A bibliometric and knowledge-map analysis of anoikis from 2003 to 2022.

This analysis covers 4494 anoikis-related publications (2003-2022). It explores annual trends, top countries, core journals, leading institutions, keywords, references, authors, and collaborations. Key findings include the United States leading in publications, Chulalongkorn University as the top institution, and Oncogene as the most prolific journal. The Journal of Biological Chemistry holds the highest influence. Burst keywords like "signal transduction," "apoptosis resistance," "metabolism," and "tumor microenvironment" highlight emerging research areas. This study offers a comprehensive overview, aiding researchers in grasping anoikis research trends, contributors, and prospects.

Tracing topics and trends in drug-resistant epilepsy research using a natural language processing-based topic modeling approach.

Epilepsy is a common neurological disorder affecting over 70 million people worldwide. Although many patients achieve seizure control with anti-epileptic drugs (AEDs), 30%-40% develop drug-resistant epilepsy (DRE), where seizures persist despite adequate trials of AEDs. DRE is associated with reduced quality of life, increased mortality and morbidity, and greater socioeconomic challenges. The continued intractability of DRE has fueled exponential growth in research that aims to understand and treat this serious condition. However, synthesizing this vast and continuously expanding DRE literature to derive insights poses considerable difficulties for investigators and clinicians. Conventional review methods are often prolonged, hampering the timely application of findings. More-efficient approaches to analyze the voluminous research are needed. In this study, we utilize a natural language processing (NLP)-based topic modeling approach to examine the DRE publication landscape, uncovering key topics and trends. Documents were retrieved from Scopus, preprocessed, and modeled using BERTopic. This technique employs transformer models like BERT (Bidirectional Encoder Representations from Transformers) for contextual understanding, thereby enabling accurate topic categorization. Analysis revealed 18 distinct topics spanning various DRE research areas. The 10 most common topics, including "AEDs," "Neuromodulation Therapy," and "Genomics," were examined further. "Cannabidiol," "Functional Brain Mapping," and "Autoimmune Encephalitis" emerged as the hottest topics of the current decade, and were examined further. This NLP methodology provided valuable insights into the evolving DRE research landscape, revealing shifting priorities and declining interests. Moreover, we demonstrate an efficient approach to synthesizing and visualizing patterns within extensive literature that could be applied to other research fields.

Simplifying synthesis of the expanding glioblastoma literature: a topic modeling approach.

PURPOSE: Our study aims to discover the leading topics within glioblastoma (GB) research, and to examine if these topics have "hot" or "cold" trends. Additionally, we aim to showcase the potential of natural language processing (NLP) in facilitating research syntheses, offering an efficient strategy to dissect the landscape of academic literature in the realm of GB research. METHODS: The Scopus database was queried using "glioblastoma" as the search term, in the "TITLE" and "KEY" fields. BERTopic, an NLP-based topic modeling (TM) method, was used for probabilistic TM. We specified a minimum topic size of 300 documents and 5% probability cutoff for outlier detection. We labeled topics based on keywords and representative documents and visualized them with word clouds. Linear regression models were utilized to identify "hot" and "cold" topic trends per decade. RESULTS: Our TM analysis categorized 43,329 articles into 15 distinct topics. The most common topics were Genomics, Survival, Drug Delivery, and Imaging, while the least common topics were Surgical Resection, MGMT Methylation, and Exosomes. The hottest topics over the 2020s were Viruses and Oncolytic Therapy, Anticancer Compounds, and Exosomes, while the cold topics were Surgical Resection, Angiogenesis, and Tumor Metabolism. CONCLUSION: Our NLP methodology provided an extensive analysis of GB literature, revealing valuable insights about historical and contemporary patterns difficult to discern with traditional techniques. The outcomes offer guidance for research directions, policy, and identifying emerging trends. Our approach could be applied across research disciplines to summarize and examine scholarly literature, guiding future exploration.

A data-driven analysis to discover research hotspots and trends of technologies for PFAS removal.

The frequent detection of persistent per- and polyfluoroalkyl substances (PFAS) in organisms and environment coupled with surging evidence for potential detrimental impacts, have attracted widespread attention throughout the world. In order to reveal research hotspots and trends of technologies for PFAS removal, herein, we performed a data-driven analysis of 3975 papers and 436 patents from Web of Science Core Collection and Derwent Innovation Index databases up to 2023. The results showed that China and the USA led the way in the research of PFAS removal with outstanding contributions to publications. The progression generally transitioned from accidental discovery of decomposition, to experimentation with removal effects and mechanisms of existing methods, and finally to enhanced defluorination and mechanism-driven design approaches. The keywords co-occurrence network and technology classification together revealed the main knowledge framework, which was constructed and correlated through contaminants, substrates, materials, processes and properties. Moreover, adsorption was demonstrated to be the dominant removal process among the current studies. Subsequently, we concluded the principles, advances and drawbacks of enrichment and separation, biological methods, advanced oxidation and reduction processes. Further exploration indicated the hotspots such as alternatives and precursors for PFAS ("genx": 1.258, "f-53b": 0.337), degradable mineralization technologies ("photocatalytic degrad": 0.529, "hydrated electron": 0.374), environment-friendly remediation technologies ("phytoremedi": 0.939, "constructed wetland": 0.462) and combination with novel materials ("metal-organic framework": 1.115, "layered double hydroxid": 0.559) as well as computer science ("molecular dynamics simul": 0.559, "machine learn"). Furthermore, the future direction of technological innovation might lie in high-performance processes that minimize secondary pollution, the development of recyclable and renewable treatment agents, and collaborative control strategies for multiple pollutants. Overall, this study offers comprehensive and objective review for researchers and industry professionals in this field, enabling rapid access to knowledge guidance and insights into research frontiers.

Advancements and turning point of artificial intelligence in ophthalmology: A comprehensive analysis of research trends and collaborative networks.

Artificial intelligence (AI) has emerged as a transformative force with great potential in various fields, including healthcare. In recent years, AI has garnered significant attention due to its potential to revolutionise ophthalmology, leading to advancements in patient care such as disease detection, diagnosis, treatment and monitoring of disease progression. This study presents a comprehensive analysis of the research trends and collaborative networks at the intersection of AI and ophthalmology. In this study, we conducted an extensive search of the Web of Science Core Collection to identify articles related to 'artificial intelligence' in ophthalmology published from 1968 to 2023. We performed co-occurrence keywords and co-authorship network analyses using VOSviewer software to explore the relationships between keywords and country collaboration. We found a remarkable surge in articles applying AI in ophthalmology after 2017, marking a turning point in the integration of AI within the medical field. The primary application of AI shifted towards the diagnosis of ocular disease, which was particularly evident through keywords such as glaucoma, diabetic retinopathy and age-related macular degeneration. Analysis of the collaboration networks of countries revealed a global expansion of ophthalmology-related AI research. This study provides valuable insights into the evolving landscape of AI integration in ophthalmology, indicating its growing potential for enhancing disease detection, diagnosis, treatment planning and monitoring of disease progression. In order to translate AI technologies into clinical practice effectively, it is imperative to comprehend the evolving research trends and advancements at the intersection of AI and ophthalmology.

Exploring trends and developments in cholesteatoma research: a bibliometric analysis.

PURPOSE: Examination of the scientific literature on cholesteatoma from past to present using bibliometric methods. METHODS: A total of 2353 articles on cholesteatoma between 1980 and 2023 were obtained from the Web of Science database and statistically analysed using bibliometric techniques. RESULTS: The journals with the highest number of contributions to the literature were Otology & Neurotology (n = 192), Acta Oto-Laryngologica (156) and Laryngoscope (149). The most impactful journals based on h-index and total citation (TC) counts were Laryngoscope (h:36, TC: 4241), Otology & Neurotology (29, 3141), and American Journal of Otology (26, 2071) respectively. The most prolific author was Kojima H. (n = 49). According to the findings of the Reference Publication Year Spectroscopy (RPYS) analysis, there has been a significant increase in academic interest in Cholesteatoma since its first description in 1959, particularly following the expansion of its definition in 1977. Subsequently, the trend of increased interest has risen rapidly in later years, with notable peaks in research intensity observed in 1989, 2006, and 2011. The highest academic trend occurred in 2006. In recent years, trending topics have included innovative approaches such as diagnostic and imaging methods particularly alongside technological developments, endoscopic surgery, recurrence and complications, prognosis and quality of life, artificial intelligence, and obliteration. CONCLUSION: In conclusion, the evolution of cholesteatoma and its research priorities reveal temporal shifts and emerging areas of interest over time. Primarily, diagnosis, treatment, and surgical approaches remain central. Additionally, research has expanded to encompass developments in surgical techniques, imaging modalities, cellular biology, pathology, and molecular mechanisms.

Current status and development trends in CKD with frailty research from 2000 to 2021: a bibliometric analysis.

INTRODUCTION: The prevalence of chronic kidney disease (CKD) is gradually increasing in the elderly population. At the same time, frailty has become one of the research hotspots in the field of geriatrics. Bibliometric analyses help to understand the direction of a field. Therefore, this study aimed to analyze the status and emerging trends of frailty in CKD patients. DATA AND METHODS: The Web of Science Core Collection (WoSCC) database was screened for relevant literature published between 1 January 2000 and 31 December 2021. Next, publications were analyzed for information including authors, journals, cited references, citing journals, institutions, countries and regions, high-frequency keywords and co-citations using VOSviewer, Microsoft Excel, and R software. RESULTS: A total of 2223 articles were obtained, from which 613 relevant articles were selected based on title and abstract screening. There was an upward trend in the number of annual publications and Johansen KL was considered the most contributing author in the field. The Clinical Journal of the American Society of Nephrology was the most productive research journal. Johns Hopkins University is the most published organization. The United States is the global leader in the field and contributes the most to research. Research hotspots focus on epidemiological studies of frailty and frailty intervention. CONCLUSIONS: This study presents a comprehensive bibliometric analysis of CKD and frailty research. Key findings highlight the current focus on early screening and assessment of frailty in CKD patients, as well as physical function interventions in frail patients.

The scientific landscape of phytoremediation of tailings: a bibliometric and scientometric analysis.

This article seeks to evaluate the scientific landscape of the phytoremediation of mine tailings through a series of bibliometric and scientometric techniques. Phytoremediation has emerged as a sustainable approach to remediate metal-contaminated mine waste areas. A scientometric analysis of 913 publications indexed in Web of Science from 1999 to 2023 was conducted using CiteSpace. The results reveal an expanding, interdisciplinary field with environmental sciences as the core category. Keyword analysis of 561 nodes and 2,825 links shows a focus on plant-metal interactions, microbial partnerships, bioavailability, and field validation. Co-citation analysis of 1,032 nodes and 2,944 links identifies seminal works on native species, plant-microbe interactions, and amendments. Temporal mapping of 15 co-citation clusters indicates a progression from early risk assessments and native plant inquiries to integrated biological systems, economic feasibility, and sustainability considerations. Recent trends emphasize multidimensional factors influencing adoption, such as plant-soil-microbe interactions, organic amendments, and field-scale performance evaluation. The findings demonstrate an intensifying translation of phytoremediation from scientific novelty to engineering practice. This quantitative and qualitative analysis of research trends aids in understanding the development of phytoremediation for mine tailings. The results provide valuable insights for researchers and practitioners in this evolving field.

Lewy body dementia: Overcoming barriers and identifying solutions.

Despite its high prevalence among dementias, Lewy body dementia (LBD) remains poorly understood with a limited, albeit growing, evidence base. The public-health burden that LBD imposes is worsened by overlapping pathologies, which contribute to misdiagnosis, and lack of treatments. For this report, we gathered and analyzed public-domain information on advocacy, funding, research outputs, and the therapeutic pipeline to identify gaps in each of these key elements. To further understand the current gaps, we also conducted interviews with leading experts in regulatory/governmental agencies, LBD advocacy, academic research, and biopharmaceutical research, as well as with funding sources. We identified wide gaps across the entire landscape, the most critical being in research. Many of the experts participated in a workshop to discuss the prioritization of research areas with a view to accelerating therapeutic development and improving patient care. This white paper outlines the opportunities for bridging the major LBD gaps and creates the framework for collaboration in that endeavor. HIGHLIGHTS: A group representing academia, government, industry, and consulting expertise was convened to discuss current progress in Dementia with Lewy Body care and research. Consideration of expert opinion,natural language processing of the literature as well as publicly available data bases, and Delphi inspired discussion led to a proposed consensus document of priorities for the field.

The evolutionary paths of clinical psychology studies in the United States: A bibliometrics illustration of 69k publications.

OBJECTIVE: We aim to uncover the hot topics and development trends in clinical psychology research in the United States. METHOD: Utilizing bibliometric analysis, we examined clinical psychology papers published in the United States from 2010 to 2022 in the Web of Science database, employing citation analysis, content analysis, author analysis, and journal analysis. RESULTS: Our analysis revealed a significant increase in clinical psychology research, notably catalyzed by the 2019 COVID-19 pandemic. This surge was particularly evident in studies addressing mental disorders such as PTSD, anxiety, and suicidal behaviors, as well as psychological trauma related to issues like family conflict, elder abuse, and collective trauma. Furthermore, there was a distinct shift towards studying diverse populations, including gender and racial minorities, mothers, and adolescents. Therapeutic approaches, such as mindfulness-based practices and AI-assisted technologies, also gained prominence. CONCLUSIONS: This study represents the first large-scale bibliometric analysis in the field of clinical psychology in the United States. Our findings suggest that the COVID-19 pandemic highlights the importance of studying psychological issues linked to major events. Also, researchers are increasingly focusing on minority groups. This trend, along with the use of new technologies like big data and artificial intelligence, guides future research in clinical psychology. PUBLIC HEALTH SIGNIFICANCE STATEMENT: The works in this review suggest that the changing landscape of clinical psychology, especially post-COVID-19. The increased research post-pandemic emphasizes addressing psychological trauma arising from societal issues like family conflicts, elder abuse, and collective trauma. A positive shift towards inclusivity is evident in research, focusing on diverse groups like gender minorities, racial minorities, mothers, and adolescents. Additionally, the amplified focus on mental disorders like PTSD, anxiety, and suicidal behaviors during the pandemic stresses the need for tailored interventions and support systems. Exploring innovative methods such as mindfulness-based practices and AI-assisted technologies showcases the field's adaptability in mental health interventions.

Analysis of dementia research trends in nursing using text mining approach.

This research aimed to analyze dementia research trends in nursing using a text mining approach. In this study, text mining approach was used to analyze the studies on dementia in the field of nursing around the world. In this study, summaries of all available publications in English between the years 1990-2023 were extracted from the database and transferred to an Excel table by using the 'Nursing' AND 'Dementia' query from the Thomson Reuters-ISI Web of Science database. According to the results of this research, a total of 21 concept groups were identified, four of which were the most important. It was determined that the most frequently used word in all of the studies obtained from the database was 'cost'. In this direction, scientific research on dementia in the field of nursing can be planned by evaluating the most frequently used concepts.

Does medical waste research during COVID-19 meet the challenge induced by the pandemic to waste management?

The COVID-19 pandemic has resulted in an unprecedented amount of medical waste, presenting significant challenges for the safe disposal of hazardous waste. A systematic review of existing research on COVID-19 and medical waste can help address these challenges by providing insights and recommendations for effective management of the massive medical waste generated during the pandemic. This study utilized bibliometric and text mining methods to survey the scientific outcomes related to COVID-19 and medical waste, drawing on data from the Scopus database. The results show that the spatial distribution of medical waste research is unbalanced. Surprisingly, developing countries rather than developed countries lead research in this area. Especially, China, a major contributor to the field, has the highest number of publications and citations, and is also a centre of international cooperation. The main study authors and research institutions are also mainly from China. And the research on medical waste is a multidisciplinary field. Text mining analysis shows that COVID-19 and medical waste research is mainly organized around four themes: (i) medical waste from personal protective equipment; (ii) research on medical waste in Wuhan, China; (iii) threats of medical waste to the environment and (iv) disposal and management of medical waste. This would serve to better understand the current state of medical waste research and to provide some implications for future research.

A Systematic Review of Lipid-Focused Cardiovascular Disease Research: Trends and Opportunities.

Lipids are important modifiers of protein function, particularly as parts of lipoproteins, which transport lipophilic substances and mediate cellular uptake of circulating lipids. As such, lipids are of particular interest as blood biological markers for cardiovascular disease (CVD) as well as for conditions linked to CVD such as atherosclerosis, diabetes mellitus, obesity and dietary states. Notably, lipid research is particularly well developed in the context of CVD because of the relevance and multiple causes and risk factors of CVD. The advent of methods for high-throughput screening of biological molecules has recently resulted in the generation of lipidomic profiles that allow monitoring of lipid compositions in biological samples in an untargeted manner. These and other earlier advances in biomedical research have shaped the knowledge we have about lipids in CVD. To evaluate the knowledge acquired on the multiple biological functions of lipids in CVD and the trends in their research, we collected a dataset of references from the PubMed database of biomedical literature focused on plasma lipids and CVD in human and mouse. Using annotations from these records, we were able to categorize significant associations between lipids and particular types of research approaches, distinguish non-biological lipids used as markers, identify differential research between human and mouse models, and detect the increasingly mechanistic nature of the results in this field. Using known associations between lipids and proteins that metabolize or transport them, we constructed a comprehensive lipid-protein network, which we used to highlight proteins strongly connected to lipids found in the CVD-lipid literature. Our approach points to a series of proteins for which lipid-focused research would bring insights into CVD, including Prostaglandin G/H synthase 2 (PTGS2, a.k.a. COX2) and Acylglycerol kinase (AGK). In this review, we summarize our findings, putting them in a historical perspective of the evolution of lipid research in CVD.

Deciphering drug discovery and microbial pathogenesis research in tuberculosis during the two decades of postgenomic era using entity mining approach.

We examined literature on Mycobacterium tuberculosis (Mtb) subsequent to its genome release, spanning years 1999-2020. We employed scientometric mapping, entity mining, visualization techniques, and PubMed and PubTator databases. Most popular keywords, most active research groups, and growth in quantity of publications were determined. By gathering annotations from the PubTator, we determined direction of research in the areas of drug hypersensitivity, drug resistance (AMR), and drug-related side effects. Additionally, we examined the patterns in research on Mtb metabolism and various forms of tuberculosis, including skin, brain, pulmonary, extrapulmonary, and latent tuberculosis. We discovered that 2011 had the highest annual growth rate of publications, at 19.94%. The USA leads the world in publications with 18,038, followed by China with 14,441, and India with 12,158 publications. Studies on isoniazid and rifampicin resistance showed an enormous increase. Non-tuberculous mycobacteria also been the subject of more research in effort to better understand Mtb physiology and as model organisms. Researchers also looked at co-infections like leprosy, hepatitis, plasmodium, HIV, and other opportunistic infections. Host perspectives like immune response, hypoxia, and reactive oxygen species, as well as comorbidities like arthritis, cancer, diabetes, and kidney disease etc. were also looked at. Symptomatic aspects like fever, coughing, and weight loss were also investigated. Vitamin D has gained popularity as a supplement during illness recovery, however, the interest of researchers declined off late. We delineated dominant researchers, journals, institutions, and leading nations globally, which is crucial for aligning ongoing and evolving landscape of TB research efforts. Recognising the dominant patterns offers important information about the areas of focus for current research, allowing biomedical scientists, clinicians, and organizations to strategically coordinate their efforts with the changing priorities in the field of tuberculosis research.

Use of topic modeling to assess research trends in the journal Gynecologic Oncology.

STUDY OBJECTIVE: There is scant research identifying thematic trends within medical research. This work may provide insight into how a given field values certain topics. We assessed the feasibility of using a machine learning approach to determine the most common research themes published in Gynecologic Oncology over a thirty-year period and to subsequently evaluate how interest in these topics changed over time. METHODS: We retrieved the abstracts of all original research published in Gynecologic Oncology from 1990 to 2020 using PubMed. Abstract text was processed through a natural language processing algorithm and clustered into topical themes using latent Dirichlet allocation (LDA) prior to manual labeling. Topics were investigated for temporal trends. RESULTS: We retrieved 12,586 original research articles, of which 11,217 were evaluable for subsequent analysis. Twenty-three research topics were selected at the completion of topic modeling. The topics of basic science genetics, epidemiologic methods, and chemotherapy experienced the greatest increase over the time period, while postoperative outcomes, reproductive age cancer management, and cervical dysplasia experienced the greatest decline. Interest in basic science research remained relatively constant. Topics were additionally reviewed for words indicative of either surgical or medical therapy. Both surgical and medical topics saw increasing interest, with surgical topics experiencing a greater increase and representing a higher proportion of published topics. CONCLUSIONS: Topic modeling, a type of unsupervised machine learning, was successfully used to identify trends in research themes. The application of this technique provided insight into how the field of gynecologic oncology values the components of its scope of practice and therefore how it may choose to allocate grant funding, disseminate research, and participate in the public discourse.

Research trends on vonoprazan-based therapy for Helicobacter pylori eradication: A bibliometric analysis from 2015 to 2023.

BACKGROUND: Vonoprazan is an emerging option for the treatment of Helicobacter pylori infection. We aimed to assess the research trends and hotspots of vonoprazan-based therapy for H. pylori eradication through bibliometric analysis. MATERIALS AND METHODS: Vonoprazan-based studies for eradicating H. pylori published from 2015 to 2023 were extracted from the Web of Science using a combination of the search terms "H. pylori" and "vonoprazan." Each study was weighted according to the number of included patients. RESULTS: A total of 65 studies were included. Japan was the most productive and cooperative country, accounting for 69.2% of publications. Vonoprazan in combination with amoxicillin and clarithromycin (41.8%) was most used for eradicating H. pylori, followed by vonoprazan in combination with amoxicillin (20.4%) and vonoprazan in combination with amoxicillin and metronidazole (19.4%). The eradication rates for first-line vonoprazan-based therapies by intention to treat were: dual therapy (82.9%, 95% CI: 77.7%-88.0%), triple (83.3%, 95% CI: 79.7%-86.8%) and quadruple therapy (91.5%, 95% CI: 85.5%-97.4%), and per protocol: dual therapy (86.1%, 95% CI: 81.5%-90.7%), triple (89.3%, 95% CI: 87.9%-90.6%) and quadruple therapy (94.0%, 95% CI: 88.6%-99.4%). Vonoprazan was superior to proton pump inhibitors in triple therapy regarding empirical therapy (RR = 1.18, 95% CI, 1.14-1.22, p < 0.01) and clarithromycin-resistant group (RR = 1.71, 95% CI, 1.33-2.20, p < 0.01), but there is no significant difference between triple therapy and dual therapy (RR = 1.02, 95% CI, 0.98-1.07, p = 0.33). CONCLUSIONS: Vonoprazan has been widely used for H. pylori eradication. Further studies are needed to optimize the best duration and dosage of vonoprazan-based regimens in different regions.

Research trends on the relationship between Helicobacter pylori and microbiota: A bibliometric analysis.

BACKGROUND: Increasing evidence has indicated that Helicobacter pylori infection is associated with the complex microbiota in the digestive tract of the human body. We aimed to assess the research trends and hotspots in the field of H. pylori and microbiota using a quantitative method. MATERIALS AND METHODS: The clinical studies on H. pylori and microbiota published from 2001 to 2022 were extracted from the Web of Science database. We visualized and analyzed countries/regions, institutions, authors, journals, and keywords through VOSviewer and CiteSpace software. The test techniques, specimen type, as well as microbiota variation after H. pylori infection and eradication were also evaluated. RESULTS: A total of 98 publications were finally identified, and the number of annual papers increased gradually. China showed its dominant position in the publication outputs, and Nanchang University was the most productive institution. Cong He, Xu Shu, and Yin Zhu published the highest number of papers, whereas Helicobacter was the most productive journal. "Helicobacter pylori" ranked highest in the keyword occurrences. 16S rRNA gene sequencing was the most frequently used method for microbiota analysis. Fecal samples had the highest frequency of use, followed by gastric mucosa and saliva. H. pylori infection was associated with the alterations of microbiota through the digestive tract, characterized by the enrichment of Helicobacter in the stomach. Triple and quadruple therapy were the most utilized eradication regimens, and probiotics supplementation therapy has been proven to reduce side effects and restore microbial diversity. CONCLUSIONS: This bibliometric analysis provides an overview of advancements in the field of H. pylori and microbiota. While numerous studies have been conducted on the correlation between H. pylori and the alterations of microbiota, future research is warranted to investigate the mechanisms underlying the interplay between H. pylori and other microbes in the development of related diseases.

A bibliometric analysis of media coverage of mental disorders between 2002 and 2022.

PURPOSE: Mental disorders are a public health concern with media as a primary source of information. This study aims to present current research on how the media reports mental disorders. METHODS: This study conducted a bibliometric analysis of 130 published studies on media coverage of mental disorders, focusing on the research themes and research trends of the published studies. RESULTS: The results show that media coverage of mental disorders was generally negative and influenced by socio-demographic factors (e.g., age, gender, and cultural values). Results also show that social media was becoming crucial for mental health communication. CONCLUSION: Media worsen the stigma against those with mental disorders, continuing prejudice and discrimination. The field has shifted from traditional media to social media, and from general to specific mental disorder descriptions.

Bibliometric Review of the Literature on Cone Snail Peptide Toxins from 2000 to 2022.

The venom of marine cone snails is mainly composed of peptide toxins called conopeptides, among which conotoxins represent those that are disulfide-rich. Publications on conopeptides frequently state that conopeptides attract considerable interest for their potent and selective activity, but there has been no analysis yet that formally quantifies the popularity of the field. We fill this gap here by providing a bibliometric analysis of the literature on cone snail toxins from 2000 to 2022. Our analysis of 3028 research articles and 393 reviews revealed that research in the conopeptide field is indeed prolific, with an average of 130 research articles per year. The data show that the research is typically carried out collaboratively and worldwide, and that discoveries are truly a community-based effort. An analysis of the keywords provided with each article revealed research trends, their evolution over the studied period, and important milestones. The most employed keywords are related to pharmacology and medicinal chemistry. In 2004, the trend in keywords changed, with the pivotal event of that year being the approval by the FDA of the first peptide toxin drug, ziconotide, a conopeptide, for the treatment of intractable pain. The corresponding research article is among the top ten most cited articles in the conopeptide literature. From the time of that article, medicinal chemistry aiming at engineering conopeptides to treat neuropathic pain ramped up, as seen by an increased focus on topological modifications (e.g., cyclization), electrophysiology, and structural biology.

The intellectual base and global trends in inflammation of diabetic kidney disease: a bibliometric analysis.

Diabetic kidney disease (DKD) is a severe complication of diabetes mellitus (DM). The literature on DKD inflammation research has experienced substantial growth. However, there is a lack of bibliometric analyses. This study aimed to examine the existing research on inflammation in DKD by analyzing articles published in the Web of Science Core Collection (WOSCC) over the past 30 years. We conducted a visualization analysis using several software, including CiteSpace and VOSviewer. We found that the literature on inflammation research in DKD has experienced substantial growth, indicating a rising interest in this developing area of study. In this field, Navarro-Gonzalez, JF is the most frequently cited author, Kidney International is the most frequently cited journal, China had the highest number of publications in the field of DKD inflammation, and Monash University emerged as the institution with the most published research. The research area on inflammation in DKD primarily centers around the investigation of 'Glycation end-products', 'chronic kidney disease', and 'diabetic nephropathy'. The emerging research trends in this field will focus on the 'Gut microbiota', 'NLRP3 inflammasome', 'autophagy', 'pyroptosis', 'sglt2 inhibitor', and 'therapeutic target'. Future research on DKD may focus on further exploring the inflammatory response, identifying specific therapeutic targets, studying biomarkers, investigating stem cell therapy and tissue engineering, and exploring gene therapy and gene editing. In summary, this study examines the main areas of study, frontiers, and trends in DKD inflammation, which have significant implications for future research.