Search Results (513)

Introduction: Night shift work (NSW) has been increasingly addressed in the scientific literature, as it is considered a probable carcinogen. In this study, we investigated the association of NSW with health problems, unhealthy behaviors, and occupational carcinogens. Methods: Cross-sectional study with a sample of 47,953 workers from the 2019 National Health Survey. NSW prevalence was estimated according to sociodemographic characteristics. To investigate the associations of NSW with all study variables, gender stratified logistic regression models were used. The odds-ratio and 95% confidence intervals were estimated. Results: Among men, there was a significant association of NSW with sleep disorders (OR = 1.39; 95% CI: 1.17–1.65), tiredness (OR = 1.68; 95% CI: 1.41–2.00), obesity (OR = 1.41; 95% CI: 1.20–1.66), unhealthy food consumption (OR = 1.28; 95% CI: 1.12–1.46), handling of radioactive material (OR = 2.45; 95% CI: 1.61–3.72), and biological material (OR = 3.18; 95% CI: 3.15–4.80). Among females, NSW was associated with the same variables except obesity, but depressive feelings (OR = 1.35 95% CI: 1.09–1.67), frequent alcohol intake (OR = 1.48; 95% CI: 1.23–1.78), handling of chemical substances (OR = 1.54; OR = 1.54; 95% CI: 1.20–1.97), and passive smoking at work (OR = 1.45; 95% CI: 1.12–1.86) were highly significant. Conclusion: Night shift workers are more vulnerable to occupational carcinogen exposure, experience greater impacts on their well-being, and are more likely to engage in unhealthy behaviors. These findings should be considered in managing and organizing night work in the workplace. Actions to promote healthy work environments should be encouraged to protect workers’ health. Full article

Introduction: Nurses are the largest group of healthcare workers, and healthcare managers should pay attention to the quality of work life and the health and working capacity of nurses in order to ensure a sufficient number of nurses and a stable workforce. Aim: The present study aimed to determine nurses’ quality of work life, work ability index and intention to leave the nursing profession and to examine the associations between nurses’ quality of work life, work ability index and intention to leave the nursing profession. Methods: An online cross-sectional study was conducted. A total of 498 nurses completed the instrument, consisting of demographic data, Brooks’ Quality of Nursing Work Life Survey (BQNWL), Work Ability Index Questionnaire (WAIQ) and questions on their intention to leave their current job or the nursing profession. Results: Most nurses had a moderate quality of work life (QWL) (73.7%) and a good work ability index (WAI) (43.78%). Men (p = 0.047), nurses who study (p = 0.021), nurses who do not have children (p = 0.000) and nurses who do not take care of their parents (p = 0.000) have a statistically significantly higher total WAIQ score. Most nurses (61.1%) had considered changing jobs in the last 12 months, and 36.9% had considered leaving the nursing profession. A statistically significant positive correlation was found between the total BQNWL and the total WAI. The study found no correlation between QWL, WAI and intention to change jobs or leave the profession, which was unexpected. Conclusions: To ensure the provision of necessary nursing care and a healthy working environment for nurses, it is necessary to regularly monitor QWL and WAI and take measures to ensure the highest quality of working life. Further longitudinal and mixed-methods research is needed to understand the relationship between QWL, WAI and intention to leave. Full article

Spores of filamentous fungi are common biological particles in indoor air that can negatively impact human health, particularly among immunocompromised individuals and patients with chronic respiratory conditions. Airborne viruses represent an equally pervasive threat, with some carrying the potential for pandemic spread, affecting both healthy individuals and the immunosuppressed alike. This study investigated the abundance and diversity of airborne fungal spores in both hospital and residential environments, using custom designed air samplers with or without the presence of negative air ions (NAIs) inside the sampler. The main purpose of investigation was the assessment of biological effects of NAIs on fungal spore viability, deposition, mycelial growth, and sporulation, as well as airborne viral load. The precise assessment of mentioned biological effects is otherwise difficult to carry out due to low concentrations of studied specimens; therefore, specially devised and designed, ion-bioaerosol interaction air samplers were used for prolonged collection of specimens of interest. The total fungal spore concentrations were quantified, and fungal isolates were identified using cultural and microscopic methods, complemented by MALDI-TOF mass spectrometry. Results indicated no significant difference in overall spore concentration between environments or treatments; however, presence of NAIs induced a delay in the sporulation process of Cladosporium herbarum, Aspergillus flavus, and Aspergillus niger within 72 h. These effects of NAIs are for the first time demonstrated in this work; most likely, they are mediated by oxidative stress mechanisms. A parallel experiment demonstrated a substantially reduced concentration of aerosolized equine herpesvirus 1 (EHV-1) DNA within 10–30 min of exposure to NAIs, with more than 98% genomic load reduction beyond natural decay. These new results on the NAIs interaction with a virus, as well as new findings regarding the fungal sporulation, resulted in part from a novel interaction setup designed for experiments with the bioaerosols. Our findings highlight the potential of NAIs as a possible approach for controlling fungal sporulation and reducing airborne viral particle quantities in indoor environments. Full article

Gait analysis is a valuable tool for aiding in the diagnosis of neurological diseases, providing objective measurements of human gait kinematics and kinetics. These data enable the quantitative estimation of movement abnormalities, which helps to diagnose disorders and assess their severity. In this regard, machine learning techniques and explainability methods offer an opportunity to enhance anomaly detection in gait measurements and support a more objective assessment of neurodegenerative disease, providing insights into the most relevant gait parameters used for disease identification. This study employs several classifiers and explainability methods to analyze gait data from a public dataset composed of patients affected by degenerative neurological diseases and healthy controls. The work investigates the relevance of spatial, temporal, and kinematic gait parameters in distinguishing such diseases. The findings are consistent among the classifiers employed and in agreement with known clinical findings about the major gait impairments for each disease. This work promotes the use of data-driven assessments in clinical settings, helping reduce subjectivity in gait evaluation and enabling broader deployment in healthcare environments. Full article

Amid the global shift toward clean energy, wind power has emerged as a critical pillar of the modern energy matrix. To improve the reliability and maintainability of wind farms, this work proposes a novel hybrid fault detection approach that combines expert-driven diagnostic knowledge with data-driven modeling. The framework integrates autoencoder-based neural networks with Failure Mode and Symptoms Analysis, leveraging the strengths of both methodologies to enhance anomaly detection, feature selection, and fault localization. The methodology comprises five main stages: (i) the identification of failure modes and their observable symptoms using FMSA, (ii) the acquisition and preprocessing of SCADA monitoring data, (iii) the development of dedicated autoencoder models trained exclusively on healthy operational data, (iv) the implementation of an anomaly detection strategy based on the reconstruction error and a persistence-based rule to reduce false positives, and (v) evaluation using performance metrics. The approach adopts a fault-specific modeling strategy, in which each turbine and failure mode is associated with a customized autoencoder. The methodology was first validated using OpenFAST 3.5 simulated data with induced faults comprising normal conditions and a 1% mass imbalance fault on a blade, enabling the verification of its effectiveness under controlled conditions. Subsequently, the methodology was applied to a real-world SCADA data case study from wind turbines operated by EDP, employing historical operational data from turbines, including thermal measurements and operational variables such as wind speed and generated power. The proposed system achieved 99% classification accuracy on simulated data detect anomalies up to 60 days before reported failures in real operational conditions, successfully identifying degradations in components such as the transformer, gearbox, generator, and hydraulic group. The integration of FMSA improves feature selection and fault localization, enhancing both the interpretability and precision of the detection system. This hybrid approach demonstrates the potential to support predictive maintenance in complex industrial environments. Full article

Background/Objectives: The COVID-19 pandemic has caused sickness and death among many health care workers. However, the apparent resistance of health care workers to SARS-CoV-2 infection despite their high-risk work environment remains unclear. To investigate if inflammation and circadian disruption contribute to resistance or diminished susceptibility to the SARS-CoV-2 virus, we retrospectively evaluated a cohort of volunteer hospital nurses (VHNs). Methods: A total of 246 apparently healthy VHNs (mean age 37.4 ± 5.9 years) who had received the BNT162b2 mRNA vaccine were asked to report their sleep quality, according to the Pittsburgh Sleep Quality Index, and number of SARS-CoV-2 infections during the observational study period (from the end of December 2020 to April 2025). The expression of inflammation-associated mediators and circadian transcription factors in peripheral blood mononuclear cells, as well as sleep quality, were examined. Results: Our findings revealed no anthropometric, biochemical, or inflammation-associated parameters but demonstrated significantly greater levels of NFE2L2, also known as nuclear factor erythroid-derived 2-like 2 (NFR2), gene expression in peripheral blood mononuclear cells among VHNs who had never been infected with SARS-CoV-2 (n = 97) than in VHNs with only one (n = 119) or with two or more (n = 35) prior SARS-CoV-2 infections (p < 0.01). This result was confirmed through one-to-one propensity score matching (p < 0.01). Moreover, NRF2 gene expression was not associated with the number of COVID-19 vaccinations (p = 0.598). Finally, NRF2 gene expression was higher among participants who reported better sleep quality (p < 0.01). Conclusions: Our findings suggest possible interactions among NRF2 gene expression, protection against SARS-CoV-2 infection, and the modulation of COVID-19 vaccination efficacy. Full article

Vegetable intake among military Service Members (SMs) is well below public health guidelines, with only 12.9% meeting the Dietary Guidelines for Americans (DGAs). Low vegetable consumption negatively impacts diet quality as measured by the Healthy Eating Index (HEI), and poses risks to health and performance. Given the high physical and mental demands of military life, improving diet quality, including through increased vegetable intake, is crucial for optimizing health and readiness. Providing meal kits may help improve vegetable intake by reducing access-related barriers for SMs living or working on a military base. Furthermore, the addition of spices and herbs is a readily modifiable accompanying approach to address taste-related barriers and increase intake that has shown promise in other populations with poor diet quality. Background/Objectives: This study aimed to evaluate whether heat-and-serve meal kits with spices and herbs could increase vegetable intake and liking among active-duty SM by simultaneously targeting barriers to healthy eating and modifiable sensory factors. Methods: Conducted at Naval Support Activity Bethesda, the study randomly distributed heat-and-serve meal kits (n = 400) featuring either spiced (n = 200) or plain versions (n = 200) of four vegetables (broccoli, carrots, cauliflower, and kale). Each kit contained a quick response (QR) code for participants to upload post-consumption photos and rate vegetable liking on a nine-point Likert scale. Food photography (SmartIntake^®) was used to estimate vegetable consumption. Paired t-tests were used to determine differences between the intake of plain and spiced vegetables. Results: Intake of the heat-and-serve vegetables was very high for both the spiced and plain preparations (1.73 out of 2 cups, 87%). There was minimal difference (p = 0.87) between the consumption of spiced (1.75 cups) and plain (1.725 cups) vegetables, suggesting that both were well accepted. Overall, convenient and accessible meal options, alongside sensory-driven strategies, appear to improve some barriers to vegetable consumption in SM populations. Conclusions: Future studies should explore long-term outcomes and adaptability across different military environments, while considering additional factors, including convenience and time constraints, that influence dietary choices in the military. Full article

The luminous environment, shaped by daylight and electric light, significantly influences visual performance, physiological responses, and perceptual experiences. While these light sources are often perceived as distinct due to their differing effects on occupants’ cognition and well-being, the underlying mechanisms remain unclear. Nine lighting conditions were evaluated, combining three spectral types—daylight (DL), conventional LED (CLED), and daylight LED (DLED)—with three horizontal illuminance levels (300 lx, 500 lx, and 1000 lx). Twelve healthy subjects completed visual performance tasks (2-back working memory test), physiological measurements (heart rate variability and critical flicker frequency), and subjective evaluations. The results revealed that 500 lx consistently yielded the most favorable outcomes: 2-back task response speed improved by 6.2% over 300 lx and 1000 lx, and the critical flicker frequency difference was smallest, indicating reduced fatigue. DLED lighting achieved cognitive and physiological levels comparable to daylight. Heart rate variability analyzes further confirmed higher alertness levels under 500 lx DLED lighting (LF/HF = 3.31). Subjective ratings corroborated these findings, with perceived alertness and comfort highest under DLED and 500 lx conditions. These results demonstrate that DLED, which offers a balanced spectral composition and improved uniformity, may serve as an effective lighting configuration for supporting both visual and non-visual performance in indoor settings lacking daylight. Full article

One of the most significant factors shaping the formation of new urban structures is climate change—including global warming and the associated emerging issues—heatwaves, storms, hurricanes, floods, droughts, fires and others. In recent times, new threats have emerged, including war risks, radiation, pandemics and other potential factors, whose devastating consequences are no less severe than those of climate change. Concerning these and other potential threats, this work aims to develop a new, sustainable urban structure element—a territorial unit or complex to be used in creating a new city planning framework. The formation of this sustainable urban unit or complex is based on three fundamental sustainability principles—social, ecological and economic—the harmonious interaction of which can enable the creation of a safe, healthy and convenient urban environment for living, working and leisure. Such a structural urban complex would consist of a group of neighbourhoods with various building densities, enclosed by public transport streets that integrate the complex into the city’s overall spatial structure. To support the complex’s functioning, a structural element—a green core—is planned at its centre, serving as a space for residents’ recreation, protection from various threats and social interaction. Given that this technical, structural and urban territorial unit, in terms of its autonomous functionality, structure, composition, significance and other characteristics, is identical to a natural cell, it is proposed (based on the principles of bionics) to name this structural urban territorial unit an ‘urban cell’ or ‘urbocell’ for semantic clarity. Full article

Sedentary behavior is increasingly recognized as a risk factor for various health issues, including cognitive decline. Objectives: This pilot study examined the acute and chronic effects of workplace-integrated exercise snacks—short, vigorous bouts of exercise—on cognitive performance in middle-aged adults. Methods: Twenty-five sedentary but healthy office workers (51.4 ± 5.3 years; 6 m/19 f) participated. The intervention group (n = 12) performed three 1 min bouts of vigorous exercise (running on the spot) daily, four days a week, for four weeks, while the control group (n = 13) maintained their usual routine. Cognitive performance was assessed pre-intervention, shortly following the first exercise bout (acute effects), and post-intervention (chronic effects) using the Trail Making Test and the Stroop Color–Word Test. Results: Significant acute improvements (p ≤ 0.05) were observed across all cognitive outcomes following a single bout of exercise. Significant interaction effects (p ≤ 0.05) were found across all cognitive outcomes after four weeks, indicating sustained cognitive benefits. Conclusion: These findings suggest that workplace-integrated brief, vigorous exercise may lead to both immediate and sustained enhancement in executive functions such as working memory, processing speed, and inhibitory control. They highlight the potential cognitive health benefits of incorporating exercise snacks into sedentary workplace environments. Full article

Cutaneous larva migrans (CLM) is attributed to zoonotic infection with animal hookworm larvae penetrating the human skin, usually the feet and legs. There is, however, a broad range of differential diagnoses, with the implicated hookworm species usually remaining speculative. Single-dose ivermectin is the most recommended current therapy, with repeat ivermectin doses sometimes required. With the massive global expansion of macrocytic lactone use in both livestock and companion animals, ivermectin resistance is being increasingly described in both helminths and ectoparasites. A case of CLM involving the foot of a healthy 37-year-old is described, with the failure of two doses of ivermectin 15 mg (240 μg/kg) a week apart. This occurred in the context of a remote work environment in tropical Australia with both companion animals (dogs and cats) and wildlife exposed to antiparasitic agents including ivermectin. A combination regimen of multiple doses of albendazole and ivermectin was curative. Parasites with multidrug resistance being described from animals now include hookworms in dogs which are resistant to pyrantel, benzimidazoles such as mebendazole and ivermectin. For relapsed CLM we now recommend a combination of ivermectin and albendazole therapy. This report supports the critical role for a One Health/Planetary Health approach to surveillance and response for emerging zoonoses and antimicrobial resistance in human and animal pathogens. This requires support for systematic approaches to foster and normalize communications and collaborations between human and animal health professionals, environmental scientists and ecologists and First Nations scientists who are the holders of Indigenous knowledge. Full article

Powdery mildew is one of the most common diseases affecting strawberry yield and quality. Accurate and timely detection is essential to reduce pesticide usage and labor costs. However, recognizing strawberry powdery mildew in complex field environments remains a significant challenge. In this study, an HSV-based image segmentation method was employed to enhance the extraction of disease regions from complex backgrounds. A total of 14 widely used deep learning models—including SqueezeNet, GoogLeNet, ResNet-50, AlexNet, and others—were systematically evaluated for their classification performance. To address sample imbalance, data augmentation was applied to 2372 healthy and 553 diseased leaf images, resulting in 11,860 training samples. Experimental results showed that InceptionV4, DenseNet-121, and ResNet-50 achieved superior performance across metrics such as accuracy, F1-score, recall, and loss. Models such as MobileNetV2, AlexNet, VGG-16, and InceptionV3 demonstrated certain strengths, and models like SqueezeNet, VGG-19, EfficientNet, and even ResNet-50 showed room for further improvement in performance. These findings demonstrate that CNN models originally developed for other crop diseases can be effectively adapted to detect strawberry powdery mildew under complex conditions. Future work will focus on enhancing model robustness and deploying the system for real-time field monitoring. Full article

Continuous human population growth, industrialization, and technical progress have increased the demand for a new design and synthesis of chemical compounds. Developing eco-friendly chemical compounds has been a priority for fostering a sustainable and healthy environment, which is directly linked to human well-being. In this context, green chemistry and circular economy principles have been applied to generate valuable new chemicals, such as surfactants, with high market value. Surfactants play a crucial role in various products for both domestic and industrial applications, leading to their large-scale production a diverse array of chemical structures. However, the advantages of their use must be balanced against their negative environmental impact as pollutants. Thus, there is an increasing demand for the development of new eco-friendly surfactants. Additionally, life cycle assessment (LCA) studies of new surfactants are essential for evaluating their environmental impact, enhancing energy efficiency and facilitating the transition toward sustainable energy resources. In this work, we present the chemical synthesis of oligomeric and polymeric thiophene-based surfactants with potential applications in biosensors, organic transistors, and various other fields. The newly synthesized oligomeric and polymeric thiophene-based surfactants demonstrated medium-to-high biodegradation potential and showed no significant ecotoxicological effects on bacterial communities. However, the LCA of their synthesis revealed a negative impact on the environment and human health, particularly concerning polymeric thiophene-based surfactants. The LCA identified specific chemical steps that could be optimized to develop a new generation of eco-friendly surfactants. Full article

This paper presents the development and initial evaluation of a novel protocol for robot-assisted lower limb rehabilitation. It integrates dual-modal patient interaction, employing mirror therapy and an auto-adaptive EMG-driven control system, designed to enhance lower limb rehabilitation in patients with hemiparesis impairments. The system features a robotic platform specifically engineered for lower limb rehabilitation, which operates in conjunction with a virtual reality (VR) environment. This immersive environment comprises a digital twin of the robotic system alongside a human avatar representing the patient and a set of virtual targets to be reached by the patient. To implement mirror therapy, the proposed protocol utilizes a set of inertial sensors placed on the patient’s healthy limb to capture real-time motion data. The auto-adaptive protocol takes as input the EMG signals (if any) from sensors placed on the impaired limb and performs the required motions to reach the virtual targets in the VR application. By synchronizing the motions of the healthy limb with the digital twin in the VR space, the system aims to promote neuroplasticity, reduce pain perception, and encourage engagement in rehabilitation exercises. Initial laboratory trials demonstrate promising outcomes in terms of improved motor function and subject motivation. This research not only underscores the efficacy of integrating robotics and virtual reality in rehabilitation but also opens avenues for advanced personalized therapies in clinical settings. Future work will investigate the efficiency of the proposed solution using patients, thus demonstrating clinical usability, and explore the potential integration of additional feedback mechanisms to further enhance the therapeutic efficacy of the system. Full article

The development of automatic methods for early cognitive impairment (CI) detection has a crucial role to play in helping people obtain suitable treatment and care. Video-based analysis offers a promising, low-cost alternative to resource-intensive clinical assessments. This paper investigates visual features (eye blink rate (EBR), head turn rate (HTR), and head movement statistical features (HMSFs)) for distinguishing between neurodegenerative disorders (NDs), mild cognitive impairment (MCI), functional memory disorders (FMDs), and healthy controls (HCs). Following prior work, we improve the multiple thresholds (MTs) approach specifically for EBR calculation to enhance performance and robustness, while the HTR and HMSFs are extracted using methods from previous work. The EBR, HTR, and HMSFs are evaluated using an in-the-wild video dataset captured in challenging environments. This method leverages clinically validated cues and automatically extracts features to enable classification. Experiments show that the proposed approach achieves competitive performance in distinguishing between ND, MCI, FMD, and HCs on in-the-wild datasets, with results comparable to audiovisual-based methods conducted in a lab-controlled environment. The findings highlight the potential of visual-based approaches to complement existing diagnostic tools and provide an efficient home-based monitoring system. This work advances the field by addressing traditional limitations and offering a scalable, cost-effective solution for early detection. Full article