Hasil untuk "Industrial hygiene. Industrial welfare"

Francesco Ragusa, Rosario Leonardi, Michele Mazzamuto et al.

Understanding human behavior from complementary egocentric (ego) and exocentric (exo) points of view enables the development of systems that can support workers in industrial environments and enhance their safety. However, progress in this area is hindered by the lack of datasets capturing both views in realistic industrial scenarios. To address this gap, we propose ENIGMA-360, a new ego-exo dataset acquired in a real industrial scenario. The dataset is composed of 180 egocentric and 180 exocentric procedural videos temporally synchronized offering complementary information of the same scene. The 360 videos have been labeled with temporal and spatial annotations, enabling the study of different aspects of human behavior in industrial domain. We provide baseline experiments for 3 foundational tasks for human behavior understanding: 1) Temporal Action Segmentation, 2) Keystep Recognition and 3) Egocentric Human-Object Interaction Detection, showing the limits of state-of-the-art approaches on this challenging scenario. These results highlight the need for new models capable of robust ego-exo understanding in real-world environments. We publicly release the dataset and its annotations at https://fpv-iplab.github.io/ENIGMA-360/.

Annemarie Jutte, Uraz Odyurt

Industrial Cyber-Physical Systems (CPS) are sensitive infrastructure from both safety and economics perspectives, making their reliability critically important. Machine Learning (ML), specifically deep learning, is increasingly integrated in industrial CPS, but the inherent complexity of ML models results in non-transparent operation. Rigorous evaluation is needed to prevent models from exhibiting unexpected behaviour on future, unseen data. Explainable AI (XAI) can be used to uncover model reasoning, allowing a more extensive analysis of behaviour. We apply XAI to improve predictive performance of ML models intended for an industrial CPS use-case. We analyse the effects of components from time-series data decomposition on model predictions using SHAP values. Through this method, we observe evidence on the lack of sufficient contextual information during model training. By increasing the window size of data instances, informed by the XAI findings for this use-case, we are able to improve model performance.

Luan Aparecido Alexandre Elias, Marcia Cristina Nobukuni, Herica Emilia Félix de Carvalho et al.

To map and describe the scientific evidence on germicidal ultraviolet C (UV-C) light for hospital surface disinfection, this scoping review examined device types, reported operational parameters, microbiological and clinical outcomes, and implementation aspects. Primary studies conducted in hospital settings and evaluating UV-C or ultraviolet germicidal irradiation on environmental surfaces were searched in four databases without date restrictions. Data were synthesized descriptively in tables and narrative form following JBI and PRISMA-ScR guidance. Eleven studies (2007–2025) met the inclusion criteria. Reported microbial reductions ranged from 1 to ≥5 log₁₀. Higher and more consistent reductions were predominantly observed under laboratory or controlled experimental conditions, whereas reductions in real-world hospital surface sampling were more variable and influenced by pathogen type, surface material, room geometry, and shadowing. Integration of UV-C with manual cleaning and multi-position irradiation cycles was associated with greater effectiveness. Reporting of key radiometric parameters (dose, exposure time, and distance) was frequently incomplete, limiting reproducibility and cross-study comparability. Clinical findings were heterogeneous: some interrupted time-series analyses suggested reductions in healthcare-associated infections, although effects were not uniform across microorganisms. Implementation reports described room-level cycle times compatible with turnover, variable staffing requirements, and limited economic evaluation. Overall, UV-C appears to be a promising adjunct to standard cleaning practices in hospital environments. However, standardized radiometric reporting, multicenter studies, and robust clinical and economic evaluations are necessary to support safe, reproducible, and sustainable large-scale implementation.

Theofanis P. Raptis, Andrea Passarella, Marco Conti

Energy efficiency and reliability are vital design requirements of recent industrial networking solutions. Increased energy consumption, poor data access rates and unpredictable end-to-end data access latencies are catastrophic when transferring high volumes of critical industrial data in strict temporal deadlines. These requirements might become impossible to meet later on, due to node failures, or excessive degradation of the performance of wireless links. In this paper, we focus on maintaining the network functionality required by the industrial, best effort, low-latency applications after such events, by sacrificing latency guarantees to improve energy consumption and reliability. We avoid continuously recomputing the network configuration centrally, by designing an energy efficient, local and distributed path reconfiguration method. Specifically, given the operational parameters required by the applications, our method locally reconfigures the data distribution paths, when a network node fails. Additionally, our method also regulates the return to an operational state of nodes that have been offline in the past. We compare the performance of our method through simulations to the performance of other state of the art protocols and we demonstrate performance gains in terms of energy consumption, data delivery success rate, and in some cases, end-to-end data access latency. We conclude by providing some emerging key insights which can lead to further performance improvements.

Huajie Liang, Di Wang, Yuchao Lu et al.

With the advancement of Industry 4.0, intelligent manufacturing extensively employs sensors for real-time multidimensional data collection, playing a crucial role in equipment monitoring, process optimisation, and efficiency enhancement. Industrial data exhibit characteristics such as multi-source heterogeneity, nonlinearity, strong coupling, and temporal interactions, while also being affected by noise interference. These complexities make it challenging for traditional anomaly detection methods to extract key features, impacting detection accuracy and stability. Traditional machine learning approaches often struggle with such complex data due to limitations in processing capacity and generalisation ability, making them inadequate for practical applications. While deep learning feature extraction modules have demonstrated remarkable performance in image and text processing, they remain ineffective when applied to multi-source heterogeneous industrial data lacking explicit correlations. Moreover, existing multi-source heterogeneous data processing techniques still rely on dimensionality reduction and feature selection, which can lead to information loss and difficulty in capturing high-order interactions. To address these challenges, this study applies the EAPCR and Time-EAPCR models proposed in previous research and introduces a new model, Time-EAPCR-T, where Transformer replaces the LSTM module in the time-series processing component of Time-EAPCR. This modification effectively addresses multi-source data heterogeneity, facilitates efficient multi-source feature fusion, and enhances the temporal feature extraction capabilities of multi-source industrial data.Experimental results demonstrate that the proposed method outperforms existing approaches across four industrial datasets, highlighting its broad application potential.

Chaozheng Wang, Zezhou Yang, Shuzheng Gao et al.

Code completion, a crucial practice in industrial settings, helps developers improve programming efficiency by automatically suggesting code snippets during development. With the emergence of Large Code Models (LCMs), this field has witnessed significant advancements. Due to the natural differences between open-source and industrial codebases, such as coding patterns and unique internal dependencies, it is a common practice for developers to conduct domain adaptation when adopting LCMs in industry. There exist multiple adaptation approaches, among which retrieval-augmented generation (RAG) and fine-tuning are the two most popular paradigms. However, no prior research has explored the trade-off of the two approaches in industrial scenarios. To mitigate the gap, we comprehensively compare the two paradigms including Retrieval-Augmented Generation (RAG) and Fine-tuning (FT), for industrial code completion in this paper. In collaboration with Tencent's WXG department, we collect over 160,000 internal C++ files as our codebase. We then compare the two types of adaptation approaches from three dimensions that are concerned by industrial practitioners, including effectiveness, efficiency, and parameter sensitivity, using six LCMs. Our findings reveal that RAG, when implemented with appropriate embedding models that map code snippets into dense vector representations, can achieve higher accuracy than fine-tuning alone. Specifically, BM25 presents superior retrieval effectiveness and efficiency among studied RAG methods. Moreover, RAG and fine-tuning are orthogonal and their combination leads to further improvement. We also observe that RAG demonstrates better scalability than FT, showing more sustained performance gains with larger scales of codebase.

Changhong Fu, Hua Lin, Haobo Zuo et al.

Text spotting for industrial panels is a key task for intelligent monitoring. However, achieving efficient and accurate text spotting for complex industrial panels remains challenging due to issues such as cross-scale localization and ambiguous boundaries in dense text regions. Moreover, most existing methods primarily focus on representing a single text shape, neglecting a comprehensive exploration of multi-scale feature information across different texts. To address these issues, this work proposes a novel multi-scale dense text spotter for edge AI-based vision system (EdgeSpotter) to achieve accurate and robust industrial panel monitoring. Specifically, a novel Transformer with efficient mixer is developed to learn the interdependencies among multi-level features, integrating multi-layer spatial and semantic cues. In addition, a new feature sampling with catmull-rom splines is designed, which explicitly encodes the shape, position, and semantic information of text, thereby alleviating missed detections and reducing recognition errors caused by multi-scale or dense text regions. Furthermore, a new benchmark dataset for industrial panel monitoring (IPM) is constructed. Extensive qualitative and quantitative evaluations on this challenging benchmark dataset validate the superior performance of the proposed method in different challenging panel monitoring tasks. Finally, practical tests based on the self-designed edge AI-based vision system demonstrate the practicality of the method. The code and demo will be available at https://github.com/vision4robotics/EdgeSpotter.

Aida Navas de Serrato

Para recordar y conmemorar 15 años de la realización del primer Congreso de la Federación Mundial de Terapeutas Ocupacionales en América del Sur, como documento histórico se presenta el texto en el que la entonces presidenta de la Asociación Colombiana de Terapia Ocupacional reseñó su vivencia de este evento. Este escrito se publicó en junio de 2010 en el boletín informativo encontACTO, un impreso que, para la época, producía y distribuía la Asociación.

Hyungmin Kim, Donghun Kim, Pyunghwan Ahn et al.

While deep neural networks have achieved remarkable performance, data augmentation has emerged as a crucial strategy to mitigate overfitting and enhance network performance. These techniques hold particular significance in industrial manufacturing contexts. Recently, image mixing-based methods have been introduced, exhibiting improved performance on public benchmark datasets. However, their application to industrial tasks remains challenging. The manufacturing environment generates massive amounts of unlabeled data on a daily basis, with only a few instances of abnormal data occurrences. This leads to severe data imbalance. Thus, creating well-balanced datasets is not straightforward due to the high costs associated with labeling. Nonetheless, this is a crucial step for enhancing productivity. For this reason, we introduce ContextMix, a method tailored for industrial applications and benchmark datasets. ContextMix generates novel data by resizing entire images and integrating them into other images within the batch. This approach enables our method to learn discriminative features based on varying sizes from resized images and train informative secondary features for object recognition using occluded images. With the minimal additional computation cost of image resizing, ContextMix enhances performance compared to existing augmentation techniques. We evaluate its effectiveness across classification, detection, and segmentation tasks using various network architectures on public benchmark datasets. Our proposed method demonstrates improved results across a range of robustness tasks. Its efficacy in real industrial environments is particularly noteworthy, as demonstrated using the passive component dataset.

Heyuan Huang, Xingyu Lou, Chaochao Chen et al.

Cross-Domain Recommendation (CDR) have received widespread attention due to their ability to utilize rich information across domains. However, most existing CDR methods assume an ideal static condition that is not practical in industrial recommendation systems (RS). Therefore, simply applying existing CDR methods in the industrial RS environment may lead to low effectiveness and efficiency. To fill this gap, we propose DIIT, an end-to-end Domain-Invariant Information Transfer method for industrial cross-domain recommendation. Specifically, We first simulate the industrial RS environment that maintains respective models in multiple domains, each of them is trained in the incremental mode. Then, for improving the effectiveness, we design two extractors to fully extract domain-invariant information from the latest source domain models at the domain level and the representation level respectively. Finally, for improving the efficiency, we design a migrator to transfer the extracted information to the latest target domain model, which only need the target domain model for inference. Experiments conducted on one production dataset and two public datasets verify the effectiveness and efficiency of DIIT.

Wei Gao, Jingqiang Wang, Xinv Zhu et al.

We would like industrial robots to handle unstructured environments with cameras and perception pipelines. In contrast to traditional industrial robots that replay offline-crafted trajectories, online behavior planning is required for these perception-guided industrial applications. Aside from perception and planning algorithms, deploying perception-guided manipulators also requires substantial effort in integration. One approach is writing scripts in a traditional language (such as Python) to construct the planning problem and perform integration with other algorithmic modules & external devices. While scripting in Python is feasible for a handful of robots and applications, deploying perception-guided manipulation at scale (e.g., more than 10000 robot workstations in over 2000 customer sites) becomes intractable. To resolve this challenge, we propose a Domain-Specific Language (DSL) for perception-guided manipulation applications. To scale up the deployment,our DSL provides: 1) an easily accessible interface to construct & solve a sub-class of Task and Motion Planning (TAMP) problems that are important in practical applications; and 2) a mechanism to implement flexible control flow to perform integration and address customized requirements of distinct industrial application. Combined with an intuitive graphical programming frontend, our DSL is mainly used by machine operators without coding experience in traditional programming languages. Within hours of training, operators are capable of orchestrating interesting sophisticated manipulation behaviors with our DSL. Extensive practical deployments demonstrate the efficacy of our method.

Vitowe Batch, Martina Kress, Ezekiel Luhanga et al.

This study assessed knowledge, attitudes, and practices related to food hygiene among mothers from Malawi’s rural communities against the WHO Five Keys to Safer Food (WHO-FKSF) and good pre-and post-harvest practices (GPPHPs) as reference points. Five hundred twenty-two mothers from six rural communities across two districts were selected for the survey. The results indicated limited knowledge among participating mothers regarding managing food hazards, including mycotoxins, bacteria, viruses, and parasites (BVPs). A significant proportion (89.5%) of women reported inconsistent or no handwashing with soap after using the toilet. In addition, 48.7% failed to plant with the first good rains, 38.7% neglected to check for moldy cobs during harvesting, 57.4% dried maize on bare soil, and 99.2% bought maize with noticeable mold. Higher education, knowledge, and positive attitudes were associated with enhanced BVP control practices, while larger households and positive attitudes were linked to improved mold/mycotoxin management (<i>p</i> < 0.05). Mothers showed lower (<i>p</i> < 0.05) knowledge and attitude levels regarding molds than BVPs but demonstrated relatively better practices for mold control. A comprehensive education program based on the WHO Five Keys to Safer Foods, tailored to local socio-cultural norms and incorporating mold and mycotoxin management guidelines, is recommended.

Dong-Keun Lee, Gyuri Kim, Muthuchamy Maruthupandy et al.

Abstract Background Alveolar macrophages (AMs) have been predicted to affect the pulmonary clearance of nanomaterials; however, their qualitative and quantitative roles are poorly understood. In this study, carbon black nanoparticles (CBNPs) were instilled into the lungs of Wistar rats at 30, 100, and 300 µg/rat. The concentrations of particles in organs, including the lung, lung-associated lymph nodes (LALN), liver, spleen, and kidney, were evaluated at days 0 (immediately after instillation), 1, 7, 28, 60, and 90 post-instillation. Results The results indicated a multimodal pulmonary clearance pattern for CBNPs: slow clearance until day 28, fast clearance from days 28 to 60, and slow clearance from days 60 to 90. To determine the mechanism of this unique clearance pattern, CBNPs were instilled into AM-depleted rats using clodronate liposomes (CLO). At 28 days after instillation, the CBNP levels in the lungs treated with CLO showed about 31% higher reduction than in normal rats. In addition, the concentration of CBNPs in LALN treated with CLO significantly increased on day 28, whereas in normal rats, no detectable levels were observed. Conclusions This result highlights that the prolonged retention of poorly soluble NPs in the lung until day 28 is mediated by the phagocytosis of AMs, and the fast clearance between days 28–60 is due to the turnover time of AMs, estimated around 1–2 months after birth. Similarly, new generations of AMs mediate the slow phase between days 60 and 90. However, further studies are needed to understand the multimodal clearance mechanism and the modulation of pulmonary clearance of poorly soluble NPs.

Xinpei Lin, Wei Wang, Xiangyu Chang et al.

Abstract Background As the demand and application of engineered nanomaterials have increased, their potential toxicity to the central nervous system has drawn increasing attention. Tunneling nanotubes (TNTs) are novel cell–cell communication that plays a crucial role in pathology and physiology. However, the relationship between TNTs and nanomaterials neurotoxicity remains unclear. Here, three types of commonly used engineered nanomaterials, namely cobalt nanoparticles (CoNPs), titanium dioxide nanoparticles (TiO2NPs), and multi-walled carbon nanotubes (MWCNTs), were selected to address this limitation. Results After the complete characterization of the nanomaterials, the induction of TNTs formation with all of the nanomaterials was observed using high-content screening system and confocal microscopy in both primary astrocytes and U251 cells. It was further revealed that TNT formation protected against nanomaterial-induced neurotoxicity due to cell apoptosis and disrupted ATP production. We then determined the mechanism underlying the protective role of TNTs. Since oxidative stress is a common mechanism in nanotoxicity, we first observed a significant increase in total and mitochondrial reactive oxygen species (namely ROS, mtROS), causing mitochondrial damage. Moreover, pretreatment of U251 cells with either the ROS scavenger N-acetylcysteine or the mtROS scavenger mitoquinone attenuated nanomaterial-induced neurotoxicity and TNTs generation, suggesting a central role of ROS in nanomaterials-induced TNTs formation. Furthermore, a vigorous downstream pathway of ROS, the PI3K/AKT/mTOR pathway, was found to be actively involved in nanomaterials-promoted TNTs development, which was abolished by LY294002, Perifosine and Rapamycin, inhibitors of PI3K, AKT, and mTOR, respectively. Finally, western blot analysis demonstrated that ROS and mtROS scavengers suppressed the PI3K/AKT/mTOR pathway, which abrogated TNTs formation. Conclusion Despite their biophysical properties, various types of nanomaterials promote TNTs formation and mitochondrial transfer, preventing cell apoptosis and disrupting ATP production induced by nanomaterials. ROS/mtROS and the activation of the downstream PI3K/AKT/mTOR pathway are common mechanisms to regulate TNTs formation and mitochondrial transfer. Our study reveals that engineered nanomaterials share the same molecular mechanism of TNTs formation and intercellular mitochondrial transfer, and the proposed adverse outcome pathway contributes to a better understanding of the intercellular protection mechanism against nanomaterials-induced neurotoxicity. Graphical abstract

Chaofan Wang, Weiwei Jiang, Kangning Yang et al.

Hand hygiene has long been promoted as the most effective way to prevent the transmission of infection. However, due to the low compliance and quality of hand hygiene reported in previous studies, constant monitoring of healthcare workers' hand hygiene compliance and quality is crucial. In this study, we investigate the feasibility of using a thermal camera together with an RGB camera to detect hand coverage of alcohol-based formulation, thereby monitoring handrub quality. The system yields promising results in terms of accuracy (93.5%) and Dice coefficient (87.1%) when observations take place 10 seconds after performing handrub. In addition, we also examine the system performance change over a 60-second observation period, and the accuracy and Dice coefficient still remain at about 92.4% and 85.7% when observation happens at the 60-second time point. Given these encouraging results, thermal imaging shows its potential feasibility in providing accurate, constant, and systematic hand hygiene quality monitoring.

Rui Zhao, Zhongze Yang, Dong Liang et al.

This paper explores the application of automated machine learning (AutoML) techniques to the construction industry, a sector vital to the global economy. Traditional ML model construction methods were complex, time-consuming, reliant on data science expertise, and expensive. AutoML shows the potential to automate many tasks in ML construction and to create outperformed ML models. This paper aims to verify the feasibility of applying AutoML to industrial datasets for the smart construction domain, with a specific case study demonstrating its effectiveness. Two data challenges that were unique to industrial construction datasets are focused on, in addition to the normal steps of dataset preparation, model training, and evaluation. A real-world application case of construction project type prediction is provided to illustrate the accessibility of AutoML. By leveraging AutoML, construction professionals without data science expertise can now utilize software to process industrial data into ML models that assist in project management. The findings in this paper may bridge the gap between data-intensive smart construction practices and the emerging field of AutoML, encouraging its adoption for improved decision-making, project outcomes, and efficiency

Chelsea M Cary, Talia N Seymore, Dilpreet Singh et al.

Abstract Background Exposure to micro- and nanoplastic particles (MNPs) in humans is being identified in both the indoor and outdoor environment. Detection of these materials in the air has made inhalation exposure to MNPs a major cause for concern. One type of plastic polymer found in indoor and outdoor settings is polyamide, often referred to as nylon. Inhalation of combustion-derived, metallic, and carbonaceous aerosols generate pulmonary inflammation, cardiovascular dysfunction, and systemic inflammation. Additionally, due to the additives present in plastics, MNPs may act as endocrine disruptors. Currently there is limited knowledge on potential health effects caused by polyamide or general MNP inhalation. Objective The purpose of this study is to assess the toxicological consequences of a single inhalation exposure of female rats to polyamide MNP during estrus by means of aerosolization of MNP. Methods Bulk polyamide powder (i.e., nylon) served as a representative MNP. Polyamide aerosolization was characterized using particle sizers, cascade impactors, and aerosol samplers. Multiple-Path Particle Dosimetry (MPPD) modeling was used to evaluate pulmonary deposition of MNPs. Pulmonary inflammation was assessed by bronchoalveolar lavage (BAL) cell content and H&E-stained tissue sections. Mean arterial pressure (MAP), wire myography of the aorta and uterine artery, and pressure myography of the radial artery was used to assess cardiovascular function. Systemic inflammation and endocrine disruption were quantified by measurement of proinflammatory cytokines and reproductive hormones. Results Our aerosolization exposure platform was found to generate particles within the micro- and nano-size ranges (thereby constituting MNPs). Inhaled particles were predicted to deposit in all regions of the lung; no overt pulmonary inflammation was observed. Conversely, increased blood pressure and impaired dilation in the uterine vasculature was noted while aortic vascular reactivity was unaffected. Inhalation of MNPs resulted in systemic inflammation as measured by increased plasma levels of IL-6. Decreased levels of 17β-estradiol were also observed suggesting that MNPs have endocrine disrupting activity. Conclusions These data demonstrate aerosolization of MNPs in our inhalation exposure platform. Inhaled MNP aerosols were found to alter inflammatory, cardiovascular, and endocrine activity. These novel findings will contribute to a better understanding of inhaled plastic particle toxicity.

Manuel Alejandro Herrera-Rodríguez, María del Pilar Ramos-Godinez, Agustina Cano-Martínez et al.

Abstract Background Metallic nanoparticles (NPs) are widely used as food additives for human consumption. NPs reach the bloodstream given their small size, getting in contact with all body organs and cells. NPs have adverse effects on the respiratory and intestinal tract; however, few studies have focused on the toxic consequences of orally ingested metallic NPs on the cardiovascular system. Here, the effects of two food-grade additives on the cardiovascular system were analyzed. Methods Titanium dioxide labeled as E171 and zinc oxide (ZnO) NPs were orally administered to Wistar rats using an esophageal cannula at 10 mg/kg bw every other day for 90 days. We evaluated cardiac cell morphology and death, expression of apoptotic and autophagic proteins in cardiac mitochondria, mitochondrial dysfunction, and concentration of metals on cardiac tissue. Results Heart histology showed important morphological changes such as presence of cellular infiltrates, collagen deposition and mitochondrial alterations in hearts from rats exposed to E171 and ZnO NPs. Intracellular Cyt-C levels dropped, while TUNEL positive cells increased. No significant changes in the expression of inflammatory cytokines were detected. Both NPs altered mitochondrial function indicating cardiac dysfunction, which was associated with an elevated concentration of calcium. ZnO NPs induced expression of caspases 3 and 9 and two autophagic proteins, LC3B and beclin-1, and had the strongest effect compared to E171. Conclusions E171 and ZnO NPs induce adverse cardiovascular effects in rats after 90 days of exposure, thus food intake containing these additives, should be taken into consideration, since they translocate into the bloodstream and cause cardiovascular damage.

Kukka Aimonen, Monireh Imani, Mira Hartikainen et al.

Abstract Background Cellulose nanofibrils (CNFs) have emerged as a sustainable and environmentally friendly option for a broad range of applications. The fibrous nature and high biopersistence of CNFs call for a thorough toxicity assessment, but it is presently unclear which physico-chemical properties could play a role in determining the potential toxic response to CNF. Here, we assessed whether surface composition and size could modulate the genotoxicity of CNFs in human bronchial epithelial BEAS-2B cells. We examined three size fractions (fine, medium and coarse) of four CNFs with different surface chemistry: unmodified (U-CNF) and functionalized with 2,2,6,6-tetramethyl-piperidin-1-oxyl (TEMPO) (T-CNF), carboxymethyl (C-CNF) and epoxypropyltrimethylammonium chloride (EPTMAC) (E-CNF). In addition, the source fibre was also evaluated as a non-nanosized material. Results The presence of the surface charged groups in the functionalized CNF samples resulted in higher amounts of individual nanofibrils and less aggregation compared with the U-CNF. T-CNF was the most homogenous, in agreement with its high surface group density. However, the colloidal stability of all the CNF samples dropped when dispersed in cell culture medium, especially in the case of T-CNF. CNF was internalized by a minority of BEAS-2B cells. No remarkable cytotoxic effects were induced by any of the cellulosic materials. All cellulosic materials, except the medium fraction of U-CNF, induced a dose-dependent intracellular formation of reactive oxygen species (ROS). The fine fraction of E-CNF, which induced DNA damage (measured by the comet assay) and chromosome damage (measured by the micronucleus assay), and the coarse fraction of C-CNF, which produced chromosome damage, also showed the most effective induction of ROS in their respective size fractions. Conclusions Surface chemistry and size modulate the in vitro intracellular ROS formation and the induction of genotoxic effects by fibrillated celluloses. One cationic (fine E-CNF) and one anionic (coarse C-CNF) CNF showed primary genotoxic effects, possibly partly through ROS generation. However, the conclusions cannot be generalized to all types of CNFs, as the synthesis process and the dispersion method used for testing affect their physico-chemical properties and, hence, their toxic effects.

T. Tsarenko, L. Korniienko

In Ukraine zoonoses are a permanent threat to human health, some of them are bacterial diseases associated with farm animals. Complete avoidance of outbreaks of bacterial zoonoses is not possible but it is appropriate to study them to reduce the risks of transmission of zoonosis pathogens from industrial farms to the human population and the environment. The article highlights the results of a literature review on the potential role of industrial livestock farms in the spread of major bacterial zoonoses in Ukraine. About half of all of the country’s farmed animals are kept on farms using industrial technology; more than half of the establishments are classified as medium and large. The technology of keeping animals on such farms contributes to the development of diseases of obligate hosts caused by fecal bacteria. The systematic search and selection of literary sources, which are relevant to the topic of the study were carried out. The vast majority of analyzed publications are published in Ukrainian in local peer-reviewed scientific journals. An analysis of open-access official statistics from the state authorities of Ukraine was also conducted. The authors analyzed statistics and scientific papers published over the last 10–15 years discussing the outbreaks of food-borne zoonoses among humans and the studying their pathogens (Campylobacter spp., Salmonella spp., Escherichia coli (STEC strains), Listeria spp.) on industrial livestock farms. The main source of Campylobacter spp. and Salmonella spp. distribution are industrial poultry, including broilers and chickens, respectively. The STEC strains E. coli carriers are various types of farm animals, including cattle and pigs. The majority of infections documented in Ukraine are cases of salmonellosis in humans and animals. Despite reports of a significant prevalence of campylobacteriosis, colibacillosis and listeriosis in livestock farms, their association with outbreaks of food-borne zoonoses in humans remains poorly understood. The concept of an industrial livestock farm involves a permanent presence of a risk of outbreaks of bacterial zoonoses and their rapid spreading to the human population. This is due to concentrated maintenance of animals, standardized feeding, the priority of achieving the highest productivity of animals and economic indicators. Under such conditions, disturbance of hygienic norms and technologies significantly increases the risk of bacterial zoonoses on industrial farms. It is important to enforce the continuous control of the level of microbial pollution of farms, animal health, hygiene of milk production and processing, meat, eggs, etc. Farms have a negative impact on the ecological welfare of the surrounding territories. The problem of spread of antibiotic-resistant strains of bacterial zoonoses is a very serious one. Efforts for the formation of a national system of epidemiological supervision over bacterial zoonoses, comprising epidemiological, epizootological, ecological, microbiological, serological and molecular genetic monitoring, as well as the development on this basis of effective prophylactic and anti-epidemic measures are relevant and necessary.