Search Results (670)

This study introduces the Machine Learning (ML)-Driven Pattern Synthesis for Digital Forensics in Synthetic Log Analysis (ML-PSDFA) framework to address critical gaps in digital forensics, including the reliance on real-world data, limited pattern diversity, and forensic integration challenges. A key innovation is the introduction of a novel temporal forensics loss ${\mathcal{L}}_{\mathrm{TFL}}$ in the Synthetic Attack Pattern Generator (SAPG), which enhances the preservation of temporal sequences in synthetic logs that are crucial for forensic analysis. The framework employs the SAPG with hybrid seed data (UNSW-NB15 and CICIDS2017) to create 500,000 synthetic log entries using Google Colab, achieving a realism score of 0.96, a temporal consistency score of 0.90, and an entropy of 4.0. The methodology employs a three-layer architecture that integrates data generation, pattern analysis, and forensic training, utilizing TimeGAN, XGBoost classification with hyperparameter tuning via Optuna, and reinforcement learning (RL) to optimize the extraction of evidence. Due to enhanced synthetic data quality and advanced modeling, the results exhibit an average classification precision of 98.5% (best fold 98.7%) 98.5% (best fold 98.7%), outperforming previously reported approaches. Feature importance analysis highlights timestamps (0.40) and event types (0.30), while the RL workflow reduces false positives by 17% over 1000 episodes, aligning with RL benchmarks. The temporal forensics loss improves the realism score from 0.92 to 0.96 and introduces a temporal consistency score of 0.90, demonstrating enhanced forensic relevance. This work presents a scalable and accessible training platform for legally constrained environments, as well as a novel RL-based evidence extraction method. Limitations include a lack of real-system validation and resource constraints. Future work will explore dynamic reward tuning and simulated benchmarks to enhance precision and generalizability. Full article

Background: Trelegy Ellipta is a widely prescribed triple inhaler therapy for chronic obstructive pulmonary disease (COPD). Although its clinical efficacy is well established, evidence on sex-specific differences in adverse event (AE) profiles from real-world pharmacovigilance data remains limited. In addition, some AEs may reflect underlying disease characteristics rather than drug exposure, which complicates interpretation of safety signals. Objective: To explore sex-related differences in AEs associated with Trelegy Ellipta using the FDA Adverse Event Reporting System (FAERS). The study aimed to identify potential safety signals while accounting for alternative explanations, including comorbidity burden and disease-related variation. Methods: We retrospectively analyzed FAERS reports from January 2018 to April 2025, identifying 4555 AEs attributed to Trelegy Ellipta. Events were coded by System Organ Class (SOC) and stratified by patient sex. Frequencies were compared between male (n = 1621) and female (n = 2934) patients using chi-square tests, and associations were expressed as reporting odds ratios (RORs) with 95% confidence intervals (CIs). Results: Male patients more frequently reported hypertension (63.4% vs. 47.0%; p = 0.01), pneumonia (87.8% vs. 76.8%; p < 0.001), anxiety (91.0% vs. 66.9%; p < 0.001), sleep disorders (20.1% vs. 6.8%; p < 0.001), and hyperglycemia (92.7% vs. 52.1%; p < 0.001). Female patients more often reported headache (56.7% vs. 32.6%; p < 0.001), depression (33.1% vs. 9.0%; p < 0.001), and osteoporosis (41.7% vs. 2.4%; p < 0.001). Further variation was observed across neurological, musculoskeletal, and respiratory categories, suggesting a multidimensional pattern of sex differences. Conclusions: This FAERS-based analysis indicates distinct sex-specific safety signals for Trelegy Ellipta, particularly in cardiovascular, neuropsychiatric, and steroid-related domains. These findings are hypothesis-generating and highlight the importance of incorporating sex-disaggregated analyses into future pharmacovigilance and clinical studies. Full article

Surgical site infections (SSIs) remain a significant complication in spine surgery, especially in instrumented procedures with long operative times. Although guidelines recommend cefazolin as the first-line agent due to its efficacy against Staphylococcus aureus, predictable pharmacokinetics, and safety, its real-world practice is highly variable, with inappropriate and prolonged regimens reported across Europe. Vancomycin is often used as the first choice of therapy empirically and without screening, exposing patients to risks such as delayed infusion, nephrotoxicity, and the emergence of vancomycin-resistant enterococci (VRE).This review assesses the present function of vancomycin in relation to cefazolin for spinal prophylaxis and examines wider trends in the misuse of surgical antibiotic prophylaxis, which were identified through PubMed and Scopus searches. Evidence from randomized and prospective studies consistently supports cefazolin as the preferred prophylactic agent in clean spinal surgery. Observational data suggest that adjunctive or topical vancomycin may reduce infection rates in selected high-risk or revision cases, though the results are inconsistent and frequently limited by retrospective designs and heterogeneous outcome reporting. Importantly, the most rigorous randomized controlled trial found no benefit of intrawound vancomycin over the placebo. A small number of available investigations in vancomycin use with major design limitations have resulted in no significant VRE emergency. Unexpectedly, widespread use of vancomycin was followed by a notable transition toward Gram-negative and opportunistic organisms. In summary, vancomycin may only be considered in patients with documented MRSA colonization, β-lactam allergy, or selected revision procedures, but its widespread empirical use as a first-choice therapy is not supported. Full article

Background: Dementia is a progressive neurodegenerative condition that impairs cognitive functions such as memory, language comprehension, and problem-solving. Assistive technologies can provide vital support at various stages of dementia, significantly improving the quality of life by aiding daily activities and care. However, for these technologies to be effective and widely adopted, a human-centred design (HCD) approach is of consequence for both their development and evaluation. Objectives: This scoping review aims to explore how HCD principles have been applied in the design of assistive technologies for people with dementia and to identify the extent and nature of their involvement in the design process. Eligibility Criteria: Studies published between 2017 and 2025 were included if they applied HCD methods in the design of assistive technologies for individuals at any stage of dementia. Priority was given to studies that directly involved people with dementia in the design or evaluation process. Sources of Evidence: A systematic search was conducted across five databases: Web of Science, JSTOR, Scopus, and ProQuest. Charting Methods: Articles were screened in two stages: title/abstract screening (n = 350) and full-text review (n = 89). Data from eligible studies (n = 49) were extracted and thematically analysed to identify design approaches, types of technologies, and user involvement. Results: The 49 included studies covered a variety of assistive technologies, such as robotic systems, augmented and virtual reality tools, mobile applications, and Internet of Things (IoT) devices. A wide range of HCD approaches were employed, with varying degrees of user involvement. Conclusions: HCD plays a critical role in enhancing the development and effectiveness of assistive technologies for dementia care. The review underscores the importance of involving people with dementia and their carers in the design process to ensure that solutions are practical, meaningful, and capable of improving quality of life. However, several key gaps remain. There is no standardised HCD framework for healthcare, stakeholder involvement is often inconsistent, and evidence on real-world impact is limited. Addressing these gaps is crucial to advancing the field and delivering scalable, sustainable innovations. Full article

Background and Objective: Prostate cancer (PC) is the most common cancer among males in the Western World. Androgens are key growth regulators both in normal and malignant prostate growth. Several new types of androgen pathway inhibitors (ARPIs) have been developed for the treatment of PC. Despite the lack of evidence, sequential use of ARPIs has been adopted into everyday clinical practice. This study aimed to assess real-life ARPI use patterns, especially sequential use and treatment costs, in Finland. Methods: Nationwide register data on all ARPI (enzalutamide, apalutamide, darolutamide, abiraterone) purchases recorded in the National Health Insurance scheme register maintained by the Social Insurance Institution of Finland from January 2012 to December 2023 were used. The data included patient demographics and medicine purchase details, which were descriptively analysed. Results: During the study period, 8369 patients initiated ARPIs. The median age of the users was 75.1 years. Of these, 32.1% (n = 2685) used at least two ARPIs sequentially. The proportion of treatment initiations leading to sequential use increased from 36% in 2012 to 56% in 2017, then decreased to 14% in 2022. The total cost of sequential use was €43.8 million. Limitations include the unrecorded phase of PC. The study’s strength is its inclusion of all reimbursed ARPI purchases nationwide. Conclusions: Despite the lack of evidence, sequential ARPI use was initially prevalent but declined after the introduction of new guidelines. Randomised trials are needed to guide the sequential use of these medicines. Patient summary: Androgen pathway inhibitors (ARPIs) are widely used in prostate cancer in Finland. One-third of patients use at least two ARPIs sequentially to inhibit testosterone effect. However, there are no large clinical trials published demonstrating the benefits of sequential treatment. More evidence is needed to justify sequential use. Full article

Background: Fixed-duration venetoclax plus rituximab (VR) is a standard therapy for relapsed/refractory (R/R) chronic lymphocytic leukemia (CLL). However, evidence supporting its use after covalent BTK inhibitor (c-BTKi) therapy is scarce in clinical trials and limited in real-world settings. Objectives: To assess the efficacy and safety of VR in a real-world cohort of patients with R/R CLL, including cBTKi-pretreated individuals, and to contextualize outcomes alongside published real-world studies and registrational trials of alternative therapies. Methods: We retrospectively analyzed 37 patients with R/R CLL treated with VR at our center between April 2018 and November 2024. Baseline characteristics, treatment responses, minimal residual disease (MRD), and adverse events were recorded. Survival was estimated using the Kaplan–Meier method. A structured review of relevant real-world evidence and pirtobrutinib clinical trials was also conducted. Results: Median age was 67 years; 35.1% had prior cBTKi exposure. The overall response rate (ORR) was 91.7% (22/24 evaluable patients), with 66.7% achieving complete remission (CR). Among evaluable c-BTKi-pretreated patients, the ORR was 87.5% (7/8) and the CR rate was 62.5%. Undetectable MRD (uMRD) rates were 78.6% in peripheral blood and 71.4% in bone marrow. Thirty-month progression-free survival (PFS), time to next treatment (TTNT), and overall survival (OS) were >90% for the whole cohort and for c-BTKi-pretreated patients. The most frequent adverse event was neutropenia grade ≥ 3, especially during combination therapy, which is easily managed with GCSF support. Conclusions: Our real-world evidence shows that VR is an effective and well-tolerated option even after c-BTKi therapy in R/R CLL. These data complement evidence from emerging therapies and inform post-c-BTKi treatment selection in clinical practice. Full article

Background/Objectives: The execution of clinical trials is increasingly constrained by operational complexity, regulatory requirements, and variability in site performance. These challenges have direct implications for the reliability of trial outcomes. However, standardized methods to evaluate site-level performance remain underdeveloped. This study introduces the Clinical Trial Site Performance Measure (CT-SPM), a novel framework designed to systematically capture site-level operational quality and to provide a scalable short form for routine monitoring. Methods: We conducted a multicenter study across six Italian academic hospitals (January–June 2025). Candidate performance indicators were identified through a systematic review and expert consultation, followed by validation and reduction using advanced statistical approaches, including factor modeling, ROC curve analysis, and nonparametric scaling methods. The CT-SPM was assessed for structural validity, discriminative capacity, and feasibility for use in real-world settings. Results: From 126 potential indicators, 18 were retained and organized into four domains: Participant Retention and Consent, Data Completeness and Timeliness, Adverse Event Reporting, and Protocol Compliance. A bifactor model revealed two higher-order dimensions (participant-facing and data-facing performance), highlighting the multidimensional nature of site operations. A short form comprising four items demonstrated good scalability and sufficient accuracy to identify underperforming sites. Conclusions: The CT-SPM represents an innovative, evidence-based instrument for monitoring trial execution at the site level. By linking methodological rigor with real-world applicability, it offers a practical solution for benchmarking, resource allocation, and regulatory compliance. This approach contributes to advancing clinical research by providing a standardized, data-driven method to evaluate and improve performance across networks. Full article

Objectives: To examine long-term outcomes and predictors of structural and functional remission in rheumatoid arthritis (RA) after 10-year disease-modifying antirheumatic drug (DMARD) therapy under tight control. Methods: We used real-world cohort data from RA patients who completed 10-year DMARD therapy toward remission or low disease activity based on every-3-month measurements between April 2001 and July 2024. Baseline characteristics, disease control during follow-up, and outcomes after 10 years were examined. Results: Among 204 patients, 76% received biological and/or non-biological targeted DMARDs. Clinical remission, structural remission defined as an increase in modified total Sharp score (mTSS) ≤ 5 per 10 years, and functional remission defined as health assessment questionnaire-disability index (HAQ-DI) ≤ 0.5 were achieved by 68.1%, 73.0%, and 81.4% of patients, respectively. The mean increase (∆) in mTSS was 5.4 for 10 years (∆erosion score, 1.2; ∆joint space narrowing [JSN] score, 4.2), and 28.9% of patients had no structural progression (51% for erosion score and 34.8% for JSN score). Mean HAQ-DI was 0.26. During a 10-year follow-up, 8.8% of patients experienced high or moderate disease activity lasting for ≥12 months and they had a low structural remission rate (11.1%) and functional remission rate (16.6%). According to multivariable logistic regression analysis, baseline mTSS and JNS score (but not erosion score) were strong predictors for structural and functional remission after 10 years. Conclusions: Structural damage progression and functional loss are limited during 10-year tightly controlled DMARD therapy. Compared with bone erosion, JSN appears to be of much higher relevance to structural and functional outcomes. Full article

Pregnancy in women with type 1 diabetes mellitus (T1DM) is associated with a high risk of maternal and perinatal complications, and achieving optimal glycaemic control remains a clinical challenge. This article presents a narrative review of the evidence on advanced hybrid closed loop (AHCL) insulin delivery systems in pregnancy, with a focus on maternal glycaemic outcomes, neonatal outcomes, and psychosocial aspects. The relevant literature was identified through a structured search of PubMed, Scopus, and Web of Science (2010–2025), supplemented by guideline documents and reference screening. Eligible studies included randomised controlled trials, observational studies, and qualitative investigations. Data were synthesised thematically. Findings from key trials, including CONCEPTT, AiDAPT, and CRISTAL, demonstrate that AHCL systems improve time in range, lower mean glucose, and reduce hyperglycaemia without increasing hypoglycaemia. Some evidence also suggests improved neonatal outcomes, though statistical significance varies. Qualitative studies highlight reduced anxiety, improved sleep, and enhanced quality of life for women using AHCL during pregnancy. In conclusion, AHCL systems show strong promise in optimising maternal glycaemic control and potentially improving perinatal outcomes. However, larger, unbiased studies and real-world evaluations are needed to confirm their benefits and support broader clinical implementation. Full article

Radioiodine-refractory differentiated thyroid cancer (RAIR-DTC) is associated with poor prognosis and limited systemic options. This narrative review focuses on lenvatinib (LEN), a multitarget tyrosine kinase inhibitor that significantly prolongs progression-free survival. Evidence from the SELECT trial and real-world data indicates that its benefits can be enhanced through early initiation, maintenance of a high relative dose intensity, and proactive toxicity management. Planned drug holidays help sustain treatment while avoiding prolonged unplanned interruptions. In selected patients with locally advanced, initially unresectable disease, neoadjuvant LEN may enable conversion surgery, facilitating subsequent treatments. However, the current data are mainly from case series and early-phase studies. After dose reduction, re-escalation can restore disease control, and LEN rechallenge after a drug-free interval may restore sensitivity in later lines. Thus, LEN should be integrated into personalized multidisciplinary care to optimize outcomes across treatment courses. Nevertheless, key limitations remain, as much of the supporting evidence is derived from post hoc or retrospective analyses. Prospective studies are required to validate the optimization strategies, define stage-specific benefits, and determine their impact on overall survival. Full article

Background: Engineered nanoparticles (NPs)—titanium dioxide, silver, zinc oxide and silica—are widely used in cosmetics for UV protection, antimicrobial activity and texturising effects. Chronic consumer-level exposure may impair skin-barrier integrity, disturb microbiome composition and dysregulate immune signalling via the gut–skin axis. Current regulatory frameworks typically omit chronic- or microbiome-focused safety assessments, leaving potential gaps. Objectives: This study aimed to evaluate the long-term effects of cosmetic-relevant NPs (titanium dioxide, silver, zinc oxide, silica) on skin and gut microbiota, epithelial-barrier integrity and immune signalling—including telocyte- and exosome-mediated pathways—and to identify regulatory shortcomings, particularly the absence of microbiome endpoints, validated chronic models and consideration of vulnerable populations. Methods: Following PRISMA 2020, PubMed, Scopus and Web of Science were searched for English-language in vivo animal or human studies (December 2014–April 2025) meeting chronic-exposure criteria (≥90 days in rodents or >10% of lifespan in other species; for humans, prolonged, repetitive application over months to years consistent with cosmetic use). Although not registered in PROSPERO, the review adhered to a pre-specified protocol. Two independent reviewers screened studies; risk of bias was assessed using a modified SYRCLE tool (animal) or adapted NIH guidance (zebrafish). Owing to heterogeneity, findings were synthesised narratively. Results: Of 600 records, 450 unique articles were screened, 50 full texts were assessed and 12 studies were included. Oral exposure predominated and was associated with dysbiosis, barrier impairment, immune modulation and metabolic effects. Dermal models showed outcomes from minimal change to pronounced immune activation, contingent on host susceptibility. Comparative human–animal findings are summarised; telocyte and exosome pathways were largely unexplored. Regulatory reviews (EU SCCS, US FDA and selected Asian frameworks) revealed no requirements for chronic microbiome endpoints. Limitations: Evidence is limited by the small number of eligible studies, heterogeneity in NP characteristics and exposure routes, predominance of animal models and a scarcity of longitudinal human data. Conclusions: Cosmetic nanoparticles may disrupt the microbiome, compromise barrier integrity and trigger immune dysregulation—risks amplified in vulnerable users. Existing regulations lack requirements for chronic exposure, microbiome endpoints and testing in vulnerable groups, and neglect mechanistic pathways involving telocytes and exosomes. Long-term, real-world exposure studies integrating gut–skin microbiome and immune outcomes, and harmonised global nanomaterial-safety standards, are needed to ensure safer cosmetic innovation. Full article

Heavy metal contamination of aquatic systems represents a critical environmental and public health concern due to the persistence, toxicity, and bioaccumulative potential of these elements. Geographic information systems (GISs) have emerged as indispensable tools for the spatial assessment and management of heavy metals (HMs) in water resources. This review systematically synthesizes current research on GIS applications in detecting, monitoring, and modeling heavy metal pollution in surface and groundwater. A bibliometric analysis highlights five principal research directions: (i) global research trends on GISs and heavy metals in water, (ii) occurrence of HMs in relation to World Health Organization (WHO) permissible limits, (iii) GIS-based modeling frameworks for contamination assessment, (iv) identification of pollution sources, and (v) health risk evaluations through geospatial analyses. Case studies demonstrate the adaptability of GISs across multiple spatial scales, ranging from localized aquifers and river basins to regional hydrological systems, with frequent integration of advanced statistical techniques, remote sensing data, and machine learning approaches. Evidence indicates that concentrations of some HMs often surpass WHO thresholds, posing substantial risks to human health and aquatic ecosystems. Furthermore, GIS-supported analyses increasingly function as decision support systems, providing actionable insights for policymakers, environmental managers, and public health authorities. The synthesis presented herein confirms that the GIS is evolving beyond a descriptive mapping tool into a predictive, integrative framework for environmental governance. Future research directions should focus on coupling GISs with real-time monitoring networks, artificial intelligence, and transdisciplinary collaborations to enhance the precision, accessibility, and policy relevance of heavy metal risk assessments in water resources. Full article

(1) Background: The convergence of digital twin technology, artificial intelligence, and multimodal biomarkers heralds a transformative era in neuropsychological assessment and intervention. Digital twin cognition represents an emerging paradigm that creates dynamic, personalized virtual models of individual cognitive systems, enabling continuous monitoring, predictive modeling, and precision interventions. This systematic review comprehensively examines the integration of AI-driven biomarkers within biomimetic neuropsychological frameworks to advance personalized cognitive health. (2) Methods: Following PRISMA 2020 guidelines, we conducted a systematic search across six major databases spanning medical, neuroscience, and computer science disciplines for literature published between 2014 and 2024. The review synthesized evidence addressing five research questions examining framework integration, predictive accuracy, clinical translation, algorithm effectiveness, and neuropsychological validity. (3) Results: Analysis revealed that multimodal integration approaches combining neuroimaging, physiological, behavioral, and digital phenotyping data substantially outperformed single-modality assessments. Deep learning architectures demonstrated superior pattern recognition capabilities, while traditional machine learning maintained advantages in interpretability and clinical implementation. Successful frameworks, particularly for neurodegenerative diseases and multiple sclerosis, achieved earlier detection, improved treatment personalization, and enhanced patient outcomes. However, significant challenges persist in algorithm interpretability, population generalizability, and the integration of healthcare systems. Critical analysis reveals that high-accuracy claims (85–95%) predominantly derive from small, homogeneous cohorts with limited external validation. Real-world performance in diverse clinical settings likely ranges 10–15% lower, emphasizing the need for large-scale, multi-site validation studies before clinical deployment. (4) Conclusions: Digital twin cognition establishes a new frontier in personalized neuropsychology, offering unprecedented opportunities for early detection, continuous monitoring, and adaptive interventions while requiring continued advancement in standardization, validation, and ethical frameworks. Full article

This study presents the development and implementation of an integrated survey system designed to evaluate the impact of MaaS in the context of Cairo’s rapidly evolving urban landscape. The research employs a dual-survey methodology, combining an RP household travel survey with an innovative, context-aware SP experiment focused on MaaS. The system is tailored to address the complexities of Cairo’s formal and informal transport networks and the transformative potential of new public transit infrastructure associated with Cairo’s urban expansion and the introduction of the New Administrative Capital. The paper outlines the methodological framework, including the design of the survey instruments, drawing upon established guidelines and the integration of real-world transportation data for realistic scenario generation in the SP component. While this paper primarily focuses on the development of the survey system and its design principles, it also incorporates some preliminary findings from a 313-participant full-scale survey to illustrate the potential of this comprehensive approach for understanding current travel behaviour, socio-demographic determinants of mobility, and the prospects of context-aware SP data to assess user preferences for potential MaaS offerings. Results highlight the methodological advances in survey design for developing cities and aim to offer policy-relevant evidence for sustainable mobility interventions. Full article

Background/Objectives: As global aging accelerates, there is a pressing and empirically substantiated demand for integrated and sustainable strategies, as evidenced by the rising prevalence rates of chronic conditions, social isolation, and digital exclusion among older adults worldwide. These factors underscore the urgent need for multidimensional interventions that simultaneously target physical, psychological, and social well-being. The HOPE-FIT (Hybrid Outreach Program for Exercise and Follow-up Integrated Training) model and the SAGE (Senior Active Guided Exercise) program were designed to address this need through a hybrid framework. These programs foster inclusive aging by explicitly bridging digitally underserved groups and mobility-restricted populations into mainstream health promotion systems through tailored exercise, psychosocial support, and smart-home technologies, thereby functioning as a scalable meta-model across healthcare, community, and policy domains. Methods: HOPE-FIT was developed through a formative, multi-phase process grounded in the RE-AIM framework and a Hybrid Type II effectiveness–implementation design. The program combines professional health coaching, home-based and digital exercise routines, Acceptance and Commitment Performance Training (ACPT)-based psychological strategies, and smart-home monitoring technologies. Empirical data from pilot studies, large-scale surveys (N = 1000), and in-depth user evaluations were incorporated to strengthen validity and contextual adaptation. Culturally tailored content and participatory feedback from older adults further informed ecological validity and program refinement. Implementation Strategy/Framework: The theoretical foundation integrates implementation science with behavioral and digital health. The RE-AIM framework guided reach, fidelity, and maintenance planning, while the Hybrid E–I design enabled the concurrent evaluation of effectiveness outcomes and contextual implementation strategies. Institutional partnerships with community centers, public health organizations, and welfare agencies further facilitated the translation of the model into real-world aging contexts. Dissemination Plan: The multi-pronged dissemination strategy includes international symposia, interdisciplinary academic networks, policy briefs, localized community deployment, and secure, authenticated data sharing for reproducibility. This design facilitates evidence-informed policy, empowers practitioners, and advances digital health equity. Ultimately, HOPE-FIT constitutes a scalable and inclusive model that concretely addresses health disparities and promotes active, dignified aging across systems and disciplines. Full article