Search Results (6,188)

In the construction industry, struck-by accidents involving heavy equipment such as crawler excavators are a leading cause of worker fatalities and injuries. Existing vision-based hazard detection methods are limited by approximate evaluations, reliance on specific references, and neglect of spatial relationships between equipment and workers, making them inadequate for complex dynamic construction environments. This study aims to address these limitations by proposing a precise and adaptable struck-by hazard detection method. The method integrates four core modules: object tracking via the YOLOv5-DeepSORT model to detect workers, excavators, and their key components; activity recognition to identify the operational states of excavators, working or static, and workers, driver or field worker; proximity estimation based on stereo vision using the BGNet model and camera calibration to calculate 3D spatial distances; and safety identification to assess worker safety status in real time. Validated through three virtual construction scenarios, flat ground, rugged terrain, slope, the method achieved high safety status identification accuracies of 92.71%, 90.04%, and 94.25% respectively. The results demonstrate its robustness in adapting to diverse construction environments and accurately capturing equipment–worker spatial interactions. This research expands the application scope of hazard monitoring in complex settings, enhances safety identification efficiency, and provides a reliable technical solution for improving construction site safety management. Full article

Heavy metal contamination remains a critical threat to water quality, particularly in effluents associated with industrial activities such as electroplating. This study presents an exploratory proof of concept for a simplified and low-requirement method to fabricate bovine serum albumin (BSA) hydrogels crosslinked with glutaraldehyde (GA) as protein-based adsorbents for Cu²⁺, Ni²⁺, and Co²⁺ removal. Hydrogel slabs were prepared using BSA concentrations of 20% and 25% (w/v) and GA in the 0.6–1.0% (v/v) range, with formulation adjustments guided by handling and aqueous stability. Swelling behavior was monitored for 23 days, and 0.9% (v/v) GA was selected to balance network expansion with hydrogel consistency. FT-IR confirmed preservation of protein functional groups in the crosslinked network, and TGA/DTG demonstrated multi-step thermal behavior consistent with hydrated protein matrices and a stabilizing effect of increased GA content. Metal removal tests at 50–100 ppm (Cu²⁺, Ni²⁺) and 70–100 ppm (Co²⁺) showed rapid removal approaching equilibrium within the first hours and improved performance at higher BSA content, achieving maximum removal percentages of 99.258% for Cu²⁺, 80.733% for Ni²⁺, and 76.070% for Co²⁺. Adsorption behaviors for Cu²⁺ and Co²⁺ aligned with the Langmuir model, while Ni²⁺ was better described by the Freundlich model. Although the scope is intentionally preliminary and limited to controlled synthetic systems, these results support GA-crosslinked BSA hydrogels as promising, easily fabricated adsorbents and establish a foundation for future studies on broader ion selectivity, competitive adsorption, and adsorption–desorption performance. Full article

Stem cell therapy represents an intrinsic part of regenerative medicine, with expanding applications in orthopedic and musculoskeletal research. Although studies span from small-animal models to early-phase clinical trials, the field remains fragmented, with wide variation in stem cell types, delivery methods, and target tissues. A consolidated overview is needed to inform future directions and bridge the gap between preclinical promise and clinical application. This scoping review synthesized evidence from 500 preclinical and clinical studies, identified through systematic searches and screened in accordance with PRISMA-ScR guidelines. Data were extracted on stem cell type and source, delivery approach, targeted tissue and organ, and disease indication. We found that autologous bone marrow-derived mesenchymal stem cells were the most used, with adipose- and perinatal-derived cells gaining prominence in recent years. Small-animal models such as rats and rabbits predominated, while large-animal and human studies focused mainly on knee osteoarthritis. Intra-articular injection was the principal delivery method across both preclinical and clinical settings. By mapping prevailing practices and emerging trends, this review provides a comprehensive reference for researchers, clinicians, and regulatory stakeholders. It highlights translational pathways, identifies critical gaps, and offers evidence to guide the design of safe, effective, and scalable regenerative therapies in orthopedics. Full article

Genome-wide association studies (GWAS) have successfully identified thousands of genetic loci associated with complex traits and diseases, providing critical insights into genetic architecture, biological pathways, and disease mechanisms. With the advance of machine learning, the analytical scope of GWAS can be substantially expanded by enabling joint modeling, nonlinear effects, and integrative analysis. However, deep learning approaches remain underutilized in augmenting traditional GWAS frameworks, particularly in the presence of cryptic relatedness among sampled individuals. In this paper, we propose a deep neural network (DNN)-based machine learning method to assist genetic association testing in samples with related individuals. By approximating the phenotype–genotype relationships in classical association tests and combining approximations across multiple tests, the proposed method aims to improve predictive performance in the identification of associated variants. Simulation studies demonstrate that our approach effectively complements conventional statistical methods and generally achieves increased power for detecting genetic associations. We further apply the method to data from the Framingham Heart Study, illustrating how DNN-based machine learning can facilitate the identification of genome-wide SNPs associated with average systolic blood pressure. Full article

Background: Artificial Intelligence as a Medical Device (AIaMD) introduces regulatory, methodological, ethical, and clinical challenges that are not fully addressed by traditional device trial frameworks. Given rapidly evolving and jurisdiction-specific guidance, a consolidated mapping of trial design expectations and regulatory requirements is needed. Objective: To map regulatory requirements and clinical trial design approaches for AIaMD across major jurisdictions and to identify key methodological and implementation gaps relevant to adaptive/continuously learning systems. Methods: A scoping review was conducted in accordance with the PRISMA-ScR reporting guideline. Peer-reviewed literature (2017–2025) was searched in PubMed, Embase, Web of Science, and the Cochrane Library. Gray literature was identified from major regulators and policy bodies (FDA, EMA, MHRA, PMDA, WHO, CDSCO). Eligible records addressed AIaMD clinical evaluation, trial design, regulatory pathways, post-market surveillance, or reporting standards. Data were charted using a predefined extraction framework and synthesized descriptively with thematic analysis across regulatory, methodological, ethical, and clinical implementation domains. Results: Included sources demonstrate substantial heterogeneity in evidence expectations and AI-specific pathways across jurisdictions. Recurrent themes include the need for predefined change management, performance monitoring and drift controls, dataset representativeness and bias evaluation, transparency and versioning, cybersecurity, and real-world evidence integration. Reporting frameworks (SPIRIT-AI, CONSORT-AI, MI-CLAIM) are frequently cited as mechanisms to improve reproducibility and regulatory readiness. Conclusions: Evidence and regulatory expectations for AIaMD remain fragmented. Harmonization of terminology, trial design principles, and post-market governance—supported by standardized reporting—would improve clinical validity, safety assurance, and scalability across regions. This review has several limitations. As a scoping synthesis, it prioritizes breadth of coverage rather than quantitative meta-analysis. Included sources vary in methodological rigor and reporting detail, and evolving regulatory guidance may change rapidly over time. Nevertheless, integrating peer-reviewed and regulatory evidence provides a comprehensive overview of current expectations and emerging gaps. In conclusion, effective evaluation of AIaMD requires a shift from static, one-time validation toward continuous lifecycle oversight that integrates adaptive trial designs, transparent reporting standards, bias surveillance, and structured post-market monitoring. Regulatory heterogeneity currently poses significant barriers to multinational development; however, coordinated adoption of standardized evidence frameworks and collaborative governance mechanisms may reduce duplication while preserving patient safety. By translating methodological principles into operational guidance, this review aims to support regulators, sponsors, and clinical investigators in designing trials that are both scientifically rigorous and practically implementable for continuously learning systems. Full article

Antimicrobial resistance (AMR) threatens global health, particularly through the rise of multidrug-resistant tuberculosis (MDR-TB) and other critical bacterial infections such as methicillin-resistant Staphylococcus aureus (MRSA). Rifamycins remain frontline antibiotics but are increasingly undermined by resistance. Here, we introduce a click-enabled platform for the synthesis of C8-functionalized rifamycins, which can be converted in a single additional step into efficacious 3′-hydroxy-5′-aminobenzoxazinorifamycins (bxRifs) and enzymatically into 25-deacetylated rifamycins (deAcRifs), providing access to novel antibacterial scaffolds that expand beyond the scope of traditional C8 modifications. Accordingly, we establish a modular strategy for late-stage analog development of the complex natural product rifamycin S, wherein azido and alkyne functionalities are installed via tailored core chemistry and converted into 1,2,3-triazoles through copper(I)-catalyzed click chemistry. Another key feature of this work is the development of systematic HPLC purification methods, enabling the isolation of analytically pure compounds despite structural complexity. The resulting analogs exhibit distinct antibacterial profiles, notably against Gram-positive bacteria including MRSA and Streptococcus mutans, informing structure–activity relationships and offering a foundation for further optimization. This approach supports the rapid diversification of rifamycin scaffolds to combat the escalating threat of AMR, while also establishing a foundation for future discovery through bioorthogonal applications. Full article

Extensive research has been developed on the success factors and benefits of Digital Transformation projects for both public and private sectors. In this spectrum, the parameters that drive company administrations to proceed with such projects have been the subject of research as distinct attributes, with scholars analyzing whether the parameters were sufficiently evaluated and to what extent digital transformation project delivery met initial expectations. In this study we present a scoping review of 61 peer-reviewed academic journal articles published between 2013 and 2024, retrieved from the Scopus and Web of Science databases, to assess which are the most crucial evaluation parameters that could support corporate administrations in taking calculated decisions on initiating new digital transformation projects. We selected eligible articles based on predefined inclusion and exclusion criteria related to publication type, language, publication period, and scientific rigor. We analyzed the selected articles using thematic and content analysis, enabling the identification and synthesis of recurring evaluation dimensions. The findings indicate that key parameters—vision, scope, duration, and budget—consistently emerge as critical determinants of project success. Moreover, we propose the integration of these parameters into a digital transformation plan, which organizations could use in future as a control method to support a digital transformation project’s successful delivery. Full article

Objectives: Obesity and related metabolic disorders, including insulin resistance, dyslipidemia, and non-alcoholic fatty liver disease, represent major global health challenges. Growing interest in complementary strategies has brought propolis, a resinous bee-derived product rich in phenolic and flavonoid compounds, into focus. This scoping review aimed to map and synthesize available in vitro, in vivo, and clinical evidence regarding the metabolic effects of whole propolis preparations and propolis-derived bioactive compounds in obesity-related contexts. Methods: The review was conducted in accordance with the PRISMA-ScR framework and included experimental and human studies evaluating adipogenesis, lipid and glucose metabolism, oxidative stress, inflammatory signaling, non-alcoholic fatty liver disease-related outcomes, and gut microbiota modulation. Results: Across preclinical models, propolis preparations have been associated with modulation of antioxidant defenses, attenuation of inflammatory signaling, regulation of adipogenic transcriptional programs, and alterations in gut microbiota composition and barrier integrity. Clinical evidence suggests modest improvements in selected metabolic and inflammatory biomarkers; however, effects on body weight and adiposity remain inconsistent. Interpretation is limited by heterogeneity in propolis type, extraction method, chemical standardization, dosing strategies, and study design. Full article

Background/Objectives: To identify US military unknowns, the Armed Forces Medical Examiner System’s Armed Forces DNA Identification Laboratory has historically relied upon mitochondrial DNA and Y-chromosomal short tandem repeat testing. Where no appropriate family reference sample (FRS) is available or skeletal samples are degraded, autosomal single nucleotide polymorphism (SNP) testing with next-generation sequencing could assist. Methods: A method utilizing hybridization capture enrichment of a 95,000 (95K) SNP panel, amenable to FRS and extremely challenging samples, was validated. The Parabon Fx Forensic Analysis Platform was used for analysis and extended kinship inference. Skeletal samples (n = 65) and associated FRS (n = 64) were selected for a performance evaluation and case-type sample study. Results: Considering FRS with ≥7 ng DNA input into library preparation, 94% yielded ≥66,320 SNPs at ≥5X coverage. SNP recovery for skeletal samples at ≥1X coverage ranged from 5 to 94,197 SNPs, averaging 40,770 SNPs. When skeletal samples resulted in ≥13,000 SNPs, the most likely relationship category was consistent with the expected relationship. A log10 likelihood ratio of ≥4 and a posterior probability of ≥99.99% were established as thresholds for strong statistical support, and 87% of inferences met these thresholds while 13% were considered inconclusive. Pairwise kinship inference between unrelated individuals yielded an unrelated result in 85% of comparisons, 66% with strong statistical support. There were 170 instances of false positive 4th degree relationship inferences with strong statistical support. All false positives involved skeletal samples from individuals of admixed ancestry. Conclusions: With this approach, autosomal SNP testing can result in reliable kinship inferences between related individuals out to 3rd, and in some cases 4th, degree relationships, increasing the scope of eligible FRS to aid in identifications. Full article

To ensure personnel safety and prevent serious accidents, it is crucial to monitor parameters such as temperature, pressure, and gas composition concentrations in confined spaces. This study proposes a multi-parameter simultaneous inversion algorithm based on tunable diode laser absorption spectroscopy (TDLAS). The algorithm integrates the Levenberg–Marquardt (L-M) fitting method, single-line thermometry and manometry methods, spectral separation, and alternating iteration techniques, with an adaptive feedback mechanism adding to enhance convergence stability. Through this approach, simultaneous inversion of H₂O, CO₂, CO, and O₂ concentrations, temperature, and pressure was successfully achieved. Simulation results demonstrated that the measurement accuracy meets practical requirements. This study provides an effective monitoring method for multi-parameter detection in confined spaces within conventional environments and lays a foundation for expanding the application scope of TDLAS technology. Full article

Background/Objectives: The rapid expansion of generative artificial intelligence (AI) and large language model-based chatbots has accelerated their adoption in higher education, including nursing. This scoping review mapped the use of AI-based chatbots in nursing education, including curricular domains, pedagogical approaches, educational outcomes, and implementation challenges. Methods: A scoping review was conducted following the Joanna Briggs Institute methodology and reported in accordance with the PRISMA-ScR guideline. Searches were performed across major bibliographic databases and grey literature sources. Quantitative, qualitative, and mixed-methods studies addressing the use of AI chatbots in nursing education or professional training were included. Data were extracted using a standardized instrument and synthesized through descriptive statistics and qualitative content analysis. Results: Sixty-six studies (2019–2025) were included, with significant growth observed after 2023. Most studies employed quasi-experimental designs (37.9%) and were implemented in academic settings (83.3%). Application formats varied across online, hybrid, simulation-based, and classroom models. Reported benefits included improved learning performance, clinical reasoning, and student engagement. Key challenges involved the reliability of AI outputs, academic integrity, data protection, and limited institutional governance. Conclusions: AI-based chatbots represent promising tools to enhance nursing education, particularly when integrated into structured pedagogical strategies with active faculty supervision. Their use can support the development of clinical reasoning, student engagement, and personalized learning. However, methodological heterogeneity, ethical concerns, and governance gaps highlight the need for careful implementation and further rigorous research to ensure safe, effective, and pedagogically sound integration. Full article

Background: Worldwide breastfeeding initiation and exclusive rates at 6 months remain lower than recommended. Our scoping review aimed to identify interventions to improve breastfeeding duration in primiparous women. We assessed interventions’ effectiveness during the prenatal and postnatal periods separately or combined. Methods: Eight databases and grey literature were searched in March 2023, using a keyword search strategy. Results: We identified 16,161 articles from 2013 to 2023, and 35 met our eligibility criteria. The studies were conducted mostly in low–middle income countries (62.9%), and they proposed a variety of interventions in the prenatal period (n = 8), the postnatal period (n = 11) and in a combination of both periods (n = 16). It appears that a combination of various interventions, in both the prenatal and postnatal periods, targeting young women who intended to breastfeed, with low education levels, and with a partner, showed positive effects on exclusive breastfeeding rates until 6 months. Combined approaches such as workshops or individual education and support sessions during the prenatal period with support by professionals or peers until at least 6 months also showed improvements on breastfeeding duration. Conclusions: Our scoping review was the first to have identified potentially effective interventions, alone or in combination, to improve breastfeeding duration among primiparous women. Further studies should be conducted to cover a longer period, beyond six months. They should also explore the role of sociodemographic factors, such as ethnicity, in interventions’ effects. Full article

Newborn screening (NBS) is a well-established public health program that enables early detection and treatment of rare disorders in newborns, preventing severe complications or death. Despite its recognized importance, the scope and implementation of NBS programs vary across Southeastern (SE) and Central Europe. This study aimed to evaluate the current status of NBS in 16 countries of SE and Central Europe and assess progress since the previous survey in 2021. A structured questionnaire was distributed to national experts between April and December 2025, collecting data on program organization, coverage, diseases included, laboratory methods, confirmatory testing, consent practices, and future expansion plans. All countries reported universal screening for congenital hypothyroidism, except Kosovo, where a national NBS is in the process of being established. Expanded NBS using tandem mass spectrometry was available in Austria, Bulgaria, Croatia, Cyprus, Greece, Hungary, North Macedonia, Romania, and Slovenia. Spinal muscular atrophy screening became universal in Austria, Croatia, Hungary, Serbia, and Slovenia. Most countries reported plans for further expansion, with congenital adrenal hyperplasia, severe combined immunodeficiency, spinal muscular atrophy, and cystic fibrosis being the most frequently targeted conditions. Although notable infrastructural progress has been achieved, financial constraints, lack of staff, and organizational barriers remain key challenges. The study’s assessment of program effectiveness was further limited by the absence of region-wide systems for capturing end-to-end performance indicators, such as the age of the infant at treatment initiation or missed cases. Regional collaboration and adoption of best practices are therefore vital to ensure equitable access and continuous advancement of NBS programs. Full article

Background/Objectives: Although they differ in terms of epidemiological incidence, melanoma and sarcoma are rare, aggressive cancers with a substantial impact on patients’ quality of life and that of their caregivers. Caregivers, who are often family members, experience high levels of stress, anxiety, and caregiving burden, leading to significant psychological, social, and economic repercussions. The aim of this scoping review was to identify the assessment tools used to study the quality of life and psychological well-being of caregivers of patients with melanoma and/or sarcoma, and to highlight the main areas of difficulty. Methods: A systematic search of PubMed, Embase, and PsycINFO databases identified 325 studies, of which 16 met the inclusion criteria. Eligible studies were English language publications focusing on caregivers of adult patients (>18 years) with melanoma or sarcoma. Reviews, case reports, animal studies, or clinical trials, abstracts, books or book chapters, and studies without clear information on assessment tools or involving the pediatric population were excluded. Results: The studies included (2007–2024) involve Full article

Background/Objectives: Most patients with metastatic renal cell carcinoma (mRCC) progress on first-line immune checkpoint inhibitor (ICI). Subsequent vascular endothelial growth factor (VEGF) tyrosine kinase inhibitor (TKI) is standard. Due to the rapid evolution in treatment landscape, data directly comparing outcomes of VEGF-TKI following ICI versus VEGF-TKI alone are limited. This scoping review aimed to explore whether VEGF-TKI following prior ICI is associated with improved outcome, potentially reflecting a treatment sequence effect. Methods: PubMed/MEDLINE and ClinicalTrials.gov were searched systematically to identify phase 2/3 prospective clinical trials that investigated VEGF-TKI in patients who had progressed after ≥1 therapy published from 2004. Included studies were summarised by prior therapy and reported outcomes. Data from subgroups/arms were extracted and weighted overall response rate (ORR), progression free survival (PFS) and overall survival (OS) calculated for patients pretreated with VEGF-TKI versus ICI. An exploratory, hypothesis generating analysis was performed comparing outcomes between patients who received prior VEGF-TKI only or ICI-based therapy. Results: In total, 17 clinical trials were included: 2538 patients had prior VEGF-TKI (15 subgroups/arms) and 724 prior ICI-based therapy (11 subgroups/arms). In prior VEGF-TKI, weighted mORR was 8% (IQR 6–16%) versus 28% (IQR 20–41%) post-ICI. Weighted mPFS was 3.9 m (IQR 3.6–5.4) with prior VEGF-TKI versus 8.3 m (IQR 7.4–10.3) in prior ICI group. Weighted mOS was 15.2 m (IQR 11.1–16.6) versus 22.1 m (IQR 10.9–22.1) with prior ICI. Conclusions: Improved outcomes in ICI pretreated population in this exploratory analysis suggests ongoing biological benefit of ICI therapy. As prospective 2L randomised studies are not feasible, we conclude that VEGF therapy in pretreated mRCC is at least as good, if not better, since the introduction of 1st line ICI. Full article