As the contribution of de novo mutations (DNMs) to human genetic diseases has been gradually uncovered, analyzing the global research landscape over the past 20 years is essential. Because of the large and rapidly increasing number of publications in this field, understanding the current landscape of the contribution of DNMs in the human genome to genetic diseases remains a challenge. Bibliometric analysis provides an approach for visualizing these studies using information in published records in a specific field. This study aimed to illustrate the current global research status and explore trends in the field of DNMs underlying genetic diseases. Bibliometric analyses were performed using the Bibliometrix Package based on the R language version 4.1.3 and CiteSpace version 6.1.R2 software for publications from 2000 to 2021 indexed under the Web of Science Core Collection (WoSCC) about DNMs underlying genetic diseases on 17 September 2022. We identified 3435 records, which were published in 731 journals by 26,538 authors from 6052 institutes in 66 countries. There was an upward trend in the number of publications since 2013. The USA, China, and Germany contributed the majority of the records included. The University of Washington, Columbia University, and Baylor College of Medicine were the top-producing institutions. Evan E Eichler of the University of Washington, Stephan J Sanders of the Yale University School of Medicine, and Ingrid E Scheffer of the University of Melbourne were the most high-ranked authors. Keyword co-occurrence analysis suggested that DNMs in neurodevelopmental disorders and intellectual disabilities were research hotspots and trends. In conclusion, our data show that DNMs have a significant effect on human genetic diseases, with a noticeable increase in annual publications over the last 5 years. Furthermore, potential hotspots are shifting toward understanding the causative role and clinical interpretation of newly identified or low-frequency DNMs observed in patients.

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.

BACKGROUND: The Coronavirus Disease 2019 (COVID-19) pandemic was a huge shock to society, and the ensuing information problems had a huge impact on society at the same time. The urgent need to understand the Infodemic, i.e., the importance of the spread of false information related to the epidemic, has been highlighted. However, while there is a growing interest in this phenomenon, studies on the topic discovery, data collection, and data preparation phases of the information analysis process have been lacking.
OBJECTIVE: Since the epidemic is unprecedented and has not ended to this day, we aimed to examine the existing Infodemic-related literature from January 2019 to December 2022.
METHODS: We have systematically searched ScienceDirect and IEEE Xplore databases with some search limitations. From the searched literature we selected titles, abstracts and keywords, and limitations sections. We conducted an extensive structured literature search and analysis by filtering the literature and sorting out the available information.
RESULTS: A total of 47 papers ended up meeting the requirements of this review. Researchers in all of these literatures encountered different challenges, most of which were focused on the data collection step, with few challenges encountered in the data preparation phase and almost none in the topic discovery section. The challenges were mainly divided into the points of how to collect data quickly, how to get the required data samples, how to filter the data, what to do if the data set is too small, how to pick the right classifier and how to deal with topic drift and diversity. In addition, researchers have proposed partial solutions to the challenges, and we have also proposed possible solutions.
CONCLUSIONS: This review found that Infodemic is a rapidly growing research area that attracts the interest of researchers from different disciplines. The number of studies in this field has increased significantly in recent years, with researchers from different countries, including the United States, India, and China. Infodemic topic discovery, data collection, and data preparation are not easy, and each step faces different challenges. While there is some research in this emerging field, there are still many challenges that need to be addressed. These findings highlight the need for more articles to address these issues and fill these gaps.

The COVID-19 epidemic in December 2019 had a significant negative impact on people\'s health and economies all across the world. The most effective preventive measure against COVID-19 is vaccination. Therefore, the development and production of COVID-19 vaccines is booming worldwide. This study aimed to analyze the current state of that research and its development tendency by bibliometrics. We conducted a thorough search of the Web of Science Core Collection. VOSviewer1.6.18 was used to perform the bibliometric analysis of these papers. A total of 6,325 papers were finally included. The USA maintained a top position worldwide. Shimabukuro Tom T and Harvard University were the most prolific author and institution. The Vaccines was the most published journal. The research hotspots of COVID-19 vaccines can be classified into vaccine hesitancy, vaccine safety and effectiveness, vaccine immunogenicity, and adverse reactions to vaccines. Studies on various vaccination types have also concentrated on efficacy against continuously developing virus strains, immunogenicity, side effects, and safety.

Background. There is a lack of knowledge on the scope and nature of the research by faculty members in occupational science (OS) and/or occupational therapy (OT) programs in Canada. Purpose. To describe the research activities of faculty members in these programs and directions. Method. A cross-sectional survey was distributed to 173 faculty members across all 14 Canadian OT that addressed: 1) research topics and methods, 2) populations, and 3) funding. Findings. Based on respondents (N  =  121), research is focused on a range of topics and populations with most conducting qualitative research. Many conduct research examining the effectiveness of interventions, with few respondents focused on OS research. Federal and provincial grants agencies were the largest source of funding. Implications. Research topics studied were not always proportional to practice although emerging areas were being investigated that can expand the evidence base and scope of practice. Despite limited occupation-specific funding options, respondents were accessing funding from varied sources. Collaborations among faculty members, clinicians, and individuals with lived experience can create priorities for future OS and/or OT research in Canada.

Sentiment analysis, one of the research hotspots in the natural language processing field, has attracted the attention of researchers, and research papers on the field are increasingly published. Many literature reviews on sentiment analysis involving techniques, methods, and applications have been produced using different survey methodologies and tools, but there has not been a survey dedicated to the evolution of research methods and topics of sentiment analysis. There have also been few survey works leveraging keyword co-occurrence on sentiment analysis. Therefore, this study presents a survey of sentiment analysis focusing on the evolution of research methods and topics. It incorporates keyword co-occurrence analysis with a community detection algorithm. This survey not only compares and analyzes the connections between research methods and topics over the past two decades but also uncovers the hotspots and trends over time, thus providing guidance for researchers. Furthermore, this paper presents broad practical insights into the methods and topics of sentiment analysis, while also identifying technical directions, limitations, and future work.

Much research is being carried out using publicly available Twitter data in the field of public health, but the types of research questions that these data are being used to answer and the extent to which these projects require ethical oversight are not clear.
This review describes the current state of public health research using Twitter data in terms of methods and research questions, geographic focus, and ethical considerations including obtaining informed consent from Twitter handlers.
We implemented a systematic review, following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, of articles published between January 2006 and October 31, 2019, using Twitter data in secondary analyses for public health research, which were found using standardized search criteria on SocINDEX, PsycINFO, and PubMed. Studies were excluded when using Twitter for primary data collection, such as for study recruitment or as part of a dissemination intervention.
We identified 367 articles that met eligibility criteria. Infectious disease (n=80, 22%) and substance use (n=66, 18%) were the most common topics for these studies, and sentiment mining (n=227, 62%), surveillance (n=224, 61%), and thematic exploration (n=217, 59%) were the most common methodologies employed. Approximately one-third of articles had a global or worldwide geographic focus; another one-third focused on the United States. The majority (n=222, 60%) of articles used a native Twitter application programming interface, and a significant amount of the remainder (n=102, 28%) used a third-party application programming interface. Only one-third (n=119, 32%) of studies sought ethical approval from an institutional review board, while 17% of them (n=62) included identifying information on Twitter users or tweets and 36% of them (n=131) attempted to anonymize identifiers. Most studies (n=272, 79%) included a discussion on the validity of the measures and reliability of coding (70% for interreliability of human coding and 70% for computer algorithm checks), but less attention was paid to the sampling frame, and what underlying population the sample represented.
Twitter data may be useful in public health research, given its access to publicly available information. However, studies should exercise greater caution in considering the data sources, accession method, and external validity of the sampling frame. Further, an ethical framework is necessary to help guide future research in this area, especially when individual, identifiable Twitter users and tweets are shared and discussed.
PROSPERO CRD42020148170; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=148170.

This study aimed to explore the core knowledge topics and future research trends in neuroscience in the field of education (NIE). In this study, we have explored the diffusion of neuroscience and different neuroscience methods (e.g., electroencephalography, functional magnetic resonance imaging, eye tracking) through and within education fields. A total of 549 existing scholarly articles and 25,886 references on neuroscience in the field of education (NIE) from the Web of Science Core Collection databases were examined during the following two periods: 1995-2013 and 2014-2022. The science mapping software Vosviewer and Bibliometrix were employed for data analysis and visualization of relevant literature. Furthermore, performance analysis, collaboration network analysis, co-citation network analysis, and strategic diagram analysis were conducted to systematically sort out the core knowledge in NIE. The results showed that children and cognitive neuroscience, students and medical education, emotion and empathy, and education and brain are the core intellectual themes of current research in NIE. Curriculum reform and children\'s skill development have remained central research issues in NIE, and several topics on pediatric research are emerging. The core intellectual themes of NIE revealed in this study can help scholars to better understand NIE, save research time, and explore a new research question. To the best of our knowledge, this study is one of the earliest documents to outline the NIE core intellectual themes and identify the research opportunities emerging in the field.

The COVID-19 outbreak highlighted the importance of rapid access to research.
The aim of this study was to investigate research communication related to COVID-19, the level of openness of papers, and the main topics of research into this disease.
Open access (OA) uptake (typologies, license use) and the topic evolution of publications were analyzed from the start of the pandemic (January 1, 2020) until the end of a year of widespread lockdown (March 1, 2021).
The sample included 95,605 publications; 94.1% were published in an OA form, 44% of which were published as Bronze OA. Among these OA publications, 42% do not have a license, which can limit the number of citations and thus the impact. Using a topic modeling approach, we found that articles in Hybrid and Green OA publications are more focused on patients and their effects, whereas the strategy to combat the pandemic adopted by different countries was the main topic of articles selecting publication via the Gold OA route.
Although OA scientific production has increased, some weaknesses in OA practice, such as lack of licensing or under-researched topics, still hold back its effective use for further research.

OBJECTIVE: Ever since Leo Kanner first described autism in 1943, the research in this field has grown immensely. In 2021 alone, 5837 SCOPUS indexed documents were published with a title that contained the words: \"autism\", \"autistic\", or \"ASD\". The purpose of this study was to examine the most common topics of autism research in 2021 and present a geographical contribution to this research.
METHODS: We performed a content analysis of 1102 abstracts from the articles published in 11 Autism journals in 2021. The following journals, indexed by the SCOPUS database, were included: Autism, Autism Research, Molecular Autism, Journal of Autism and Developmental Disorders, Research in Autism Spectrum Disorders, Focus on Autism and Other Developmental Disabilities, Education and Training in Autism and Developmental Disabilities, Review Journal of Autism and Developmental Disorders, Advances in Autism, Autism and Developmental Language Impairments, and Autism in Adulthood.
RESULTS: According to the analysis, the main research topics were: mental health, social communication, social skills, quality of life, parenting stress, ADHD, Covid-19, self-efficacy, special education, and theory of mind. In relation to geographic distribution, most studies came from the USA, followed by the UK, Australia, and Canada.
CONCLUSIONS: Research topics were aligned with the priorities set by stakeholders in autism, most notably persons with autism themselves and their family members. There is a big gap in research production between developed countries and developing countries.