The increasing scientific interest in antioxidants and naturally derived compounds as potential remedies for obesity and non-alcoholic fatty liver disease (NAFLD) has led to extensive research. The objective of this bibliometric analysis is to present an updated perspective on the topic of antioxidants, herbs, phytochemicals, and natural compounds, in the control of obesity and NAFLD, to identify new areas for future research. Publications from the years 2012-2022 were retrieved using the Scopus database. The research trends were analyzed using the Biblioshiny and VOSviewer tools. The field has seen a significant increase in research activity, as indicated by an annual growth rate of 10 % in the number of published manuscripts. China, Korea, and the USA emerged as the most prominent contributors in this specific field, supported by their notable volumes of publications and citations. The density analysis revealed that the most frequently occurring authors\' keywords related to herbal species are, in rank order, Camelia sinensis, Momordica charantia, Curcuma longa, Ilex paraguariensis, Panax ginseng, Moringa oleifera, Garcinia cambogia, Garcinia mangostana, Zingiber officinale, and Cinnamomum verum. In the group of antioxidants, phytochemicals, and natural compounds, the top 10 were resveratrol, curcumin, quercetin, vitamin E, alpha-lipoic acid, vitamin C, chlorogenic acid, lycopene, fucoxanthin, and berberine. The co-occurrence analysis unveiled significant themes and potential trends, including a notable interest in the impact of herbal species, antioxidants, phytochemicals, and natural compounds on obesity and NAFLD through the modulation of the gut microbiome. Another recurring theme that arises, is the ongoing investigation of molecular targets that demonstrate anti-adipogenesis properties. The analysis presented in this study provides valuable insights for researchers investigating the efficacy of antioxidants, herbs, phytochemicals, and natural compounds in addressing obesity and NAFLD. Through the use of bibliometric methods, the study offers a comprehensive overview. Furthermore, the findings of this analysis can serve as a foundation for future research in this specific domain.

This bibliometric study analyzes the evolving field of thermotherapy, a medical treatment that utilizes heat to treat various conditions, including cancer, by applying controlled temperatures to targeted tissues. Utilizing bibliographic data from the core collection of Web of Science and analysis software Biblioshiny and VOSviewer, we analyzed several key metrics to gain insights into the development and trends in thermotherapy research. The annual scientific production revealed a significant increase in publications over the past two decades, reflecting growing interest in this field. Analysis of the most relevant authors and sources highlighted key contributors and influential journals. Trend topics demonstrated a shift from early focus areas like hyperthermia and laser-induced thermotherapy to recent advancements involving nanoparticles and combination therapies. The thematic map provided insights into core, emerging, and niche areas within the research landscape. A historiograph traced the chronological development of significant publications, while the co-occurrence of keywords and bibliographic coupling of documents identified major themes and interconnections in the literature. International collaborations were mapped, showing the global nature of thermotherapy research. The study identified several research gaps, including the need for large-scale clinical trials, interdisciplinary approaches, and standardized treatment protocols. Practical implications suggest focusing on targeted delivery systems, expanding cancer research, and fostering collaborative projects to advance the field.

The place of digital twin technology in any healthcare system is a truly disruptive innovation that has profound consequences all across medical research and practice. Digital twins represent the virtual replicas that correspond to some physical entity by pulling real-time data streams from different sources to model biological systems for health monitoring and personalization of treatment strategies. This paper presents a detailed review of the current research landscape into digital twins for healthcare. Through bibliometric analysis, we obtained 1,663 publications from 2012 to 2024, basically sourced from the Scopus database, establishing a portion of the trends, productive authors, influential sources, and collaboration networks in this fast-evolving field. Descriptively, our results indicate that although research into this area started way back, the bulk of research began to be realized from 2018 onwards, with appreciable contributions coming in from interdisciplinary fields of artificial intelligence, machine learning, and data analytics. Even with challenges to data interoperability and other privacy concerns, this change brought on by digital twin technology is undoubtedly a considerable promise for chronic disease management, predictive analytics, drug discovery, and surgical planning. This work brings immense insight into this new domain of digital twins in health, which shall set up a strong foundation for future research and innovation in this area.

Biomedical physics is the interdisciplinary field that links the scientific concepts in physics to the practice of medicine and biology, in an effort to understand biological processes, help in the development of medical technologies, to improve human health. This bibliometric study investigates the interdisciplinary field of biomedical physics, which integrates the principles of physics with biological and medical sciences to develop innovative diagnostic and therapeutic technologies. Utilizing the Web of Science database for bibliographic data collection, the analysis employs advanced bibliometric software tools, including Biblioshiny and VOSviewer, to comprehensively map the research landscape. Our findings delineate the annual scientific production, highlighting growth trends and identifying the most influential authors and key publication venues in the field. A thematic analysis reveals prevailing research topics and the evolution of scientific interests over time, providing insights into the shifting focus areas within biomedical physics. The factorial analysis goes further to clarify the conceptual structure of the discipline by providing a topological image of how the different research areas are involved. It helps to recognize topical fields and the possibility of the topicalization of other subjects. Keyword co-occurrence assumes the leading themes and measures the value of the topology. Meanwhile, bibliographical information defines the authors\' network, and co-citation analysis identifies the critical authors\' pool. The last points to the topic dependence and the network of research collaboration on a global scale. As a result, a survey identifies the deficits and rules of recommendations for the further development of research. It adds practical implications that are necessary for the development and identifies influences for popularization that it might have in the future.

With the advancement in artificial intelligence, the use of machine learning algorithms for clinical prediction has increased tremendously. Logistic regression is one of the powerful machine learning algorithms that can be used to predict the probability of a variable. Logistic regression is very popular among medical researchers owing to its simplicity, interpretability, and solid statistical foundation. This study aims to investigate the research productivity of heart disease classification using a logistic regression model to analyze the current patterns and potential future trends through bibliometric analysis. Additionally, it aims to highlight the impact and quality of research in the area, identify prominent research groups, the countries actively contributing to the field, which will help the researchers and healthcare professionals to pinpoint research gaps, influential authors, and make informed decisions and invest resources accordingly. The data is collected from a database of Scopus spanning from 2019 to 2023. We have used two bibliometric software, Biblioshiny (Aria and Cuccurullo, 2017) and VOSviewer (Centre for Science and Technology Studies (CWTS), Leiden University, the Netherlands), to analyze the bibliographic data regarding the citation count, contribution of authors, publication count, the contribution of institutions, etc. There are 2331 documents under study which were fed into both software to analyze the data. With 700 documents, China topped the list of most productive countries indicating the vast contribution of the country followed by India and the United States. Contributions of the Harvard Medical School, Boston, MA, United States are found to be the greatest with six papers. The most productive author is Wang Y with 73 documents. Analysis of trending topics reveals that the field progressing towards using support vector machines (SVM), k-nearest neighbours (KNN), and naïve Bayes algorithms. The article has only considered data from Scopus excluding literature indexed in other databases which limits the potential coverage of the data. Also, the work focuses on recent developments excluding older literature from 2019 which could be a limitation. Furthermore, since the study is a bibliometric analysis targeting the use of logistic regression for heart disease prediction, powerful techniques such as SVM, decision trees, random forests, neural networks and deep learning have not been included, which could be another limitation.

Mental health conditions, such as depression, anxiety, and stress-related disorders, are often difficult to diagnose and monitor using traditional methods. Salivary biomarkers offer a promising alternative due to their non-invasive nature, ease of collection, and the potential to reflect real-time physiological changes associated with mental health. This bibliometric analysis examines 95 clinical trials on stress biomarkers for mental health, published between 2003 and 2024. The field is characterized by extensive collaboration and global participation, involving 593 authors and publications across 73 journals. Despite a consistent annual publication rate, notable increases in 2011, 2014, and 2018 indicate growing research interest. The United States leads in research output, followed by Australia, Germany, and Japan, with Psychoneuroendocrinology being the most prominent journal. Co-occurrence analysis identified nine research clusters, suggesting diverse directions such as the impact of stress-related hormones, circadian rhythms, mindfulness, various therapies, aging, psychological adaptation mechanisms, exercise therapy, anxiety disorders, and the autonomic nervous system on salivary biomarkers. Key terms such as \"biomarkers/metabolism,\" AND \"hydrocortisone/metabolism,\" AND \"saliva/metabolism\" were central, with significant activity from 2012 to 2018. This analysis highlights a growing focus on the metabolic processes and therapeutic applications of salivary biomarkers in mental health. This bibliometric analysis calls attention to the promising potential of salivary biomarkers to revolutionize mental health diagnostics and treatment through non-invasive methods, fostering interdisciplinary research, technological advancements, and global health improvements.

Social media addiction is a behavioral dependency characterized by excessive and compulsive use of social media platforms, leading to negative impacts on various aspects of an individual\'s life. Bibliometric analysis is a research method used to quantitatively analyze academic literature, such as articles, books, and conference papers. It involves the application of statistical and mathematical tools to study the patterns and trends in scientific publications. This bibliometric study provides a comprehensive analysis of the literature on social media addiction, revealing patterns and dynamics within the field. Utilizing Web of Science for bibliographic data, the study employs advanced bibliometric tools like Biblioshiny and CiteSpace to map the scientific landscape. Annual scientific production, top contributing authors, key sources, trending topics, and thematic maps were identified using Biblioshiny. Additionally, network visualizations, such as co-citation networks of authors, time zone network visualizations of keyword co-occurrence, and timeline network visualizations of country collaborations, were created using CiteSpace. Our findings present an increasing trend in publications over the years, highlighting a growing recognition of social media addiction\'s significance. We detail the most relevant authors and sources, pinpointing key contributors and influential journals that shape the discourse. Trend topics analysis uncovers the prevalent themes, with \"internet addiction\" and \"adolescents\" at the forefront, reflecting the field\'s concentration on the younger population. The thematic map categorizes the research into motor themes (driving research areas), basic themes (fundamental and well-established areas), and niche themes (specialized and emerging topics), providing insight into the central and evolving topics. The study also delves into the co-occurrence of all keywords and the co-citation of authors, illustrating the interconnected nature of the research community. A timeline network visualization of country collaborations underscores the global scope of research efforts. Importantly, the study identifies critical research gaps such as underexplored demographics and emerging digital concerns and discusses practical implications, including the need for targeted intervention programs and informed policy-making. Collectively, this study charts the trajectory of social media addiction research and lays a foundation for future explorations to address identified lacunae.

Suicide remains a critical global health issue despite advancements in mental health treatment. The purpose of this analysis is to emphasize the development, patterns, and noteworthy outcomes of suicide prediction research. It also helps to uncover gaps and areas of under-researched topics within suicide prediction. A scientometric analysis was conducted using Biblioshiny and VOSviewer. To thoroughly assess the academic literature on suicide prediction, various scientometric methodologies such as trend analysis and citation analysis were employed. We utilized the temporal features of the Web of Science to analyze publication trends over time. Author affiliation data were used to investigate the geographic distribution of research. Cluster analysis was performed by grouping related keywords into clusters to identify overarching themes within the literature. A total of 1,703 articles from 828 different sources, spanning from 1942 to 2023, were collected for the analysis. Machine learning techniques might have a big influence on suicide-related event prediction, which would enhance attempts at suicide prevention and intervention. The conceptual understanding of suicide prediction is enhanced by scientometric analysis, which further uncovers the research gap and literature in this area. Suicide prediction research underscores that suicidal behavior is not caused by a single factor but is the result of a complex interplay of multiple factors. These factors may include biological, psychological, social, and environmental factors. Understanding and integrating these factors into predictive models is a theoretical advancement in the field. Unlike previous bibliometric studies in the field of suicide prediction that have typically focused on specific subtopics or data sources, our analysis offers a comprehensive mapping of the entire landscape. We encompass a wide range of suicide prediction literature, including research from medical, psychological, and social science domains, thus providing a holistic overview.

This research provides a comprehensive bibliometric analysis of polycythemia vera (PV) research trends, encompassing data from 1969 to 2024. Utilizing advanced tools, key findings reveal a notable increase in scientific production over time, reflecting growing interest and investment in PV research. Prominent themes include genetic studies, targeted therapies, and precision medicine approaches. The analysis identifies leading authors, institutions, and countries contributing to PV research, highlighting the importance of global collaboration. The study emphasizes the need to broaden genetic investigations, explore the bone marrow microenvironment, and enhance precision medicine strategies. The implications of this research extend to clinical practice, with potential advancements in diagnostics, treatments, and patient outcomes. Ultimately, addressing these challenges and embracing emerging opportunities can propel PV research forward, fostering innovation and improving the lives of affected individuals.

Caenorhabditis elegans (C. elegans) is a nematode and model organism whose entire genome has been mapped, which allows for easy observation of the organism\'s development due to its transparent structure, and which is appealing due to its ease of crossover, ease of culture, and low cost. Despite being separated by nearly a billion years of evolution, C. elegans homologs have been identified for the vast majority of human genes and are associated with C. elegans for many biological processes such as apoptosis, cell signaling, cell cycle, cell polarity, metabolism, and aging. A detailed bibliometric study is performed here to examine publication trends in this field. Data were taken from the Web of Science database and analyzed using the bibliometric application Biblioshiny (RStudio). In terms of publication, the results indicated a gradual increase each year between 1980 and 2023. A total of 20,322 records were issued in 96 countries, the majority of which were in the USA, China, and Japan. The most prolific writers, the journals most engaged in the area, the nations, institutions, and keywords used by authors were all determined using the Web of Science database and bibliometric rules. The number of papers in the C. elegans research field is increasing exponentially, and Genetics is the journal with the highest number of articles. This study presents how research patterns have evolved throughout time. As a result, worldwide cooperation and a potential field can be developed.