Search Results (1,917)

Background: Cervical cancer poses a threat to the health of women globally. Adolescent girls are the primary target population for HPV vaccination, and guardians’ attitude towards the HPV vaccine plays a significant role in determining the vaccination status among adolescent girls. Objectives: This study aimed to explore the factors influencing guardians’ HPV vaccine acceptance for their girls and provide clues for the development of health intervention strategies. Methods: Combining the health belief model as a theoretical framework, a questionnaire-based survey was conducted. A total of 2157 adolescent girls and their guardians were recruited. The multivariable logistic model was applied to explore associated factors. Results: The guardians had a high HPV vaccine acceptance rate (86.7%) for their girls, and they demonstrated a relatively good level of awareness regarding HPV and HPV vaccines. Factors influencing guardians’ HPV vaccine acceptance for girls included guardians’ education background (OR = 0.57, 95%CI = 0.37–0.87), family income (OR = 1.94, 95%CI = 1.14–3.32), risk of HPV infection (OR = 3.15, 95%CI = 1.40–7.10) or importance of the HPV vaccine for their girls (OR = 6.70, 95%CI = 1.61–27.83), vaccination status surrounding them (OR = 2.03, 95%CI = 1.41–2.92), awareness of negative information about HPV vaccines (OR = 0.59, 95%CI = 0.43–0.82), and recommendations from medical staff (OR = 2.32, 95%CI = 1.65–3.25). Also, guardians preferred to get digital information on vaccines via government or CDC platforms, WeChat platforms, and medical knowledge platforms. Conclusions: Though HPV vaccine willingness was high among Chinese guardians, they preferred to vaccinate their daughters at the age of 17–18 years, later than WHO’s recommended optimal age period (9–14 years old), coupled with safety concerns. Future work should be conducted based on these findings to explore digital intervention effects on girls’ vaccination compliance. Full article

Machine learning and artificial intelligence have transformed fault detection and maintenance strategies for industrial machinery. This study applies well-established data-driven techniques to a rarely explored industrial application—the condition monitoring of high-precision paper cutting machines—enhancing condition-based maintenance to improve operational efficiency, safety, and cost-effectiveness. A key element of the proposed approach is the integration of an infrared pyrometer into vibration monitoring, utilizing accelerometer data to evaluate the state of health of machinery. Unlike traditional fault detection studies that focus on extreme degradation states, this work successfully identifies subtle deviations from optimal, which even expert technicians struggle to detect. Building on a feasibility study conducted with Tecnau SRL, a comprehensive diagnostic system suitable for industrial deployment is developed. Endurance tests pave the way for continuous monitoring under various operating conditions, enabling real-time industrial diagnostic applications. Multi-scale signal analysis highlights the significance of transient and steady-state phase detection, improving the effectiveness of real-time monitoring strategies. Despite the physical similarity of the classified states, simple time-series statistics combined with machine learning algorithms demonstrate high sensitivity to early-stage deviations, confirming the reliability of the approach. Additionally, a systematic analysis to downgrade acquisition system specifications identifies cost-effective sensor configurations, ensuring the feasibility of industrial implementation. Full article

The rapid growth of electric vehicles (EVs) and new energy systems has put lithium-ion batteries at the center of the clean energy change. Nevertheless, to achieve the best battery performance, safety, and sustainability in many changing circumstances, major innovations are needed in Battery Management Systems (BMS). This review paper explores how artificial intelligence (AI) and digital twin (DT) technologies can be integrated to enable the intelligent BMS of the future. It investigates how powerful data approaches such as deep learning, ensembles, and models that rely on physics improve the accuracy of predicting state of charge (SOC), state of health (SOH), and remaining useful life (RUL). Additionally, the paper reviews progress in AI features for cooling, fast charging, fault detection, and intelligible AI models. Working together, cloud and edge computing technology with DTs means better diagnostics, predictive support, and improved management for any use of EVs, stored energy, and recycling. The review underlines recent successes in AI-driven material research, renewable battery production, and plans for used systems, along with new problems in cybersecurity, combining data and mass rollout. We spotlight important research themes, existing problems, and future drawbacks following careful analysis of different up-to-date approaches and systems. Uniting physical modeling with AI-based analytics on cloud-edge-DT platforms supports the development of tough, intelligent, and ecologically responsible batteries that line up with future mobility and wider use of renewable energy. Full article

Contamination of food with heavy metals is an important factor leading to serious health concerns. Rapid identification of these heavy metals is of utmost priority. There are several methods to identify traces of heavy metals in food. Conventional methods for the detection of heavy metal residues have their limitations in terms of cost, analysis time, and complexity. In the last decade, voltammetric analysis has emerged as the most prominent electrochemical determination method for heavy metals. Voltammetry is a reliable, cost-effective, and rapid determination method. This review provides a detailed primer on recent advances in the development and application of graphene-based electrochemical sensors for heavy metal monitoring over the last decade. We critically examine aspects of graphene modification (fabrication process, stability, cost, reproducibility) and analytical properties (sensitivity, selectivity, rapid detection, lower detection, and matrix effects) of these sensors. Furthermore, to our knowledge, meta-analyses were performed for the first time for all investigated parameters, categorized based on graphene materials and heavy metal types. We also examined the pass–fail criteria according to the WHO drinking water guidelines. In addition, the effects of heavy metal toxicity on human health and the environment are discussed. Finally, the contribution of heavy metal contamination to the seventeen Sustainable Development Goals (SDGs) stated by the United Nations in 2015 is discussed in detail. The results confirm the significant impact of heavy metal contamination across twelve SDGs. This review critically examines the existing knowledge in this field and highlights significant research gaps and future opportunities. It is intended as a resource for researchers working on graphene-based electrochemical sensors for the detection of heavy metals in food safety, with the ultimate goal of improving consumer health protection. Full article

The lateral flow immunoassay (LFIA) is a widely utilized, rapid diagnostic technique characterized by its short analysis duration, cost efficiency, visual result interpretation, portability and suitability for point-of-care applications. However, conventional LFIAs have limited sensitivity, a challenge that can be overcome by the introduction of gold nanoparticles, which provide enhanced sensitivity and selectivity (compared, for example, to latex beads or carbon nanoparticles) for the detection of target analytes, due to their optical properties, chemical stability and ease of functionalization. In this work, gold nanoparticle-based LFIAs are developed for the detection of aflatoxin B1, and the relative performance of different morphology particles is evaluated. LFIA using gold nano-labels allowed for aflatoxin B1 detection over a range of 0.01 ng/mL–100 ng/mL. Compared to spherical gold nanoparticles and gold nano-flowers, star-shaped gold nanoparticles show increased antibody binding efficiency of 86% due to their greater surface area. Gold nano-stars demonstrated the highest sensitivity, achieving a limit of detection of 0.01ng/mL, surpassing the performance of both spherical gold nanoparticles and gold nano-flowers. The use of star-shaped particles as nano-labels has demonstrated a five-fold improvement in sensitivity, underscoring the potential of integrating diverse nanostructures into LFIA for significantly improving analyte detection. Moreover, the robustness and feasibility of gold nano-stars employed as labels in LFIA was assessed in detecting aflatoxin B1 in a wheat matrix. Improved sensitivity with gold nano-stars holds promise for applications in food safety monitoring, public health diagnostics and rapid point-of-care diagnostics. This work opens the pathway for further development of LFIA utilizing novel nanostructures to achieve unparallel precision in diagnostics and sensing. Full article

The use of opioids for cancer-related pain is essential but poses significant challenges due to the risk of misuse and the development of opioid use disorder (OUD). This review takes a multidisciplinary perspective based on the current scientific literature to analyze the pharmacological mechanisms, classification, and therapeutic roles of opioids in oncology. Key risk factors for opioid misuse—including psychiatric comorbidities, prior substance use, and insufficient clinical monitoring—are discussed in conjunction with validated tools for pain assessment and international guidelines. The review emphasizes the importance of integrating toxicological, pharmacological, physiological, and public health perspectives to promote rational opioid use. Pharmacogenetic variability is explored as a determinant of treatment response and addiction risk, underscoring the value of personalized medicine. Evidence-based strategies such as early screening, psychosocial interventions, and the use of buprenorphine-naloxone are presented as effective measures for managing OUD in cancer patients. Ultimately, this work advocates for safe, patient-centered opioid prescribing practices that ensure effective pain relief without compromising safety or quality of life. Full article

Improving occupational health and safety (OHS) in the construction industry can contribute to the advancement of the Sustainable Development Goals (SDGs), particularly Goals 3 (Good Health and Well-being) and 8 (Decent Work and Economic Growth). Yet, workers’ risk-taking behaviors (RTBs) remain a persistent challenge. Drawing on Social Cognitive Theory and Social Information Processing Theory, this study develops and tests a social influence model to examine how foremen’s safety attitudes (SAs) shape workers’ RTBs. Drawing on survey data from 301 construction workers in China, structural equation modeling reveals that foremen’s SAs significantly and negatively predict workers’ RTBs. However, the three dimensions of SAs—cognitive, affective, and behavioral—exert their influence through different pathways. Risk perception (RP) plays a key mediating role, particularly for the cognitive and behavioral dimensions. Furthermore, interpersonal trust (IPT) functions as a significant moderator in some of these relationships. By identifying the micro-social pathways that link foremen’s attitudes to workers’ safety behaviors, this study offers a testable theoretical framework for implementing the Sustainable Development Goals (particularly Goals 3 and 8) at the frontline workplace level. The findings provide empirical support for organizations to move beyond rule-based management and instead build more resilient OHS governance systems by systematically cultivating the multidimensional attitudes of frontline leaders. Full article

This study analyzes the relationship between ergonomic and psychosocial risk factors and labor productivity using a bibliometric approach through a general analysis and one that includes inclusion criteria such as English language, open access, and primary research publications to identify only those articles that explicitly address the relationship between ergonomic and psychosocial risk factors and labor productivity. It is recognized that both physical and psychosocial conditions of the work environment directly influence workers’ health and organizational performance. For this purpose, a bibliometric review was conducted in academic databases, including Scopus, Web of Science, ScienceDirect, and Taylor & Francis, resulting in the selection of 4794 relevant articles for general analysis. Additionally, 116 relevant articles were selected based on the inclusion criteria. Tools and methodologies, such as Rayyan, Excel, VOSviewer 1.6.20, and PRISMA, were used to classify the studies and identify trends, collaboration networks, and geographical distribution. The results reveal a sustained growth in scientific production, with clusters on occupational safety and health, work environment factors, and the characteristics of the population, approach, and methodologies used in the studies. Likewise, Procedia Manufacturing, International Journal of Occupational Safety and Ergonomics, and Ergonomics stand out as the main sources of publication, while countries such as Sweden, Poland, and the United States lead the scientific production in this field. In addition, the network of co-occurrence of keywords evidences a comprehensive approach that articulates physical or ergonomic and psychosocial risk factors with organizational performance, while the network of authors shows consolidated collaborations and studies focused on analyzing the relationship between physical demands and musculoskeletal disorders from advanced ergonomic approaches. Full article

The current proceedings summarize the presentations delivered during the third International Electronic Conference on Microbiology (ECM 2025), which was held online from 1 to 3 April 2025, via the SciForum platform. This virtual event brought together researchers from around the world to share recent advances in microbiological sciences. The ECM 2025 highlighted recent developments across a broad spectrum of microbiological research, including antimicrobial resistance, gut microbiota, infectious diseases, and environmental microbiomes. Participants shared their work through online presentations and abstracts, with selected submissions invited for full publication. The event fostered global collaboration, promoted open-access science, and showcased innovative tools for studying and managing microbial systems in health, agriculture, and industry. The multidisciplinary program was organized into several thematic sessions: S1. Gut Microbiota and Health Disease. S2. Foodborne Pathogens and Food Safety. S3. Antimicrobial Agents and Resistance. S4. Emerging Infectious Diseases. S5. Microbiome and Soil Science. S6. Microbial Characterization and Bioprocess. S7. Microbe–Plant Interactions. This conference report presents summaries of the contributions made by participating authors over the three-day event. Full article

The high pH and heavy metal leaching of argon oxygen decarburization (AOD) slag limit its application in agriculture. Slag carbonation can aid in decreasing slag alkalinity and inhibit heavy metal release; the environmental safety of utilizing carbonated AOD slag (CAS) as a fertilizer remains a topic of significant debate, however. In this work, pakchoi (Brassica chinensis L.) was planted in CAS-fertilized soil to investigate the accumulation and migration behavior of heavy metals in the soil–plant system and perform an associated risk assessment. Our results demonstrated that CAS addition increases Ca, Si, and Cr concentrations but decreases Mg and Fe concentrations in soil leachates. Low rates (0.25–1%) of CAS fertilization facilitate the growth of pakchoi, resulting in the absence of soil contamination and posing no threat to human health. At the optimal slag addition rate of 0.25%, the pakchoi leaf biomass, stem biomass, leaf area, and seedling height increased by 34.2%, 17.2%, 26.3%, and 8.7%, respectively. The accumulation of heavy metals results in diverging characteristics in pakchoi. Cr primarily accumulates in the roots; in comparison, Pb, Cd, Ni, and Hg preferentially accumulate in the leaves. The migration rate of the investigated heavy metals from the soil to pakchoi follows the order of Cr > Cd > Hg > Ni > Pb; in comparison, that from the roots to the leaves follows the order Cd > Ni > Hg > Cr > Pb. Appropriate utilization of CAS as a mineral fertilizer can aid in improving pakchoi yield, achieving sustainable economic benefits, and preventing environmental pollution. Full article

Per- and polyfluoroalkyl substances (PFAS) have been found worldwide in water, soil, plants, and animals, including humans. A primary route of exposure for humans and animals to PFAS is through the diet and drinking water. Perfluorooctane sulfonate (PFOS), a long-chain PFAS with a relatively long half-life, has been associated with adverse health effects in humans and laboratory animals. There are few toxicokinetic studies on PFOS in domestic livestock raised for human food consumption, which are critical for assessing human food safety. This work aimed to develop a simple daily accumulation model (DAM) for predicting PFOS residues in edible beef cattle muscle. A one-compartment toxicokinetic model in a spreadsheet format was developed using simple calculations to account for daily PFAS into and out of the animal. The DAM was used to simulate two case studies to predict resultant PFOS residues in edible beef cattle tissues. The results demonstrated that the model can reasonably predict PFOS concentrations in beef cattle muscle in a real-world scenario. The DAM was then used to simulate dietary PFOS exposure in beef cattle throughout a typical lifespan in order to derive a generic bioaccumulation factor. The DAM is expected to work well for other PFAS in beef cattle, PFAS in other livestock species raised for meat, and other chemical contaminants with relatively long half-lives. Full article

The measurement of national well-being has become central to both academic and policy debates, particularly within the framework of sustainable development. In this context, this study investigates the relationship between macroeconomic conditions and subjective well-being in Portugal. Using annual data from 2004 to 2022, we explore the effects of GDP per capita, unemployment, and inflation on the Global Well-Being Index (GWBI). Employing ordinary least squares (OLS) regression, the results indicate a significant positive relationship between GDP per capita and subjective well-being, while inflation is negatively associated. Contrary to expectations, the unemployment rate showed a positive and significant association with the GWBI. This counterintuitive result may reflect institutional buffering effects, such as social safety nets, strong family structures, or lagged responses in perceptions of well-being. Similar patterns were observed in other southern European countries with strong informal social support systems. These findings contribute to a deeper understanding of how economic indicators relate to perceived well-being, particularly in the context of a southern European country. The study offers relevant insights for public policy, including the alignment of macroeconomic management with the Sustainable Development Goals (SDGs), especially SDG 3 (Good Health and Well-being) and SDG 8 (Decent Work and Economic Growth). Full article

Antimicrobial resistance (AMR) has emerged as one of the most pressing global health challenges of our time. Alarming projections of increasing mortality from resistant infections highlight the urgent need for innovative solutions. While many candidates have shown promise in preliminary studies, they often encounter challenges in terms of efficacy and safety during clinical translation. This review examines cutting-edge approaches to combat AMR, with a focus on engineered antimicrobial peptides, functionalized nanoparticles, and advanced genomic therapies, including Clustered Regularly Interspaced Short Palindromic Repeats-associated proteins (CRISPR-Cas systems) and phage therapy. Recent advancements in these fields are critically analyzed, with a focus on their mechanisms of action, therapeutic potential, and current limitations. Emphasis is given to strategies targeting biofilm disruption and quorum sensing interference, which address key mechanisms of resistance. By synthesizing current knowledge, this work provides researchers with a comprehensive framework for developing next-generation antimicrobials, highlighting the most promising approaches for overcoming AMR through rational drug design and targeted therapies. Ultimately, this review aims to bridge the gap between experimental innovation and clinical application, providing valuable insights for developing effective and resistance-proof antimicrobial agents. Full article

Infant clothing represents a critical yet overlooked exposure pathway for heavy metals, with significant implications for child health and sustainable consumption. This study investigates cadmium (Cd) and chromium (Cr) contamination in 33 textile samples, integrating in vitro bioaccessibility assays, cytotoxicity analysis, and risk assessment models to evaluate dermal exposure risks. Results reveal that 80% of samples exceeded OEKO-TEX Class I limits for As (mean 1.01 mg/kg), Cd (max 0.25 mg/kg), and Cr (max 4.32 mg/kg), with infant clothing showing unacceptable hazard indices (HI = 1.13) due to Cd (HQ = 1.12). Artificial sweat extraction demonstrated high bioaccessibility for Cr (37.8%) and Ni (28.5%), while keratinocyte exposure triggered oxidative stress (131% ROS increase) and dose-dependent cytotoxicity (22–59% viability reduction). Dark-colored synthetic fabrics exhibited elevated metal loads, linking industrial dye practices to health hazards. These findings underscore systemic gaps in textile safety regulations, particularly for low- and middle-income countries reliant on cost-effective apparel. We propose three policy levers: (1) tightening infant textile standards for Cd/Cr, (2) incentivizing non-toxic dye technologies, and (3) harmonizing global labeling requirements. By bridging toxicological evidence with circular economy principles, this work advances strategies to mitigate heavy metal exposure while supporting Sustainable Development Goals (SDGs) 3 (health), 12 (responsible consumption), and 12.4 (chemical safety). Full article

Background. Healthcare workers’ health can be influenced by physical, psychological, social, emotional, and work-related stress. MountainTherapy Activities (MTAs) are an integrated therapeutic approach that uses nature to enhance their well-being through group activities like hiking. This cross-sectional study examines well-being levels among Italian Departments of Mental Health workers who do or do not participate in MTAs. It hypothesizes that MTAs may reduce burnout, boost psychological resilience, and increase job satisfaction. Methods. The study involved 167 healthcare workers from 11 Italian Local Health Authorities, divided into MTA (who take part in MTA; n = 83) and non-MTA (who have never participated in MTA; n = 84) groups. They completed five validated questionnaires on psychological distress, burnout, resilience, job engagement, and psychological safety. Data were compared between groups, considering MTA frequency and well-being differences during MTAs versus workplace activities. Results. MTA participants scored higher in psychological well-being (t(117.282) = −1.721, p = 0.044) and general dysphoria (t(116.955) = −1.721, p = 0.042). Additionally, during MTAs, they showed greater job engagement (vigor: t(66) = −8.322, p < 0.001; devotion: t(66) = −4.500, p < 0.001; emotional involvement: t(66) = −8.322, p = 0.002) and psychological safety (general: t(66) = −5.819, p < 0.001; self-expression: t(66) = −5.609, p < 0.001) compared to other activities. Conclusions. MTAs can be considered a valid intervention for the promotion of the mental health of healthcare workers. Full article