At the edge of the network, the exponential increase of data produced along with the growing concerns over data privacy coming from regulations and society have all together triggered the rise of Federated Learning (FL) as the main approach in distributed machine learning (DML). Fed learning allows the model training to be performed on decentralized devices or data silos even without the raw data being transferred. Hence, FL is completely in line with the objectives of the privacy-preserving techniques. In this paper, we carry out a scientometric analysis on the 200 most cited papers, which are the first 200 papers at the intersection of "Distributed Machine Learning," "Federated Learning," and "Privacy-Preserving" published between 2020 and 2024, and the Scopus database is where they are indexed. The literature of publication trends, prominent authors and works, the thematic clusters, and research fronts that are changing are all systematically examined in this study; hence, the intellectual landscape of this fast developing field is mapped out. Our findings point to the existence of certain streams of research such as the algorithms with differential privacy being the mainstay, secure aggregation methods through the use of homomorphic encryption and multi-party computation, blockchain-based FL systems which ensure security and trust, and resource-efficient FL that supports IoT and edge computing. The results also show an area that is nearly enjoying a complete transformation as a result of the overpowering need to address the triad of model quality, data protection, and system efficiency. The review not only encourages researchers, and practitioners but also helps the policymakers by providing the current trend to which the key challenges can be identified and the future directions in privacy-preserving distributed intelligence anticipated.

The emergence of large language models (LLMs), like ChatGPT, has caused a major shift in all academic and professional fields; education being the one impacted the most deeply. The first 200 most cited articles from Scopus on the topic of "large language models" and "education" were selected for a scientometric analysis of this survey. The aim is to map the intellectual landscape, specifying dominant research themes, key contributors, methodological trends, and the emergent challenges that researchers faced during this initial, explosive phase of research. This review systematically analyzes the publication trends, authorship patterns, geographical and institutional contributions, and the conceptual structure of the literature to provide a quantitative and qualitative snapshot of a field that is rapidly changing. The analysis uncovers a domain where exploratory studies, conceptual debates, and early empirical validations dominate, and where the focus of research is mainly on higher education and medical training. The major issues being discussed are academic integrity, assessment redesign, and the ethical integration of AI, while promising applications are personalized learning, automated feedback, and content generation. This survey is a reference for a better understanding of the roots and forefronts of LLM-related educational research as it passes from the initial reaction to systematic integration.

The introduction of Large Language Models (LLMs) in the healthcare sector is a major step, which can for sure transform all aspects of medicine including practice, research, and education. This review of publications presents the first 200 most cited articles which were obtained from a Scopus search on the terms "large language models" and "healthcare". The purpose was to elucidate the knowledge and trends in this fast-growing area. The analysis looks at the yearly publications, the main journals, the most prominent authors and institutions, the leading research areas, the methodological approaches used, and the ethical and regulatory issues that are the most talked about. The results show an increase in the number of scholars interested in this field, especially in 2023 and 2024. Besides that, it was found that there is a lot of high-impact publications going on in the leading multidisciplinary and specialized medical journals areas. The largest research areas that were found are: clinical trials and their outcomes, ethical and governance frameworks, educational integration, and technological advancements and surveys. The research is mainly focused on testing the performance of LLMs like ChatGPT in particular medical tasks but in the background, there are profound concerns about their accuracy, bias, and safety. This review presents the current knowledge state, points out the most active and leading research fronts, and recognizes the gaps with future directions hence it offers the most basic reference for researchers, doctors, and policymakers who will be dealing with the LLMs incorporation into the healthcare system.

The deployment of Large Language Models (LLMs) in the financial field is one of the most forward-looking and quickly changing AI research areas. This paper presents a thorough scientometric study of the top 200 most cited articles, which deal with the conjunction of LLMs, finance, and stock markets, and are indexed in Scopus. The analysis carries out a detailed examination of the publication patterns, influential authors and institutions, main research areas, methods used, and the intellectual framework of this new field. The disclose of the research output has been increased enormously; the main form of it is conference papers, and it is all through collaboration between the global academic and industrial research centers. Financial sentiment analysis and market prediction, the building and evaluating of domain-specific financial LLMs (FinLLMs), and lastly the application of the mentioned FinLLMs in areas such as financial analytics and decision-making support are examples of research that has been clustered together. Nevertheless, a big part of the research is directed towards trust, ethics, and risks in the financial domain. Moreover, the study points out that practical applications, such as algorithmic trading, risk management, and compliance with regulations, are among the most highlighted in the fields of LLMs in finance. This scientometric review offers a primary map of the vast conceptual territory marked by high-impact research, which means it can be very helpful to researchers, practitioners, and policymakers in getting a better understanding of the existing situation and thus, pointing out future research directions in the area of LLMs in finance.

The deployment of Large Language Models (LLMs) in the financial field is one of the most forward-looking and quickly changing AI research areas. This paper presents a thorough scientometric study of the top 200 most cited articles, which deal with the conjunction of LLMs, finance, and stock markets, and are indexed in Scopus. The analysis carries out a detailed examination of the publication patterns, influential authors and institutions, main research areas, methods used, and the intellectual framework of this new field. The disclose of the research output has been increased enormously; the main form of it is conference papers, and it is all through collaboration between the global academic and industrial research centers. Financial sentiment analysis and market prediction, the building and evaluating of domain-specific financial LLMs (FinLLMs), and lastly the application of the mentioned FinLLMs in areas such as financial analytics and decision-making support are examples of research that has been clustered together. Nevertheless, a big part of the research is directed towards trust, ethics, and risks in the financial domain. Moreover, the study points out that practical applications, such as algorithmic trading, risk management, and compliance with regulations, are among the most highlighted in the fields of LLMs in finance. This scientometric review offers a primary map of the vast conceptual territory marked by high-impact research, which means it can be very helpful to researchers, practitioners, and policymakers in getting a better understanding of the existing situation and thus, pointing out future research directions in the area of LLMs in finance.

Climate change that is happening all around the world and is global that way poses a great threat to the productivity of agriculture and therefore the need for both resilient crops and sustainable farming systems is very strong. Climate-resilient biotechnology has become a major field that is dealing with this issue through the use of the most advanced genetic, microbial, and molecular tools. The present study is a scientometric one and it makes use of data from the Scopus database consisting of 122 scientific publications to map the research field, find the main trends and specify the intellectual structure of this area from 2013 to 2026. The research shows the existence of a very fast-growing field where the main focus of the research is on genome editing especially CRISPR-Cas9 for introducing tolerance to abiotic and biotic stress, the use of plant-growth-promoting microbes for the purpose of improving the plant's resilience, and the combination of omics for trait discovery. The major research areas are: the staples such as wheat, rice and maize, the underutilized nature-resilient crops, and the coming together of biotechnology with AI (artificial intelligence) and models of sustainable systems. The results of the study give evidence of a change in paradigm moving towards precision breeding and biological control measures, and this change is accompanied by the highlighting of the role of biotechnology as critical in securing future food supply under the conditions of volatile climate.

This scientific analysis of 18,441 solid-state battery articles in the Scopus database gives us a picture not only of the whole global research but also of the most dynamic parts of it. The issue leads to a conclusion that the Chinese researchers are the ones that produce the most papers, which is an expression of the Chinese government's great attention to energy innovation. It is also noteworthy that the two major Chinese governmental funding organizations support more than half of the research, which is an indication of the strong institutional commitment to the development of battery technologies. The distribution of disciplines shows a strong interdisciplinary base, with Material Science, Chemistry, Energy, and Engineering being the main contributors. These areas are working together to make breakthroughs in solid electrolytes, interface stability, and scalable manufacturing. The analysis points out that the solid-state battery research is not only technically involved but also geopolitically focused, with China as the key player in determining the direction and influence of the research. This research presents an all-encompassing picture of the intellectual and institutional map, giving clues to the main themes and funding trends that are crucial for the progress of the future energy storage systems.

The worldwide prevalence of type 2 diabetes (T2D) and obesity has been a driving force behind the heavy research on glucagon-like peptide-1 receptor agonists (GLP-1RAs). Orforglipron, an innovative, oral, non-peptide GLP-1RA, is a major development in the therapeutic field, and it has the potential to make the delivery of drugs easier and more patient-friendly than the usual injections. The purpose of this scientometric study is to present the scientific works on orforglipron and thus portray its research landscape, important topics, and growing evidence base. A detailed examination of 101 scientific works (from 2023 to 2025) taken from the Scopus database has been done in this study. The documents were divided into categories depending on the type (e.g., clinical trial, review, meta-analysis), the theme dealt with, and the writer's background. The synthesis included research trends in focus, drug efficacy and safety outcomes, and positioning of the drug within the larger area of pharmacotherapy. The literature on orforglipron has just begun, with the first publications coming out in 2023. The data set consists of a substantial primary research foundation comprising clinical trials of all three Phases, plus a great number of reviews, meta-analyses, and editorial commentaries. The major research themes are: (1) Efficacy in Type 2 Diabetes and obesity, showing pronounced drops in HbA1c and body weight; (2) Safety and tolerability, where the main concern is with gastrointestinal adverse events; (3) Comparative effectiveness against other GLP-1RAs and multi-agonists; (4) Pharmacological characterization as a small-molecule agonist; and (5) The potential role of the drug in cardiovascular risk reduction and other areas that are yet to be explored. The scientific literature surrounding orforglipron is growing at a rapid pace and this indicates that there is a high interest in the drug's potential to change the management of T2D and obesity. The current evidence places it as a powerful, oral option to be administered instead of GLP-1RAs that are delivered by injection. The future research initiatives will probably be directed towards the investigation of the long-term cardiovascular outcomes, real-world effectiveness, and the use in a broader range of patients with different diseases.

Glucagon-like peptide-1 receptor agonists (GLP-1 RAs), originally designed for the management of type 2 diabetes (T2DM) and weight loss, have turned out to be a potential therapeutic target for neurodegenerative diseases. The present scientometrics study carries out a detailed analysis of 119 scienctific studies to the front of publications in the Scopus database (2011-2026) and maps the academic structure, thematic development, and main research areas in this rapidly growing field. The examination points out a strong increase in the number of publications, particularly from 2022 onward, which marks a major paradigm shift in considering GLP-1 RAs as possible neuroprotectors. Among the main research areas are: (1) Mechanistic Preclinical Studies, which account for the majority of publications and explain insulin signaling, neuroinflammation, oxidative stress, apoptosis and synaptic plasticity through the different pathways; (2) Clinical and Real-World Evidence, comprising of retrospective cohort studies and the first randomized controlled trials (RCTs) that show a decrease in dementia and Parkinson's disease (PD) among T2DM patients; (3) Drug-Specific Investigations, that discuss the effects of liraglutide, semaglutide, exendin-4, and the dual agonist tirzepatide; and (4) Disease-Specific Applications, primarily concentrating on Alzheimer's disease (AD) and PD. The field is characterized by a powerful shift from animal models to human trials and is starting to test applications in other areas besides the classic neurodegeneration such as cognitive dysfunction in obesity, psychiatric disorders and other CNS conditions. This survey encapsulates the present knowledge, puts the limelight on the contributions of pivotal Ais and research groups, and points out the future directions for this very promising area of therapeutic development.

Semaglutide, which is a glucagon-like peptide-1 receptor agonist (GLP-1 RA), has shown significant effects for weight loss in RCTs. However, good results obtained during clinical trials are not necessarily reflected in everyday practice because of factors such as adherence, dosing, patient differences, and healthcare system limitations. In this review, we present the results of 67 studies as real-world evidence (RWE) that help to assess semaglutide's effectiveness in weight control. RWE gives a constant message that semaglutide does indeed cause weight loss that is clinically significant in varieties of patients including those with type 2 diabetes (T2D) and simply obesity. Although the reduction in weight in real clinical situations is generally somewhat less than in RCTs, the studies speak of weight losses of 4-6% over 6-7 months and 9-15% over 12 months, with even greater losses among the most compliant patients. Some of the main barriers are identified as high dropout rates, inadequate dose titration, and the effects of both access and cost. Nevertheless, semaglutide proves to be a very effective option in standard care, with its positive effects on glycemic control, cardiovascular risk factors, and patient-reported outcomes. This paper highlights the importance of RWE in confirming, and sometimes even augmenting, RCT outcomes, as well as in providing support for clinical decision making in weight loss therapy.