Search Results (186)

Objectives: The diagnosis of infective endocarditis (IE) is clinically challenging. This study aimed to examine an endocarditis network and the effects of delayed diagnosis. Methods: We reviewed the patients who were admitted for infective endocarditis at our institution between January 2012 and December 2021. Infective endocarditis was diagnosed according to ESC/EACTS guidelines for the management of endocarditis. Details of admitting hospitals were obtained from the German Hospital Directory. Data are presented as medians (25th–75th quartiles) or absolute values (percentages) unless otherwise specified. Results: A total of 812 consecutive patients were admitted to our centre for IE. Exact records on the time to diagnosis were available for 707 patients (87.1%). The patients were divided into two groups based on the time to diagnosis, i.e., up to 7 days (n = 509; 72.0% group ED) and more than 7 days (n = 198; 28.0% group LD). The EuroSCORE II (p = 0.001) and the EndoSCORE (p = 0.019) were significantly higher in the LD group. The median time to diagnosis was shorter in university hospitals as compared to non-teaching hospitals (p = 0.008) and among patients admitted to cardiology and cardiac surgery departments (p < 0.001). Patients diagnosed later had higher rates of tracheostomy (p < 0.001), longer ICU (p = 0.004) and hospital stays (p < 0.001) and higher in-hospital mortality (p = 0.027). We found that a delayed diagnosis (p = 0.040), stroke (p = 0.004), age > 75 years (p = 0.044) and atrial fibrillation (p < 0.001) were independently associated with in-hospital mortality. Furthermore, survival at 1 and 5 years was significantly higher in the ED group (p < 0.001). Conclusions: The diagnosis of IE may be influenced by a multitude of factors. Our results indicate that a delayed diagnosis is independently associated with an increased rate of in-hospital mortality. According to our results, an early diagnosis of IE may be associated with improved outcomes. Full article

This paper presents the design, implementation, and evaluation of a low-code document management and approval system developed on the Microsoft Power Platform. The solution integrates Power Apps, Power Automate, SharePoint Online, and Azure Active Directory to enable secure, traceable, and device-independent workflows for managing organizational documents. By combining graphical interfaces, automated approval logic, and enterprise-grade identity management, the system supports real-time collaboration and compliance with records’ governance standards. A comparative analysis with traditional enterprise content management and open-source web architectures demonstrates substantial advantages in deployment speed, scalability, and auditability. Empirical results from a six-week pilot involving multiple users indicate a reduction in approval cycle time, high user satisfaction, and strong cost-efficiency relative to conventional development models. The findings highlight how low-code ecosystems operationalize digital transformation by empowering non-technical users to automate complex workflows while maintaining security and governance integrity. This work contributes to the understanding of information system democratization, showing that low-code platforms can extend digital participation, improve organizational agility, and support sustainable operational efficiency across distributed environments. Full article

Open access (OA) has expanded scholarly publishing, yet concerns remain about the sustainability of journals indexed in selective databases. This study analyzes editorial predictors of discontinuation among 8730 journals simultaneously registered in the Directory of Open Access Journals (DOAJ) and indexed in Scopus, including 58 (0.66%) discontinued titles as of June 2025 (latest available update at the time of data extraction). The analyses revealed that a journal’s history of prior discontinuation was the strongest and most consistent predictor of future instability, confirming that discontinuation follows a path-dependent pattern rather than isolated events. Financial structure also played a decisive role: journals applying other editorial fees beyond standard article processing charges (APCs) were nearly four times more likely to experience discontinuation (IRR = 3.877, p = 0.048), while those following standardized APC models showed a protective but non-significant tendency (IRR = 0.378, p = 0.084). Journal age exhibited a modest yet significant positive effect (IRR = 1.032, p = 0.031), suggesting that older titles face a gradual accumulation of risk over time. By contrast, editorial practices such as plagiarism detection, waiver policies, and turnaround time showed no significant association. Overall, the findings indicate that discontinuation in Scopus-indexed OA journals is statistically associated with historical trajectories, financial transparency, and governance capacity, rather than by routine editorial procedures. Full article

Waardenburg syndrome (WS) represents a group of genetic conditions characterized by auditory and pigmentation defects. Pathogenic variants in PAX3, MITF, SOX10, EDN3, EDNRB, SNAI2, and KITLG genes have been associated with WS across multiple populations; a comprehensive study of WS in Africa has not yet been reported. We conducted a systematic review of clinical expressions and genetics of WS across Africa. The Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines were followed, and the study protocol was registered on PROSPERO, the International Prospective Register of Systematic Reviews (2025 CRD420250655744). A literature search was performed on Google Scholar, PubMed, Scopus, Directory of Open Access Journals (DOAJ), Global Index Medicus, African-Wide Information, ScienceDirect, Connecting Repositories (CORE), and the Web of Science databases. We reviewed a total of 15 articles describing 84 WS cases, which showed no gender bias and a mean age at reporting of 17.5 years. Congenital, sensorineural, and profound hearing loss was described in most cases (66.7%; n = 56/84). WS type 2 (WS2), with characteristically no dystopia canthorum, is the predominant subtype (36.9%; n = 31/84). Pathogenic variants in four WS known genes, i.e., PAX3 (13 families), SOX10 (7 families), EDNRB (4 families), and EDN3 (1 family), were reported in Morocco, Tunisia, and South Africa. One candidate gene (PAX8) was described in one family in Ghana. Two non-syndromic hearing loss (NSHL) genes (BDP1 and MYO6) were reported in two separate families in South Africa, suggesting a possible phenotypic expansion. The highest number of WS cases was described in South Africa (38.1%; n = 32/84) and Tunisia (26.2%; n = 22/84). Gene variants were missense (27/43), deletion (7/43), splicing (5/43), nonsense (2/43), indel (1/43), and duplication (1/43), chiefly segregating in an autosomal dominant inheritance mode. There was no functional data to support the pathogenicity of putative causative variants. This review showed that WS2 is the most common in Africa. Variants in PAX3 and SOX10 were the predominant genetic causes. This study emphasizes the need to further investigate in-depth clinical characterization, molecular landscape, and the pathobiology of WS in Africa. Full article

The COVID-19 outbreak has had a tremendous socioeconomic impact around the world, and although there are currently some drugs that have been granted authorization by the U.S. FDA for the treatment of COVID-19, there are still some restrictions on their use. As a result, it is still necessary to urgently carry out related drug development research. Deep generative models and cheminformatics were used in this study to design and screen novel candidates for potential anti-SARS-CoV-2 small molecule compounds. In this study, the small molecule structure of Molnupiravir which has been authorized by the U.S. FDA for emergency use was used to be a model in a similarity search based on the BIOVIA Available Chemicals Directory (BIOVIA ACD) database using the BIOVIA Discovery Studio (DS) software (version 2022). There were 61,480 similar structures of Molnupiravir, which were used as training dataset for the deep generative model, and then the reinforcement learning model was used to generate 6000 small molecule structures. To further confirm whether those molecule structures potentially possess the ability of anti-SARS-CoV-2, cheminformatics techniques were used to assess 38 small molecule compounds with potential anti-SARS-CoV-2 activity. The suitability of 38 small molecule structures was calculated using ADMET analysis. Finally, one compound structure, Molecule_36, passed ADMET and was unpatented. This study demonstrates that Molecule_36 may have better potential than Molnupiravir does in affinity with SARS-CoV-2 RdRp and ADMET. We provide a combination of generative deep neural networks and cheminformatics for developing new anti-SARS-CoV-2 compounds. However, additional chemical refinement and experimental validation will be required to determine its stability, mechanism of action, and antiviral efficacy. Full article

This paper presents a markerless augmented reality (AR) navigation system for guiding users across a university campus, independent of internet or wireless connectivity, integrating machine learning (ML) and deep learning techniques. The system employs computer vision to detect campus signage “Meeting Point” and “Directory”, and classifies them through a binary classifier (BC) and convolutional neural networks (CNNs). The BC distinguishes between the two types of signs using RGB values with algorithms such as Perceptron, Bayesian classification, and k-Nearest Neighbors (KNN), while the CNN identifies the specific sign ID to link it to a campus location. Navigation routes are generated with the Floyd–Warshall algorithm, which computes the shortest path between nodes on a digital campus map. Directional arrows are then overlaid in AR on the user’s device via ARCore, updated every 200 milliseconds using sensor data and direction vectors. The prototype, developed in Android Studio, achieved over 99.5% accuracy with CNNs and 100% accuracy with the BC, even when signs were worn or partially occluded. A usability study with 27 participants showed that 85.2% successfully reached their destinations, with more than half rating the system as easy or very easy to use. Users also expressed strong interest in extending the application to other environments, such as shopping malls or airports. Overall, the solution is lightweight, scalable, and sustainable, requiring no additional infrastructure beyond existing campus signage. Full article

Cities face mounting pressures to deliver reliable, low-carbon services amid rapid urbanization and budget constraints. Artificial Intelligence (AI), Machine Learning (ML), Deep Learning (DL), and the Internet of Things (IoT) are widely promoted to automate operations and strengthen decision-support across the built environment; however, it remains unclear whether these interventions are both effective and systemically integrated across domains. We conducted a Preferred Reporting Items for Systematic Reviews (PRISMA) aligned systematic review and meta-analysis (January 2015–July 2025) of empirical AI/ML/DL/IoT interventions in urban infrastructure. Searches across five open-access indices Multidisciplinary Digital Publishing Institute (MDPI), Directory of Open Access Journals (DOAJ), Connecting Repositories (CORE), Bielefeld Academic Search Engine (BASE), and Open Access Infrastructure for Research in Europe (OpenAIRE)returned 7432 records; after screening, 71 studies met the inclusion criteria for quantitative synthesis. A random-effects model shows a large, pooled effect (Hedges’ g = 0.92; 95% CI: 0.78–1.06; p < 0.001) for within-domain performance/sustainability outcomes. Yet 91.5% of implementations operate at integration Levels 0–1 (isolated or minimal data sharing), and only 1.4% achieve real-time multi-domain integration (Level 3). Publication bias is likely (Egger’s test p = 0.03); a conservative bias-adjusted estimate suggests a still-positive effect of g ≈ 0.68–0.70. Findings indicate a dual reality: high efficacy in silos but pervasive fragmentation that prevents cross-domain synergies. We outline actions, mandating open standards and APIs, establishing city-level data governance, funding Level-2/3 integration pilots, and adopting cross-domain evaluation metrics to translate local gains into system-wide value. Overall certainty of evidence is rated Moderate based on Grading of Recommendations Assessment, Development, and Evaluation (GRADE) due to heterogeneity and small-study effects, offset by the magnitude and consistency of benefits. Full article

Advancements in targeted therapies and immunotherapies have improved survival for individuals with metastatic non-small-cell lung cancer (mNSCLC), creating a growing population of Canadians living long-term with the disease. These individuals face ongoing physical, emotional, and practical challenges, yet existing supportive care services are often designed for patients receiving curative intent treatment and may not adequately address the challenges of those undergoing continuous treatment. To explore these experiences and inform the development of supports tailored to their needs, eight participants with mNSCLC completed one-on-one virtual interviews. They described limited support for managing side effects and psychosocial concerns despite general satisfaction with oncology care. Fatigue and cognitive challenges impacted daily functioning, and emotional challenges (e.g., fear of progression, stigma, and difficulty finding meaning) impacted quality of life. Financial burden, including unexpected costs and loss of income, further affected their well-being. Existing supports, such as exercise programs, were viewed positively but were often difficult to access, were offered only short-term, and required patients to find them independently. Recommendations included improved coordination and communication across the healthcare system, alongside tailored interventions such as navigation services, resource directories, health promotion supports, and expanded peer support. Overall, people living long term with mNSCLC face distinct challenges and unmet supportive care needs, highlighting the importance of integrating supportive services into routine oncology care. Full article

With the rapid growth of the Internet of Things, efficient resource discovery has become essential for effective resource management. Although Message Queuing Telemetry Transport is one of the most widely adopted IoT communication protocols, it lacks a native resource discovery mechanism or any resource discovery standards. The existing Message Queuing Telemetry Transport resource discovery relies on symmetric full-mesh synchronization, which causes excessive traffic and unacceptable latency as the system scales up: this restricts its use to only small-size deployments. To overcome these limitations, this paper proposes a Hierarchical Message Queuing Telemetry Transport resource discovery and distribution framework, inspired by the hierarchical design of the Domain Name System. By introducing hierarchical asymmetry, the framework reduces communication overhead, enhances scalability, and maintains efficient real-time query performance, as demonstrated by implementation and simulation results. Full article

(1) Chronic heart failure (CHF) is a typical component of the polymorbid profile of an elderly patient. The aim of this systematic review was to search for data from pharmacokinetic (PK) studies of any drugs in patients with CHF to systematize information on changes in PK parameters depending on the physicochemical properties (PCPs) of the drug and route of its administration. (2) A systematic review of PK studies in patients with CHF was performed using Elibrary.ru, United States National Library of Medicine (PubMed), China National Knowledge Infrastructure (CNKI), and Directory of Open Access Journals (DOAJ). The final number of included articles was 106. A descriptive and correlation analysis of PK data and PCPs of drugs included in the study was carried out. Inclusion criteria: PK study, available PK parameters, demographic data, and diagnosed CHF. Risk of bias was assessed using ROBINS-I. (3) Evaluation of correlations between PCPs of drugs and their PK revealed a link between (i) plasma protein binding (PPB) and volume of distribution for lipophilic drugs; (ii) PCPs, half-life, and clearance for drugs with high PPB; and (iii) PPB and clearance for hydrophilic and amphiphilic drugs. (4) Hypoalbuminemia associated with CHF may lead to an increased volume of distribution of lipophilic drugs; lipophilic drugs used in CHF patients may be associated with prolongation of the half-life period and reduction in clearance; highly protein-bound drugs may manifest with reduced clearance. PK characteristics identified in this review should guide modifications to dosing regimens in CHF patients receiving medications from different groups. Full article

With the rapid development of the Internet of Things (IoT) applications, the ability to automatically discover and retrieve resource information has become increasingly enhanced. Despite being one of the most commonly used IoT communication protocols, Message Queuing Telemetry Transport (MQTT) does not natively support resource discovery. To address this limitation, MQTT-resource discovery (MQTT-RD), a resource discovery mechanism based on MQTT, has been used for resource management. In this study, we tested and evaluated MQTT-RD using the Sniffer system that manages the resource directory and synchronizes data via MQTT. When too many Sniffers are activated, the MQTT-RD system becomes unsustainable. However, the experimental results in this study revealed that frequent updates to the resource directory (RD) and high-frequency heartbeat messages (pingalive) significantly increase network traffic and system load. In this study, we identified performance and stability issues to propose improvement strategies, including refining the topic design, reducing message transmission frequency, and improving the synchronization mechanism. Additionally, the feasibility of incorporating centralized management was explored to enhance system efficiency. Full article

This study examines journalists’ perceptions of the impact of artificial intelligence (AI) on disinformation, a growing concern in journalism due to the rapid expansion of generative AI and its influence on news production and media organizations. Using a quantitative approach, a structured survey was administered to 504 journalists in the Basque Country, identified through official media directories and with the support of the Basque Association of Journalists. This survey, conducted online and via telephone between May and June 2024, included questions on sociodemographic and professional variables, as well as attitudes toward AI’s impact on journalism. The results indicate that a large majority of journalists (89.88%) believe AI will considerably or significantly increase the risks of disinformation, and this perception is consistent across genders and media types, but more pronounced among those with greater professional experience. Statistical analyses reveal a significant association between years of experience and perceived risk, and between AI use and risk perception. The main risks identified are the difficulty in detecting false content and deepfakes, and the risk of obtaining inaccurate or erroneous data. Co-occurrence analysis shows that these risks are often perceived as interconnected. These findings highlight the complex and multifaceted concerns of journalists regarding AI’s role in the information ecosystem. Full article

Malicious actors often exploit persistence mechanisms, such as unauthorized modifications to Windows startup directories or registry keys, to achieve privilege escalation and maintain access on compromised systems. While information technology (IT) teams legitimately use these AutoStart Extension Points (ASEPs), adversaries frequently deploy malicious binaries with non-standard naming conventions or execute files from transient directories (e.g., Temp or Public folders). This study proposes a threat-hunting framework using a custom Elasticsearch Security Information and Event Management (SIEM) system to detect such persistence tactics. Two hypothesis-driven investigations were conducted: the first focused on identifying unauthorized ASEP registry key modifications during user logon events, while the second targeted malicious Dynamic Link Library (DLL) injections within temporary directories. By correlating Sysmon event logs (e.g., registry key creation/modification and process creation events), the researchers identified attack chains involving sequential registry edits and malicious file executions. Analysis confirmed that Sysmon Event ID 12 (registry object creation) and Event ID 7 (DLL loading) provided critical forensic evidence for detecting these tactics. The findings underscore the efficacy of real-time event correlation in SIEM systems in disrupting adversarial workflows, enabling rapid mitigation through the removal of malicious entries. This approach advances proactive defense strategies against privilege escalation and persistence, emphasizing the need for granular monitoring of registry and filesystem activities in enterprise environments. Full article

The rapid growth of internet usage in daily life has led to a significant increase in cyber threats, with malicious URLs serving as a common cybercrime. Traditional detection methods often suffer from high false alarm rates and struggle to keep pace with evolving threats due to outdated feature extraction techniques and datasets. To address these limitations, we propose a deep learning-based approach aimed at developing an effective model for detecting malicious URLs. Our proposed method, the Char2B model, leverages a fusion of BERT and CharBiGRU embedding, further enhanced by a Conv1D layer with a kernel size of three and unit-sized stride and padding. After combining the embedding, we used the BERT model as a baseline for comparison. The study involved collecting a dataset of 87,216 URLs, comprising both benign and malicious samples sourced from the open project directory (DMOZ), PhishTank, and Any.Run. Models were trained using the training set and evaluated on the test set using standard metrics, including accuracy, precision, recall, and F1-score. Through iterative refinement, we optimized the model’s performance to maximize its effectiveness. As a result, our proposed model achieved 98.50% accuracy, 98.27% precision, 98.69% recall, and a 98.48% F1-score, outperforming the baseline BERT model. Additionally, the false positive rate of our model was 0.017 better than the baseline model’s 0.018. By effectively extracting and utilizing informative features, the model accurately classified URLs into benign and malicious categories, thereby improving detection capabilities. This study highlights the significance of our deep learning approach in strengthening cybersecurity by integrating advanced algorithms that enhance detection accuracy, bolster defense mechanisms, and contribute to a safer digital environment. Full article

This study aims to analyze how women’s empowerment in sustainable entrepreneurial leadership transforms social, environmental, and economic challenges into growth opportunities within B Corps-certified companies in Latin America. A total of 9536 companies were identified in the global B Corps registry, of which more than 1000 belonged to the Latin America and Caribbean directory. Particular attention was given to 130 companies located in Chile, with a presence in countries such as Peru, Mexico, Colombia, Brazil, Uruguay, Paraguay, and Argentina. The methodology adopted a post-positivist approach with a hermeneutic analysis rooted in organizational studies, using the Straussian grounded theory method. Testimonies from 16 female entrepreneurs were explored, identified through the B Corps directory and the main social media networks of the B system in Latin America. This approach enabled a deeper understanding of the human complexity surrounding sustainability, equity, and gender equality. Findings show that female leadership promotes inclusive and strategic actions that challenge traditional structures and generate positive impacts. Five categories emerged: female entrepreneurial leadership; gender equality stakeholders; social contribution; women’s economic development; and sustainable decision-making. These converge in the central category of female empowerment in sustainable entrepreneurial leadership. In conclusion, the emerging theory expands the understanding of women-led leadership in Latin America, revealing socially responsible business models that promote sustainability, inclusion, and challenge dominant power structures in the business world. Full article