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.
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.
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.
N-linked glycosylation is critical for protein function and stability, yet identifying glycosylated sites remains challenging because glycosylation depends on sequence motifs and structural context. Many available computational approaches focus on motif-centred sequence windows and provide limited support for whole-protein inspection of candidate sites. SGGly is a freely accessible web server for structure-guided analysis of candidate N-linked glycosylation sites across full-length proteins. The server uses ProtBERT transformer-based embeddings with sequon and structure-derived residue descriptors to generate residue-level candidate-site predictions and returns downloadable residue-level predictions together with interactive 3D visualisation. Using a dual evaluation framework, SGGly achieved a Matthews correlation coefficient of 0.888 and receiver operating characteristic area under the curve of 0.987 under a strict, publication-supported regime. On the independent N-GlyDE benchmark, SGGly demonstrated strong generalisability, achieving the strongest specificity (0.941), sensitivity (0.993), and accuracy (0.946) among compared methods. SGGly provides a practical web resource for whole-protein glycosylation candidate mapping, structural inspection, and prioritisation of sites for follow-up analysis, guiding experimental design and interpreting glycoproteomic observations. SGGly is available at https://biosig.lab.uq.edu.au/sggly/. This website is free and open to all users, and there is no login requirement.
Telemonitoring has the potential to improve access to care and continuity of follow-up after kidney transplantation. Advanced practice nurses (APNs) play an increasingly important role in coordinating remote care pathways. This study evaluated patient experience with telemonitoring after renal transplantation, identified determinants of adherence, and clarified the role of APNs in this model. We conducted a single-center retrospective observational study including adult kidney transplant recipients enrolled in a telemonitoring program between April 2020 and April 2022. Patients were classified as active users (TOUCO), discontinued users (STOPCO), or never users (JAMCO). Satisfaction and experience were assessed through questionnaires. Platform activity and APN workload were analyzed using descriptive statistics. Among 207 eligible patients, 110 responded to the survey (53%): 64 TOUCO (71%), 11 STOPCO (47%), and 35 JAMCO (37%). Active users reported high satisfaction with response time (89%), improved access to care (81%), and increased reassurance (75%). Ease of use (86%) and adequate information at enrollment were significantly associated with continued use. Major barriers included technical difficulties (≈80%) and loss of login credentials (>50%). During the study period, 5,214 platform events and more than 4,000 secure messages were recorded, reflecting sustained engagement. APNs required a mean workload of 3 hours per day to manage all active users on a daily basis. Telemonitoring after kidney transplantation is feasible and well accepted, improving perceived access to care and enhancing patient reassurance without measured clinical outcome differences. Adherence is driven primarily by organizational and technological factors rather than patient characteristics. APNs play a central role in ensuring continuity of care, triaging data, and maintaining patient engagement. Future studies should evaluate clinical outcomes and cost-effectiveness to support broader implementation.
BilboMD is a web-accessible platform that facilitates integrative modeling of flexible macromolecules using experimental small-angle X-ray and neutron scattering data. BilboMD combines a browser-based interface with high-performance back-end services hosted locally at the SIBYLS beamline and at the National Energy Research Scientific Computing Center (NERSC). BilboMD guides users through a workflow that includes preparing compatible input files, conformational sampling through molecular dynamics (MD) simulations, small-angle X-ray scattering (SAXS) fitting with FoXS, and establishing multi-state models with MultiFoXS. Input assistants (Inp Jiffy and PAE Jiffy) streamline the process of defining rigid and flexible regions for conformational sampling, including automatic inference from AlphaFold PAE matrices. All analyses are containerized to ensure reproducibility, with job metadata and provenance stored in a central database. By leveraging GPU-enabled MD engines, such as OpenMM (on NERSC resources), BilboMD significantly accelerates conformational sampling and expands the set of models used for SAXS fitting. The platform reduces technical barriers for non-specialists while providing API access for advanced users to automate and integrate with custom workflows. Thus, BilboMD democratizes ensemble-based SAXS modeling, accelerates high-throughput SAXS/small-angle neutron scattering analysis, and lays the groundwork for future integration of AI-driven structure prediction with experimental scattering data. It is freely available without a login requirement at https://bilbomd.bl1231.als.lbl.gov.
Despite the 21st Century Cures Act mandating open access to clinical notes, parent engagement remains low, with less than 10% accessing notes during their child's hospitalization at our children's hospital across all services. Parents play a critical role in patient safety but often experience challenges staying informed due to limited access to written clinical information. Evaluate parent access to and experiences with clinical notes using a bedside tablet during their child's oncology hospitalization. In this mixed methods study, parents of children younger than 12 years admitted to an oncology service from August 2022 to June 2023 received real-time note access via a 4-digit login to the patient portal application on a bedside tablet. Note access was electronically tracked. Parent experiences were assessed using OpenNotes survey items and semistructured interviews analyzed using conventional content analysis. All 25 parents accessed at least 1 note, with a mean of 31 notes per parent (range: 1-134). Of 1599 notes written, 1594 (99.7%) were shared to the portal, and 781 (49.0%) were accessed. Most parents found notes understandable (92%) and useful for tracking daily plans and discharge goals (80%). Five parents (20%) identified possible inaccuracies; 60% were confirmed safety concerns (eg, medication error). Note access increased parent self-efficacy in understanding their child's care, evoked a spectrum of emotions, and required management of complex medical information. Although most parents found notes beneficial, efforts are needed to support parent navigation of complex information and systematically address parent-identified safety concerns.
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.
In recent years, the development of peptide drugs has seen significant growth. These molecules often go beyond simple linear chains composed of the standard 20 amino acids. Peptide drugs frequently incorporate non-standard amino acids, non-amino components, and can exhibit mono- or multicyclic structures, branching, and other complex topologies. Consequently, there is a growing need for accessible tools that allow researchers to easily generate and modify 1D, 2D, and 3D representations of these complex peptides, serving as a starting point for further optimization. PEP-EDIT was created to meet this need. It offers a user-friendly, interactive web interface for generating complex peptide representations from 1D BILN (Boehringer Ingelheim Line Notation) sequences, using a customizable monomer library. Building on the pyPept library, PEP-EDIT enhances its functionality with options such as pH-dependent protonation and simplified specification of conformational constraints. The platform leverages interactive 2D and 3D visualizations to guide peptide design, offers intuitive management of monomers and 3D models, and includes collaborative and interactive visualization tools. PEP-EDIT is available at https://pep-edit.rpbs.univ-paris-diderot.fr. This website is free and open to all users and there is no login requirement.
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/.
Periorbital measurements such as margin to reflex distances, palpebral fissure height, and scleral show are critical in diagnosing and managing conditions like ptosis and disorders of the eyelid. However, deployment of automated periorbital measurement algorithms in structured research workflows remains limited by the lack of integrated capture and data management infrastructure. We developed and evaluated Glorbit, a lightweight, browser-based application for automated periorbital distance measurement using artificial intelligence (AI). The objective was to evaluate end-to-end workflow feasibility of the platform under simulated, operator-run conditions. The application integrates a DeepLabV3 segmentation model into a modular image processing pipeline with secure, site-specific Google Cloud storage, supporting local preprocessing and cloud upload through Firebase-authenticated logins. The full workflow-metadata entry, facial image capture, segmentation, and upload-was tested. After the session, the participants completed a Likert-style survey. Glorbit successfully ran on all tested platforms, including laptops, tablets, and mobile phones across major browsers. A total of 15 volunteers were enrolled in this study in which the app completed predefined workflow steps in all simulated, operator-run sessions. The segmentation model produced outputs on all images, and the average session duration was 101.7 (SD 17.5) seconds. Simulated experience scores on a 5-point Likert scale were uniformly high. Glorbit is a cross-platform application that supports structured periorbital image capture and automated inference within a unified workflow. In simulated, operator-run testing, the platform demonstrated successful execution of predefined workflow steps across devices. These findings support the technical feasibility of the system as a research-oriented data collection framework and may inform future evaluations in broader research settings.
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.
Inefficient hardware configuration in Health Information Systems (HIS) is a global informatics challenge that undermines healthcare delivery. This study designed and content-validated a novel quantitative hardware assessment framework to enable systematic hardware evaluation. We developed an informatics-driven framework through a methodological study in 2024. Initial items were derived from a scoping literature review and expert input from computer engineering. Content validity was assessed using Content Validity Ratio (CVR) and Index (CVI). Inter-rater reliability was evaluated via Intraclass Correlation Coefficient (ICC) across 122 identical computers. The final framework comprises 23 items across three domains. A quantitative scoring model (0-150 points) was established, demonstrating excellent inter-rater reliability (ICC = 0.87). The tool effectively differentiates between obsolete (score < 50), marginal (50-80), and optimal (> 80) configurations, providing IT managers with a clear upgrade priority map. This study presents a development and content validation of a reliable and content-valid informatics framework that bridges hardware assessment with system performance evaluation. Predictive validity against real-world HIS performance metrics (e.g., login time, response latency, freeze frequency) has not yet been empirically tested and remains a critical next step.
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.
To develop the authentication and authorization module for iAgree, a privacy-preserving platform that enables patients to manage consent preferences and share electronic health record (EHR) data across multiple health systems with researchers. The consent and blockchain modules are described elsewhere. We developed the authentication and authorization module of iAgree. This module supports account creation and cross-institution identity binding as part of the iAgree workflow to enable patients to authenticate using federated credentials (eg, Google, Facebook). Patients link their identities across institutions by signing into each participating health system's patient portal. Identity attributes retrieved via a Fast Healthcare Interoperability Resources (FHIR®) application programming interface (API) are cryptographically transformed into de-identified tokens so no raw identifiers are stored. Consent preferences are recorded immutably using blockchain technology. iAgree guides patients through account creation, cross-institution identity binding, and selection of granular data-sharing preferences. In this paper, we describe the design and implementation of the authentication and authorization modules, not the full workflow of the platform. We installed the iAgree platform in a test or proof-of-concept environment at three health systems: Cedars-Sinai Medical Center, University of Colorado, and University of California, San Francisco. Functional testing with test patients demonstrated that authentication, identity binding, and secure multi-site record linkage could safely bind the identities of patients across sites, enabling patients to manage their data sharing consent preferences. The results demonstrate the feasibility of a privacy-preserving multi-institutional data-sharing architecture that employs federated login, secure privacy-preserving multi-site record linkage, and blockchain-based consent tracking to align with privacy regulations while increasing transparency and patient autonomy. This effort demonstrated that the authentication and authorization module enables iAgree to be a feasible and secure platform for patient-directed sharing of EHR data across health systems with researchers. Future work will evaluate usability, patient engagement, and future real-world deployment.
Protein energy networks (PENs)-residue interaction networks weighted by force-field-based pairwise non-bonded interaction energies-provide a physically grounded framework for identifying functionally important residues, allosteric communication pathways, and ligand-induced network rewiring from molecular dynamics (MD) simulation data. We previously developed gRINN (get Residue Interaction eNergies and Networks), a standalone tool for PEN analysis of biomolecular simulation trajectories, which has seen wide adoption but also suffered from compatibility issues with modern MD engines and operating systems. Here, we present i-gRINN (interactive platform for gRINN), a completely redesigned web server for calculation of non-bonded residue interaction energies and PEN analysis of GROMACS trajectories or PDB ensembles. i-gRINN introduces two major advances over the original tool: first, pairwise interaction energy calculations are extended beyond standard amino acids to include small molecule ligands and non-standard residues; and second, the result dashboard integrates an LLM-powered chatbot that allows users to interrogate interaction energy matrices and PEN metrics using natural language queries, with a two-stage biological interpretation pipeline grounded in UniProt annotations and PubMed literature. Interactive visualization is provided through dedicated panels for pairwise energies, the full interaction energy matrix heatmap, network analysis, and an integrated 3D structure viewer. i-gRINN is freely available without any login requirement at https://grinn.bio-cloud.site.
In recent years, digital patient portals have become an increasingly common feature of care in various medical fields. Despite growing scientific evidence of their effectiveness and the benefits they offer to patients and caregivers, their implementation, especially in hospital mental health settings, lags behind expectations. The study aimed to identify the barriers and facilitators to implementing a patient portal in a public mental health hospital setting in Germany. Moreover, it aimed to develop recommendations for implementing a patient portal. Three psychiatric clinics in the early stages of implementing an online portal for patients participated in this implementation study. We assessed objective usage data (log data from the patient portal) and performed qualitative interviews with professionals and questionnaire surveys with both patients and professionals. We combined the results to develop generic recommendations for the implementation of patient portals in a mental hospital setting using a 2-stage Delphi method with a group of professionals and patients. Portal log data from 71 patients indicated variation in the use of the portal functions. On average, users logged in 9.5 (SD 14.9) times (median 4, IQR 2-7 times). The variability in the number of logins per patient, ranging from 1 to 72, indicated a high variance in the frequency of use. On average, the portal was used for 47 (SD 59) days (median 27, IQR 2-62 days). Questionnaire data from 27 patients showed satisfaction with the portal and elucidated perceived barriers to usage. Qualitative interview data from 15 professionals revealed patient-related, professional-related, organizational, structural, and technical facilitators and barriers to the implementation process. We developed 10 actionable recommendations for the implementation of digital patient portals in psychiatric hospitals, which were rated by an expert group on different dimensions. To our knowledge, this is the first implementation study in a German mental health hospital setting that provides experience-based recommendations for advancing the implementation of digital patient portals in hospital mental health care. The next steps will include the analysis of a larger number of users and functions, which will help to specify recommendations for different target groups and settings.
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.
Postdural puncture headache (PDPH) following spinal anaesthesia significantly affects postpartum recovery. We evaluated the efficacy of nebulised dexmedetomidine and aminophylline in women with moderate to severe PDPH after caesarean delivery. In this single-centre, double-blind, randomised controlled trial, 96 postpartum women, with American Society of Anesthesiologists physical status II, Visual Analogue Scale (VAS), with PDPH (VAS>4; Lybecker>2) after subarachnoid block were allocated to receive nebulised dexmedetomidine (1 µg/kg), aminophylline (1.5 mg/kg) or saline placebo. All participants received standardised conservative treatment and remained supine for 72 hours. Nebulisation was administered two times per day for ≤72 hours. The primary endpoint was change in VAS pain score at 24 hours. Secondary endpoints included VAS and Lybecker scores at 48 hours and 72 hours, Patient Global Impression of Change (PGIC) and adverse events. Baseline characteristics were comparable between groups. At 24 hours, median VAS was significantly lower with dexmedetomidine (3 (3-4)) and aminophylline (4 (3-6)) compared with control (6 (5.5-6)) (p<0.001). Pain reduction persisted at 48 hours and 72 hours, with dexmedetomidine producing the fastest resolution. Lybecker scores improved earlier in the treatment groups (p<0.001). PGIC showed 'very much improved' in 100% (dexmedetomidine), 53.1% (aminophylline) and 0% (control) (p<0.001). Adverse events were mild and infrequent, with cough and nausea occurring only in the aminophylline group. Nebulised dexmedetomidine and aminophylline provided effective, well-tolerated, non-invasive treatment for PDPH. Dexmedetomidine produced the greatest and earliest improvement. These therapies may represent practical alternatives for PDPH management in postpartum patients; larger multicentre trials allowing early mobilisation are warranted. Clinical Trials Registry-India (CTRI/2024/01/061322; date of prospective trial registration: 10 January 2024, date of first patient enrolment: 15 January 2024, https://ctri.nic.in/Clinicaltrials/login.php).
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/.