increased in oral cancer patients. At the species level, C. durum, L. umeaens, N. subflava, A. massiliensis, and V. dispar were significantly lower, while G. haemolysans was significantly increased (p < 0.05). Major periodontopathogens associated with periodontal disease (P. gingivalis and F.nucleatum) increased 6.5- and 2.8-fold, respectively. Conclusion These data suggested that patients with oral cancer had worse oral health conditions and a distinct oral microbiome composition that is affected by personal daily habits and may be associated with the pathogenicity of the disease and interspecies interactions. Clinical relevance This paper demonstrates the link between oral bacteria and oral cancers, identifying mechanistic interactions between species of oral microbiome.