Global research Activity on olfactory marker protein (OMP): A bibliometric and visualized analysis

Olfactory marker protein (OMP) is extensively studied in mature olfactory receptor neurons (ORNs) for understanding olfaction physiology. However, no bibliometric analysis on this topic exists. We conducted a bibliometric analysis of OMP research articles, wherein the publication count was assessed by year, country, journal, and author, collaboration by country, and productivity of the authors. Additionally, key terms and research themes were identified. Using the search phrase "olfactory marker protein" in Scopus, we retrieved 691 original research articles by 2487 authors since 1974. Publications showed an increasing trend, with the United States leading in quantity and collaboration. Our thematic map highlights "Olfactory bulb, regeneration, olfactory" as the primary research domain, while "olfaction, olfactory sensory neuron, glomerulus" and "olfactory receptor neurons, apoptosis, olfactory dysfunction" emerge as essential future research topics. These bibliometric findings offer insights into the global OMP research landscape, guiding researchers in potential collaborations and intriguing future research fields.

Gene and schizophrenia in the pregenome and postgenome-wide association studies era: a bibliometric analysis and network visualization

This study aimed to perform a bibliometric analysis on genetic studies in schizophrenia in the pregenome-wide association studies (GWAS) and post-GWAS era. We searched the literature on genes and schizophrenia using the Scopus database. The documents increased with time, especially after the human genome project and International HapMap Project, with the highest citation in 2008. The top occurrence author keywords were discovered to be different in the pre-GWAS and post-GWAS eras, reflecting the progress of genetic studies connected to schizophrenia. Emerging keywords highlighted a trend towards an application of precision medicine, showing an interplay of environmental exposures as well as genetic factors in schizophrenia pathogenesis, progression, and response to therapy. In conclusion, the gene and schizophrenia literature has grown rapidly after the human genome project, and the temporal variation in the author keywords pattern reflects the trend of genetic studies related to schizophrenia in the pre-GWAS and post-GWAS era.

Topic Modeling for Automatic Analysis of Natural Language: A Case Study in an Italian Customer Support Center

This paper focuses on the automatic analysis of conversation transcriptions in the call center of a customer care service. The goal is to recognize topics related to problems and complaints discussed in several dialogues between customers and agents. Our study aims to implement a framework able to automatically cluster conversation transcriptions into cohesive and well-separated groups based on the content of the data. The framework can alleviate the analyst selecting proper values for the analysis and the clustering processes. To pursue this goal, we consider a probabilistic model based on the latent Dirichlet allocation, which associates transcriptions with a mixture of topics in different proportions. A case study consisting of transcriptions in the Italian natural language, and collected in a customer support center of an energy supplier, is considered in the paper. Performance comparison of different inference techniques is discussed using the case study. The experimental results demonstrate the approach’s efficacy in clustering Italian conversation transcriptions. It also results in a practical tool to simplify the analytic process and off-load the parameter tuning from the end-user. According to recent works in the literature, this paper may be valuable for introducing latent Dirichlet allocation approaches in topic modeling for the Italian natural language.

An overview of literature on COVID-19, MERS and SARS: Using text mining and latent Dirichlet allocation

The unprecedented outbreak of COVID-19 is one of the most serious global threats to public health in this century. During this crisis, specialists in information science could play key roles to support the efforts of scientists in the health and medical community for combatting COVID-19. In this article, we demonstrate that information specialists can support health and medical community by applying text mining technique with latent Dirichlet allocation procedure to perform an overview of a mass of coronavirus literature. This overview presents the generic research themes of the coronavirus diseases: COVID-19, MERS and SARS, reveals the representative literature per main research theme and displays a network visualisation to explore the overlapping, similarity and difference among these themes. The overview can help the health and medical communities to extract useful information and interrelationships from coronavirus-related studies.

Profiling the Research Landscape on Cognitive Aging: A Bibliometric Analysis and Network Visualization

Objectives

This study aimed to profile the cognitive aging research landscape from 1956 to 2021.

Methods

A total of 3,779 documents were retrieved from the Scopus database for the bibliometric analysis and network visualization. By comparing each keyword's overall connection strength (centrality), frequency (density), and average year of publication (novelty) to the calculated median values acquired from the overlay view of the VOSviewer map, the enhanced strategic diagrams (ESDs) were constructed.

Results

The findings showed an increasing trend in the number of publications. The United States leads the contributing countries in cognitive aging research. The scientific productivity pattern obeyed Lotka's law. The most productive researcher was Deary, I. J., with the highest number of publications. The collaborative index showed an increasing trend from 1980 onwards. Frontiers in Aging Neuroscience is the most prestigious journal in the field of cognitive aging research. In Bradford core journals zone 1, the top 10 core journals of cognitive aging research provided more than half of the total articles (697, or 55.36 percent).

Conclusions

For the next decades, the trending topics in cognitive aging research include neuropsychological assessment, functional connectivity, human immunodeficiency virus (HIV), decision-making, gender, compensation, default mode network, learning and memory, brain-derived neurotrophic factor (BDNF), obesity, D-galactose, epigenetics, frailty, mortality, mini-mental state examination (MMSE), anxiety, and gait speed.

Thematic analysis of multiple sclerosis research by enhanced strategic diagram

Recent interest in multiple sclerosis research warrants literature analysis to evaluate the current state of the discipline and new research domains. This bibliometric review summarised the research trends and analysed research areas in multiple sclerosis over the last decade. The documents containing the term 'multiple sclerosis' in the article title were retrieved from the Scopus database. We used Harzing's Publish or Perish and VOSviewer for citation analysis and data visualisation, respectively. We found a total of 18,003 articles published in journals in the English language between 2012 and 2021. The emerging keywords identified utilising the enhanced strategic diagram were 'covid-19', 'teriflunomide', 'clinical trial', 'microglia', 'b cells', 'myelin', 'brain', 'white matter', 'functional connectivity', 'pain', 'employment', 'health-related quality of life', 'meta-analysis' and 'comorbidity'. This study demonstrates the tremendous growth of multiple sclerosis literature worldwide, which is expected to grow more than double during the next decade especially in the identified emerging topics.

The Development History and Research Tendency of Medical Informatics: Topic Evolution Analysis

BACKGROUND

Medical informatics has attracted the attention of researchers worldwide. It is necessary to understand the development of its research hot spots as well as directions for future research.

OBJECTIVE

The aim of this study is to explore the evolution of medical informatics research topics by analyzing research articles published between 1964 and 2020.

METHODS

A total of 56,466 publications were collected from 27 representative medical informatics journals indexed by the Web of Science Core Collection. We identified the research stages based on the literature growth curve, extracted research topics using the latent Dirichlet allocation model, and analyzed topic evolution patterns by calculating the cosine similarity between topics from the adjacent stages.

RESULTS

The following three research stages were identified: early birth, early development, and rapid development. Medical informatics has entered the fast development stage, with literature growing exponentially. Research topics in medical informatics can be classified into the following two categories: data-centered studies and people-centered studies. Medical data analysis has been a research hot spot across all 3 stages, and the integration of emerging technologies into data analysis might be a future hot spot. Researchers have focused more on user needs in the last 2 stages. Another potential hot spot might be how to meet user needs and improve the usability of health tools.

CONCLUSIONS

Our study provides a comprehensive understanding of research hot spots in medical informatics, as well as evolution patterns among them, which was helpful for researchers to grasp research trends and design their studies.