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Health information management education faces challenges in teaching claims data. Regulations prohibit the use of real, non-deidentified patient claims data within classroom settings. This limits hands-on experience with real-world data formats such as X12 837 institutional (837i) transaction files. Most existing approaches rely on textbook examples or static datasets that have been heavily redacted, which may lack the complexity required to prepare students for roles involving the handling of claims-related data. In this report, the authors describe the design and deployment of an open-source synthetic claims generator and parser designed to democratize access to realistic health care claims data, bridging the gap between theoretical knowledge and practical skills. A Python-based system was developed that generates realistic X12 formatted 837i claims using real Centers for Medicare & Medicaid Services (CMS) provider and payer databases. The tool features a free browser-based interface, plus local deployment via a command-line interface (CLI) or application programming interface (API) for advanced educational contexts. Finally, a modular two-lecture curriculum framework for integration into existing health information management courses is provided. The browser-based application enables instant access without installation, eliminating technical and privacy barriers. The tool supports generating 1-25 claims per session via the web interface, with the ability to perform batch generation of thousands of claims via CLI/API for analytics projects locally on a user's own machine. All generated files were validated against X12 5010 837i specifications using two independent electronic data interchange validation platforms, confirming zero structural errors and full conformance to industry standards. The framework aligns with American Health Information Management Association core competencies, offering a reproducible and scalable method to support workforce readiness for revenue cycle analyst, medical coding, and health care data analyst roles. By eliminating privacy concerns, financial barriers, and installation complexity, this method offers a scalable, reproducible model for teaching critical electronic data interchange and revenue cycle competencies related to the handling of claims data files. The open-source nature and multiple deployment options of this tool enable adoption across diverse institutional contexts, from community colleges to research universities, and from technical coding to nontechnical users.

Haplo2D6 is a free, browser-based tool that automates the translation of CYP2D6 genotype data into metabolizer phenotypes. Using haplotype reconstruction with the PHASE algorithm, integration of copy number variation estimates, and curated definitions from ClinPGx and PharmVar, Haplo2D6 assigns star alleles, incorporates gene deletions and duplications, calculates activity scores (AS), and predicts phenotype. This standardized, high-throughput approach improves reproducibility and reduces interpretation errors compared to manual workflows. It runs in any modern web browser, requires no installation, supports batch processing, and generates downloadable, human-readable results. Haplo2D6 is accessible at https://bioinfo.dcc.ufmg.br/Haplo2D6/ without registration.

Riparian willows (Salix spp.) in Yellowstone National Park have long been shaped by ungulate browsing, yet the specific contribution of individual herbivore species remains unclear. We applied a bite-DNA metabarcoding approach, extracting saliva DNA from browsed willow twigs, to directly identify the browsing community across six northern range riparian sites. Mammalian DNA was successfully assigned for more than half of the collected bite samples, revealing browsing by moose (Alces alces), North American bison (Bison bison), elk (Cervus canadensis), deer (Odocoileus sp.), bighorn sheep (Ovis canadensis), and jackrabbit (Lepus townsendii). Contrary to the traditional view of bison as primarily grazers, bite-DNA showed that bison were the most frequent browsers of willows, present at all sites and contributing the majority of browsing bites. Elk, historically considered the primary browser on riparian shrubs, were detected less often, whereas mule deer browsing was consistently recorded and frequently exceeded elk. Browsing height largely overlapped among species and was significantly higher for bighorn sheep than for bison and mule deer. Diameter of browsed twigs did not differ significantly between species. Browsing composition varied locally without clear spatial patterns, suggesting that site-level factors shape where different ungulates browse willows. Our results demonstrate substantial bison browsing on riparian willows and highlight shifting herbivore impacts on Yellowstone's riparian ecosystems.

Primer design remains a fundamental yet non-trivial step in modern molecular biology workflows. Although numerous tools are available, they are often fragmented across disparate applications, constrained by commercial licensing, or dependent on external servers, limiting workflow integration and compromising sequence confidentiality. To address this challenge, we developed PrimerWeaver (https://ignea.lab.mcgill.ca/primerweaver), a free browser-based tool that integrates primer design, quality control, and support for diverse cloning workflows within a single platform. PrimerWeaver enables overlap PCR, site-directed mutagenesis, multiplex PCR, restriction enzyme-based cloning (including Type II and Golden Gate assembly), and homology-directed methods such as Gibson assembly and Uracil-Specific Excision Reagent (USER) cloning. Primers were generated by optimizing the 3' annealing region for efficient amplification while appending workflow-specific 5' sequences according to the selected application. In silico and wet lab validation across diverse cloning workflows demonstrated consistent results relative to established molecular cloning software. All calculations are performed locally in the user's browser without sequence upload, preserving data confidentiality and supporting reproducible performance across systems. Overall, PrimerWeaver streamlines primer workflows for users ranging from undergraduate students to experienced researchers by integrating multiple PCR and cloning strategies into a single browser-based platform.

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.

Allergic asthma (AA) is a heterogeneous chronic inflammatory airway disorder. In this study, we performed a retrospective bioinformatics analysis based on public transcriptome datasets to identify critical genes associated with immune cell infiltration in AA and to establish a novel predictive model. Two transcriptome datasets (GSE73482 and GSE40889) were analyzed to explore key genes implicated in AA. Functional enrichment analyses, including Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway analyses, were performed using Metascape. Least absolute shrinkage and selection operator regression was applied to screen feature genes and construct a diagnostic prediction model. Weighted gene co-expression network analysis (WGCNA) was conducted to identify AA-related gene modules. The fractions of infiltrating immune cells were estimated using single-sample gene set enrichment analysis (ssGSEA). Gene set variation analysis and gene set enrichment analysis (GSEA) were performed to explore the biological functions and related signaling pathways of the key genes. The Cistrome Data Browser database was used to predict transcription factors that potentially regulate these key genes. We identified 4 highly significant genes in the brown module: membrane associated O acetyltransferase 1 (MBOAT1), leucine rich repeats and immunoglobulin-like domains 1 (LRIG1), LOC401357, and G protein regulated inducer of neurite outgrowth 3 (GPRIN3). GSEA results revealed that these key genes were significantly enriched in multiple immune-related signaling pathways. To further explore the regulatory network of these genes, transcription factors were predicted using the Cistrome Data Browser database, and the regulatory network was visualized using Cytoscape software. MBOAT1, LRIG1, LOC401357, and GPRIN3 are candidate AA-associated genes identified through retrospective modeling. The identification of these genes offers potential opportunities to utilize them as biomarkers and targets for immunotherapy in AA.

BeetleAtlas is an online resource for tissue- and stage-specific transcriptomics in the red flour beetle, Tribolium castaneum. On updating from the original Tcas5.2 genome assembly to the more recent improved icTriCast1.1 genome assembly it became evident that there were major discrepancies between the gene models of the two genome annotations in use: the OGS3 and the NCBI gene sets. As neither was clearly superior we implemented a new design in BeetleAtlas 2 (beetleatlas.org) comprising two parallel 'modes' - one incorporating results using the NCBI gene models and a second incorporating those using the OGS3 gene models. This allows direct comparison where equivalent gene models exist: 50-57% of cases. To aid resolution of discrepancies between the two gene model sets and verification of results, gene models are linked to a custom visualization of RNA-seq read coverage of the genome in the UCSC Genome Browser. This displays reads from 22 tissues and life stages superimposed on the icTriCast1.1 genome assembly. Reference tracks show the NCBI gene models, the OGS3 gene models after translation of their coordinates from the Tcas5.2 assembly, and 1050 discontinued NCBI gene models from the previous assembly after a similar transfer of coordinates. We document various situations in which distinct patterns of expression of the tissues can be used to confirm and extend correlations between the two gene sets, resolve discrepancies between them, make corrections and identify putative genes or exons absent from the current gene sets. BeetleAtlas 2 allows those involved in Tribolium research to avoid the pitfalls inherent in incorrect gene models when planning experiments on specific genes and interpreting the results. It also demonstrates how BeetleAtlas 2 might play an important role in establishing a revised gene set for Tribolium castaneum in the future.

This investigation explores whether differences between fricatives become compressed or shifted during remote recordings. Inputs for all test conditions (six devices across five teleconferencing platforms, two browsers, and internal and external microphones) were near-minimal English fricative-monophthong pairs read by a male speaker and female speaker. Differences of Mel frequency cepstral coefficients (MFCCs), spectral measures, and loudness between /s/ and each of /f, ʃ, ʒ/ under each test condition were compared to the original. MFCCs often showed expanded differences; other measures often showed compression. Even reversals in differences were observed. The findings highlight the need to carefully select and document remote recording conditions for fricative measures.

This study aimed to evaluate the content characteristics, quality, and reliability of cognitive impairment educational videos on Douyin and Bilibili and examine whether video duration and user engagement are associated with information quality. This cross-sectional content analysis searched the Chinese domestic version of TikTok (Douyin; https://www.douyin.com/) and Bilibili (https://www.bilibili.com/) through their official web interfaces on March 12, 2026. Searches were performed in logged-out mode using the keyword "cognitive impairment" via a desktop browser. The first 150 results from each platform were screened, and 250 eligible videos were included (133 from Douyin and 117 from Bilibili). Video quality and reliability were evaluated with the Global Quality Score, modified DISCERN, Journal of the American Medical Association benchmark criteria, and Video Information and Quality Index. Overall educational quality was moderate. Across both platforms, content focused primarily on clinical manifestations and treatment, whereas epidemiology, diagnosis, and prognosis were insufficiently covered. Douyin videos had significantly higher Global Quality Score, modified DISCERN, Journal of the American Medical Association, and Video Information and Quality Index scores than Bilibili videos (all P&#x2005;<&#x2005;.001), despite being substantially shorter. Videos uploaded by doctors or other health professionals showed the highest quality and reliability, whereas videos uploaded by individual users generated stronger engagement. Correlations between engagement indicators and quality scores were weak, indicating that popularity did not reliably reflect educational value. Cognitive impairment videos on Douyin and Bilibili showed substantial variability in quality and incomplete content coverage. Professional participation, clearer source disclosure, and platform-level governance may improve the accuracy and practical utility of short-video health education on cognitive impairment.

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&#x2009;=&#x2009;[Formula: see text]&#x2009;=&#x2009;-0.392X2&#x2009;+&#x2009;0.927X71&#x2009;+&#x2009;1.695X72&#x2009;+&#x2009;0.537X111&#x2009;+&#x2009;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.

Molecular dynamics is a key technique for exploring biomolecular systems at the atomic level. The rapid growth in accessible system sizes and time scales has intensified the need for efficient postprocessing methods that extract meaningful insights from the resulting data. Interaction fingerprint (IFP) analyses are a valuable tool for elucidating key atomic interactions within molecular ensembles; yet current specialized software often struggle with extensive trajectories or complex systems. Here, we introduce InterMap, a Python package designed to accelerate IFP detection on large-scale molecular ensembles. By actively exploiting k-d trees, InterMap efficiently handles the massive amount of distance calculations necessary to detect IFPs, particularly when dealing with intramolecular interactions. The seamless integration with MDAnalysis ensures broad format compatibility and allows using SMARTS patterns for flexible interaction definitions. InterMap adopts a deeply compressed binary encoding to manage IFPs, which makes it very memory-friendly. Furthermore, convenient interactive visualizations are provided to enhance data interpretation through a locally hosted web-browser application. Benchmark results indicate that InterMap significantly outperforms existing tools for processing complex biomolecular systems, achieving up to a 99% reduction in both runtime and peak memory usage. InterMap's code and issue tracker are available at https://github.com/rglez/intermap, while documentation and tutorials can be found at https://rglez.github.io/intermap/.

Large-scale genome-wide association studies (GWAS) and rare variant association studies (RVAS) from population biobanks provide valuable resources for gene discovery in complex human traits. We present an analysis of the All of Us Research Program v8 release, which includes whole genome sequencing data and harmonized phenotypic information of 392,030 participants after quality control, enabling a unified investigation of rare and common variants across a spectrum of human traits and diseases. We build an extensive phenome- and genome-wide ("All by All") computational framework to perform GWAS and RVAS on 3,602 phenotypes and identify 49,863 approximately independent, high-quality single-variant and gene-level associations. Meta-analyses of All of Us and UK Biobank, with sample sizes as large as 786,871 participants, further enhance statistical power and find 193 pLoF gene-phenotype associations that are not significant in either cohort alone, including 22 associations not highlighted by previous studies. We also present a public interactive browser that integrates association results for common and rare variants to facilitate interpretation and rapid querying of summary statistics, along with supporting documentation, and a Featured Workspace in the All of Us Researcher Workbench. Our framework will apply to iterative data releases as All of Us grows, empowering researchers worldwide to uncover insights into the functional effects of genetic components on complex traits and diseases.

Illicit websites depend upon abusive Traffic Distribution Systems (TDSs) to generate user traffic for malicious. Traffic Distribution Systems are the intermediate websites that redirect the HTTP traffic from online advertisements. However, such systems also started to promote abusive activities such as phishing, scams, ad frauds, malicious downloads, and social engineering attacks. In this study, we present Online Abusive Traffic Finder (OATF), an enhanced web security protection system designed to investigate and evaluate abusive TDSs and their associated threats. A total of 10,746 webpages were collected over a one-month period (May 15, 2024-June 14, 2024) from four diverse traffic sources, including advertisement-based URL shortening services, typosquatting websites, unlicensed online pharmacy sites, and the PhishTank dataset. We use these sources due to their diverse nature to redirect users toward abusive and malicious sites. During data collection process, we collect destination web pages screenshots, browser, and content logs. We semi automatically label collected pages and use labeled data to automatically examine page content to understand the threats from these traffic sources. To protect users from abusive TDSs, a Convolutional neural network (CNN) based classifier is integrated as a supporting component for automated detection of abusive webpages using visual features. The CNN model achieved the highest accuracy (91.92%) within the proposed framework. The proposed approach provides deeper insights into the operational behavior of abusive traffic ecosystems and contributes toward improving web security against evolving malicious distribution strategies.

The global surge in fluoroquinolone resistance (FQR) underscores the urgent need for robust environmental surveillance. From a One Health perspective, rivers serve as critical conduits and hotspots for antimicrobial resistance (AMR) dissemination. To address this issue, we conducted a systematic metagenomic surveillance of FQR across spatially prioritized freshwater ecosystems using distribution data of five major markers (gyr, par, qnr, aac, and qep) retrieved from the National Center for Biotechnology Information Pathogen Detection Isolate Browser. Among 164 riverine metagenomic datasets, 31 high-quality datasets from the Mississippi, Yukon, Saint Lawrence, Yangtze, and Pearl Rivers were analysed. FQR genes were detected in 12 datasets, with normalized abundances ranging from 0.01 to 1.22 copies per bacterial cell. Plasmid-mediated qnrS2 and efflux pump genes (qepA2 and AbaQ) emerged as the most prevalent determinants. Multivariate analyses revealed river-specific clustering patterns and strong correlations with metal resistance genes, highlighting co-selection pressures. The predominance of conjugative mobile genetic elements indicated an elevated potential for horizontal gene transfer. Taxonomic profiling further revealed enrichment of clinically important and World Health Organization priority pathogens. Community structure analyses (permutational multivariate analysis, R&#xb2; = 0.7598, P = 0.003) confirmed significant microbial variations across rivers. Collectively, this integrative approach identifies environmental reservoirs of FQR genes, supporting river-based AMR surveillance. These insights are pivotal for shaping evidence-driven mitigation strategies and informing both national and global AMR policies.