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As online platforms continue to grow, the need for strong authentication mechanisms becomes increasingly important to protect sensitive information and networks. Risk-Based Authentication (RBA) is an adaptive approach that dynamically adjusts authentication decisions based on user behavior and contextual information, thereby improving both security and user experience. This study proposes a hybrid RBA framework that integrates machine learning ensemble techniques, fuzzy logic, clustering, and optimization to enhance account takeover detection and dynamic risk assessment. The ensemble classifier, combining Gradient Boosting, SVM, and XGBoost, predicts the probability of account compromise based on login behavior, device attributes, and network information. K-Means clustering is used to generate initial risk thresholds (low, medium, and high), which are further refined using a fuzzy logic system to map probabilities to risk levels. The L-BFGS-B optimization algorithm is employed to fine-tune fuzzy membership boundaries and improve threshold consistency. Experimental results demonstrate strong performance, achieving 97.77% accuracy, 99.41% precision, 98.04% recall, 98.72% F1-score, and an EER of 0.0303. On large-scale datasets ranging from 2M to 30M records, the proposed framework demonstrates consistent improvement in authentication decisions. For the 2M dataset, Allow Login actions increase from 349,432-349,923, while Deny Login actions decrease from 1,462-1,228, along with a slight reduction in additional authentication prompts. Furthermore, the use of Explainable AI techniques, particularly SHAP, enhances the transparency and interpretability of the model, supporting more informed decision-making. Overall, the proposed framework is accurate, adaptive, and suitable for real-world risk-based authentication applications.
Long-term retention and engagement with digital asthma self-management tools remain challenging in underserved adults. We examined predictors of study dropout and sustained engagement with ASTHMAXcel PRO, an asthma self-management mobile health app, over 12 months in an underserved adult population. In this randomized controlled trial, adults with asthma who were on daily controller medications were recruited from 3 Bronx primary care clinics and randomized 1:1 to intervention (ASTHMAXcel PRO plus standard care) or control (standard care alone). Baseline questionnaires collected demographic, clinical (eg, asthma control, exacerbation history, and comorbidity), and health literacy data. Attrition was analyzed using Cox proportional hazards models. Engagement metrics in the intervention arm (logins, chapters read, and subject-reported outcomes) were evaluated with negative binomial mixed models, adjusting sequentially for sociodemographic and clinical covariates. Among 99 participants (77% female; 97% non-White), 39 (39%) dropped out before the fourth study visit. Study dropout was associated with some college or technical education versus high school or less (hazard ratio [HR] 2.11, 95% CI 1.01-4.42, P = .048), assignment to the intervention versus control group (HR 2.11, 95% CI 1.05-4.24, P = .036), and the presence of at least 1 comorbidity (HR 2.14, 95% CI 1.12-4.09, P = .02). Engagement declined over time but was strongly predicted by higher early app use (logins incidence rate ratio [IRR] 40.95; chapters read IRR 7.23; subject-reported outcomes IRR 15.35; all P < .001). Higher educational attainment was also associated with greater sustained engagement across multiple metrics. In this urban underserved cohort, comorbidities and some college/technical education predicted dropout, whereas greater early app use and higher education predicted sustained engagement. These findings highlight the importance of early identification of participants at risk of dropout to enable targeted retention strategies that support sustained self-management behaviors critical to asthma outcomes.
To develop SXRNSCLC-PRSP software which can predict the prognostic risk and survival of resected T1-3N0-2M0 (according to the 9th AJCC/UICC TNM stage of lung cancer) non-small cell lung cancer (NSCLC) patients in Shanxi Province China more comprehensively, accurately and conveniently, and provide reference and help for clinicians tailoring patients'follow-up adjuvant therapy and care. Patients with NSCLC whose tumor stage is T1-3N0-2M0 underwent surgical treatment only were selected from the medical records of Shanxi Tumor Hospital. The clinicopathological features that may affect the prognosis of these patients'survival outcome and survival time were collected (there are no missing data), and then the survival data set was established. In the survival data set, 70% of the patients were randomly selected as the training set, and the rest were composed of the test set. A prognostic model of resected T1-3N0-2M0 NSCLC patients in Shanxi Province China was constructed using the training set, and the model was validated using the test set. SXRNSCLC-PRSP software was developed to implement the model for prognostic risk and survival prediction in such patients. The software can be used free of charge by clinicians who log on to a specific website. After they register and log on to the software, they can select the corresponding clinicopathological characteristics of the patient and obtain the prognostic risk and survival prediction results of the patient. Using a Cox proportional hazard regression model, we determined the independent prognostic factors and obtained a prognostic index (PI) eq. PI = [Formula: see text] = -0.392X2 + 0.927X71 + 1.695X72 + 0.537X111 + 0.401X112-0.434X113. Using the PI equation, we determined the PI value of every patient. According to the quantile of the PI value, patients were divided into three risk groups: low-, intermediate-, and high-risk groups with significantly different survival rates. Meanwhile, we obtained the restricted mean survival times and 1-5-year survival rates of the three groups. Based on the construction of prognostic risk and survival prediction model and the programming in JAVA language, we developed the SXRNSCLC-PRSP software to determine the prognostic risk and associated survival of patients with resected T1-3N0-2M0 NSCLC in Shanxi Province China. At last, we have established a Risk Assessment System(RAS). In this system, clinicians can use the software. clinicians can input URL https://www.sxrnsclcpps.com into one of browsers (latest versions of Chrome, Firefox, Safari, Microsoft Edge which have passed the compatibility test for the login function) to reach its login screen. By processing clinical parameter inputs, the software stratifies patient risk levels and generates Restricted Mean Survival Time (RMST) estimates and survival rate projections, providing clinical support for follow-up care planning, adjuvant therapy selection, and patient screening. After prognostic factor analysis, prognostic risk grouping and corresponding survival assessment, we developed a novel software program and established the Risk Assessment System (RAS). It is practical and convenient for clinicians to evaluate the prognostic risk and corresponding survival of patients with resected T1-3N0-2M0 NSCLC in Shanxi Province China. Additionally, it has guiding significance for clinicians to make decisions about complementary treatment for patients.
The early prediction of student success through machine learning holds transformative potential for improving educational outcomes. However, the development of robust predictive models is fundamentally constrained by the scarcity of large-scale, high-quality educational datasets and privacy regulations. This paper introduces a machine learning framework that addresses these challenges through privacy-preserving synthetic educational data, with the key insight that synthetic data provides maximum value as a pre-training foundation rather than a complete replacement for real data. The framework is built upon SynEdu-HEDL, a novel synthetic dataset comprising 20,000 students across 180 courses with six interconnected tables. The dataset was generated using a hybrid rule-based and probabilistic approach that provides strong privacy guarantees (no real student records are used). Using this synthetic foundation, we evaluate predictive architectures including LSTM networks with attention, transformers, graph neural networks, and a novel hybrid LSTM-GNN architecture. Experimental results on the real-world OULAD dataset demonstrate that while direct transfer from synthetic to real data yields limited performance (AUC-ROC = 0.714), fine-tuning with only 5% of real data achieves an AUC-ROC of 0.781-a 12.7% relative improvement over training from scratch on the same limited real data (0.693). With 20% real data, fine-tuning reaches AUC-ROC of 0.831, approaching the full-data upper bound of 0.842. This synthetic pre-training benefit represents the central practical contribution of this work. Temporal analysis reveals that meaningful early warnings can be generated by week four, with the LSTM-GNN model correctly identifying 47.3% of students who would ultimately drop out. Fairness evaluation identifies acceptable disparities for gender and program type but meaningful differences for first-generation students (disparate impact ratio 0.84 on OULAD), which is successfully mitigated with only 1.8% reduction in predictive performance. Domain gap analysis reveals that performance degradation stems primarily from differences in forum participation (KS=0.34) and login frequency (KS=0.21) between synthetic and real data. The SynEdu-HEDL dataset and all code are publicly released. The framework provides validated evidence that synthetic educational data offers the greatest practical value when used for pre-training, enabling institutions with limited historical data to develop effective early warning systems without compromising student privacy.
With the rapid growth of mobile, IoT, and edge-based ecosystems, the need for secure, seamless, and privacy-preserving authentication mechanisms has become increasingly critical. Traditional authentication methods, such as passwords and single-step biometrics, verify users only at the initial login stage and fail to ensure continuous user verification throughout an active session, exposing systems to significant security risks. To address this limitation, this study proposes the Privacy-Aware Continuous Federated Biometric Authentication (PACFBA) framework, a novel Federated Learning (FL)-based approach for continuous, decentralized user authentication. PACFBA integrates a hybrid Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) architecture to jointly capture spatial and temporal biometric patterns, improving adaptability in heterogeneous environments. To ensure secure model updates and protect user data, PACFBA incorporates Differential Privacy (DP) and Homomorphic Encryption (HE) within the FL process, preventing the transfer of sensitive biometric information to central servers. Experimental results demonstrate that PACFBA achieves an accuracy of 92.5%, precision of 90.8%, recall of 91.2%, and an F1-score of 0.91, while reducing communication overhead by 20% and improving privacy preservation by 35% compared to centralized models. These results confirm PACFBA's potential for enhancing continuous authentication in mobile, IoT, and edge-based environments. However, further empirical validation using large-scale, heterogeneous, real-world datasets is necessary before the framework can be considered fully deployment-ready. This research contributes to designing scalable, secure, and adaptive authentication frameworks to address emerging challenges in decentralized digital ecosystems.
This study aimed to evaluate the effectiveness of Low-concentration Whitestrips with 3% hydrogen peroxide (HP) on extrinsic discoloration of incisors, and to evaluate the safety of whitening process. Enrolled adults with extrinsic discolored incisors (Vita shade guide value >1M1.5) were randomized into treatment and control groups. Treatment group used Low-concentration Whitestrips, control group used placebo. Tooth color was assessed at baseline and follow-up visits at Day 1, Day 3 and Day 14. Tooth color was assessed by digital shade meter (primary outcomes), and VITA 3D-Master shade guide (secondary outcomes), respectively. Adverse events (AEs) and serious adverse events (SAEs) including oral and systemic symptoms based on clinical examination and participants' description were also recorded. 70 adults participated. The primary and secondary outcomes objectively and subjectively demonstrated that the treatment group exhibited better whitening effects than the control. Additionally, the treatment group was observed to attain better effects with prolonged use, with W* values based on digital shade meter increasing from 72.86 to 78.73, and the VITA 3D-Master shade guide color values decreasing from 14.91 to 11.23 (P < 0.05). Five AEs occurred with no significant difference between groups (P > 0.05). No SAEs were reported. From both objective and subjective perspectives, Low-concentration Whitestrips were found to be effective in extrinsic discoloration tooth bleaching and were well-tolerated by participants. Chinese Clinical Trial Registry: https://www.chictr.org.cn/ (ChiCTR2300073971) and National Health Information Platform: http://114.255.48.20/login (MR-51-23-033324).
The Austrian Electronic Health Record (ELGA) aims to enhance healthcare coordination and patient empowerment, yet public uptake remains limited. This study explored citizens' motivations and barriers toward ELGA use and reflected on the potential of science communication events to foster dialogue on digital health. During the Long Night of Science at UMIT TIROL, 35 participants anonymously shared on a whiteboard their reasons for using or not using ELGA. Statements were thematically analyzed using the Context-Mechanism-Outcome (CMO) framework. Major barriers were concerns about data security and privacy, login complexity, and perceived lack of necessity. Facilitators included fast access to medical data, reduced paperwork, and improved continuity of care. Participants balanced digital convenience with privacy concerns. Public events such as the Long Night of Science provide valuable opportunities not only to inform citizens about digital health but to let them actively participate in science, exchange perspectives, and learn from their lived experiences.
SNPnexus is a long-standing web-based platform for the functional annotation and prioritization of genetic variants. Since its previous release, SNPnexus has undergone substantial backend re-engineering resulting in major performance improvements and a complete restructuring of the underlying datasets. A redesigned interface now enables larger queries and multi-sample analysis enabling comparative workflows such as identifying shared pathogenic variants in disease cohorts and divergent mutations in cancer evolution studies. SNPnexus increased its capacity to 150 000 variants per query and introduced pre-annotation filters that allow users to restrict analyses to selected genes or genomic regions, improving efficiency while enabling targeted interrogation of high-priority targets. SNPnexus integrates updated and expanded annotations across both GRCh37 and GRCh38, covering genomic consequences, in silico pathogenicity predictions, population allele frequencies, evolutionary conservation, regulatory elements, biological pathways, and clinical associations. The refreshed result interface provides interactive visualizations for single-sample and cohort-level outputs, together with advanced filtering and export options. Optional user accounts now support query history and real-time job monitoring while preserving full, unregistered access for all users. SNPnexus remains free and open to all users without login requirements at https://snpnexus.org/.
People with dementia or mild cognitive impairment (MCI) are increasingly using the internet, but cognitive and functional changes may amplify online safety risks. This scoping review mapped academic evidence and publicly available guidance on online safety for people affected by dementia/MCI. In April 2025, seven databases and Google Scholar were searched for academic studies. Publicly available guidance was identified via Google and targeted searches of relevant organisations. Academic studies were synthesized narratively, and public resources underwent content analysis. Of 2,014 academic articles screened, 13 were included. Studies were organised into three themes: 1) 'Vulnerability to scams and misinformation', including cybercrime victimisation, malicious links, inadvertent sharing of personal details, and misleading content; 2) 'Online harms', including psychological distress from negative interactions and upsetting content; and 3) 'Safeguarding approaches', characterised by labour-intensive, carer-led monitoring and reactive strategies. The 14 publicly available resources focused primarily on email, social media, and scams, but offered limited guidance on managing distressing content, misinformation, online abuse, or decisions around sharing login information. Evidence on online safety for people with dementia/MCI remains limited. Our review highlights the importance of co-designed online safety initiatives, effective moderation, improved technology design, and policy supporting safe digital engagement.
India bears the world's largest tuberculosis (TB) burden, and under-reporting from private-sector providers continues to hinder elimination goals. In the Akot Tuberculosis Unit (TU) of Maharashtra, private notifications remained disproportionately low despite extensive private healthcare presence. This study implemented a structured quality-improvement (QI) model using Plan-Do-Study-Act (PDSA) cycles to enhance TB case notifications from private providers.A mixed-method quality-improvement study was conducted from August to December 2023. Four iterative PDSA cycles were implemented targeting private practitioners, pharmacists, and laboratory technicians. Interventions included sensitization workshops, weekly WhatsApp-based digital outreach, and personalized follow-up visits by Public-Private Mix (PPM) coordinators. Quantitative data were extracted from the Ni-kshay platform and validated through Tuberculosis Unit records. Private notification trends (2018-2024) were analysed using segmented regression to assess temporal trends, while qualitative insights from key informant interviews (n = 6) explored barriers and enablers influencing notification practices. A total of 90 stakeholders participated (45 practitioners, 30 pharmacists, and 15 laboratory technicians). Private TB notifications increased from 16 of 90 total cases (17.8%) during January-July 2023-63 of 121 cases (52.1%) during November-December 2023, reflecting increase during the post-intervention period. The interrupted time-series analysis demonstrated a positive post-intervention trend in private TB notifications (β = +50.5, p = 0.032). Qualitative findings revealed that personalized digital mentoring and hands-on technical support helped address barriers such as Ni-kshay login issues, misconceptions about notification responsibility, and perceived workload. The implementation of a structured PDSA-based quality-improvement approach was associated with improvements in private-sector TB notifications in a previously low-performing tuberculosis unit. The findings suggest that integrating digital engagement, capacity building, and supportive supervision within existing NTEP structures may strengthen TB surveillance and private-sector engagement. These insights may inform scalable strategies to improve TB notification systems in similar programmatic settings.
The number of digital health applications has rapidly grown over recent years, but many of them are limited by sustainability and scalability. Paradata, the detailed interactions of a user with a piece of software, is straightforward to collect without disrupting the user-experience, and can provide a nuanced understanding of in-app user behavior. In this work we analyze paradata from the mLab App arm of a three-arm, multi-site randomized clinical trial (NYC and Chicago), in a sub-study that evaluates longitudinal, session-level interaction logs with multiple sessions per participant, (registered with Clinicaltrials.gov as NCT03803683), a mobile health application to facilitate at-home HIV testing in at-risk populations. We investigated application-level usage statistics to identify feature usage, as well as common navigational paths within the application. Temporal patterns were observed for login events and test-taking patterns. Despite being under-collected and underreported, paradata reveal feature gaps, guide targeted revisions for subsequent research and implementation, and deepen understanding of user behavior-enabling digital-health applications with lasting impact.
Protein structure prediction models released in recent years have presented tectonic changes in the field of structural biology. However, their potential has not yet been harnessed to its fullest due to their demands on hardware and technical expertise required for their usage. In this paper, we present Foldify, which makes prediction models accessible, integrating AlphaFold 3, AlphaFold 2, ColabFold, OmegaFold, and ESMFold into a single user-friendly, easy-to-use graphical interface, and ensures their stable operation within a scalable high-performance computing environment. Foldify accepts protein sequences, submitted through a web-based graphical interface as input, and allows executing multiple prediction models on the same protein sequence. The predicted protein structures can be directly visualized online through Mol* Viewer or can be downloaded from the website. Furthermore, the multiresult comparison mode allows visualization of multiple predicted structures in a single Mol* window, accompanied by qualitative metrics of the models' prediction similarity. The Foldify application is freely available at https://foldify-open.cloud.e-infra.cz/ with no login required.
Plasmids play a central role in bacterial adaptation and in the dissemination of antimicrobial resistance, driving a growing need for accessible tools that support their comparative analysis without requiring local computational infrastructure. Although several circular genome visualization platforms exist, most are designed for general bacterial genome analysis rather than focused on plasmid comparison. Host element reference-based aligner (HERA) is a web server for intuitive visualization and comparison of plasmids and other circular molecules through BLAST alignment against reference sequences. Built on interactive circular genome visualization, HERA simplifies comparative genomics by providing an accessible interface for exploring sequence similarity, identifying conserved regions, and analyzing genetic elements without the complexity of traditional local tools. HERA includes a plasmid-oriented annotation pipeline covering replicon and mobility typing, antimicrobial resistance detection, mobile element identification, and homology search against the PLSDB plasmid database. HERA also provides an automatic selection of the reference which is the most appropriate from the uploaded sequences. The web server is available without login or any restriction at https://web.ccb.uni-saarland.de/hera/.
PockFlex is a web server designed to analyse pockets across protein structural ensembles and support the reconstruction, characterisation, and prioritisation of recurrent binding site organisations. Applicable to ensembles derived from molecular dynamics simulations, multiple experimental structures, or protein structure predictions, PockFlex detects pockets independently in each conformation, retains those overlapping a user-defined region of interest, and groups them across the ensemble by residue-level similarity. This residue-centred clustering framework identifies recurrent binding site clusters, quantifies residue recurrence and variability, and distinguishes persistent from transient binding site regions across the ensemble. Pocket-level druggability, predicted using the PockDrug workflow, is summarised at the cluster level to support binding site prioritisation under conformational variability while preserving access to individual pocket scores. The web application provides interactive, residue-level insights into pocket organisation, variability, and druggability in structural ensembles. The web server is free and open to all users, without login requirement, at https://pockflex.rpbs.univ-paris-diderot.fr/.
The HMMER web server, available at https://www.ebi.ac.uk/Tools/hmmer, provides online access to tools from the HMMER software suite (http://hmmer.org/) for protein analysis using profile hidden Markov models. Users can perform sequence similarity searches against a range of regularly updated protein sequence databases or annotate protein sequences with domains and families using profile HMM libraries from protein family databases. Since the 2018 update, the continued exponential growth of sequence databases has necessitated substantial infrastructural improvements to maintain search performance speed and service reliability. To achieve this, the web interface has been completely reengineered using modern web technologies (JavaScript and React), providing users with an enhanced experience, including session-based search history and streamlined results visualization. The web application programming interface has been rewritten to better support programmatic access with updated endpoints and JSON-based responses. The infrastructure has been redesigned to efficiently handle searches against much larger databases through horizontal scaling and asynchronous job processing. Target database offerings have been updated to reflect current usage patterns and data availability. The HMMER web server is free and open to all users, and there is no login requirement.
Esophageal cancer is associated with substantial morbidity and mortality worldwide and is frequently diagnosed at advanced stages, leading patients and their relatives to seek health-related information beyond traditional clinical encounters. In recent years, YouTube has become a popular source of medical information. Nevertheless, questions persist regarding the accuracy, credibility, and overall reliability of the content available on the platform. This cross-sectional study evaluated publicly available YouTube videos related to esophageal cancer. Data were collected on December 3, 2025, using a browser without a user login to minimize algorithm-driven bias. Viewer engagement metrics (views, likes, and comments), source categories, and country of origin were recorded for each video. Content quality and reliability were assessed using the DISCERN instrument, Journal of the American Medical Association (JAMA) benchmark criteria, and Global Quality Score (GQS). Non-parametric statistical analyses were used to compare quality outcomes across source categories and evaluate the correlations between the engagement metrics and quality scores. A total of 78 videos met the inclusion criteria, most of which originated in the USA (83.3%). Health-related channels constituted the largest source category (35.9%), followed by patient experience-based videos (23.1%), and private institutions (20.5%). Viewer engagement metrics (views, likes, and comments) did not differ significantly among source types (p > 0.05). In contrast, the content quality varied substantially. Videos produced by public institutions achieved the highest DISCERN, JAMA, and GQS values, whereas patient-experience-based videos demonstrated significantly lower quality and reliability (p < 0.001). Engagement metrics were strongly intercorrelated but showed no association with quality scores. YouTube videos related to esophageal cancer frequently exhibit moderate informational quality, and popularity metrics do not reflect content reliability. Source credibility plays a critical role in determining video quality, underscoring the need for greater involvement of healthcare professionals and public institutions in digital health content production.
Variants in promoters and enhancers can alter the binding of transcription factors (TFs), but their functional assessment remains difficult. FABIAN-variant is a web application that predicts the effects of DNA variants on TF binding by comparing position weight matrix (PWM) and transcription factor flexible model (TFFM) scores between reference and variant alleles. Here, we present FABIAN-variant 2026, a major update that expands the prediction model library from ~5000 to over 40 000 models for >1500 human TFs, sourced from nine PWM databases and including 1290 TFFMs. The application now supports the mouse genome (GRCm38 and GRCm39) with over 35 000 models for >1100 mouse TFs. An optional BPNet deep learning scorer provides neural network-based binding predictions for 240 human TFs. Known TF binding site information has been expanded from three to five sources. Predictions for over 1400 heterodimer TF complexes have been added. The web server has been rewritten in Rust and the scoring engine optimized, reducing runtime by ~70%. A RESTful JSON API and a standalone command-line version enable programmatic access and local high-throughput analysis. FABIAN-variant 2026 is available at https://fabianapp.org/variant26/. The web server is free and open to all users and there is no login requirement.
Routine financial activities are now conducted primarily through digital channels. Many such systems remain inaccessible to more than 2.2 billion people globally living with vision impairment, limiting independent financial management. Constrained access can create financial strain and social disadvantage, reducing access to health-enabling resources, and contributing to avoidable health inequities. This scoping review maps evidence on the accessibility of digital financial services for individuals with visual impairment (VI) as a digital determinant of health. We synthesized barriers and facilitators, characterized study designs, settings, and populations, and identified evidence gaps to inform inclusive design, digital health research priorities, and policy. A scoping review was conducted using the Joanna Briggs Institute framework and reported in line with PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines. Eight databases (PubMed, MEDLINE, CINAHL, Scopus, Web of Science, Business Source Complete, ProQuest, and IEEE Xplore) were searched for peer-reviewed papers in English published between 1995 and 2026. Searches featured controlled vocabulary and free-text terms structured in 3 conceptual blocks (VI, digital financial services, and accessibility or usability). A random sample of 20% of titles, abstracts, full texts, and included studies was independently screened or charted by 2 reviewers to calibrate decisions; the remainder were screened and charted by a single reviewer. Data were charted using a standardized extraction form, and results were synthesized descriptively and thematically. Twenty-three studies met the inclusion criteria. Studies were conducted across 12 countries, with the largest number from India (n=7), Indonesia (n=2), Thailand (n=2), and the United States (n=2). Study designs included qualitative studies (n=6), mixed methods studies (n=1), cross-sectional studies (n=4), nonrandomized experimental studies (n=2), and technical or design-focused evaluations (n=6). One study was a large population survey (n=19,136), and the remaining studies with human participants had sample sizes ranging from 4 to 36 participants. Accessibility barriers were reported across all platform types, with authentication-related barriers described in 18 studies and screen reader incompatibility in 17 studies. Reported barriers included reliance on sighted assistance for tasks such as login, verification, and payments, compromising privacy and independence. Facilitators included assistive technology support, logical navigation order, nonvisual feedback mechanisms, and accessible authentication alternatives. Evidence mapping revealed recurrent barrier patterns across Android, iOS, and web platforms. No longitudinal or intervention-based evaluations were identified. This review provides a focused synthesis of accessibility evidence at the intersection of digital financial services and VI, a domain addressed by neither prior digital accessibility reviews nor financial inclusion for people with disabilities. Authentication methods, interface labeling, and navigation were identified as persistent cross-platform accessibility barriers. The findings carry implications for financial technology developers, accessibility auditors, and policymakers implementing accessibility legislation and extend the digital determinants of health framework by demonstrating how inaccessible financial technology may compound health inequities.
Interpreting genome-wide epigenomic experiments, such as DNA methylation profiling and chromatin accessibility assays, requires tools that can identify which regulatory programs underlie coordinated changes across genomic regions. Without this regulatory context, lists of differential regions remain largely descriptive and difficult to interpret mechanistically. Existing approaches either apply hard significance cutoffs that discard moderate but biologically meaningful signals, or rely on gene-centric annotations that neglect enhancers and intergenic space, introducing bias into the interpretation. RegRegSEA addresses both shortcomings by adapting the Gene Set Enrichment Analysis framework directly to genomic coordinates. The server accepts a standard differential analysis table, ranks all tested intervals by a signed statistic, and computes enrichment scores against curated regulatory databases including transcription factor binding site collections. Results are returned as an interactive, publication-ready report featuring dynamic visualizations of enrichment profiles and regulatory annotations, along with downloadable leading-edge regions for downstream analyses. We demonstrate the utility of this approach through re-analysis of Down syndrome brain methylation data and chromatin accessibility in ageing mouse liver. The server is freely available at https://web.ccb.uni-saarland.de/regregsea/ and open to all users with no login required.
The transition of high-risk neonates from the neonatal intensive care unit (NICU) to home remains a complex process, often hindered by inadequate and fragmented discharge education. This study aimed to develop, implement, and evaluate an interactive telenursing web application designed to enhance discharge education for mothers of high-risk neonates. A multi-method study with a three-phase approach (design, implementation, and evaluation) was conducted to develop and assess a bilingual telenursing web application. The application was developed using the waterfall model. Implementation and evaluation phase in this study was conducted as a quasi-experimental study, involving 60 mothers of high-risk neonates. These mothers were randomly assigned to either an intervention group (N = 30), which received interactive, multimedia-based discharge education via the web application, or a control group (N = 30) that received standard discharge education. Maternal knowledge and user satisfaction were the primary outcome measures. The findings are structured into two distinct components: (1) web application design and (2) implementation, and its evaluation. This application was designed as a bilingual (Persian/English) platform. Its various components include the following: A- Login Page (this web application has three user roles: Mother, nurse, and Supervisor (Editor)), B- Home Page, C- Educational Icons Page, D- multiple questions to assess mothers' understanding of the educational content. E- Nurses' Page, F-Messages Page. Evaluation of the Intervention on maternal knowledge showed that the mean knowledge score in the intervention group was significantly higher than in the control group (p = 0.008). User Satisfaction and Usability questionnaire results showed that the overall mean satisfaction score was 3.90 ± 0.77 out of 5. The highest-scoring domain was Information Quality (Mean ± SD = 4.13 ± 0.63), followed by Aesthetics (Mean ± SD = 3.96 ± 0.66) and Functionality (Mean ± SD = 3.90 ± 0.84). The lowest-rated domain was Subjective Quality (Mean ± SD = 3.63 ± 0.73). Conclusion: The findings indicate that the bilingual, competency-driven bilingual telenursing web application is an effective tool for improving maternal knowledge and engagement in post-NICU care. The platform's features, including real-time nurse-mother communication and adaptive learning modalities, facilitate better discharge preparedness and may contribute to reduced emergency healthcare utilization. Future research should explore integration with electronic health record systems to enable personalized and continuous care pathways.