Top 100 cited research on COVID-19 vaccines: A bibliometric analysis and evidence mapping.

The outbreak of the COVID-19 has seriously affected the whole society, and vaccines were the most effective means to contain the epidemic. This paper aims to determine the top 100 articles cited most frequently in COVID-19 vaccines and to analyze the research status and hot spots in this field through bibliometrics, to provide a reference for future research. We conducted a comprehensive search of the Web of Science Core Collection database on November 29, 2023, and identified the top 100 articles by ranking them from highest to lowest citation frequency. In addition, we analyzed the year of publication, citation, author, country, institution, journal, and keywords with Microsoft Excel 2019 and VOSviewer 1.6.18. Research focused on vaccine immunogenicity and safety, vaccine hesitancy, and vaccination intention.

Machine Learning-Based Approach for Identifying Research Gaps: COVID-19 as a Case Study.

BACKGROUND: Research gaps refer to unanswered questions in the existing body of knowledge, either due to a lack of studies or inconclusive results. Research gaps are essential starting points and motivation in scientific research. Traditional methods for identifying research gaps, such as literature reviews and expert opinions, can be time consuming, labor intensive, and prone to bias. They may also fall short when dealing with rapidly evolving or time-sensitive subjects. Thus, innovative scalable approaches are needed to identify research gaps, systematically assess the literature, and prioritize areas for further study in the topic of interest. OBJECTIVE: In this paper, we propose a machine learning-based approach for identifying research gaps through the analysis of scientific literature. We used the COVID-19 pandemic as a case study. METHODS: We conducted an analysis to identify research gaps in COVID-19 literature using the COVID-19 Open Research (CORD-19) data set, which comprises 1,121,433 papers related to the COVID-19 pandemic. Our approach is based on the BERTopic topic modeling technique, which leverages transformers and class-based term frequency-inverse document frequency to create dense clusters allowing for easily interpretable topics. Our BERTopic-based approach involves 3 stages: embedding documents, clustering documents (dimension reduction and clustering), and representing topics (generating candidates and maximizing candidate relevance). RESULTS: After applying the study selection criteria, we included 33,206 abstracts in the analysis of this study. The final list of research gaps identified 21 different areas, which were grouped into 6 principal topics. These topics were: "virus of COVID-19," "risk factors of COVID-19," "prevention of COVID-19," "treatment of COVID-19," "health care delivery during COVID-19," "and impact of COVID-19." The most prominent topic, observed in over half of the analyzed studies, was "the impact of COVID-19." CONCLUSIONS: The proposed machine learning-based approach has the potential to identify research gaps in scientific literature. This study is not intended to replace individual literature research within a selected topic. Instead, it can serve as a guide to formulate precise literature search queries in specific areas associated with research questions that previous publications have earmarked for future exploration. Future research should leverage an up-to-date list of studies that are retrieved from the most common databases in the target area. When feasible, full texts or, at minimum, discussion sections should be analyzed rather than limiting their analysis to abstracts. Furthermore, future studies could evaluate more efficient modeling algorithms, especially those combining topic modeling with statistical uncertainty quantification, such as conformal prediction.

A bibliometric analysis of worldwide cancer research using machine learning methods.

With the progress and development of computer technology, applying machine learning methods to cancer research has become an important research field. To analyze the most recent research status and trends, main research topics, topic evolutions, research collaborations, and potential directions of this research field, this study conducts a bibliometric analysis on 6206 research articles worldwide collected from PubMed between 2011 and 2021 concerning cancer research using machine learning methods. Python is used as a tool for bibliometric analysis, Gephi is used for social network analysis, and the Latent Dirichlet Allocation model is used for topic modeling. The trend analysis of articles not only reflects the innovative research at the intersection of machine learning and cancer but also demonstrates its vigorous development and increasing impacts. In terms of journals, Nature Communications is the most influential journal and Scientific Reports is the most prolific one. The United States and Harvard University have contributed the most to cancer research using machine learning methods. As for the research topic, "Support Vector Machine," "classification," and "deep learning" have been the core focuses of the research field. Findings are helpful for scholars and related practitioners to better understand the development status and trends of cancer research using machine learning methods, as well as to have a deeper understanding of research hotspots.

An established protocol for generating transgenic wheat for wheat functional genomics via particle bombardment.

Wheat is one of the most important food crops in the world and is considered one of the top targets in crop biotechnology. With the high-quality reference genomes of wheat and its relative species and the recent burst of genomic resources in Triticeae, demands to perform gene functional studies in wheat and genetic improvement have been rapidly increasing, requiring that production of transgenic wheat should become a routine technique. While established for more than 20 years, the particle bombardment-mediated wheat transformation has not become routine yet, with only a handful of labs being proficient in this technique. This could be due to, at least partly, the low transformation efficiency and the technical difficulties. Here, we describe the current version of this method through adaptation and optimization. We report the detailed protocol of producing transgenic wheat by the particle gun, including several critical steps, from the selection of appropriate explants (i.e., immature scutella), the preparation of DNA-coated gold particles, and several established strategies of tissue culture. More importantly, with over 20 years of experience in wheat transformation in our lab, we share the many technical details and recommendations and emphasize that the particle bombardment-mediated approach has fewer limitations in genotype dependency and vector construction when compared with the Agrobacterium-mediated methods. The particle bombardment-mediated method has been successful for over 30 wheat genotypes, from the tetraploid durum wheat to the hexaploid common wheat, from modern elite varieties to landraces. In conclusion, the particle bombardment-mediated wheat transformation has demonstrated its potential and wide applications, and the full set of protocol, experience, and successful reports in many wheat genotypes described here will further its impacts, making it a routine and robust technique in crop research labs worldwide.

Future Research in General Medicine Has Diverse Topics and is Highly Promising: Opinions Based on a Questionnaire Survey.

In Japan, general medicine is still relatively new as a specialty, having been established in 2018 as the 19th primary specialty. The relevant research field has therefore not been fully established yet, and the detailed research areas in this field have not been identified. We conducted a descriptive questionnaire-based web survey of members of the Japanese Society of Hospital General Medicine. Respondents were asked to highlight their research topics from the following categories: diagnostic excellence, design (problem-solving and thinking methodology), symptomatology, physical examination, clinical epidemiology, home and community medicine, general medicine education, organizational management, hospital administration, and "none of the above (add description of your work if desired)". The respondents could choose multiple topics. There were 276 respondents (14% response rate), of whom 240 (86.9%) were male, 103 (37.3%) worked at universities, and 232 (84.1%) had previous research experience. Diagnostic excellence was the most common research topic category among generalists (n=87, 21.3%), followed by clinical epidemiology (n=83, 20.3%), symptomatology (n=41, 10.0%), home and community medicine (n=39, 9.6%), and general medicine education (n=36, 8.8%). Seventy-eight respondents (19.1%) chose "none of the above (add description of your work if desired)". The main research topics were in areas fundamental to diagnostic excellence, ie, diagnostics, diagnostic error, clinical epidemiology, and symptomatology. Home and community medicine and general medicine education were also included as research topics because of their diverse roles. The research interests of generalists are therefore diverse, and new areas and frameworks are likely to be created in the future.