Hasil untuk "Industrial medicine. Industrial hygiene"

Kashish Rathi, Nishu Devi, Bharmjeet Singh et al.

The process of ensuring the safety of the food supply is dynamic. Both the possibility of contamination and the effectiveness of safety precautions are impacted by changes in the kinds of food consumed, the geographical origins of food products, and the methods by which these foods are processed. For instance, compared to earlier generations, consumers’ general understanding of safe food preparation and handling techniques has decreased due to a higher reliance on prepackaged convenience foods. Nowadays, consumers depend increasingly on other people to make sure the food they eat is safe. Growing consumption of minimally processed foods and growing imports of fresh products from other nations have resulted from changes in consumer tastes and food processing technologies. This review aims to critically synthesize existing knowledge on microbial risks in food, focusing on their sources, mechanisms of contamination, risk evaluation methodologies, and implementation of food safety measures. Major foodborne pathogens, including <i>Salmonella</i>, <i>Escherichia coli</i>, <i>Listeria monocytogenes</i>, and <i>Norovirus</i>, are discussed alongside factors influencing their survival and transmission. Today <i>Clostridium botulinum</i>, <i>Staphylococcus aureus</i>, and <i>Salmonella</i> spp. remain among the major foodborne pathogens, but during the last two decades food-borne diseases such as shigellosis, listeriosis, campylobacteriosis, and diseases caused by pathogenic strains of <i>Escherichia coli</i> have become increasingly salient. These new concerns necessitate continued investment in research and technology development to improve the safety of the food supply. The review highlights current approaches to microbiological risk assessment, regulatory frameworks, and control strategies, while also addressing emerging challenges such as antimicrobial resistance, biofilms, and ready-to-eat foods. By integrating risk evaluation with practical implementation strategies, this review provides valuable insights for researchers, regulators, and food industry stakeholders seeking to strengthen food safety systems and reduce the burden of foodborne diseases.

Jihoon Jeong

Model Medicine is the science of understanding, diagnosing, treating, and preventing disorders in AI models, grounded in the principle that AI models -- like biological organisms -- have internal structures, dynamic processes, heritable traits, observable symptoms, classifiable conditions, and treatable states. This paper introduces Model Medicine as a research program, bridging the gap between current AI interpretability research (anatomical observation) and the systematic clinical practice that complex AI systems increasingly require. We present five contributions: (1) a discipline taxonomy organizing 15 subdisciplines across four divisions -- Basic Model Sciences, Clinical Model Sciences, Model Public Health, and Model Architectural Medicine; (2) the Four Shell Model (v3.3), a behavioral genetics framework empirically grounded in 720 agents and 24,923 decisions from the Agora-12 program, explaining how model behavior emerges from Core--Shell interaction; (3) Neural MRI (Model Resonance Imaging), a working open-source diagnostic tool mapping five medical neuroimaging modalities to AI interpretability techniques, validated through four clinical cases demonstrating imaging, comparison, localization, and predictive capability; (4) a five-layer diagnostic framework for comprehensive model assessment; and (5) clinical model sciences including the Model Temperament Index for behavioral profiling, Model Semiology for symptom description, and M-CARE for standardized case reporting. We additionally propose the Layered Core Hypothesis -- a biologically-inspired three-layer parameter architecture -- and a therapeutic framework connecting diagnosis to treatment.

Ruomu Tan, Martin W Hoffmann

The integration of artificial intelligence (AI) into the industrial sector has not only driven innovation but also expanded the ethical landscape, necessitating a reevaluation of principles governing technology and its applications and awareness in research and development of industrial AI solutions. This chapter explores how AI-empowered industrial innovation inherently intersects with ethics, as advancements in AI introduce new challenges related to transparency, accountability, and fairness. In the chapter, we then examine the ethical aspects of several examples of AI manifestation in industrial use cases and associated factors such as ethical practices in the research and development process and data sharing. With the progress of ethical industrial AI solutions, we emphasize the importance of embedding ethical principles into industrial AI systems and its potential to inspire technological breakthroughs and foster trust among stakeholders. This chapter also offers actionable insights to guide industrial research and development toward a future where AI serves as an enabler for ethical and responsible industrial progress as well as a more inclusive industrial ecosystem.

Ewa Malczyk, Agnieszka Malczyk, Joanna Wyka et al.

Background The correct nutrition of infants is crucial for their proper mental and physical development, as well as for adequate metabolic programming. Programming is the influence of environmental factors, including nutrition, during critical periods of early development (including fetal life and the first years of life) on the risk of disease in adulthood. Objective The aim of the study was to investigate the level of knowledge of mothers on infant feeding. Material and Methods The study involved 1100 mothers of different ages who were active in online groups interested in maternity and infant feeding. The inclusion criterion for the study was having a child born between 2021 and 2023. The study used a CAWI (Computer-Assisted Web Interview) method and the survey was conducted in November 2024. Results It was shown that most of the mothers surveyed had very good knowledge of infant feeding. A good level of knowledge was recorded among younger mothers of children under 6 months of age, with primary/high school education and living in rural areas. Conclusions It is recommended to provide more detailed information on expanding the diet of infants after 6 months of age, e.g. on the labels of foods dedicated to children.

Liang YR, Chang CH, Huang SY et al.

Ya-Ru Liang,1,&amp;ast; Chuan-Hsin Chang,2,&amp;ast; Shiang-Yu Huang,1 Yao-Kuang Wu,3,4 Mei-Chen Yang,3,4 Kuo-Liang Huang,3,4 I-Shiang Tzeng,2 Po-Chun Hsieh,5,6,&amp;ast; Chou-Chin Lan3,4,&amp;ast; 1Division of Respiratory Therapy, Taipei Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, New Taipei City, Taiwan; 2Department of Research, Taipei Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, New Taipei City, Taiwan; 3Division of Pulmonary Medicine, Taipei Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, New Taipei City, Taiwan; 4School of Medicine, Tzu-Chi University, Hualien, Taiwan; 5Department of Chinese Medicine, Taipei Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, New Taipei City, Taiwan; 6School of Chinese Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan&amp;ast;These authors contributed equally to this workCorrespondence: Chou-Chin Lan, Division of Pulmonary Medicine, Taipei Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, 289, Jianguo Road, Xindian City, New Taipei City, 23142, Taiwan, Tel +886-2-6628-9779 ext. 2259, Fax +886-2-6628-9009, Email bluescopy@yahoo.com.twIntroduction: Asthma causes airway inflammation, leading to symptoms that impair patients’ quality of life, making it a significant global public health issue. Inhaled corticosteroids (ICS) with long-acting beta-agonists therapy (LABA) is commonly used to manage moderate to severe asthma. For patients unresponsive to ICS with LABA, omalizumab may be added to improve asthma control. Understanding transcriptomic expressions is crucial as it provides insights into the molecular mechanisms underlying treatment. However, the impact of omalizumab on transcriptomic expressions remains unclear. Therefore, this study aims to investigate the transcriptomic expression profiles and clinical outcomes between patients receiving ICS with LABA therapy and those adding omalizumab.Materials and Methods: This is a prospective, real-world study that enrolled 26 participants, divided into three groups: Group 1, ICS with LABA (n=10); Group 2, ICS with LABA plus omalizumab (n=12); and Group 3, healthy controls (n=4). Assessments included transcriptomic expression profiles, and bioinformatics analysis, IgE, airborne allergen test, pulmonary function test, blood tests, and asthma control test (ACT).Results: ACT scores were significantly higher in Group 1 and 2 compared to Group 3. IgE levels, dust mite sensitivity, and dynamic pulmonary function changes after bronchodilator administration were notably higher in Group 2. In these patients, down-regulated genes included those related to immune response, NOD-like receptor signaling, RIG-I signaling, IL-17 signaling, and antioxidant activity. Conversely, up-regulated genes were found in the cGMP-PKG signaling pathway, cardiomyopathy-related pathways, and voltage-gated calcium channel activity.Conclusion: Patients receiving ICS with LABA plus omalizumab still exhibited more dynamic airway changes and higher IgE levels. Downregulation of immune and inflammatory pathways suggests its potential as an add-on treatment for severe asthma. However, upregulated genes were observed in the cGMP-PKG signaling pathway, cardiomyopathy-related pathways, and voltage-gated calcium channel activity.Keywords: asthma, airway inflammation, omalizumab, RNA transcriptome

Shinichi Mae, Ryousuke Yamada, Hirokatsu Kataoka

Pre-training on real-image datasets has been widely proven effective for improving instance segmentation. However, industrial applications face two key challenges: (1) legal and ethical restrictions, such as ImageNet's prohibition of commercial use, and (2) limited transferability due to the domain gap between web images and industrial imagery. Even recent vision foundation models, including the segment anything model (SAM), show notable performance degradation in industrial settings. These challenges raise critical questions: Can we build a vision foundation model for industrial applications without relying on real images or manual annotations? And can such models outperform even fine-tuned SAM on industrial datasets? To address these questions, we propose the Instance Core Segmentation Dataset (InsCore), a synthetic pre-training dataset based on formula-driven supervised learning (FDSL). InsCore generates fully annotated instance segmentation images that reflect key characteristics of industrial data, including complex occlusions, dense hierarchical masks, and diverse non-rigid shapes, distinct from typical web imagery. Unlike previous methods, InsCore requires neither real images nor human annotations. Experiments on five industrial datasets show that models pre-trained with InsCore outperform those trained on COCO and ImageNet-21k, as well as fine-tuned SAM, achieving an average improvement of 6.2 points in instance segmentation performance. This result is achieved using only 100k synthetic images, more than 100 times fewer than the 11 million images in SAM's SA-1B dataset, demonstrating the data efficiency of our approach. These findings position InsCore as a practical and license-free vision foundation model for industrial applications.

Jay Lee, Hanqi Su

Recent advancements in industrial artificial intelligence (AI) are reshaping the industry by driving smarter manufacturing, predictive maintenance, and intelligent decision-making. However, existing approaches often focus primarily on algorithms and models while overlooking the importance of systematically integrating domain knowledge, data, and models to develop more comprehensive and effective AI solutions. Therefore, the effective development and deployment of industrial AI require a more comprehensive and systematic approach. To address this gap, this paper reviews previous research, rethinks the role of industrial AI, and proposes a unified industrial AI foundation framework comprising three core modules: the knowledge module, data module, and model module. These modules help to extend and enhance the industrial AI methodology platform, supporting various industrial applications. In addition, a case study on rotating machinery diagnosis is presented to demonstrate the effectiveness of the proposed framework, and several future directions are highlighted for the development of the industrial AI foundation framework.

Hang Yu, Yuzhou Lai, Li Zhang et al.

The rapid advancement of large language models (LLMs) is fundamentally reshaping software engineering (SE), driving a paradigm shift in both academic research and industrial practice. While top-tier SE venues continue to show sustained or emerging focus on areas like automated testing and program repair, with researchers worldwide reporting continuous performance gains, the alignment of these academic advances with real industrial needs remains unclear. To bridge this gap, we first conduct a systematic analysis of 1,367 papers published in FSE, ASE, and ICSE between 2022 and 2025, identifying key research topics, commonly used benchmarks, industrial relevance, and open-source availability. We then carry out an empirical survey across 17 organizations, collecting 282 responses on six prominent topics, i.e., program analysis, automated testing, code generation/completion, issue resolution, pre-trained code models, and dependency management, through structured questionnaires. By contrasting academic capabilities with industrial feedback, we derive seven critical implications, highlighting under-addressed challenges in software requirements and architecture, the reliability and explainability of intelligent SE approaches, input assumptions in academic research, practical evaluation tensions, and ethical considerations. This study aims to refocus academic attention on these important yet under-explored problems and to guide future SE research toward greater industrial impact.

Lu Xia, Kaiqi Zhao, Sunil Kadam et al.

Paired electrolysis at industrial current densities offers an energy-efficient and sustainable alternative to thermocatalytic chemical synthesis by leveraging anodic and cathodic valorization. However, its industrial feasibility remains constrained by system integration, including reactor assembly, asymmetric electron transfer kinetics, membrane selection, mass transport limitations, and techno-economic bottlenecks. Addressing these challenges requires an engineering-driven approach that integrates reactor architecture, electrode-electrolyte interactions, reaction pairing, and process optimization. Here, we discuss scale-specific electrochemical reactor assembly strategies, transitioning from half-cell research to full-scale stack validation. We develop reaction pairing frameworks that align electrocatalyst design with electrochemical kinetics, enhancing efficiency and selectivity under industrial operating conditions. We also establish application-dependent key performance indicators (KPIs) and benchmark propylene oxidation coupled with hydrogen evolution reaction (HER) or oxygen reduction reaction (ORR) against existing industrial routes to evaluate process viability. Finally, we propose hybrid integration models that embed paired electrolysis into existing industrial workflows, overcoming adoption barriers.

Sotiris Michaelides, Daniel Eguiguren Chavez, Martin Henze

With the ongoing adoption of 5G for communication in industrial systems and critical infrastructure, the security of industrial UEs such as 5G-enabled industrial robots becomes an increasingly important topic. Most notably, to meet the stringent security requirements of industrial deployments, industrial UEs not only have to fully comply with the 5G specifications but also implement and use correctly secure communication protocols such as TLS. To ensure the security of industrial UEs, operators of industrial 5G networks rely on security testing before deploying new devices to their production networks. However, currently only isolated tests for individual security aspects of industrial UEs exist, severely hindering comprehensive testing. In this paper, we report on our ongoing efforts to alleviate this situation by creating an automated security testing framework for industrial UEs to comprehensively evaluate their security posture before deployment. With this framework, we aim to provide stakeholders with a fully automated-method to verify that higher-layer security protocols are correctly implemented, while simultaneously ensuring that the UE's protocol stack adheres to 3GPP specifications.

Connor W. Brown, Charlotte E. Goldfine, Lao-Tzu Allan-Blitz et al.

Abstract Background The global market for lithium-ion batteries (LIBs) is growing exponentially, resulting in an increase in mining activities for the metals needed for manufacturing LIBs. Cobalt, lithium, manganese, and nickel are four of the metals most used in the construction of LIBs, and each has known toxicological risks associated with exposure. Mining for these metals poses potential human health risks via occupational and environmental exposures; however, there is a paucity of data surrounding the risks of increasing mining activity. The objective of this review was to characterize these risks. Methods We conducted a review of the literature via a systematic search of the PubMed database on the health effects of mining for cobalt, lithium, manganese, and nickel. We included articles that (1) reported original research, (2) reported outcomes directly related to human health, (3) assessed exposure to mining for cobalt, lithium, manganese, or nickel, and (4) had an available English translation. We excluded all other articles. Our search identified 183 relevant articles. Results Toxicological hazards were reported in 110 studies. Exposure to cobalt and nickel mining were most associated with respiratory toxicity, while exposure to manganese mining was most associated with neurologic toxicity. Notably, no articles were identified that assessed lithium toxicity associated with mining exposure. Traumatic hazards were reported in six studies. Three articles reported infectious disease hazards, while six studies reported effects on mental health. Several studies reported increased health risks in children compared to adults. Conclusions The results of this review suggest that occupational and environmental exposure to mining metals used in LIBs presents significant risks to human health that result in both acute and chronic toxicities. Further research is needed to better characterize these risks, particularly regarding lithium mining.

Arthur Kaboth, Lena Hünefeld, Marcel Lück

Abstract Background Extending working lives due to labour market and pension regulations makes maintaining and promoting work ability necessary. The coronavirus pandemic has shown that employees in low-skilled jobs (no qualification required) contribute significantly to society and the economy. Research on these employees has been neglected in Germany for many decades despite demanding working conditions. Therefore, we investigate the relationship between low-skilled jobs and work ability. Moreover, we explore this relationship’s variation by psychosocial work demands and resources. Methods We use two waves of the German Study on Mental Health at Work (S-MGA). We calculate Ordinary-Least-Squares (OLS) regression models with pooled data (n = 6,050) to analyse the relationship between job requirement level and work ability. We also explore the contribution of job demands and resources on this relationship with interaction models. We use the Copenhagen Psychosocial Questionnaire (COPSOQ), to assess psychosocial work demands and resources. Results Employees performing low-skilled jobs report significantly less work ability than those in medium- or high-skilled jobs. Interaction models show significantly greater work ability for employees in medium- and high-skilled jobs with high influence on their work (amount or tasks). Unexpectedly, employees in low-skilled jobs have lower work ability with more influence on their work. Furthermore, high role clarity, describing responsibility, authority and work goals, is associated with lower levels of work ability among employees in low-skilled jobs. Conclusions The moderating effect of role clarity on the work ability of employees in low-skilled jobs can possibly be attributed to skills mismatch and limited responsibility, as well as a lack of self-perceived collective purpose of the job. The moderation of the influence on work dimension supports results of previous studies. Too much job autonomy can have negative effects under certain circumstances and is therefore perceived as a job demand in some studies. Consequently, mechanisms concerning psychosocial work demands and resources must be investigated in further studies with different theoretical approaches. The imbalance of job demands and resources shows that employers should invest in preserving the work ability to prevent early exit from the labour market in an aging society.

Norman Paege, Sabrina Feustel, Philip Marx-Stoelting

Abstract Risk assessment (RA) of microbial secondary metabolites (SM) is part of the EU approval process for microbial active substances (AS) used in plant protection products (PPP). As the number of potentially produced microbial SM may be high for a certain microbial strain and existing information on the metabolites often are low, data gaps are frequently identified during the RA. Often, RA cannot conclusively clarify the toxicological relevance of the individual substances. This work presents data and RA conclusions on four metabolites, Beauvericin, 2,3-deepoxy-2,3-didehydro-rhizoxin (DDR), Leucinostatin A and Swainsonin in detail as examples for the challenging process of RA. To overcome the problem of incomplete assessment reports, RA of microbial AS for PPP is in need of new approaches. In view of the Next Generation Risk Assessment (NGRA), the combination of literature data, omic-methods, in vitro and in silico methods combined in adverse outcome pathways (AOPs) can be used for an efficient and targeted identification and assessment of metabolites of concern (MoC).

Sami T. Tuomivaara, Susan J. Fisher, Steven C. Hall et al.

Abstract Background Fluoride exposure during pregnancy has been associated with various effects on offspring, including changes in behavior and IQ. To provide clues to possible mechanisms by which fluoride may affect human fetal development, we completed proteomic analyses of cord blood serum collected from second-trimester pregnant women residing in northern California, USA. Objective To identify changes in cord blood proteins associated with maternal serum fluoride concentration in pregnant women. Methods The proteomes of 19 archived second-trimester cord blood samples from women living in northern California, USA, and having varied serum fluoride concentrations, were analyzed by quantitative mass spectrometry. The 327 proteins that were quantified were characterized by their abundance relative to maternal serum fluoride concentration, and subjected to pathway analyses using PANTHER and Ingenuity Pathway Analysis processes. Results Pathway analyses showed significant increases in process related to reactive oxygen species and cellular oxidant detoxification, associated with increasing maternal serum fluoride concentrations. Pathways showing significant decreases included complement cascade, suggesting alterations in alterations in process associated with inflammation. Conclusion Maternal fluoride exposure, as measured by serum fluoride concentrations in a small, but representative sample of women from northern California, USA, showed significant changes in the second trimester cord blood proteome relative to maternal serum fluoride concentration.

Xiao Xia, Dan Zhang, Zibo Liao et al.

The modeling of industrial scenes is essential for simulations in industrial manufacturing. While large language models (LLMs) have shown significant progress in generating general 3D scenes from textual descriptions, generating industrial scenes with LLMs poses a unique challenge due to their demand for precise measurements and positioning, requiring complex planning over spatial arrangement. To address this challenge, we introduce SceneGenAgent, an LLM-based agent for generating industrial scenes through C# code. SceneGenAgent ensures precise layout planning through a structured and calculable format, layout verification, and iterative refinement to meet the quantitative requirements of industrial scenarios. Experiment results demonstrate that LLMs powered by SceneGenAgent exceed their original performance, reaching up to 81.0% success rate in real-world industrial scene generation tasks and effectively meeting most scene generation requirements. To further enhance accessibility, we construct SceneInstruct, a dataset designed for fine-tuning open-source LLMs to integrate into SceneGenAgent. Experiments show that fine-tuning open-source LLMs on SceneInstruct yields significant performance improvements, with Llama3.1-70B approaching the capabilities of GPT-4o. Our code and data are available at https://github.com/THUDM/SceneGenAgent .

Lolitta Ammann, Jorge Martinez-Gil, Michael Mayr et al.

Industrial processes generate vast amounts of time series data, yet extracting meaningful relationships and insights remains challenging. This paper introduces a framework for automated knowledge graph learning from time series data, specifically tailored for industrial applications. Our framework addresses the complexities inherent in industrial datasets, transforming them into knowledge graphs that improve decision-making, process optimization, and knowledge discovery. Additionally, it employs Granger causality to identify key attributes that can inform the design of predictive models. To illustrate the practical utility of our approach, we also present a motivating use case demonstrating the benefits of our framework in a real-world industrial scenario. Further, we demonstrate how the automated conversion of time series data into knowledge graphs can identify causal influences or dependencies between important process parameters.

Gabriele Valvano, Antonino Agostino, Giovanni De Magistris et al.

Training supervised deep neural networks that perform defect detection and segmentation requires large-scale fully-annotated datasets, which can be hard or even impossible to obtain in industrial environments. Generative AI offers opportunities to enlarge small industrial datasets artificially, thus enabling the usage of state-of-the-art supervised approaches in the industry. Unfortunately, also good generative models need a lot of data to train, while industrial datasets are often tiny. Here, we propose a new approach for reusing general-purpose pre-trained generative models on industrial data, ultimately allowing the generation of self-labelled defective images. First, we let the model learn the new concept, entailing the novel data distribution. Then, we force it to learn to condition the generative process, producing industrial images that satisfy well-defined topological characteristics and show defects with a given geometry and location. To highlight the advantage of our approach, we use the synthetic dataset to optimise a crack segmentor for a real industrial use case. When the available data is small, we observe considerable performance increase under several metrics, showing the method's potential in production environments.

Bin Han, Mohammad Asif Habibi, Bjoern Richerzhagen et al.

Having the Fifth Generation (5G) mobile communication system recently rolled out in many countries, the wireless community is now setting its eyes on the next era of Sixth Generation (6G). Inheriting from 5G its focus on industrial use cases, 6G is envisaged to become the infrastructural backbone of future intelligent industry. Especially, a combination of 6G and the emerging technologies of Digital Twins (DT) will give impetus to the next evolution of Industry 4.0 (I4.0) systems. This article provides a survey in the research area of 6G-empowered industrial DT system. With a novel vision of 6G industrial DT ecosystem, this survey discusses the ambitions and potential applications of industrial DT in the 6G era, identifying the emerging challenges as well as the key enabling technologies. The introduced ecosystem is supposed to bridge the gaps between humans, machines, and the data infrastructure, and therewith enable numerous novel application scenarios.

Javier Alejandro Ossandon Otero, Rainiero Moisés Casma López, Manuel de la Mata Herrera

Resumen Introducción: Actualmente se está produciendo un acusado cambio demográfico en el que se observa un envejecimiento en la población activa. De hecho alrededor del 50% tiene 45 o más años. Resultando por tanto un desafío y una necesidad para nuestra sociedad el tratar de mejorar la calidad de vida de los trabajadores de mediana edad. Objetivo: Identificar y evaluar si la realización de intervenciones en el lugar de trabajo mejoran la calidad de vida de los trabajadores de mediana edad. Metodología: Se ha realizado una revisión bibliográfica sistemática basada en la literatura publicada desde el 2004 hasta diciembre de 2018 en varias bases de datos científicas: MEDLINE, EMBASE, LILACS, SCIELO y SCOPUS. Resultados: La búsqueda produjo un total de 372 registros y tras la aplicación de la fórmula de búsqueda y criterios de exclusión e inclusión, se seleccionaron un total de 11 artículos (10 ensayos clínicos y 1 estudio de cohortes). Se evidenciaron resultados estadísticamente significativos en distintas intervenciones en el lugar de trabajo que lograron mejorar la calidad de vida de los trabajadores. Conclusiones: La evidencia recopilada en esta revisión sistemática resulta consistente respecto a la capacidad que tienen las intervenciones que fomentan la salud y el bienestar en el lugar de trabajo para mejorar la calidad de vida de los trabajadores de mediana edad. Sin embargo sería recomendable la realización de nuevos estudios para poder ampliar este campo de conocimiento.

Alesia M. Jung, Sara A. Jahnke, Leslie K. Dennis et al.

Abstract Background Evidence from previous studies suggests that women firefighters have greater risk of some adverse reproductive outcomes. The purpose of this study was to investigate whether women firefighters had greater risk of miscarriage compared to non-firefighters and whether there were occupational factors associated with risk of miscarriage among firefighters. Methods We studied pregnancies in the United States fire service using data from the Health and Wellness of Women Firefighters Study (n = 3181). We compared the prevalence of miscarriage among firefighters to published rates among non-firefighters using age-standardized prevalence ratios. We used generalized estimating equations to estimate relative risks (RRs) and 95% confidence intervals (CIs) between occupational factors (employment (career/volunteer), wildland firefighter status (wildland or wildland-urban-interface/structural), shift schedule, fire/rescue calls at pregnancy start) and risk of miscarriage, adjusted for age at pregnancy, education, gravidity, BMI, and smoking. We evaluated if associations varied by age at pregnancy or employment. Results Among 1074 firefighters and 1864 total pregnancies, 404 pregnancies resulted in miscarriages (22%). Among most recent pregnancies, 138 resulted in miscarriage (13%). Compared to a study of US nurses, firefighters had 2.33 times greater age-standardized prevalence of miscarriage (95% CI 1.96–2.75). Overall, we observed that volunteer firefighters had an increased risk of miscarriage which varied by wildland status (interaction p-value< 0.01). Among structural firefighters, volunteer firefighters had 1.42 times the risk of miscarriage (95% CI 1.11–1.80) compared to career firefighters. Among wildland/wildland-urban-interface firefighters, volunteer firefighters had 2.53 times the risk of miscarriage (95% CI 1.35–4.78) compared to career firefighters. Conclusions Age-standardized miscarriage prevalence among firefighters may be greater than non-firefighters and there may be variation in risk of miscarriage by fire service role. Further research is needed to clarify these associations to inform policy and decision-making.