Search Results (28)

Rapid access to building intelligence is critical for emergency response, yet paper fire safety instructions (FSi) often provide limited utility under stress. This structured narrative review addresses the “information gap” between unit arrival and decision-making by analyzing the legal admissibility, technological requirements, and security risks of digital FSi across Poland, Germany, France, Belgium, and Hungary. While no explicit prohibition of digital forms was identified, enforcement practices prioritize paper as the evidentiary master. Consequently, we propose a hybrid model: a paper master for compliance and redundancy, supplemented by a digital operational overlay accessed via “zero-install” offline-first progressive web apps (PWA). The review defines a minimum operational dataset (MOD)—prioritizing critical data like utility shut-offs and hazards over full documentation—and addresses cybersecurity threats, specifically QR-phishing (“quishing”). We conclude that the hybrid model minimizes legal and operational risks while significantly reducing time-to-information, provided that strict content identity and change management protocols are maintained. Full article

In recent years, excessive tillering caused by high temperatures during early growth has contributed to rice quality deterioration in warm regions of Japan. Accurate determination of midseason drainage timing is essential but remains difficult due to year- and cultivar-dependent variability. In this study, we developed a smartphone-based web application that estimates rice tiller number from canopy images and diagnoses the optimal timing of midseason drainage by comparing estimated tiller numbers with cultivar-specific target values. The system operates entirely on a smartphone using HTML5 canvas-based pixel extraction, JavaScript computation, and Google Apps Script-based backend processing. Field experiments conducted in Chiba Prefecture using three rice cultivars showed a strong linear relationship between estimated and observed tiller numbers (R² = 0.9439). The root mean square error (RMSE) was 42.6 tillers m⁻², with a consistent negative bias (−34.6 tillers m⁻²), indicating systematic underestimation. Considering typical tiller increase rates near midseason drainage (12.0–24.3 tillers m⁻² day⁻¹), these errors correspond to approximately 1–3 days of growth progression, which is acceptable for timing-based decision-making. Although the system does not aim to provide precise absolute tiller counts, it reliably captures relative growth-stage dynamics and supports threshold-based diagnosis. The proposed approach enables rapid, on-site decision support using only a smartphone, contributing to labor-saving and improved water management in rice production. Full article

Background/Objectives: In recent decades, new technologies have been progressively integrated into various areas of mental health care. Mobile applications are potentially effective tools that allow psychiatric patients themselves to access self-management resources and tools within the community setting. Mental health nursing plays a key role in enabling patients to take an active role in their care and in promoting activities that foster their involvement and empowerment. The primary aim of this pilot study was to develop the PsyAPP mobile application to support both nurses and individuals with mental illness in managing care and improving health outcomes, and to assess its feasibility within a real-world clinical setting. Methods: A mobile application (PSYAPP) and a complementary web-based nursing management platform were designed and implemented. A total of 20 psychiatric patients enrolled in a partial hospitalization program in Soria (Spain) participated. Participants were assigned to experimental (app users) and control groups. Psychological empowerment, global functioning, and suicide risk were assessed before and after the intervention. Results: Patients who used the application showed significantly greater psychological empowerment (W = 2.04, p ≤ 0.04) compared with the control group. Statistically significant improvements were observed in psychological, social, and occupational functioning. Regarding suicide risk, no statistically significant changes were detected between pre- and post-intervention measurements in either group. Overall, PSYAPP demonstrated feasibility and potential utility as an innovative tool to support mental health care follow-up. Conclusions: This study developed and implemented a mobile application designed to enhance mental health care by supporting both patients and psychiatric nurses. Results showed significant improvements in global functioning in both the app and control groups, suggesting that rehabilitative treatment contributed to overall progress. Suicide risk did not significantly change within groups, although improvements were seen in the full sample, likely due to clinical care rather than app use. Only the experimental group demonstrated increased psychological empowerment, indicating that the app may effectively enhance patient engagement and involvement in their own care. Full article

Androgens have been shown to be linked to cancer progression, particularly in hormone-dependent cancers such as prostate and breast cancer, but also other cancers. Amyloid precursor protein (APP), which has primarily been studied in Alzheimer’s disease, is gaining recognition for its role in tumor growth and survival. While APP overexpression and androgen receptor (AR) signaling are each associated with cancer progression, the connection between androgens and APP processing in cancer has not been thoroughly investigated. This systematic review was conducted through a comprehensive search of PubMed, Scopus, Web of Science, and EMBASE between 2000 to 2024 for studies examining the effects of androgens on APP and its cleavage enzymes in cancer. Five experimental studies met the inclusion criteria, covering prostate and breast cancer models. Data were extracted and synthesized narratively due to heterogeneity in methods and outcomes. Three studies reported that dihydrotestosterone (DHT) or AR agonists increased the expression and nuclear translocation of ADAM10, a key α-secretase enzyme in the non-amyloidogenic APP processing pathway. Two studies identified APP as an androgen-responsive gene, showing that androgens upregulated APP expression in prostate and breast cancer cells and promoted the proliferation of cancer cells. Inhibition or knockdown of APP and ADAM10 reduced proliferation, supporting their roles in tumor progression. Androgen signaling modulates APP processing in cancer, particularly through the non-amyloidogenic pathway; however, significant knowledge gaps remain. Further studies are needed to explore the interaction between androgens and APP processing in other cancer types, as well as to elucidate downstream signaling pathways regulated at the gene expression level. Full article

Introduction: Multiple sclerosis (MS) is a chronic neurodegenerative disease that entails high costs, progressive disability, and reduced quality of life (QoL). Telerehabilitation (TR), supported by new technologies, is emerging as an alternative or complement to in-person rehabilitation, potentially lowering socioeconomic impact and improving QoL. Aim: The objective of this study was to evaluate the effect of TR on the QoL of people with MS compared with in-person rehabilitation or no intervention. Materials and methods: A systematic review of randomized clinical trials was conducted (March–May 2025) following PRISMA guidelines. Searches were run in the PubMed-Medline, EMBASE, PEDro, Web of Science, and Dialnet databases. Methodological quality was assessed with the CASP scale, risk of bias with the Risk of Bias 2 tool, and evidence level and grade of recommendation with the Oxford Classification. The protocol was registered in PROSPERO (CRD420251110353). Results: Of the 151 articles initially found, 12 RCTs (598 total patients) met the inclusion criteria. Interventions included (a) four studies employing video-controlled exercise (one involving Pilates to improve fitness, another involving exercise to improve fatigue and general health, and two using exercises focused on the pelvic floor muscles); (b) three studies using a monitoring app to improve manual dexterity, symptom control, and increased physical activity; (c) two studies implementing an augmented reality system to treat cognitive deficits and sexual disorders, respectively; (d) one platform with a virtual reality headset for motor and cognitive training; (e) one study focusing on video-controlled motor imagery, along with the use of a pain management app; (f) a final study addressing cognitive training and pain reduction. Studies used eight different scales to assess QoL, finding similar improvements between groups in eight of the trials and statistically significant improvements in favor of TR in four. The included trials were of good methodological quality, with a moderate-to-low risk of bias and good levels of evidence and grades of recommendation. Conclusions: TR was more effective in improving the QoL of people with MS than no intervention, was as effective as in-person treatment in patients with EDSS ≤ 6, and appeared to be more effective than in-person intervention in patients with EDSS between 5.5 and 7.5 in terms of QoL. It may also eliminate some common barriers to accessing such treatments. Full article

Online education has become an important channel for extensive, inclusive and flexible learning experiences. However, significant gaps persist in providing truly accessible, personalized and adaptable e-learning environments, especially for students with disabilities, varied language backgrounds, or limited bandwidth. This paper presents AccessiLearnAI, an AI-driven platform, which converges accessibility-first design, multi-format content delivery, advanced personalization, and Progressive Web App (PWA) offline capabilities. Our solution is compliant with semantic HTML5 and ARIA standards, and incorporates features such as automatic alt-text generation for images using Large Language Models (LLMs), real-time functionality for summarization, translation, and text-to-speech capabilities. The platform, built on top of a modular MVC and microservices-based architecture, also integrates robust security, GDPR-aligned data protection, and a human-in-the-loop to ensure the accuracy and reliability of AI-generated outputs. Early evaluations indicate that AccessiLearnAI improves engagement and learning outcomes across multiple ranges of users, suggesting that responsible AI and universal design can successfully coexist to bring equity through digital education. Full article

This study examines how gamification can improve the satisfaction level of foreign learners studying the Indonesian language using the innovative multiplatform application named Belajar Indo. In consideration of the Indonesian government’s heightened focus on language proficiency for foreign workers and students, this study examines the challenges encountered in Bahasa Indonesia for Foreign Speakers (BIPA) programs. Integrating gamified elements into a Progressive Web App (PWA) presents an engaging alternative to traditional learning resources, which frequently fall short in terms of interactivity and accessibility. Data gathered from surveys, online observation, and user engagement metrics indicate that gamification greatly improves learner motivation, resulting in a success rate of 93.51% and a Net Promoter Score (NPS) of 8.11. The findings demonstrate that gamification enhances language acquisition while simultaneously cultivating a greater enthusiasm for learning Indonesian, proving advantageous for both beginners and advanced learners. This study advances the domain of educational technology by presenting a model that integrates language acquisition with digital innovation, highlighting gamified learning as an effective instrument for foreign language education. Future recommendations involve fine-tuning user interface components to improve usability and maintain elevated engagement levels. Full article

Progress in automatic sign language recognition and translation has been hindered by the scarcity of datasets available for the training of machine learning algorithms, a challenge that is even more acute for languages with smaller signing communities, such as Spanish. In this paper, we introduce a dataset of 300 isolated signs in Spanish Sign Language, collected online via a web application with contributions from 124 participants, resulting in a total of 8000 instances. This dataset, which is openly available, includes keypoints extracted using MediaPipe Holistic. The goal of this paper is to describe the construction and characteristics of the dataset and to provide a baseline classification method using a spatial–temporal graph convolutional network (ST-GCN) model, encouraging the scientific community to improve upon it. The experimental section offers a comparative analysis of the method’s performance on the new dataset, as well as on two other well-known datasets. The dataset, code, and web app used for data collection are freely available, and the web app can also be used to test classifier performance on-line in real-time. Full article

Background: Glaucoma is a progressive optic neuropathy characterized by the neurodegeneration and death of retinal ganglion cells (RGCs), leading to blindness. Current glaucoma interventions reduce intraocular pressure but do not address retinal neurodegeneration. In this effort, to identify new pharmacological targets for glaucoma management, we employed a network pharmacology approach. Methods: We first retrieved transcriptomic data from GEO, an NCBI database, and carried out GEO2R (an interactive web tool aimed at comparing two or more groups of samples in a GEO dataset). The GEO2R statistical analysis aimed at identifying the top differentially expressed genes (DEGs) and used these as input of STRING (Search Tool for the Retrieval of Interacting Genes/Proteins) app within Cytoscape software, which builds networks of proteins starting from input DEGs. Analyses of centrality metrics using Cytoscape were carried out to identify nodes (genes or proteins) involved in network stability. We also employed the web-server software MIRNET 2.0 to build miRNA–target interaction networks for a re-analysis of the GSE105269 dataset, which reports analyses of microRNA expressions. Results: The pharmacological targets, identified in silico through analyses of the centrality metrics carried out with Cytoscape, were rescored based on correlations with entries in the PubMed and clinicaltrials.gov databases. When there was no match (82 out of 135 identified central nodes, in 8 analyzed networks), targets were considered “potential innovative” targets for the treatment of glaucoma, after further validation studies. Conclusions: Several druggable targets, such as GPCRs (e.g., 5-hydroxytryptamine 5A (5-HT5A) and adenosine A_2B receptors) and enzymes (e.g., lactate dehydrogenase A or monoamine oxidase B), were found to be rescored as “potential innovative” pharmacological targets for glaucoma treatment. Full article

Background: Prostate cancer survivorship care is essential for the early identification of cancer recurrence and progression and the monitoring of adverse effects. Prostate cancer survivorship programs have enabled care to be shared between specialists using digital healthcare platforms. We systematically reviewed the literature to examine if prostate cancer survivorship care had been successfully digitalised. Methods: English language articles were searched on PubMed, Embase, and Cochrane Libraries. The search terms included combinations of “eHealth”, “digital health”, “prostate cancer”, “shared care”, and related keywords (studies published between [1 January 1946 and 20 March 2023]). Results: Our search strategy yielded 1722 publications, of which 17 studies were included in our final review. Diverse eHealth interventions (web platforms, apps, patient portals) for digital prostate cancer shared care enabled communication, symptom management, and holistic assessment, with potential for reducing anxiety, enhancing outcomes, and increasing engagement. The studies (9 months to 5 years duration) involved participants across different care phases (16 to 3521 participants). We identified ten eHealth platforms, which provided successful symptom tracking, needs assessment, and communications. The platform-based interventions improved some aspects of communication, symptom management, and care delivery. The ongoing clinical need for a robust digital platform that caters to all domains of shared care was identified. Conclusions: eHealth will certainly play a central role in digital prostate cancer shared care, providing better health outcomes and care delivery. Future larger studies in this field should address the implementation barriers, including cost-effectiveness and primary care remuneration. It is also crucial to refine application useability and workflow, focusing on standardization and patient-centred approaches. Full article

Cardiovascular diseases (CVDs) remain a major global health challenge and a leading cause of mortality, highlighting the need for improved predictive models. We introduce an innovative agent-based dynamic simulation technique that enhances our AI models’ capacity to predict CVD progression. This method simulates individual patient responses to various cardiovascular risk factors, improving prediction accuracy and detail. Also, by incorporating an ensemble learning model and interface of web application in the context of CVD prediction, we developed an AI dashboard-based model to enhance the accuracy of disease prediction and provide a user-friendly app. The performance of traditional algorithms was notable, with Ensemble learning and XGBoost achieving accuracies of 91% and 95%, respectively. A significant aspect of our research was the integration of these models into a streamlit-based interface, enhancing user accessibility and experience. The streamlit application achieved a predictive accuracy of 97%, demonstrating the efficacy of combining advanced AI techniques with user-centered web applications in medical prediction scenarios. This 97% confidence level was evaluated by Brier score and calibration curve. The design of the streamlit application facilitates seamless interaction between complex ML models and end-users, including clinicians and patients, supporting its use in real-time clinical settings. While the study offers new insights into AI-driven CVD prediction, we acknowledge limitations such as the dataset size. In our research, we have successfully validated our predictive proposed methodology against an external clinical setting, demonstrating its robustness and accuracy in a real-world fixture. The validation process confirmed the model’s efficacy in the early detection of CVDs, reinforcing its potential for integration into clinical workflows to aid in proactive patient care and management. Future research directions include expanding the dataset, exploring additional algorithms, and conducting clinical trials to validate our findings. This research provides a valuable foundation for future studies, aiming to make significant strides against CVDs. Full article

Background: Lifestyle interventions halt the progression of prediabetes to frank type 2 diabetes (T2D). However, the feasibility of a diabetes prevention program promoting tailored interventions on a national scale and conducted by primary care physicians is unclear. Methods: General practitioners located in ten different regions throughout Italy enrolled random subjects without known metabolic diseases to identify individuals with prediabetes and prescribe them an intervention based on physical activity. Using a simple stepwise approach, people referring to their primary care physician for any reason were screened for their diabetes risk with a web-based app of the Findrisc questionnaire. Those at risk for T2D, i.e., with a Findrisc score >9, were invited to come back after overnight fasting to measure fasting glycaemia (FG). Those with 100 ≤ FG < 126 mg/dL were considered as people with prediabetes and compiled the Physical Activity Readiness Questionnaire (PAR-Q) to then receive a personalised prescription of physical activity. Results: Overall, 5928 people were enrolled and compiled the questionnaire. Of these, 2895 (48.8%) were at risk for T2D. Among these, FG was measured in 2168 subjects (participation rate 75%). The numbers of individuals with undetected prediabetes and T2D according to FG were 755 and 79 (34.8% and 3.6% of those assessing FG), respectively. Of the 755 subjects in the prediabetes range, 739 compiled the PAR-Q and started a personalised program of physical activity (participation rate 97%). Physicians involved in the study reported a mean of 6 min to perform the screening. Conclusions: Overall, these data suggest the feasibility of a national diabetes prevention program developed by general practitioners using a simple stepwise approach starting from a web app to intercept individuals with prediabetes. Full article

Severe COVID-19 is a major cause of morbidity and mortality worldwide, especially among those with co-morbidities, the elderly, and the immunocompromised. However, the molecular determinants critical for severe COVID-19 progression remain to be fully elucidated. Meta-analyses of transcriptomic RNAseq and single-cell sequencing datasets comparing severe and mild COVID-19 patients have demonstrated that the early expansion of myeloid-derived suppressor cells (MDSCs) could be a key feature of severe COVID-19 progression. Besides serving as potential early prognostic biomarkers for severe COVID-19 progression, several studies have also indicated the functional roles of MDSCs in severe COVID-19 pathogenesis and possibly even long COVID. Given the potential links between MDSCs and severe COVID-19, we examine the existing literature summarizing the characteristics of MDSCs, provide evidence of MDSCs in facilitating severe COVID-19 pathogenesis, and discuss the potential therapeutic avenues that can be explored to reduce the risk and burden of severe COVID-19. We also provide a web app where users can visualize the temporal changes in specific genes or MDSC-related gene sets during severe COVID-19 progression and disease resolution, based on our previous study. Full article

Integrated pest management (IPM) is a wide-ranging strategy that involves tactics for the structural control of pests and diseases, also known as integrated pest control (IPC). The prime aim of this study is to explore global scientific publications regarding integrated pest management and map the recent global trends in this innovative research domain, thereby providing future research directions based on dynamic observations. Bibliometric assessment is used in the research to provide a more detailed evaluation of the existing state of IPM, which will ultimately lead policymakers to develop novel measures to promote the efficient utilisation of the IPM measure. The study explores the Web of Science (WoS) core collection database covering published articles from 1993 to 2022, and we evaluated 1217 articles. The study employed the Biblioshiny app (V4.1, K-Synth, Naples, Italy), specially designed for the Bibliometrix R package, and a robust Java-based application, namely CiteSpace (V6.2.R3, Chaomei Chen, Philadelphia, PA, USA), to convert, process, visualise and evaluate the selected dataset. Moreover, we utilised another Java-based app VOSViewer (Leiden University, Leiden, The Netherlands), to provide a comprehensive visualisation. Based on citation analysis, we presented a list of the top 10 articles based on global citation count, the top 10 institutes, the top 10 journals, and the top 10 countries. By carefully evaluating the co-occurrence network, this study structurally represents the 9 clusters and the 20 prime keywords, which will be assisted the future researcher in understanding the key terms used by the prior research and the density criteria. The strategic coordinate mapping is utilised to evaluate the keywords’ inner relationship and provide strong themes of recent research progression of IPM by evaluating the centrality and density quadrant tactics. This technique has not been rehashed previously within the IPM domain. We identify gaps related to the formulation, implementation, and assessment of the IPM mechanisms and propose avenues for future studies to fill those gaps. Full article

Background: Alternative metrics have been proposed to estimate the impact of research on the academic and social environment. The objective of the current study was to analyze the longitudinal behavior of Altmetric resources related to online engagement in orthodontic research and to explore their correlation with citations over time. Methods: The Dimensions App was searched in December 2019 and December 2021 for published items belonging to orthodontic journals listed in the Journal Citation Reports (JCR) from 2014 to 2018. Items with an AAS (Altmetric Attention Score) equal to or greater than one were selected and screened for data related to authorship and publication. The breakdown of the different Altmeric resources was collected in 2019 and updated in 2021. Citations were retrieved from Web of Science (WOS) and Scopus at the same time interval. Results: The best performing journals were Progress in Orthodontics and the European Journal of Orthodontics at both time points, with a mean AAS per published item of 1.74 and 1.63, respectively, in 2021. The topics with the highest online engagement display a change over time, while the study design remained randomized clinical trials (RCTs) in both observations. Tweets, Facebook posts, and blogs showed a very slight increase over time, while News Outlets, patent data, and policy sources longitudinally showed a significant increase. No or poor correlation was found between altmetrics and citation except for Mendeley reader count. Conclusions: Tweets, Facebook, and Blog mentions can be considered attention trackers. News Outlets, patents, and policy sources are time dependent data. Mendeley reader count, can help to identify the article with a future citation potential. Full article