The current state of inflammation-related research in prostate cancer: a bibliometric analysis and systematic review.

Background: Prostate cancer (PCa) is the second most prevalent malignancy among men globally. The diagnosis, treatment, and prognosis of prostate cancer frequently fall short of expectations. In recent years, the connection between inflammation and prostate cancer has attracted considerable attention. However, there is a lack of bibliometric studies analyzing the research on inflammation within the domain of prostate cancer. Research methods: We utilized the Web of Science Core Collection (WOSCC) as our data source to extract articles and reviews related to inflammation in prostate cancer, published up until April 12, 2024. The collected data underwent meticulous manual screening, followed by bibliometric analysis and visualization using the Biblioshiny package in R. Results: This study encompasses an analysis of 2,786 papers focusing on inflammation-related research within the realm of prostate cancer. Recent years have seen a significant proliferation of publications in this area, with the United States and China being the foremost contributors. The most prolific author in this domain is Demarzoam, with Johns Hopkins University standing out as the most influential institution. The leading journal in disseminating these studies is PROSTATE. Keyword co-occurrence analysis reveals that 'inflammation-related biomarkers', 'inflammation index', and 'tumor immune microenvironment' represent the current research hotspots and frontiers. Conclusion: The findings of this bibliometric study serve to illuminate the current landscape of inflammation-related research in the field of prostate cancer, while further augmenting the discourse on inflammation-mediated cancer therapeutics. Of particular note is the potential of these discoveries to facilitate a more nuanced understanding among researchers regarding the interplay between inflammation and prostate cancer.

The Role of Visualization in Estimating Cardiovascular Disease Risk: Scoping Review.

BACKGROUND: Supporting and understanding the health of patients with chronic diseases and cardiovascular disease (CVD) risk is often a major challenge. Health data are often used in providing feedback to patients, and visualization plays an important role in facilitating the interpretation and understanding of data and, thus, influencing patients' behavior. Visual analytics enable efficient analysis and understanding of large datasets in real time. Digital health technologies can promote healthy lifestyle choices and assist in estimating CVD risk. OBJECTIVE: This review aims to present the most-used visualization techniques to estimate CVD risk. METHODS: In this scoping review, we followed the Joanna Briggs Institute PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines. The search strategy involved searching databases, including PubMed, CINAHL Ultimate, MEDLINE, and Web of Science, and gray literature from Google Scholar. This review included English-language articles on digital health, mobile health, mobile apps, images, charts, and decision support systems for estimating CVD risk, as well as empirical studies, excluding irrelevant studies and commentaries, editorials, and systematic reviews. RESULTS: We found 774 articles and screened them against the inclusion and exclusion criteria. The final scoping review included 17 studies that used different methodologies, including descriptive, quantitative, and population-based studies. Some prognostic models, such as the Framingham Risk Profile, World Health Organization and International Society of Hypertension risk prediction charts, Cardiovascular Risk Score, and a simplified Persian atherosclerotic CVD risk stratification, were simpler and did not require laboratory tests, whereas others, including the Joint British Societies recommendations on the prevention of CVD, Systematic Coronary Risk Evaluation, and Framingham-Registre Gironí del COR, were more complex and required laboratory testing-related results. The most frequently used prognostic risk factors were age, sex, and blood pressure (16/17, 94% of the studies); smoking status (14/17, 82%); diabetes status (11/17, 65%); family history (10/17, 59%); high-density lipoprotein and total cholesterol (9/17, 53%); and triglycerides and low-density lipoprotein cholesterol (6/17, 35%). The most frequently used visualization techniques in the studies were visual cues (10/17, 59%), followed by bar charts (5/17, 29%) and graphs (4/17, 24%). CONCLUSIONS: On the basis of the scoping review, we found that visualization is very rarely included in the prognostic models themselves even though technology-based interventions improve health care worker performance, knowledge, motivation, and compliance by integrating machine learning and visual analytics into applications to identify and respond to estimation of CVD risk. Visualization aids in understanding risk factors and disease outcomes, improving bioinformatics and biomedicine. However, evidence on mobile health's effectiveness in improving CVD outcomes is limited.

Designing visual hierarchies for the communication of health data.

OBJECTIVES: Visual hierarchy underlies all visual design decisions related to information presentation. This manuscript describes the experience of a multidisciplinary health data visualization and software design team in using visual hierarchy to redesign a hereditary colorectal cancer lab report. MATERIALS AND METHODS: A series of interviews with representative users were conducted to identify target user groups and determine information hierarchy for each user type. Visual elements (eg, size, color, contrast, etc.) were then assigned to mirror the information hierarchy and workflow for each user type. RESULTS: User research identified 2 distinct user groups as consumers of the redesigned lab report. An interactive design employing a 2-level page hierarchy was developed, which stratified the content to support the needs of each user type. CONCLUSIONS: The challenges related to displaying the complex nature of digital and personal health data can be addressed by applying foundational design methods such as visual hierarchy. DISCUSSION: Visual hierarchy, a foundational design principle, can be used by visualization teams to clearly and efficiently present complex datasets associated with healthcare.

Augmenting clinicians' analytical workflow through task-based integration of data visualizations and algorithmic insights: a user-centered design study.

OBJECTIVES: To understand healthcare providers' experiences of using GlucoGuide, a mockup tool that integrates visual data analysis with algorithmic insights to support clinicians' use of patientgenerated data from Type 1 diabetes devices. MATERIALS AND METHODS: This qualitative study was conducted in three phases. In Phase 1, 11 clinicians reviewed data using commercial diabetes platforms in a think-aloud data walkthrough activity followed by semistructured interviews. In Phase 2, GlucoGuide was developed. In Phase 3, the same clinicians reviewed data using GlucoGuide in a think-aloud activity followed by semistructured interviews. Inductive thematic analysis was used to analyze transcripts of Phase 1 and Phase 3 think-aloud activity and interview. RESULTS: 3 high level tasks, 8 sub-tasks, and 4 challenges were identified in Phase 1. In Phase 2, 3 requirements for GlucoGuide were identified. Phase 3 results suggested that clinicians found GlucoGuide easier to use and experienced a lower cognitive burden as compared to the commercial diabetes data reports that were used in Phase 1. Additionally, GlucoGuide addressed the challenges experienced in Phase 1. DISCUSSION: The study suggests that the knowledge of analytical tasks and task-specific visualization strategies in implementing features of data interfaces can result in tools that lower the perceived burden of engaging with data. Additionally, supporting clinicians in contextualizing algorithmic insights by visual analysis of relevant data can positively influence clinicians' willingness to leverage algorithmic support. CONCLUSION: Task-aligned tools that combine multiple data-driven approaches, such as visualization strategies and algorithmic insights, can improve clinicians' experience in reviewing device data.

GeneSurfer Enables Transcriptome-wide Exploration and Functional Annotation of Gene Co-expression Modules in 3D Spatial Transcriptomics Data.

Gene co-expression provides crucial insights into biological functions, however, there is a lack of exploratory analysis tools for localized gene co-expression in large-scale datasets. We present GeneSurfer, an interactive interface designed to explore localized transcriptome-wide gene co-expression patterns in the 3D spatial domain. Key features of GeneSurfer include transcriptome-wide gene filtering and gene clustering based on spatial local co-expression within transcriptomically similar cells, multi-slice 3D rendering of average expression of gene clusters, and on-the-fly Gene Ontology term annotation of co-expressed gene sets. Additionally, GeneSurfer offers multiple linked views for investigating individual genes or gene co-expression in the spatial domain at each exploration stage. Demonstrating its utility with both spatial transcriptomics and single-cell RNA sequencing data from the Allen Brain Cell Atlas, GeneSurfer effectively identifies and annotates localized transcriptome-wide co-expression, providing biological insights and facilitating hypothesis generation and validation.

Improvement in Diagnosis and Management of Nosocomial Pneumonias in a Cardiovascular Surgery Intensive Care Unit: A Multidisciplinary Approach.

BACKGROUND: While criteria for the diagnosis of nosocomial pneumonias exist, objective definitions are a challenge and there is no gold standard for diagnosis. We analyzed the impact of the implementation of a logical, consensus-based diagnostic and treatment protocol for managing nosocomial pneumonias in the cardiovascular surgery intensive care unit (CVS-ICU). METHODS: We conducted a quasi-experimental, interrupted time series analysis to evaluate the impact of a diagnostic and treatment protocol for nosocomial pneumonias in the CVS-ICU. Impacts were measured relative to patient outcomes, diagnostic processes, and antimicrobial stewardship improvement. Descriptive statistics were used to analyze results. RESULTS: Overall, 35 pre-protocol and 39 post-protocol patients were included. Primary clinical variables suggesting pneumonia in pre- and post-protocol patients were new lung consolidation (50% vs. 71%), new leukocytosis (59% vs. 64%), and positive culture (32% vs. 55%). Appropriate diagnostic testing improved (23% vs. 54%, p = 0.008) after protocol implementation. The proportion of patients meeting the criteria for nosocomial pneumonia (77% vs. 87%) was not statistically significant, though more patients in the post-protocol group met probable diagnostic criteria (51% vs. 77%). Duration of therapy was not significantly different (6 days [IQR = 5.0, 10.0] vs. 7 days [IQR = 6.0, 9.0]). CONCLUSIONS: The implementation of a diagnostic and treatment protocol for management of nosocomial pneumonias in the CVS-ICU resulted in improved diagnostic accuracy, advanced antimicrobial and diagnostic stewardship efforts, and laboratory cost savings without an adverse impact on patient-centered outcomes.

TrajVis: a visual clinical decision support system to translate artificial intelligence trajectory models in the precision management of chronic kidney disease.

OBJECTIVE: Our objective is to develop and validate TrajVis, an interactive tool that assists clinicians in using artificial intelligence (AI) models to leverage patients' longitudinal electronic medical records (EMRs) for personalized precision management of chronic disease progression. MATERIALS AND METHODS: We first perform requirement analysis with clinicians and data scientists to determine the visual analytics tasks of the TrajVis system as well as its design and functionalities. A graph AI model for chronic kidney disease (CKD) trajectory inference named DisEase PrOgression Trajectory (DEPOT) is used for system development and demonstration. TrajVis is implemented as a full-stack web application with synthetic EMR data derived from the Atrium Health Wake Forest Baptist Translational Data Warehouse and the Indiana Network for Patient Care research database. A case study with a nephrologist and a user experience survey of clinicians and data scientists are conducted to evaluate the TrajVis system. RESULTS: The TrajVis clinical information system is composed of 4 panels: the Patient View for demographic and clinical information, the Trajectory View to visualize the DEPOT-derived CKD trajectories in latent space, the Clinical Indicator View to elucidate longitudinal patterns of clinical features and interpret DEPOT predictions, and the Analysis View to demonstrate personal CKD progression trajectories. System evaluations suggest that TrajVis supports clinicians in summarizing clinical data, identifying individualized risk predictors, and visualizing patient disease progression trajectories, overcoming the barriers of AI implementation in healthcare. DISCUSSION: The TrajVis system provides a novel visualization solution which is complimentary to other risk estimators such as the Kidney Failure Risk Equations. CONCLUSION: TrajVis bridges the gap between the fast-growing AI/ML modeling and the clinical use of such models for personalized and precision management of chronic diseases.

Research status and hotspots of social frailty in older adults: a bibliometric analysis from 2003 to 2022.

Background: Social Frailty is a significant public health concern affecting the elderly, particularly with the global population aging rapidly. Older adults with social frailty are at significantly higher risk of adverse outcomes such as disability, cognitive impairment, depression, and even death. In recent years, there have been more and more studies on social frailty, but no bibliometrics has been used to analyze and understand the general situation in this field. Therefore, by using CiteSpace, VOSviewer, and Bilioshiny software programs, this study aims to analyze the general situation of the research on social frailties of the older adults and determine the research trends and hot spots. Methods: A bibliometric analysis was conducted by searching relevant literature on the social frailty of the older adults from 2003 to 2022 in the Web of Science core database, using visualization software to map publication volume, country and author cooperation networks, keyword co-occurrences, and word emergence. Results: We analyzed 415 articles from 2003 to 2022. Brazil has the highest number of articles in the field of social frailty of the older adults, and the United States has the highest number of cooperative publications. Andrew MK, from Canada, is the most published and co-cited author, with primary research interests in geriatric assessment, epidemiology, and public health. "Social Vulnerability," "Health," "Frailty," "Mortality," and "Older Adult" are among the research hotspots in this field. "Dementia," "Alzheimer's disease," "Population," and "Covid-19" are emerging research trends in social frailty among the older adults. Conclusion: This scientometric study maps the research hotspots and trends for the past 20 years in social frailty among the older adults. Our findings will enable researchers to better understand trends in this field and find suitable directions and partners for future research.

Designing interactive visualizations for analyzing chronic lung diseases in a user-centered approach.

OBJECTIVES: Medical practitioners analyze numerous types of data, often using archaic representations that do not meet their needs. Pneumologists who analyze lung function exams must often consult multiple exam records manually, making comparisons cumbersome. Such shortcomings can be addressed with interactive visualizations, but these must be designed carefully with practitioners' needs in mind. MATERIALS AND METHODS: A workshop with experts was conducted to gather user requirements and common tasks. Based on the workshop results, we iteratively designed a web-based prototype, continuously consulting experts along the way. The resulting application was evaluated in a formative study via expert interviews with 3 medical practitioners. RESULTS: Participants in our study were able to solve all tasks in accordance with experts' expectations and generally viewed our system positively, though there were some usability and utility issues in the initial prototype. An improved version of our system solves these issues and includes additional customization functionalities. DISCUSSION: The study results showed that participants were able to use our system effectively to solve domain-relevant tasks, even though some shortcomings could be observed. Using a different framework with more fine-grained control over interactions and visual elements, we implemented design changes in an improved version of our prototype that needs to be evaluated in future work. CONCLUSION: Employing a user-centered design approach, we developed a visual analytics system for lung function data that allows medical practitioners to more easily analyze the progression of several key parameters over time.

V4RIN: visual analysis of regional industry network with domain knowledge.

The regional industry network (RIN) is a type of financial network derived from industry networks that possess the capability to describe the connections between specific industries within a particular region. For most investors and financial analysts lacking extensive experience, the decision-support information provided by industry networks may be too vague. Conversely, RINs express more detailed and specific industry connections both within and outside the region. As RIN analysis is domain-specific and current financial network analysis tools are designed for generalized analytical tasks and cannot be directly applied to RINs, new visual analysis approaches are needed to enhance information exploration efficiency. In this study, we collaborated with domain experts and proposed V4RIN, an interactive visualization analysis system that integrates predefined domain knowledge and data processing methods to support users in uploading custom data. Through multiple views in the system panel, users can comprehensively explore the structure, geographical distribution, and spatiotemporal variations of the RIN. Two case studies were conducted and a set of expert interviews with five domain experts to validate the usability and reliability of our system.

Ecological Trait-Based Digital Categorization of Microbial Genomes for Denitrification Potential.

Microorganisms encode proteins that function in the transformations of useful and harmful nitrogenous compounds in the global nitrogen cycle. The major transformations in the nitrogen cycle are nitrogen fixation, nitrification, denitrification, anaerobic ammonium oxidation, and ammonification. The focus of this report is the complex biogeochemical process of denitrification, which, in the complete form, consists of a series of four enzyme-catalyzed reduction reactions that transforms nitrate to nitrogen gas. Denitrification is a microbial strain-level ecological trait (characteristic), and denitrification potential (functional performance) can be inferred from trait rules that rely on the presence or absence of genes for denitrifying enzymes in microbial genomes. Despite the global significance of denitrification and associated large-scale genomic and scholarly data sources, there is lack of datasets and interactive computational tools for investigating microbial genomes according to denitrification trait rules. Therefore, our goal is to categorize archaeal and bacterial genomes by denitrification potential based on denitrification traits defined by rules of enzyme involvement in the denitrification reduction steps. We report the integration of datasets on genome, taxonomic lineage, ecosystem, and denitrifying enzymes to provide data investigations context for the denitrification potential of microbial strains. We constructed an ecosystem and taxonomic annotated denitrification potential dataset of 62,624 microbial genomes (866 archaea and 61,758 bacteria) that encode at least one of the twelve denitrifying enzymes in the four-step canonical denitrification pathway. Our four-digit binary-coding scheme categorized the microbial genomes to one of sixteen denitrification traits including complete denitrification traits assigned to 3280 genomes from 260 bacteria genera. The bacterial strains with complete denitrification potential pattern included Arcobacteraceae strains isolated or detected in diverse ecosystems including aquatic, human, plant, and Mollusca (shellfish). The dataset on microbial denitrification potential and associated interactive data investigations tools can serve as research resources for understanding the biochemical, molecular, and physiological aspects of microbial denitrification, among others. The microbial denitrification data resources produced in our research can also be useful for identifying microbial strains for synthetic denitrifying communities.

TrialView: An AI-powered Visual Analytics System for Temporal Event Data in Clinical Trials.

Randomized controlled trials (RCT) are the gold standards for evaluating the efficacy and safety of therapeutic interventions in human subjects. In addition to the pre-specified endpoints, trial participants' experience reveals the time course of the intervention. Few analytical tools exist to summarize and visualize the individual experience of trial participants. Visual analytics allows integrative examination of temporal event patterns of patient experience, thus generating insights for better care decisions. Towards this end, we introduce TrialView, an information system that combines graph artificial intelligence (AI) and visual analytics to enhance the dissemination of trial data. TrialView offers four distinct yet interconnected views: Individual, Cohort, Progression, and Statistics, enabling an interactive exploration of individual and group-level data. The TrialView system is a general-purpose analytical tool for a broad class of clinical trials. The system is powered by graph AI, knowledge-guided clustering, explanatory modeling, and graph-based agglomeration algorithms. We demonstrate the system's effectiveness in analyzing temporal event data through a case study.

Bibliometric analysis of worldwide research on Polycythemia Vera in the 21st century.

Polycythemia vera (PV) is a myeloproliferative tumor with low incidence and complex symptoms, affecting patients' quality of life and shortening their life span. Since the beginning of the 21st century, there has been an update but a need for uniform consensus regarding diagnosing and treating PV. With the continued interest of researchers in this field, a bibliometric study of PV is necessary. This paper aims to analyze articles on PV through bibliometric software to provide collaborative information and new ideas for researchers in this field. We collected PV-related publications in the Web of Science Core Collection database from 2000 to 2023. The included literature was analyzed using Citespace (6.2.R2), VOSviewer (1.6.19), and Bibliometrix. The study included country/region, institution, authors, journals, keywords, and references, and a visual knowledge network diagram was constructed. Microsoft Excel 2013 was also used for statistical analysis. A total of 1,093 articles were eventually included. The number of PV-related publications has steadily increased from 2000 to the present, with great potential for future growth. The US and US institutions have contributed more to this field, with the US ranking first in the number of publications, total citations, and centrality. Alessandro M. Vannucchi is the most published author. Tefferi, Ayalew is the most cited author. And BLOOD has the most publications, topping the list of the eleven high-productivity core source journals. The most cited article was "Acquired mutation of the tyrosine kinase JAK2 in human myeloproliferative disorders" (Baxter, EJ, 2005). By examining the keywords, we found that the diagnosis and typing of true erythrocytosis, the use of ruxolitinib, and the tyrosine kinase JAK2 are the research hotspots in the field; genetic and molecular research in the field of true erythrocytosis is a cutting-edge topic in the field; and risk factors for true erythrocytosis is a cutting-edge hotspot issue in the field. The fruitful research in this century has laid the foundation for developing the field of PV. The information in this article will provide researchers with current hotspots and future potential in the discipline, helping the field achieve more extraordinary breakthroughs. Currently, research should focus on increasing global multicenter collaborative research in diagnosis and treatment to develop scientifically recognized diagnostic and treatment protocols and new clinical drug research. Our proposed model of global innovation collaboration will provide strong support for future research.

A hyper-knowledge graph system for research on AI ethics cases.

Current studies on the artificial intelligence (AI) ethics focus either on very broad guidelines or on a very special domain. Therefore, the research outcome can hardly be converted into actionable measures or transferred to other domains. Potential correlations between various cases of AI ethics at different granularity levels are unexplored. To overcome these deficiencies, the authors designed a case-oriented ontological model (COOM) and a hyper-knowledge graph system (HKGS) for the research of collected AI ethics cases. COOM describes criteria for modelling cases by attributes from three perspectives: event attributes, relational attributes, and positional attributes on the value chain. Based on it, HKGS stores the correlation between cases as knowledge and allows advanced visual analysis. The correlations between cases and their dynamic changes on value chain can be observed and explored. In HKGS's implementation part, one of the collected ethics cases is used as an example to demonstrate how to generate a hyper-knowledge graph and to visually analyze it. The authors also anticipated how different practitioners of AI ethics, can achieve the desired outputs from HKGS in their diverse scenarios.

Visual Analytics for Data-Driven Understanding of the Substance Use Disorder Epidemic.

The substance use disorder epidemic has emerged as a serious public health crisis, presenting complex challenges. Visual analytics offers a unique approach to address this complexity and facilitate effective interventions. This paper details the development of an innovative visual analytics dashboard, aimed at enhancing our understanding of the substance use disorder epidemic. By employing record linkage techniques, we integrate diverse data sources to provide a comprehensive view of the epidemic. Adherence to responsive, open, and user-centered design principles ensures the dashboard's usefulness and usability. Our approach to data and design encourages collaboration among various stakeholders, including researchers, politicians, and healthcare practitioners. Through illustrative outputs, we demonstrate how the dashboard can deepen our understanding of the epidemic, support intervention strategies, and evaluate the effectiveness of implemented measures. The paper concludes with a discussion of the dashboard's use cases and limitations.

Smart Shelf System for Customer Behavior Tracking in Supermarkets.

Transactional data from point-of-sales systems may not consider customer behavior before purchasing decisions are finalized. A smart shelf system would be able to provide additional data for retail analytics. In previous works, the conventional approach has involved customers standing directly in front of products on a shelf. Data from instances where customers deviated from this convention, referred to as "cross-location", were typically omitted. However, recognizing instances of cross-location is crucial when contextualizing multi-person and multi-product tracking for real-world scenarios. The monitoring of product association with customer keypoints through RANSAC modeling and particle filtering (PACK-RMPF) is a system that addresses cross-location, consisting of twelve load cell pairs for product tracking and a single camera for customer tracking. In this study, the time series vision data underwent further processing with R-CNN and StrongSORT. An NTP server enabled the synchronization of timestamps between the weight and vision subsystems. Multiple particle filtering predicted the trajectory of each customer's centroid and wrist keypoints relative to the location of each product. RANSAC modeling was implemented on the particles to associate a customer with each event. Comparing system-generated customer-product interaction history with the shopping lists given to each participant, the system had a general average recall rate of 76.33% and 79% for cross-location instances over five runs.

The Evolution and Future Trends of Stromal Vascular Fraction: A Bibliometric Analysis.

The heterogeneous population of cells obtained from processed adipose tissue, known as stromal vascular fraction (SVF), exhibits immunomodulatory and angiogenic properties. The therapeutic efficacy of SVF has been substantiated in numerous diseases, instilling hope for its clinical application as a cellular therapy. This study aims to provide a comprehensive analysis of the scholarly literature on SVF, including its worldwide progression, highlighting significant literatures, temporal development, research clusters, current active topics, and emerging trends. The combination of CiteSpace, HistCite Pro, and VOS Viewer tools was used to analyze the SVF literature. The overall panorama of the field is elucidated in terms of publication count, timeline, institutional distribution, journal coverage, and authors' contributions. In addition, this analysis explores the literature and keywords through the lens of co-occurrence, citation, and co-citation frequencies. Clustering algorithms are used to track the trajectory of the literature further, providing insight into its development. The findings offer a comprehensive overview of the progress made in the SVF field, highlighting distinct phases of development: the "Seedling period" from 1980 to 2010, the "Panicle period" from 2011 to 2016, and the "Flowering period" from 2017 to 2023. Within these periods, the evolution of 10 clusters is unraveled, encompassing topics such as vascular disease, CD34 expression, adipose tissue macrophage in 2013, cell-assisted lipotransfer, and knee osteoarthritis. In summary, this bibliometric study, conducting a quantitative analysis of publications in SVF research, encompasses a global overview of research, an analysis of pivotal literature in the field, research hotspots, and emerging frontiers.

Research focus and thematic trends of transient receptor potential vanilloid member 1 research: a bibliometric analysis of the global publications (1990-2023).

Recently, various studies have been devoted to the study of transient receptor potential vanilloid member 1 (TRPV1)-related diseases, potential drugs, and related mechanisms. The objective of this investigation was to examine the significant areas and cutting-edge developments in TRPV1 study within recent decades. Articles or reviews were obtained from the Web of Science Core Collection. VOSviewer 1.6.18 and CiteSpace 6.1 R2 software were utilized to examine publication growth, distribution by country/region, institution, journal, authorship, references, and keywords. The software identified keywords with a high citation burstiness to determine emerging topics. From 1990 to 2023, the annual global publications increased by 62,000%, from 1 to 621. Journal of neuroscience published the most manuscripts and Nature produced the highest citations. The USA, Seoul National University and Di marzo V were the most productive and impactful institution, country, and author, respectively. "TRPV1," "Capsaicin receptor," "Activation," and "Pain" are the most important keywords. The burst keywords "TRPV1 channel," "Oxidative stress," "TRPV1 structure," and "Cancer" are supposed to be the research frontiers. The present study offers valuable insights into the understanding of TRPV1 and pain-related conditions. The research on TRPV1 has demonstrated a steady increase in studies related to pain-related diseases in the past few decades. The significance of TRPV1 in cancer pathogenesis and the resolution of its structure will emerge as a new academic trend in this field, providing direction for more widespread and comprehensive studies in the future.

Machine learning and user interface for cyber risk management of water infrastructure.

With the continuous modernization of water plants, the risk of cyberattacks on them potentially endangers public health and the economic efficiency of water treatment and distribution. This article signifies the importance of developing improved techniques to support cyber risk management for critical water infrastructure, given an evolving threat environment. In particular, we propose a method that uniquely combines machine learning, the theory of belief functions, operational performance metrics, and dynamic visualization to provide the required granularity for attack inference, localization, and impact estimation. We illustrate how the focus on visual domain-aware anomaly exploration leads to performance improvement, more precise anomaly localization, and effective risk prioritization. Proposed elements of the method can be used independently, supporting the exploration of various anomaly detection methods. It thus can facilitate the effective management of operational risk by providing rich context information and bridging the interpretation gap.

Using ExpressAnalyst for Comprehensive Gene Expression Analysis in Model and Non-Model Organisms.

ExpressAnalyst is a web-based platform that enables intuitive, end-to-end transcriptomics and proteomics data analysis. Users can start from FASTQ files, gene/protein abundance tables, or gene/protein lists. ExpressAnalyst will perform read quantification, gene expression table processing and normalization, differential expression analysis, or meta-analysis with complex study designs. The results are presented via various interactive visualizations such as volcano plots, heatmaps, networks, and ridgeline charts, with built-in functional enrichment analysis to allow flexible data exploration and understanding. ExpressAnalyst currently contains built-in support for 29 common organisms. For non-model organisms without good reference genomes, it can perform comprehensive transcriptome profiling directly from RNA-seq reads. These common tasks are covered in 11 Basic Protocols. © 2023 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: RNA-seq count table uploading, processing, and normalization Basic Protocol 2: Differential expression analysis with linear models Basic Protocol 3: Functional analysis with volcano plot, enrichment network, and ridgeline visualization Basic Protocol 4: Hierarchical clustering analysis of transcriptomics data using interactive heatmaps Basic Protocol 5: Cross-species gene expression analysis based on ortholog mapping results Basic Protocol 6: Proteomics and microarray data processing and normalization Basic Protocol 7: Preparing multiple gene expression tables for meta-analysis Basic Protocol 8: Statistical and functional meta-analysis of gene expression data Basic Protocol 9: Functional analysis of transcriptomics signatures Basic Protocol 10: Dose-response and time-series data analysis Basic Protocol 11: RNA-seq reads processing and quantification with and without reference transcriptomes.