Visualization Tools Research Articles

Factors Associated With Delay in Seeking Abortion Care Until the Second Trimester at Jimma University Medical Centre: A Prospective Cross‐Sectional Study Jimma, Southwest Ethiopia

ABSTRACTObjectiveAlthough the overall abortion‐related maternal mortality ratio has shown a downward trend in Ethiopia, the case‐fatality rate from abortion is still high. This study identified factors associated with delays in seeking an abortion until the second trimester at Jimma Medical Centre, Jimma, southwest Ethiopia.MethodsFrom February to August 2023, we conducted a cross‐sectional study on pregnant women seeking safe abortion care. Participants were selected through systematic sampling from a sample size of 260 calculated based on a previous study's prevalence of second‐trimester abortion (19.2%), with 80% power and a 5% margin of error. Participants were selected using systematic sampling and data were collected by an interviewer‐administered questionnaire. P‐value adjusted odds ratios (AORs) with their 95% confidence interval (CI) were used to determine the association between variables.ResultsThe study found a high prevalence of second‐trimester abortion at 58%. Factors significantly associated with delays in seeking abortion services until the second trimester included being from a rural area (AOR = 2.1, 95% CI: 1.2–3.8), experiencing social stigma (AOR = 4.2, 95% CI: 2.9–7.8), being unmarried (AOR = 1.6, 95% CI: 1.0–3.0), having limited knowledge of menstrual periods (AOR = 2.3, 95% CI: 1.2–4.6), delaying suspicion and testing for pregnancy (AOR = 18.2, 95% CI: 2.1–8.9–37.3), postponing the decision to have an abortion by more than 1 week (AOR = 4.9, CI: 1.0–23.7), and being a primigravidae (AOR = 2.1, CI: 1.1–4.1).ConclusionsBeing from a rural area, social stigma surrounding abortion, being unmarried, limited knowledge of menstrual periods, and being a primigravidae were found to be significantly associated with delays in seeking abortion services until the second trimester. A multifaceted approach and efforts should focus on improving access to healthcare resources in rural areas, reducing social stigma surrounding abortion through education and advocacy, and providing comprehensive reproductive health education to enhance knowledge about menstrual cycles and pregnancy testing adapting to local contexts including employing community‐based approaches, using visual and interactive tools, and ensuring cultural sensitivity.

Mastering trust and commitment in the virtual age

Purpose This paper aims to review the latest management developments across the globe and pinpoint practical implications from cutting-edge research and case studies. Design/methodology/approach This briefing is prepared by an independent writer who adds their own impartial comments and places the articles in context. Findings This study investigated virtual team members’ perception of trust and commitment when using computer-mediated communication across different industries. The results showed that communication method significantly impacted trust and commitment levels, while industry type alone did not produce significant differences. Visual communication methods consistently demonstrated higher trust scores across all industries compared to non-visual methods. Key managerial insights emanating from the findings emphasize the importance of strategic tool selection and the universal benefits of visual communication tools in building trust across virtual teams, regardless of industry context. Originality/value The briefing saves busy executives, strategists and researchers hours of reading time by selecting only the very best, most pertinent information and presenting it in a condensed and easy-to-digest format.

Eaglescope: an interactive visualization and cohort selection tool for biomedical data exploration.

Eaglescope: an interactive visualization and cohort selection tool for biomedical data exploration.

Advancements and limitations in polygenic risk score methods for genomic prediction: a scoping review.

This scoping review aims to identify and evaluate the landscape of Polygenic Risk Score (PRS)-based methods for genomic prediction from 2013 to 2023, highlighting their advancements, key concepts, and existing gaps in knowledge, research, and technology. Over the past decade, various PRS-based methods have emerged, each employing different statistical frameworks aimed at enhancing prediction accuracy, processing speed and memory efficiency. Despite notable advancements, challenges persist, including unrealistic assumptions regarding sample sizes and the polygenicity of traits necessary for accurate predictions, as well as limitations in exploring hyper-parameter spaces and considering environmental interactions. We included studies focusing on PRS-based methods for risk prediction that underwent methodological evaluations using valid approaches and released computational tools/software. Additionally, we restricted our selection to studies involving human participants that were published in English language. This review followed the standard protocol recommended by Joanna Briggs Institute Reviewer's Manual, systematically searching Ovid MEDLINE, Ovid Embase, Scopus and Web of Science databases. Additionally, searches included grey literature sources like pre-print servers such as bioRxiv, and articles recommended by experts to ensure comprehensive and diverse coverage of relevant records. This study identified 34 studies detailing 37 genomic prediction methods, the majority of which rely on linkage disequilibrium (LD) information and necessitate hyper-parameter tuning. Nine methods integrate functional/gene annotation, while 12 are suitable for cross-ancestry genomic prediction, with only one considering gene-environment (GxE) interaction. While some methods require individual-level data, most leverage summary statistics, offering flexibility. Despite progress, challenges remain. These include computational complexity and the need for large sample sizes for high prediction accuracy. Furthermore, recent methods exhibit varying effectiveness across traits, with absolute accuracies often falling short of clinical utility. Transferability across ancestries varies, influenced by trait heritability and diversity of training data, while handling admixed populations remains challenging. Additionally, the absence of standard error measurements for individual PRSs, crucial in clinical settings, underscores a critical gap. Another issue is the lack of customizable graphical visualization tools among current software packages. While genomic prediction methods have advanced significantly, there is still room for improvement. Addressing current challenges and embracing future research directions will lead to the development of more universally applicable, robust, and clinically relevant genomic prediction tools.

Performance of an AI-powered visualization software platform for precision surgery in breast cancer patients.

Surgery remains the primary treatment modality in the management of early-stage invasive breast cancer. Artificial intelligence (AI)-powered visualization platforms offer the compelling potential to aid surgeons in evaluating the tumor's location and morphology within the breast and accordingly optimize their surgical approach. We sought to validate an AI platform that employs dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) to render three-dimensional (3D) representations of the tumor and 5 additional chest tissues, offering clear visualizations as well as functionalities for quantifying tumor morphology, tumor-to-landmark structure distances, excision volumes, and approximate surgical margins. This retrospective study assessed the visualization platform's performance on 100 cases with ground-truth labels vetted by 2 breast-specialized radiologists. We assessed features including automatic AI-generated clinical metrics (e.g., tumor dimensions) as well as visualization tools including convex hulls at desired margins around the tumor to help visualize lumpectomy volume. The statistical performance of the platform's automated features was robust and within the range of inter-radiologist variability. These detailed 3D tumor and surrounding multi-tissue depictions offer both qualitative and quantitative comprehension of cancer topology and may aid in formulating an optimal surgical approach for breast cancer treatment. We further establish the framework for broader data integration into the platform to enhance precision cancer care.

The Evolution of Data Visualization Tools and Their Impact on Business Decision-Making : A Historical Analysis

This article examines the transformative evolution of data visualization tools and their profound impact on business decision-making processes. Through a comprehensive analysis of historical developments, theoretical frameworks, and technological advancements, the article explores how visualization tools have progressed from basic manual charts to sophisticated AI-powered analytical platforms. The investigation encompasses the cognitive foundations of visual information processing, the emergence of first-generation Business Intelligence tools, and the current landscape of modern visualization platforms. Particular attention is given to the integration of artificial intelligence in data visualization, including automated generation, pattern recognition, and predictive analytics capabilities. The article presents both quantitative and qualitative assessments of these tools' impact on organizational decision-making, highlighting significant improvements in decision speed, accuracy, and user engagement. The article reveal substantial benefits in terms of ROI, decision-making efficiency, and organizational adoption patterns. The article concludes by examining emerging trends, including the potential of extended reality technologies and edge computing, while acknowledging current limitations and providing recommendations for future research directions in this rapidly evolving field.

Visual Communication Bridging Intercultural Barriers: Collaborative Methods Supporting the Social Inclusion of Young Refugees

This paper explores the possibilities of graphic languages in an intercultural context while analysing the potential of creative methods when working with refugee youth. The practice-based aspect of this research is undertaken with Open Doors Hungary, a community-based design project that creates a programme for unaccompanied refugee minors. The aim of Open Doors is to help young refugees integrate into Hungarian society and develop means of communication and quality relations with the host society. Refugee youth arriving in Hungary face serious problems: the loss of home and community, the traumas suffered during the journey, the cultural differences, and the lack of a common language make their social integration extremely challenging. These challenges are exacerbated by xenophobia and stereotypical opinions about foreigners. Social inclusion requires frequent contact with the local society, and refugee youth need new tools (cultural knowledge, language skills, intercultural skills) to overcome their disadvantages. Since 2013, the author has been working with refugee youth using participatory design tools such as regular creative sessions, workshops, and eight camps based on the integrating role of creativity and art. During these sessions, young refugees and their Hungarian and European peers learned about different visual communication tools and created common messages in the form of videos, animations, photos, posters, booklets, and murals. Through these activities, refugee youth become part of the design process, and the final result is based on their choices. In the theoretical research and field experiences, this paper states that design thinking and the creative process allow interactive cultural exchange, value sharing, safe and open discussions, and community building. As a global language, visual communication can be a tool for intercultural communication, storytelling, and self-expression. The opportunity for refugee youth and their Hungarian peers to work in intercultural teams develops new competencies and creates shared ownership and community.

Inferring TLB Configuration with Performance Tools

Modern computing systems are primarily designed for maximum performance, which inadvertently introduces vulnerabilities at the micro-architecture level. While cache side-channel analysis has received significant attention, other Central Processing Units (CPUs) components like the Translation Lookaside Buffer (TLB) can also be exploited to leak sensitive information. This paper focuses on the TLB, a micro-architecture component that is vulnerable to side-channel attacks. Despite the coarse granularity at the page level, advancements in tools and techniques have made TLB information leakage feasible. The primary goal of this study is not to demonstrate the potential for information leakage from the TLB but to establish a comprehensive framework to reverse engineer the TLB configuration, a critical aspect of side-channel analysis attacks that have previously succeeded in extracting sensitive data. The methodology involves detailed reverse engineering efforts on Intel CPUs, complemented by analytical tools to support TLB reverse engineering. This study successfully reverse-engineered the TLB configurations for Intel CPUs and introduced visual tools for further analysis. These results can be used to explore TLB vulnerabilities in greater depth. However, when attempting to apply the same methodology to the IBM Power9, it became clear that the methodology was not transferable, as mapping functions and performance counters vary across different vendors.

The Learning Wave

Today’s learners are immersed in engaging virtual activities such as video games, social media, and streaming services. These platforms set high expectations for user engagement, providing experiences that are user-centred and intrinsically motivating. Online learning must now compete with these digital environments for learners' attention, necessitating a transformation in its approach to increase retention and satisfaction. The Learning Wave is a novel Learning Experience Design framework that addresses this challenge by two key psychological theories: Self-Determination Theory (SDT) and Flow Theory. SDT, developed by Deci and Ryan (2000), posits that intrinsic motivation is driven by three psychological needs: autonomy, competence, and relatedness. Flow Theory, introduced by Csikszentmihalyi (1990), is a highly functional state helping learners boost their academic performance through immersion in the activity and provides incentives for personal growth (Rodriguez-Ardura & Meseguer-Artola 2016). By applying these theories, The Learning Wave generates learning experiences that positively impact learning behaviour through enhanced engagement and motivation. The framework applies strategies from digital and serious games, combining them with storytelling techniques and instructional design models to create a modern pedagogical approach to the design of online courses. At its core, The Learning Wave utilises a three-phase pattern: learn, reflect, and challenge. This structure enables learners to progressively build understanding, review and reflect on their learning, and apply their knowledge and skills to real-world scenarios. The framework employs Semantic Waves (Maton, 2013) as a construction methodology, ensuring a thoughtful progression of the learner from basic understanding to advanced application of concepts, and facilitating the creation of Flow states by gradually increasing complexity to match learner skill development. Complementing the learner experience is the use of storytelling. By incorporating narrative elements, the learning experience is enriched with contextual depth, diverse perspectives, and emotional engagement, factors known to enhance retention and application of knowledge (Landrum et al., 2019). This approach also supports the relatedness aspect of SDT, fostering a sense of connection to the learning material. As a visual tool, The Learning Wave allows instructional designers to map out the entire learning experience, illustrating how learners progress through different phases. It incorporates a series of learning moments, points of reflection, and challenge events, charting the learner's journey from novice to competent problem-solver. The framework's flexibility accommodates both high-level designs and intricate, complex experiences. The Learning Wave has been incorporated as the foundational pedagogy in the development of twelve postgraduate microcredentials at Western Sydney University, with qualitative and quantitative feedback showing learners are motivated and more confident with the knowledge and skills they are developing. Further analytics, articles, case studies, and publications are forthcoming. This poster presentation demonstrates how The Learning Wave framework transforms online learning. It showcases the 3-stage pattern, alignment with storytelling techniques, and process of gaining strategic knowledge through information deconstruction and reconstruction. It highlights how this approach, grounded in educational theories and modern engagement techniques, can enhance learner motivation, improve knowledge retention, and lead to more effective online educational experiences in our digital world.

Visualizing the Philippines’ SDG 11.3.1 Status and Progress Using Open-Source Geospatial Software and Earth Observation Datasets

Abstract. This paper presents the development and implementation of a web GIS-based application designed to visualize the status and progress of Sustainable Development Goals (SDGs). Focusing specifically on SDG 11.3.1, which pertains to sustainable urbanization, we propose a prototype (accessible at https://ccgeoinformatics.github.io/sdg1131ph/) that leverages open-source geospatial software and earth observation (EO) datasets. By following the "where-when-what-how" framework of SDG visualization system development, we address the need for effective visualization methods while showcasing the potential of accessible datasets and technology in supporting sustainable development monitoring efforts across different administrative levels. We demonstrate the feasibility of the system in aiding policymakers and stakeholders in their decision-making processes toward achieving the SDGs by 2030. Additionally, our work exemplified how open-source software and EO datasets can address the challenges associated with SDG reporting, particularly in areas where both data and visualization tools are scarce or inaccessible. This paper also contributes to the ongoing discourse on SDG visualization and underscores the importance of localized analysis and visualization in understanding and addressing the challenges of sustainable urbanization using the example of the Philippines.

Bibliometric analysis of research hotspots and trends on the relationship between the gut microbiota and depression from 2020 to 2024

IntroductionIn recent years, an increasing body of research has illustrated a strong correlation between gut microbiota and depression. However, there has yet to be a comprehensive discussion or summary of the latest advancements and trends in this field.MethodsWe retrieved research articles focused on gut microbiota and depression through the WOS database from 2020 to 2024, using visual text analysis tools such as CiteSpace and VOSviewer.ResultsThe literature on the relationship between gut microbiota and depression surged from 396 papers in 2020 to 711 by 2024. During this period, the journal with the highest publication rate was Nutrients. China led the countries in contributions, while University College Cork topped the institutions. Kenji Hashimoto emerged as the most prolific author. The most cited paper was authored by Cryan JF et al., published in 2019 in Physiol Rev. The keywords “gut microbiota,” “depression,” and “anxiety” appeared most frequently, while recent years saw explosive increases in terms such as “growth performance,” “receptors,” “depression-like phenotypes,” “stress response,” “gastrointestinal symptoms,” “reliability,” and “neurogenesis.”DiscussionOur article displayed the overview of the relationship between the gut microbiome and depression from 2020 to 2024 using bibliometric methods, providing perspectives and research hotspots for studies exploring the correlation between the gut microbiome and depression.

A bibliometric analysis and visualization of non-suicidal self-injury in adolescents

BackgroundNon-suicidal self-injury(NSSI)is a widespread occurrence among adolescents, and this behavior can bring serious consequences. In recent years, the prevalence of NSSI continues to rise, which has attracted the attention of many researchers. But currently there is no research exploring the overall research distribution of NSSI in adolescents through quantitative analysis. Therefore, it is necessary to understand the status of development and main research hotspots of NSSI in adolescents via bibliometric analysis.MethodsWe searched the relevant studies from the Web of Science Core Collection(WoSCC)from January 1, 2014 to December 31, 2023. Using CiteSpace and VOSviewer visual analysis tools, we analyzed studies from the perspectives of country, region, institution, journal, author, and keywords.ResultA total of 2177 studies related to NSSI in adolescents were included. USA and Harvard University were the leading country and institution in this research field. Penelope Hasking was the most prolific author. Frontiers in Psychiatry and the Journal of Affective Disorders were the most productive journals. The most high-frequency keywords were ‘depression’, ‘mental health’, ‘emotion regulation’ and ‘borderline personality disorder’. ‘mindfully’, ‘intervention’, ‘self-compassion’ and ‘ecological momentary assessment’ were the emerging keywords.ConclusionsExploring the relevant factors and mechanisms of comorbidities, identifying etiology and risk/protective factors, and finding the impact of NSSI on adolescents are the hot topics. Moreover, intervention measures and interdisciplinary collaborative research for NSSI in adolescents will emerge as frontiers in the future.

Assuring and Improving Learning Outcomes

Universities have a responsibility to measure and assure graduate learning outcomes ("Higher Education Standards Framework (Threshold Standards)," 2021; TEQSA, 2017). However, while assuring learning outcomes is a priority across the sector, many universities fail to collect meaningful data on student achievement of these outcomes (Martin & Mahat, 2017). Some institutions are investigating technologically enhanced processes to improve the efficiency and accuracy of learning-achievement data (Burrack & Musselwhite Thompson, 2021; El Marsafawy, 2022). Over the past four years, the University of Technology Sydney (UTS) Business School has made substantial enhancements to assurance of learning (AoL) practices, leveraging learning management system (LMS) functionality and data visualisation software to create a streamlined process, which better facilitates the continuous improvement of programs. This work is of particular interest to academic and professional staff involved in collecting and reporting on student achievement data, in program continuous improvement and evaluation, or in leveraging technology-enhanced learning analytics. The UTS Business School’s AACSB accreditation necessitates the collection, tracking, and reporting of student achievement of program-level learning outcomes (AACSB, 2020). Each program identifies subjects to "develop" and "assure" learning outcomes, with student achievement measured within specific assessment tasks and collated for program-level analysis. This process can be administratively complex, and historically involved multiple manually collated spreadsheets. This represented a substantial time cost for the faculty and often resulted in unreliable data. It was, for example, common practice to report overall subject marks rather than the specific learning outcome to determine achievement level, resulting in assessment practices that diverged from the recommended standards of constructive alignment (Biggs, 2014). In 2021, faculty T&L and central learning technologies teams commenced a pilot using LMS tools and the interactive data visualization tool PowerBI to streamline data collection, collation, and visualisation. Canvas Outcomes set up at the faculty level allows for centralised achievement-data collection, which, when linked to student and program information in PowerBI, can be used to obtain program-level insights. Beyond administrative efficiency, the project was also designed to improve and foreground the constructive alignment (Biggs, 2014) of measured learning outcomes. The revised process requires the embedding of learning-outcome-aligned assessment criteria within rubrics, resulting in improved constructive alignment and data transparency. The initial pilot was evaluated using Human Centred Design (IDEO.org, 2015) methods, which concluded the new system was an improvement on previous processes. Since the pilot, the faculty has completed a full rollout to over 60 subjects and 20 programs. Once this foundational AoL process was in place, program and subject-level continuous-improvement projects could be built upon it. Previous reporting methods had limited usefulness to Program Directors and were no longer fit for purpose due to changes in AACSB requirements. Reporting processes were therefore adjusted to focus on continuous improvement of key program metrics. In the new process, Program Directors and teaching teams review program data to identify curriculum or student-experience improvements that will enhance student achievement of learning outcomes. For example, a master's program identified that a high percentage of students were falling below expectations in two outcomes. Investigating the causes identified a need for substantial changes, with new subjects taking assurance responsibilities, and revisions to the program's overall assessment strategy. Recent data shows significant improvements in the target outcomes. This presentation will outline these updated technology-enhanced AoL processes demonstrating how the collected data can be used to improve university programs and the student learning experience.

Exploring mediagrams as a visual tool to encourage teacher critical multilingual awareness through parent-school interaction in Norway

Teachers hold a key position for processes of inclusion and exclusion in parent-teacher relationships. Through teachers’ critical analysis of their own contributions in these processes, inclusion can be enhanced. This article explores how a visual representation of a person’s linguistic and media repertoires, a mediagram, can foster such teacher critical awareness. More specifically, it investigates the research question: How can mediagrams contribute to teachers’ critical awareness related to existing language ideologies in parent-teacher interaction in multilingual settings? To answer this question, an innovative use of the mediagram, where pupils and parents created a shared mediagram, is presented and discussed. Qualitative in-depth interviews with three teachers and four parents who took part in the activity are analysed. Interviews with the teachers are explored in terms of different dimensions of language awareness before and after implementation of the mediagram activity, while interviews with parents complement the teachers’ perspectives with parents’ perspectives. The study shows that the mediagram has promising potential to enhance teacher critical multilingual awareness in an extended understanding of the concept which also includes digital resources in interaction. Yet, it also points to important discrepancies in the parents’ and teachers’ evaluations of the tool.

DbPTM 2025 update: comprehensive integration of PTMs and proteomic data for advanced insights into cancer research.

Post-translational modifications (PTMs) are essential for modulating protein function and influencing stability, activity and signaling processes. The dbPTM 2025 update significantly expands the database to include over 2.79 million PTM sites, of which 2.243 million are experimentally validated from 48 databases and over 80 000 research articles. This version integrates proteomic data from 13 cancer types, with a particular focus on phosphoproteomic data and kinase activity profiles, allowing the exploration of personalized phosphorylation patterns in tumor samples. Integrating kinase-substrate phosphorylations with E3 ligase-substrate interactions, dbPTM 2025 provides a detailed map of PTM regulatory networks, offering insights into cancer-specific post-translational regulations. This update also includes advanced search capabilities, enabling users to efficiently query PTM data across species, PTM typesand modified residues. The platform's new features-interactive visualization tools and streamlined data downloads-allow researchers to access and analyze PTM data easily. dbPTM 2025 also enhances functional annotations, regulatory networks and disease associations, broadening its application for cancer research and the study of disease-associated PTMs. Through these enhancements, dbPTM 2025 is a comprehensive, user-friendly resource, facilitating the study of PTMs and their roles in cancer research. The database is now freely accessible at https://biomics.lab.nycu.edu.tw/dbPTM/.

CTNI-16. MONOGERM, A NOVEL PROOF-OF-PRINCIPLE BAYESIAN PHASE II TRIAL DESIGN OF CARBOPLATIN OR VINBLASTINE MONOTHERAPY INDUCTION PRIOR TO RADIOTHERAPY FOR INTRACRANIAL GERMINOMA

Abstract BACKGROUND Current European standard-of-care for localised intracranial germinoma is multi-agent chemotherapy (carboPEI: carboplatin/etoposide/ifosfamide) followed by definitive radiotherapy, with excellent survival. MonoGerm is a de-escalation, non-inferiority trial aiming to reduce toxicity. Twelve-week carboplatin (PMID:8039122) AUC10 or vinblastine (PMIDs:32642701/34520101) induction will be evaluated to test if as effective as carboPEI from SIOP-CNS-GCT-II. A novel trial design was required to answer this question pragmatically/safely. METHODS Clinical trials in rare diseases recruit slowly, allowing continuous monitoring of efficacy outcomes. Efficacy-transition-pathways (ETP) are innovative visual tools to aid determination of trial design parameters, and an extension of the dose-transition-pathways concept introduced for dose-finding trials (PMID:28733440). RESULTS MonoGerm includes two monotherapies, with each single arm recruiting six cohorts of three patients, with interim assessment after each recruited cohort and final analysis at 18 patients (total n=36). Insufficient tumour volume response (<30%) at 6-week safety MRI results in 12-weeks carboPEI. Primary outcome is radiological complete response (CR) by 12-weeks of induction monotherapy. A beta-binomial conjugate analysis will generate posterior probability distributions, combining observed trial data as realisations from a binomial distribution with a minimally informative Beta (1,1) prior. Decision criteria to allow early stopping at interim analyses and go/no-go decisions at final analysis are based on probabilities from these posterior distributions. ETP visually maps out parameters used to assert decisions after each interim assessment as a pyramid decision tree. For each recruited cohort and every CR outcome, estimates of the true CR rate and probabilities with associated decisions are mapped out. ETP allows clear communication between statisticians, clinicians, and patient-public-involvement (PPI) teams, facilitating informed decisions in an efficient/realistic trial design. CONCLUSION MonoGerm, a novel Bayesian de-escalation trial, funded by Little Princess Trust (https://www.littleprincesses.org.uk/), uses ETP and continuous monitoring with built-in stopping rules to ensure patient safety in this treatment de-escalation trial.

Topic modeling and content analysis of people's anxiety-related concerns raised on a computer-mediated health platform.

About one in four Chinese people might suffer or have already suffered from anxiety conditions, with a lifetime prevalence rate of 4.8%. However, many of those who could have benefited from psychological or pharmacological treatments fail to be recognized or treated due to the lack of timely recognition and accurate diagnosis. This study used a topic modeling approach to explore people's anxiety-related concerns raised on a computer-mediated Chinese health platform, YOU WEN BI DA (questioning and answering), to provide implications for accurate diagnosis, targeted education, tailored intervention, and informed policy-making in the course of addressing this condition of public concern. First, we extracted data from YOU WEN BI DA between May 2022 and February 2023. After cleaning the extracted data both using the Python text processing tool spaCy and manually, we ascertained the optimal number of topics by drawing on the coherence scores and used latent Dirichlet allocation (LDA) topic modeling to generate the most salient topics and related terms. We then categorized the topics ascertained into different classes of themes by plotting them onto a 2D plane via multidimensional scaling using the pyLDAvis visualization tool. Finally, we analyzed these topics and themes qualitatively to better understand people's anxiety-related concerns. 5 topics with different overall prevalence were ascertained through data analysis. Topic 2 (tinnitus phobia-incurred concerns, n = 639) is the most popular dominant topic, occurring in 25.1% of the 2545 collected concerns, closely followed by Topics 1 (neurosis-incurred concerns, n = 512;) and 3 (sleep, dyskinesia, bipolar, cognitive, and somatic disorders-incurred concerns, n = 619), which appeared in 20.1% and 24.3% of the 2545 concerns respectively. Topic 5 (social phobia-incurred concerns, n = 428) ranks as the fourth most popular dominant topic, showing up in 16.8% of the 2545 concerns. Topic 4 (autonomic nerve dysfunction-incurred concerns, n = 347) accounts for 13.6% of the 2545 concerns. The t-distributed Stochastic Neighbor Embedding analysis reveals partial similarities between Topics 2 and 5 as well as between Topics 4 and 5 because many concerns involved in Topics 2 and 5 pertain to people's psychological status of fear and anxiety and the relief and dispelling of such symptoms through medication, and many concerns involved in Topics 4 and 5 relate to people's worries about the negative impact on their nerves and the adjustment and conditioning of such effects through medication. This was the first study that investigated Chinese people's anxiety-related concerns raised on YOU WEN BI DA using the topic modeling technique. The automatic text analysis and complementary manual interpretation of the collected data allowed for the discovery of the dominant topics hidden in the data and the categorization of these topics into different themes to reveal the overall status of people's anxiety-related concerns. The research findings can provide some practice implications for health and medical educators, practitioners, and policy-makers to make joint efforts to address this common public concern effectively and efficiently.

EPCO-33. OMICON: A CLOUD PLATFORM FOR FAIR RESEARCH ON GENE COEXPRESSION NETWORKS IN NORMAL HUMAN BRAIN AND BRAIN TUMORS

Abstract Genome-wide coexpression analysis of bulk human brain samples is a powerful approach for identifying highly reproducible transcriptional signatures of cell types and cell states, since it can survey huge numbers of individuals, cells, and transcripts. However, it is difficult to optimize gene coexpression network construction, compare gene coexpression modules from independent datasets, or even locate gene expression datasets with similar attributes. To address these challenges, we have developed the OMICON platform (theomicon.ucsf.edu) to promote FAIR (Findable, Accessible, Interoperable, Reusable) research practices involving human brain gene coexpression networks. OMICON contains structured gene expression data for >17K bulk human brain samples (~10K normal and ~7K malignant glioma), which were collected from diverse public repositories and consortia (e.g., GEO, TCGA, CGGA, CPTAC, REMBRANDT, IvyGAP, GTEx, NABEC, the Allen Institute). To promote interoperability, we have standardized metadata for hundreds of dataset and sample attributes, including single-nucleotide and copy-number mutations. For each dataset, we have used the FindModules R function to construct dozens of gene coexpression networks by iterating over module detection parameters. These efforts have identified ~100K gene coexpression modules, which have been extensively characterized via enrichment analysis with thousands of curated gene sets. All datasets, metadata, gene coexpression networks, enrichment results, and analysis steps can be browsed with an interactive workflow visualization tool, which promotes accessibility, reusability, and reproducibility by maintaining complete data provenance with unique identifiers. To promote findability, we have built an advanced search engine to identify datasets, samples, modules, and more, by filtering standardized metadata (e.g., find gene coexpression modules in human glioblastoma datasets that are significantly enriched with markers of T-cells). A detailed description of OMICON’s functionality is described on our Help page: theomicon.ucsf.edu/home/help. Through this functionality, OMICON seeks to build community and focus therapeutic efforts around reproducible analyses of transcriptional variation in normal human brain and brain tumors.

WebTWAS 2.0: update platform for identifying complex disease susceptibility genes through transcriptome-wide association study.

Transcriptome-wide association study (TWAS) has successfully identified numerous complex disease susceptibility genes in the post-genome-wide association study (GWAS) era. Over the past 3 years, the focus of TWAS algorithms has shifted from merely identifying associations to understanding how single nucleotide polymorphisms(SNPs)regulate gene expression, with a growing emphasis on incorporating fine-mapping techniques. Additionally, the rapid increase in GWAS summary statistics, driven largely by the UK Biobank and other consortia, has made it essential to update our webTWAS resource. To address these challenges and meet the growing needs of researchers, we developed webTWAS 2.0, an updated platform for identifying susceptibility genes for human complex diseases using TWAS. Additionally, webTWAS 2.0 provides an online TWAS analysis tool that simplifies conducting TWAS analyses. The updated resource includes 7247 GWAS summary statistics covering 1588 complex human diseases from 192 publications. It also incorporates multiple TWAS methods, such as sTF-TWAS, 3'aTWAS and GIFT, along with an updated interactive visualization tool that allows users to easily explore significant associations across different methods. Other upgrades include a personalized online analysis tool for user-submitted GWAS data and a refined search function that makes it easier to identify relevant associations and meet diverse user needs more efficiently. webTWAS 2.0 is freely accessible at http://www.webtwas.net.

Serum vitamin C levels and risk of osteoporosis: results from a cross-sectional study and Mendelian randomization analysis

BackgroundThe role of vitamin C as an antioxidant in guarding against osteoporosis in adults is still debated. This research employs both a cross-sectional study and a two-sample bidirectional Mendelian randomization (MR) analysis to explore how serum vitamin C levels correlate with the incidence of osteoporosis among adults.MethodsIn this study, we utilized data from the National Health and Nutrition Examination Survey (NHANES) database for the years 2003–2006, and 2017–2018 to conduct both a cross-sectional analysis and MR to investigate the relationship between serum vitamin C levels and the risk of osteoporosis in adults. We adjusted our analyses for essential demographic and lifestyle variables, and applied logistic regression techniques. Genetic determinants of vitamin C levels were analyzed through MR, using methods like inverse-variance weighted (IVW) and MR-Egger to assess causality. Statistical computations were carried out in R, incorporating visual tools such as restricted cubic spline curves (RCS) and forest plots to clarify the dose–response dynamics and variations across different subgroups. This study was approved by the NCHS Ethics Review Board, and informed consent was obtained from all participants.ResultsIn our investigation, we analyzed data from 3,940 participants, among whom 291 were diagnosed with osteoporosis. The logistic regression analysis of serum vitamin C quartiles did not indicate a significant trend. The most adjusted model showed a slight, albeit inconsistent, protective effect in the highest quartile (OR = 0.68, 95% CI: 0.47–0.99, P = 0.22). Mendelian randomization, employing methods such as IVW, reinforced the absence of a significant causal relationship between serum vitamin C levels and osteoporosis risk (IVW OR = 1.000, 95% CI: 0.999–1.001, P = 0.601).Subgroup analyses, visualized through forest plots and restricted cubic spline (RCS) curves, supported the primary findings, showing no significant effects or interactions between vitamin C levels and osteoporosis risk across different demographic and lifestyle subgroups. The RCS analysis particularly highlighted a lack of significant non-linear relationships between serum vitamin C concentration and the odds of osteoporosis (P for nonlinear = 0.840).ConclusionsThe cross-sectional study revealed that higher serum vitamin C levels do not consistently correlate with a reduced risk of osteoporosis. Meanwhile, the Mendelian randomization analysis confirmed that there is no genetic evidence to suggest a causal relationship between vitamin C levels and osteoporosis risk. Recent research highlights the polygenic nature of osteoporosis, with genetic predispositions playing a significant role in disease risk. The relationship between serum vitamin C and osteoporosis requires further research. This suggests the need for further investigation into the connection between vitamin C and bone health.