Twenty years of research on borderline personality disorder: a scientometric analysis of hotspots, bursts, and research trends.

Borderline personality disorder (BPD), a complex and severe psychiatric disorder, has become a topic of considerable interest to current researchers due to its high incidence and severity of consequences. There is a lack of a bibliometric analysis to visualize the history and developmental trends of researches in BPD. We retrieved 7919 relevant publications on the Web of Science platform and analyzed them using software CiteSpace (6.2.R4). The results showed that there has been an overall upward trend in research interest in BPD over the past two decades. Current research trends in BPD include neuroimaging, biological mechanisms, and cognitive, behavioral, and pathological studies. Recent trends have been identified as "prevention and early intervention", "non-pharmacological treatment" and "pathogenesis". The results are like a reference program that will help determine future research directions and priorities.

Half a Century of Research on Posttraumatic Stress Disorder: A Scientometric Analysis.

We conducted a scientometric analysis to outline clinical research on posttraumatic stress disorder (PTSD). Our primary objective was to perform a broad-ranging scientometric analysis to evaluate key themes and trends over the past decades. Our secondary objective was to measure research network performance. We conducted a systematic search in the Web of Science Core Collection up to 15 August 2022 for publications on PTSD. We identified 42,170 publications published between 1945 and 2022. We used CiteSpace to retrieve the co-cited reference network (1978-2022) that presented significant modularity and mean silhouette scores, indicating highly credible clusters (Q = 0.915, S = 0.795). Four major trends of research were identified: 'war veterans and refugees', 'treatment of PTSD/neuroimaging', 'evidence syntheses', and 'somatic symptoms of PTSD'. The largest cluster of research concerned evidence synthesis for genetic predisposition and environmental exposures leading to PTSD occurrence. Research on war-related trauma has shifted from battlefield-related in-person exposure trauma to drone operator trauma and is being out published by civilian-related trauma research, such as the 'COVID-19' pandemic impact, 'postpartum', and 'grief disorder'. The focus on the most recent trends in the research revealed a burst in the 'treatment of PTSD' with the development of Mhealth, virtual reality, and psychedelic drugs. The collaboration networks reveal a central place for the USA research network, and although relatively isolated, a recent surge of publications from China was found. Compared to other psychiatric disorders, we found a lack of high-quality randomized controlled trials for pharmacological and nonpharmacological treatments. These results can inform funding agencies and future research.

Ultrasonographic diagnosis of ovarian tumors through the deep convolutional neural network.

OBJECTIVES: The objective of this study was to develop and validate an ovarian tumor ultrasonographic diagnostic model based on deep convolutional neural networks (DCNN) and compare its diagnostic performance with that of human experts. MATERIAL AND METHODS: We collected 486 ultrasound images of 192 women with malignant ovarian tumors and 617 ultrasound images of 213 women with benign ovarian tumors, all confirmed by pathological examination. The image dataset was split into a training set and a validation set according to a 7:3 ratio. We selected 5 DCNNs to develop our model: MobileNet, Xception, Inception, ResNet and DenseNet. We compared the performance of the five models through the area under the curve (AUC), sensitivity, specificity, and accuracy. We then randomly selected 200 images from the validation set as the test set. We asked three expert radiologists to diagnose the images to compare the performance of radiologists and the DCNN model. RESULTS: In the validation set, AUC of DenseNet was 0.997 while AUC was 0.988 of ResNet, 0.987 of Inception, 0.968 of Xception and 0.836 of MobileNet. In the test set, the accuracy was 0.975 with the DenseNet model versus 0.825 (p < 0.0001) with the radiologists, and sensitivity was 0.975 versus 0.700 (p < 0.0001), and specificity was 0.975 versus 0.908 (p < 0.001). CONCLUSIONS: DensNet performed better than other DCNNs and expert radiologists in identifying malignant ovarian tumors from benign ovarian tumors based on ultrasound images, a finding that needs to be further explored in clinical trials.

The trend of breeding value research in animal science: bibliometric analysis.

This study aims to identify trends and hot topics in breeding value to support researchers in finding new directions for future research in that area. The data of this study consist of 7072 academic studies on breeding value in the Web of Science database. Network visualizations and in-depth bibliometric analysis were performed on cited references, authors, countries, institutions, journals, and keywords through CiteSpace. VanRaden (2008) is the most cited work and has an essential place in the field. The most prolific writer is Ignacy Misztal. While the most productive country in breeding value studies is the United States, the People's Republic of China is an influential country that has experienced a strong citation burst in the last 3 years. The National Institute for Agricultural Research and Wageningen University are important institutions that play a critical role in connecting other institutions. Also, these two institutions have the highest centrality values. "Genomic prediction" is the outstanding sub-study field in the active clusters appearing in the analysis results. We have summarized the literature on breeding value, including publication information, country, institution, author, and journal. We can say that hot topics today are "genome-wide association", "feed efficiency", and "genomic prediction". While the studies conducted in the past years have focused on economic value and accuracy, the studies conducted in recent years have started to be studies that consider technological developments and changing world conditions such as global warming and carbon emission.

A century of research on neuromodulation interventions: A scientometric analysis of trends and knowledge maps.

Interest in neurostimulation interventions has significantly grown in recent decades, yet a scientometric analysis objectively mapping scientific knowledge and recent trends remains unpublished. Using relevant keywords, we conducted a search in the Web of Science Core Collection on September 23, 2022, retrieving a total of 47,681 documents with 987,979 references. We identified two prominent research trends: 'noninvasive brain stimulation' and 'invasive brain stimulation.' These methods have interconnected over time, forming a cluster focused on evidence synthesis. Noteworthy emerging research trends encompassed 'transcutaneous auricular vagus nerve stimulation,' 'DBS/epilepsy in the pediatric population,' 'spinal cord stimulation,' and 'brain-machine interface.' While progress has been made for various neurostimulation interventions, their approval as adjuvant treatments remains limited, and optimal stimulation parameters lack consensus. Enhancing communication between experts of both neurostimulation types and encouraging novel translational research could foster further development. These findings offer valuable insights for funding agencies and research groups, guiding future directions in the field.

Thirty years of research on negative symptoms of schizophrenia: A scientometric analysis of hotspots, bursts, and research trends.

Research on negative symptoms of schizophrenia has received renewed interest since the 1980s. A scientometric analysis that objectively maps scientific knowledge, with changes in recent trends, is currently lacking. We searched the Web of Science Core Collection (WOSCC) on December 17, 2021 using relevant keywords. R-bibliometrix and CiteSpace were used to perform the analysis. We retrieved 27,568 references published between 1966 and 2022. An exponential rise in scientific interest was observed, with an average annual growth rate in publications of 16.56% from 1990 to 2010. The co-cited reference network that was retrieved presented 24 different clusters with a well-structured network (Q=0.7921; S=0.9016). Two distinct major research trends were identified: research on the conceptualization and treatment of negative symptoms. The latest trends in research on negative symptoms include evidence synthesis, nonpharmacological treatments, and computational psychiatry. Scientometric analyses provide a useful summary of changes in negative symptom research across time by identifying intellectual turning point papers and emerging trends. These results will be informative for systematic reviews, meta-analyses, and generating novel hypotheses.

A century of research on psychedelics: A scientometric analysis on trends and knowledge maps of hallucinogens, entactogens, entheogens and dissociative drugs.

A scientometric analysis was realized to outline clinical research on psychedelics over the last century. Web of Science Core Collection was searched up to March 18, 2022, for publications on psychedelics. Network analyses and bibliometrics were combined, to identify research themes and trends with Bibliometrix and CiteSpace. The primary aim was to measure research trends evolution over time, and the secondary aims were to identify bibliometric performance and influence networks of publications, authors, institutions, and countries. Sensitivity analyses were conducted for 2016-2022, and 2021 time periods. We included 31,687 documents (591,329 references), which aggregated into a well-structured network with credible clustering. Research productivity was split into an early less productive period mainly focusing on safety issues, and a "psychedelic renaissance" after the 1990s. Major trends were identified for hallucinogens/entheogens, entactogens, novel psychoactive substances (NPS), and on dissociative substances. There was a translational evolution from the bench to the bedside, with phase 2 and 3 trials and/or evidence synthesis in particular. The most recent trends concerned NPS, ketamine-associated brain changes, and ayahuasca-assisted psychotherapy. The USA and Canada were the most productive settings for the research overall, and more recently this geographical distribution became more prominent, reflecting legislative context/policy making. A translational evolution of psychedelics has been occurring, that has brought approval of esketamine for depression and will likely lead to approval of additional psychedelics across mental and physical conditions. Toxicology screening tools for NPS are urgently needed, which in turn might follow the same translational evolution of psychedelics in the future.

Thirty years of research on physical activity, mental health, and wellbeing: A scientometric analysis of hotspots and trends.

The sheer volume of research publications on physical activity, mental health, and wellbeing is overwhelming. The aim of this study was to perform a broad-ranging scientometric analysis to evaluate key themes and trends over the past decades, informing future lines of research. We searched the Web of Science Core Collection from inception until December 7, 2021, using the appropriate search terms such as "physical activity" or "mental health," with no limitation of language or time. Eligible studies were articles, reviews, editorial material, and proceeding papers. We retrieved 55,353 documents published between 1905 and 2021. The annual scientific production is exponential with a mean annual growth rate of 6.8% since 1989. The 1988-2021 co-cited reference network identified 50 distinct clusters that presented significant modularity and silhouette scores indicating highly credible clusters (Q = 0.848, S = 0.939). This network identified 6 major research trends on physical activity, namely cardiovascular diseases, somatic disorders, cognitive decline/dementia, mental illness, athletes' performance, related health issues, and eating disorders, and the COVID-19 pandemic. A focus on the latest research trends found that greenness/urbanicity (2014), concussion/chronic traumatic encephalopathy (2015), and COVID-19 (2019) were the most active clusters of research. The USA research network was the most central, and the Chinese research network, although important in size, was relatively isolated. Our results strengthen and expand the central role of physical activity in public health, calling for the systematic involvement of physical activity professionals as stakeholders in public health decision-making process.

Half a century of research on Attention-Deficit/Hyperactivity Disorder: A scientometric study.

We performed a scientometric analysis of the scientific literature on ADHD to evaluate key themes and trends over the past decades, informing future lines of research. We conducted a systematic search in Web of Science Core Collection up to 15 November, 2021 for scientific publications on ADHD. We retrieved 28,381 publications. We identified four major research trends: 1) ADHD treatment, risks factors and evidence synthesis; 2) neurophysiology, neuropsychology and neuroimaging; 3) genetics; 4) comorbidity. In chronological order, identified clusters of themes included: tricyclic antidepressants, ADHD diagnosis/treatment, bipolar disorder, EEG, polymorphisms, sleep, executive functions, pharmacology, genetics, environmental risk factors, emotional dysregulation, neuroimaging, non-pharmacological interventions, default mode network, Tourette, polygenic risk score, sluggish cognitive tempo, evidence-synthesis, toxins/chemicals, psychoneuroimmunology, Covid-19, and physical exercise. In conclusion, research on ADHD over the past decades has been driven mainly by a medical model. Whereas the neurobiological correlates of ADHD are undeniable and crucial, we look forward to further research on relevant psychosocial aspects related to ADHD, such as societal pressure, the concept of neurodiversity, and stigma.

Half a century of research on antipsychotics and schizophrenia: A scientometric study of hotspots, nodes, bursts, and trends.

Changes over 50 years of research on antipsychotics in schizophrenia have occurred. A scientometric synthesis of such changes over time and a measure of researchers' networks and scientific productivity is currently lacking. We searched Web of Science Core Collection from inception until November 5, 2021, using the appropriate key. Our primary objective was to conduct systematic mapping with CiteSpace to show how clusters of keywords have evolved over time and obtain clusters' structure and credibility. Our secondary objective was to measure research network performance (countries, institutions, and authors) using CiteSpace, VOSviewer, and Bibliometrix. We included 32,240 studies published between 1955 and 2021. The co-cited reference network identified 25 clusters with a well-structured network (Q=0.8166) and highly credible clustering (S=0.91). The main trends of research were: 1) antipsychotic efficacy; 2) cognition in schizophrenia; 3) side effects of antipsychotics. Last five years research trends were: 'ultra-resistance schizophrenia' (S=0.925), 'efficacy/dose-response' (S=0.775), 'evidence-synthesis' (S=0.737), 'real-world effectiveness' (S=0.794), 'cannabidiol' (S=0.989), and 'gut microbiome' (S=0.842). These results can inform funding agencies and research groups' future directions.

The boundary-spanning mechanisms of Nobel Prize winning papers.

The breakthrough potentials of research papers can be explained by their boundary-spanning qualities. Here, for the first time, we apply the structural variation analysis (SVA) model and its affiliated metrics to investigate the extent to which such qualities characterize a group of Nobel Prize winning papers. We find that these papers share remarkable boundary-spanning traits, marked by exceptional abilities to connect disparate and topically-diverse clusters of research papers. Further, their publications exert structural variations on a scale that significantly alters the betweenness centrality distributions in existing intellectual space. Overall, SVA not only provides a set of leading indicators for describing future Nobel Prize winning papers, but also broadens our understanding of similar prize-winning properties that may have been overlooked among other regular publications.

An Extensive Knowledge Mapping Review of Measurement and Validity in Language Assessment and SLA Research.

This study set out to investigate intellectual domains as well as the use of measurement and validation methods in language assessment research and second language acquisition (SLA) published in English in peer-reviewed journals. Using Scopus, we created two datasets: (i) a dataset of core journals consisting of 1,561 articles published in four language assessment journals, and (ii) a dataset of general journals consisting of 3,175 articles on language assessment published in the top journals of SLA and applied linguistics. We applied document co-citation analysis to detect thematically distinct research clusters. Next, we coded citing papers in each cluster based on an analytical framework for measurement and validation. We found that the focus of the core journals was more exclusively on reading and listening comprehension assessment (primary), facets of speaking and writing performance such as raters and validation (secondary), as well as feedback, corpus linguistics, and washback (tertiary). By contrast, the primary focus of assessment research in the general journals was on vocabulary, oral proficiency, essay writing, grammar, and reading. The secondary focus was on affective schemata, awareness, memory, language proficiency, explicit vs. implicit language knowledge, language or semantic awareness, and semantic complexity. With the exception of language proficiency, this second area of focus was absent in the core journals. It was further found that the majority of citing publications in the two datasets did not carry out inference-based validation on their instruments before using them. More research is needed to determine what motivates authors to select and investigate a topic, how thoroughly they cite past research, and what internal (within a field) and external (between fields) factors lead to the sustainability of a Research Topic in language assessment.

Measuring researchers' potential scholarly impact with structural variations: Four types of researchers in information science (1979-2018).

We propose a method to measure the potential scholarly impact of researchers based on network structural variations they introduced to the underlying author co-citation network of their field. We applied the method to the information science field based on 91,978 papers published between 1979 and 2018 from the Web of Science. We divided the entire period into eight consecutive intervals and measured structural variation change rates (ΔM) of individual authors in corresponding author co-citation networks. Four types of researchers are identified in terms of temporal dynamics of their potential scholarly impact-1) Increasing, 2) Decreasing, 3) Sustained, and 4) Transient. The study contributes to the understanding of how researchers' scholarly impact might evolve in a broad context of the corresponding research community. Specifically, this study illustrated a crucial role played by structural variation metrics in measuring and explaining the potential scholarly impact of a researcher. This method based on the structural variation analysis offers a theoretical framework and a practical platform to analyze the potential scholarly impact of researchers and their specific contributions.

A Glimpse of the First Eight Months of the COVID-19 Literature on Microsoft Academic Graph: Themes, Citation Contexts, and Uncertainties.

As scientists worldwide search for answers to the overwhelmingly unknown behind the deadly pandemic, the literature concerning COVID-19 has been growing exponentially. Keeping abreast of the body of literature at such a rapidly advancing pace poses significant challenges not only to active researchers but also to society as a whole. Although numerous data resources have been made openly available, the analytic and synthetic process that is essential in effectively navigating through the vast amount of information with heightened levels of uncertainty remains a significant bottleneck. We introduce a generic method that facilitates the data collection and sense-making process when dealing with a rapidly growing landscape of a research domain such as COVID-19 at multiple levels of granularity. The method integrates the analysis of structural and temporal patterns in scholarly publications with the delineation of thematic concentrations and the types of uncertainties that may offer additional insights into the complexity of the unknown. We demonstrate the application of the method in a study of the COVID-19 literature.

Corrigendum: An Extensive Knowledge Mapping Review of Measurement and Validity in Language Assessment and SLA Research.

[This corrects the article DOI: 10.3389/fpsyg.2020.01941.].

Visualizing a field of research: A methodology of systematic scientometric reviews.

Systematic scientometric reviews, empowered by computational and visual analytic approaches, offer opportunities to improve the timeliness, accessibility, and reproducibility of studies of the literature of a field of research. On the other hand, effectively and adequately identifying the most representative body of scholarly publications as the basis of subsequent analyses remains a common bottleneck in the current practice. What can we do to reduce the risk of missing something potentially significant? How can we compare different search strategies in terms of the relevance and specificity of topical areas covered? In this study, we introduce a flexible and generic methodology based on a significant extension of the general conceptual framework of citation indexing for delineating the literature of a research field. The method, through cascading citation expansion, provides a practical connection between studies of science from local and global perspectives. We demonstrate an application of the methodology to the research of literature-based discovery (LBD) and compare five datasets constructed based on three use scenarios and corresponding retrieval strategies, namely a query-based lexical search (one dataset), forward expansions starting from a groundbreaking article of LBD (two datasets), and backward expansions starting from a recently published review article by a prominent expert in LBD (two datasets). We particularly discuss the relevance of areas captured by expansion processes with reference to the query-based scientometric visualization. The method used in this study for comparing bibliometric datasets is applicable to comparative studies of search strategies.

Big, Deep, and Smart Data in Scanning Probe Microscopy.

Scanning probe microscopy (SPM) techniques have opened the door to nanoscience and nanotechnology by enabling imaging and manipulation of the structure and functionality of matter at nanometer and atomic scales. Here, we analyze the scientific discovery process in SPM by following the information flow from the tip-surface junction, to knowledge adoption by the wider scientific community. We further discuss the challenges and opportunities offered by merging SPM with advanced data mining, visual analytics, and knowledge discovery technologies.

Emerging trends and new developments in regenerative medicine: a scientometric update (2000 - 2014).

INTRODUCTION: Our previous scientometric review of regenerative medicine provides a snapshot of the fast-growing field up to the end of 2011. The new review identifies emerging trends and new developments appearing in the literature of regenerative medicine based on relevant articles and reviews published between 2000 and the first month of 2014. AREAS COVERED: Multiple datasets of publications relevant to regenerative medicine are constructed through topic search and citation expansion to ensure adequate coverage of the field. Networks of co-cited references representing the literature of regenerative medicine are constructed and visualized based on a combined dataset of 71,393 articles published between 2000 and 2014. Structural and temporal dynamics are identified in terms of most active topical areas and cited references. New developments are identified in terms of newly emerged clusters and research areas. Disciplinary-level patterns are visualized in dual-map overlays. EXPERT OPINION: While research in induced pluripotent stem cells remains the most prominent area in the field of regenerative medicine, research related to clinical and therapeutic applications in regenerative medicine has experienced a considerable growth. In addition, clinical and therapeutic developments in regenerative medicine have demonstrated profound connections with the induced pluripotent stem cell research and stem cell research in general. A rapid adaptation of graphene-based nanomaterials in regenerative medicine is evident. Both basic research represented by stem cell research and application-oriented research typically found in tissue engineering are now increasingly integrated in the scientometric landscape of regenerative medicine. Tissue engineering is an interdisciplinary field in its own right. Advances in multiple disciplines such as stem cell research and graphene research have strengthened the connections between tissue engineering and regenerative medicine.