Search Results (143)

Hyperthermal environments are encountered in many situations, and significant heat stress can exacerbate the fatigue perception of individuals and potentially threaten their safety. Heat acclimation (HA) interventions have many benefits in preventing the risk of incidents. However, whether HA interventions in specific environments can cope with other different hyperthermal environments remains uncertain. In this study, forty-three young male participants were heat-acclimated over 10 days of training on a motorized treadmill in a fixed hyperthermal environment, and they were tested in different hyperthermal environments. Physiological indices (rectal temperature (T_r), heart rate (HR), skin temperature (T_sk), and total sweat loss (M_sl)) and subjective perception (rating of perceived exertion (RPE) and thermal sensation votes (TSVs)) were measured during both the heat stress test (HST) sessions and HA training sessions. The results show that HR and T_sk significantly differed between pre- and post-heat acclimation (p < 0.05 for all) following the acclimation program. However, after heat acclimation training, the reduction in T_r (ΔT_r) was more notable in lower-ET* environments, and M_sl showed distinct changes in different ET* environments. The RPE and TSV decreased after HA interventions, although the difference was not significant. The results indicate that HA can effectively reduce the peak of physiological parameters. However, when subjected to stronger heat stress, the improvement effects of heat acclimation on human responses will be affected. In addition, HA can alleviate physiological thermal strain, thereby reducing the adverse effects on mobility, but it has no effect on the supervisor’s ability to perceive the environment. This study suggests that additional HA training can reduce the risk of activities in high-temperature environments but exhibits different effects under different environmental conditions, indicating that hot acclimation suits have selective effects on the environment. This study provides recommendations for additional HA training before high-temperature activities. Full article

Objective: Tumor progression is regulated by systemic immune status, nutritional metabolism, and the inflammatory microenvironment. This study aims to investigate inflammatory–nutritional biomarkers associated with metachronous liver metastasis (MLM) in colorectal cancer (CRC) and develop a machine learning model for accurate prediction. Methods: This study enrolled 680 patients with CRC who underwent curative resection, randomly allocated into a training set (n = 477) and a validation set (n = 203) in a 7:3 ratio. Feature selection was performed using Boruta and Lasso algorithms, identifying nine core prognostic factors through variable intersection. Seven machine learning (ML) models were constructed using the training set, with the optimal predictive model selected based on comprehensive evaluation metrics. An interactive visualization tool was developed to interpret the dynamic impact of key features on individual predictions. The partial dependence plots (PDPs) revealed a potential dose–response relationship between inflammatory–nutritional markers and MLM risk. Results: Among 680 patients with CRC, the cumulative incidence of MLM at 6 months postoperatively was 39.1%. Multimodal feature selection identified nine key predictors, including the N stage, vascular invasion, carcinoembryonic antigen (CEA), systemic immune–inflammation index (SII), albumin–bilirubin index (ALBI), differentiation grade, prognostic nutritional index (PNI), fatty liver, and T stage. The gradient boosting machine (GBM) demonstrated the best overall performance (AUROC: 0.916, sensitivity: 0.772, specificity: 0.871). The generalized additive model (GAM)-fitted SHAP analysis established, for the first time, risk thresholds for four continuous variables (CEA > 8.14 μg/L, PNI < 44.46, SII > 856.36, ALBI > −2.67), confirming their significant association with MLM development. Conclusions: This study developed a GBM model incorporating inflammatory-nutritional biomarkers and clinical features to accurately predict MLM in colorectal cancer. Integrated with dynamic visualization tools, the model enables real-time risk stratification via a freely accessible web calculator, guiding individualized surveillance planning and optimizing clinical decision-making for precision postoperative care. Full article

Endurance training induces significant cardiac remodeling, with evidence suggesting that prolonged high-intensity exercise may increase the risk of atrial fibrillation (AF). However, physiological responses differ by event type. This review compares AF-related markers in marathon and ultramarathon runners, focusing on structural adaptations, inflammatory and endothelial biomarkers, and the incidence of arrhythmias. A systematic analysis of 29 studies revealed consistent left atrial (LA) enlargement in marathon runners linked to elevated AF risk and fibrosis markers such as Galectin-3 and PIIINP. In contrast, ultramarathon runners exhibited right atrial (RA) dilation and increased systemic inflammation, as indicated by elevated high-sensitivity C-reactive protein (hs-CRP) and soluble E-selectin levels. AF incidence in marathoners ranged from 0.43 per 100 person-years to 4.4%, while direct AF incidence data remain unavailable for ultramarathon populations, highlighting a critical evidence gap. These findings suggest distinct remodeling patterns and pathophysiological profiles between endurance disciplines, with implications for athlete screening and cardiovascular risk stratification. Full article

Background and Objective: Despite growing emphasis on quality and safety in healthcare, there remains a limited understanding of how Quality Improvement and Patient Safety (QI/PS) training for health workers has evolved in response to global events like the COVID-19 pandemic and the WHO Global Patient Safety Action Plan. This rapid scoping review aimed to not only identify existing curricula but also uncover trends, innovation gaps, and global inequities in QI/PS education—providing timely insights for reshaping future training strategies. Methods: We searched MEDLINE and Scopus for English-language studies published between January 2020 and April 2024, describing QI and/or PS curricula across graduate, postgraduate, and continuing education levels. All healthcare worker groups were eligible, with no geographic limitations. Two reviewers conducted independent screening and data extraction; a third verified the results. Results: Among 3290 records, 74 curricula met inclusion criteria, with a majority originating from the US (58, 78.4%) and targeting physicians—especially residents and fellows (43/46, 93.5%). Only 27% of curricula were multidisciplinary. While traditional didactic (66.2%) and interactive (73%) approaches remained prevalent, curricula launched after 2020 introduced novel formats such as Massive Open Online Courses and gamification, with long-term programs uniformly leveraging web-based platforms. Common thematic content included Root Cause Analysis, Plan-Do-Study-Act cycles, QI tools, communication skills, and incident reporting. English-language peer-reviewed published literature indicated a marked lack of structured QI/PS training in Europe, Asia, and Africa. Conclusions: This review reveals both an uneven development and fragmentation in global QI/PS training efforts, alongside emerging opportunities catalyzed by digital transformation and pandemic-era innovation. The findings highlight a critical gap: while interest in QI/PS is growing, scalable, inclusive, and evidence-based curricula remain largely concentrated in a few high-income countries. By mapping these disparities and innovations, this review provides actionable direction for advancing more equitable and modern QI/PS education worldwide, whilst showcasing the need to systematically delve into QI/PS training in underrepresented regions. Full article

Background: Disparities in neuro-oncological care between high-income and low- and middle-income countries (LMICs) are well documented, yet region-specific data from Latin America remain limited. This review evaluates epidemiologic trends, access to care, and systemic challenges in brain tumor management across Latin American LMICs, using Argentina as a case study. Methods: A systematic review of peer-reviewed literature was conducted focusing on brain tumor incidence, mortality, risk factors, and availability of diagnostics and treatments in Latin America. Socioeconomic, cultural, and systemic barriers were also analyzed. Results: Latin America exhibits some of the highest global brain tumor mortality rates, with Brazil reporting age-standardized rates exceeding 4.5 per 100,000. Glioblastomas are frequently diagnosed at younger ages, often in the fifth decade of life, compared to the global average. Meningioma incidence has increased by 15–20% over the last decade, yet region-wide data remain fragmented. Access to neuroimaging, neurosurgery, radiotherapy, and chemotherapy is limited, with up to 60% of patients relying solely on under-resourced public health systems. Less than 30% of hospitals in rural areas have MRI availability, and continuous professional training is infrequent. Innovative adaptations, such as awake craniotomy, are used in some LMIC centers in response to equipment scarcity. Conclusions: Brain tumor care in Latin America is hindered by limited epidemiological data, restricted access to diagnostics and treatment, and insufficient workforce training. Targeted investments in healthcare infrastructure, international educational collaborations, and policy-level reforms are critical to reducing disparities and improving outcomes in neuro-oncology across the region. Full article

The growing complexity and risk profile of fire and emergency incidents necessitate advanced training methodologies that go beyond traditional approaches. Live-fire drills and classroom-based instruction, while foundational, often fall short in providing safe, repeatable, and scalable training environments that accurately reflect the dynamic nature of real-world emergencies. Recent advancements in immersive technologies, including virtual reality (VR), augmented reality (AR), mixed reality (MR), extended reality (XR), and simulation-based systems, offer promising alternatives to address these challenges. This review provides a comprehensive overview of the integration of VR, AR, MR, XR, and simulation technologies into firefighter and incident commander training. It examines current practices across fire services and emergency response agencies, highlighting the capabilities of immersive and interactive platforms to enhance operational readiness, decision-making, situational awareness, and team coordination. This paper analyzes the benefits of these technologies, such as increased safety, cost-efficiency, data-driven performance assessment, and personalized learning pathways, while also identifying persistent challenges, including technological limitations, realism gaps, and cultural barriers to adoption. Emerging trends, such as AI-enhanced scenario generation, biometric feedback integration, and cloud-based collaborative environments, are discussed as future directions that may further revolutionize fire service education. This review aims to support researchers, training developers, and emergency service stakeholders in understanding the evolving landscape of digital training solutions, with the goal of fostering more resilient, adaptive, and effective emergency response systems. Full article

The rapid expansion of urban rail networks has resulted in increasingly complex passenger flow patterns, presenting significant challenges for operational management, especially during incidents and emergencies. Disruptions such as power equipment failures, trackside faults, and train malfunctions can severely impact transit efficiency and reliability, leading to congestion and cascading network effects. Existing models for predicting passenger origin–destination (OD) matrices struggle to provide accurate and timely predictions under these disrupted conditions. This study proposes a deep counterfactual inference model that improves both the prediction accuracy and interpretability of OD matrices during incidents. The model uses a dual-channel framework based on multi-task learning, where the factual channel predicts OD matrices under normal conditions and the counterfactual channel estimates OD matrices during incidents, enabling the quantification of the spatiotemporal impacts of disruptions. Our approach which incorporates KL divergence-based propensity matching enhances prediction accuracy by 4.761% to 12.982% compared to baseline models, while also providing interpretable insights into disruption mechanisms. The model reveals that incident types vary in delay magnitude, with power equipment incidents causing the largest delays, and shows that incidents have time-lag effects on OD flows, with immediate impacts on origin stations and progressively delayed effects on destination and neighboring stations. This research offers practical tools for urban rail transit operators to estimate incident-affected passenger volumes and implement more efficient emergency response strategies, advancing emergency response capabilities in smart transit systems. Full article

This study investigates the spread patterns of tunnel fires and examines issues related to emergency response. It focuses on the temperature characteristics, spread patterns, conditions leading to multi-source fires, and the efficacy of water mist suppression methods in heavy-haul railway tunnel fires. The research employs theoretical derivations and numerical simulations to achieve its objectives. It was discovered that, during a fire in a heavy-haul railway tunnel, the temperature inside the tunnel can exceed 500 °C. Furthermore, depending on the nature of the goods transported by the train and under specific wind speed conditions, the fire source has the potential to spread to other carriages, resulting in a multi-source fire. Using the numerical simulation software Pyrosim 2022, various wind speed conditions were simulated. The results revealed that at lower wind speeds, the smoke demonstrates a reverse flow phenomenon. Concurrently, when the adjacent carriage on the leeward side of the fire is ignited, the high-temperature reverse flow smoke, along with the thermal radiation from the flames, ignites combustible materials in the adjacent carriage on the windward side of the burning carriage. Through theoretical derivation and numerical simulation, the critical wind speed for the working conditions was determined to be 2.14 m/s. It was found that while a higher wind speed can lead to a decrease in temperature, it also increases the flame deflection angle. When the wind speed exceeds 2.4 m/s, although the temperature significantly drops in a short period, the proximity of combustible materials on the leeward side of the carriage becomes a concern. At this wind speed, the flame deflection angle causes heat radiation on the leeward side, specifically between 0.5 m and 3 m, to ignite the combustible materials on the carriage surface, resulting in fire spread and multiple fire incidents. The relationship between wind speed and the angle of deflection from the fire source was determined using relevant physics principles. Additionally, the relationship between wind speed and the trajectory of water mist spraying was established. It was proposed to optimize the position of the water mist based on its deviation, and the results indicated that under critical wind speed conditions, when the water mist spraying is offset approximately 5 m towards the upwind side of the fire source, it can act more directly on the surface of the fire source. Numerical simulation results show a significant reduction in the maximum temperature and effective control of fire spread. Under critical wind speed conditions, the localized average temperature of the fire decreased by approximately 140 °C when spraying was applied, compared to the conditions without spraying, and the peak temperature decreased by about 190 °C. This modification scheme can effectively suppress the threat of fire to personnel evacuation under simulated working conditions, reflecting effective control over fires. Additionally, it provides theoretical support for the study of fire patterns in tunnels and emergency response measures. Full article

Lamb waves offer a series of desirable features for Structural Health Monitoring (SHM) applications, such as the ability to detect small defects, allowing to detect damage at early stages of its evolution. On the downside, their propagation through media with multiple geometrical features results in complicated patterns, which complicate the task of damage detection, thus hindering the realization of their full potential. This is exacerbated by the fact that numerical models for Lamb waves, which could aid in both the prediction and interpretation of such patterns, are computationally expensive. The present paper provides a flexible surrogate to rapidly evaluate the sensor response in scenarios where Lamb waves propagate in plates that include multiple features or defects. To this end, an offline–online ray tracing approach is combined with Frequency Response Functions (FRFs) and transmissibility functions. Each ray is thereby represented either by a parametrized FRFs, if the origin of the ray lies in the actuator, or by a parametrized transmissibility function, if the origin of the ray lies in a feature. By exploiting the mechanical properties of propagating waves, it is possible to minimize the number of training simulations needed for the surrogate, thus avoiding the repeated evaluation of large models. The efficiency of the surrogate is demonstrated numerically, through an example, including different types of features, in particular through holes and notches, which result in both reflection and conversion of incident waves. For most sensor locations, the surrogate achieves an error between 1% and 4%, while providing a computational speedup of three to four orders of magnitude. Full article

Several studies showed that the incidence of Alzheimer’s disease (AD) is significantly lower in patients with non-muscle invasive bladder cancer (NMIBC) treated with intravesical bacillus Calmette–Guérin (BCG) instillations compared to treatment by alternative methods. Hypothetically, failure to clear misfolded and aggregated proteins (i.e., beta-amyloid) in AD brains and peripheral blood mononuclear cells (PBMCs) implicates BCG in upgrading the unfolded protein response (UPR). To test this hypothesis, pre- versus post-BCG PBMC proteins of the UPR pathway were compared in six NMIBC patients by capillary immunoelectrophoresis on an Abby instrument. PERK, the endoplasmic reticulum (ER) resident kinase, a stress-activated sensor, and its substrate alpha component of the eIF2 translation factor (eIF2a) complex inactivation were considered as potentially proapoptotic via a downstream proapoptotic transcription factor only if persistently high. GAPDH, a glycolytic marker of innate immunocyte training by BCG, and eight other UPR proteins were considered antiapoptotic. Summation of antiapoptotic %change scores per patient showed that the older the age, the lower the antiapoptotic %change. Higher antiapoptotic scores were observed upon a longer time from BCG treatment (with the exception of the patient in her ninth decade of life). Studies with more individuals could substantiate that BCG enhances the antiapoptotic aggregate-clearance effect of the UPR in PBMCs of NMIBC patients, which hypothetically protects brain cells against AD. Full article

Introduction: Para-cycling is a competitive sport governed by the World Body for Cycling, Union Cycliste Internationale (UCI), encompassing various cycling disciplines tailored to athletes with physical or visual impairments. This study aimed to prospectively monitor the incidence of injury and illness in Para cyclists during the 2024 Paralympic season. Methods: This prospective, observational study included ten professional Para cyclists (five male, five female) with impairments ranging from spinal cord-related, neuromuscular, and musculoskeletal conditions to vision impairment. The definitions of an ‘athlete health problem’, ‘injury’, and ‘illnesses’ followed the Para sport translation of the IOC consensus. Injury and illness data were collected weekly using the Oslo Sports Trauma Research Centre Questionnaire on Health Problems V2 (OSTRC-H2), with the addition of subjective markers of well-being and training load, between February 2024 and October 2024. All medical contacts for any injury or illness were logged in line with consensus statement recommendations. Results: The OSTRC-H2 questionnaire had a response rate of 76.5% (±12.2%, range 55–88%) across the 35 weeks. Athletes reported 7.36 (95% CI: 5.41–9.46) health problems per 365 days, with a medical attention rate of 5.56 (95% CI: 3.91–7.36) per 365 days. The overall injury rate was 1.94 per 365 athlete days (95% CI: 1.23–2.93), with a higher incidence in males (2.44, 95% CI: 1.53–3.67) than in females (1.51, 95% CI: 0.68–2.95). Conversely, illness rates were higher in females (5.40, 95% CI: 3.00–8.11) than in males (1.80, 95% CI: 0.60–3.30), with an overall illness rate of 3.60 per 365 days (95% CI: 2.29–5.10). Conclusions: This is the first study to present prospective injury and illness epidemiology rates in Para cyclists in combination with subjective well-being markers. The findings underscore the importance and feasibility of longitudinal health monitoring in Para cyclists, ensuring that both physical and mental health concerns are systematically tracked and addressed. This enables a proactive, multidisciplinary support system to respond effectively to fluctuations in well-being, particularly during periods of injury or illness. Full article

Background/Objectives: Breast cancer is the most common malignancy among women globally, with an increasing incidence, particularly in younger populations. Achieving complete surgical excision is essential to reduce recurrence. Artificial intelligence (AI), including large language models like ChatGPT, has potential for supporting diagnostic tasks, though its role in surgical oncology remains limited. Methods: This retrospective study evaluated ChatGPT’s performance (ChatGPT-4, OpenAI, March 2025) in predicting surgical margin status (R0 or R1) based on intraoperative mammograms of lumpectomy specimens. AI-generated responses were compared with histopathological findings. Performance was evaluated using sensitivity, specificity, accuracy, positive predictive value (PPV), negative predictive value (NPV), F1 score, and Cohen’s kappa coefficient. Results: Out of a total of 100 patients, ChatGPT achieved an accuracy of 84.0% in predicting surgical margin status. Sensitivity for identifying R1 cases (incomplete excision) was 60.0%, while specificity for R0 (complete excision) was 86.7%. The positive predictive value (PPV) was 33.3%, and the negative predictive value (NPV) was 95.1%. The F1 score for R1 classification was 0.43, and Cohen’s kappa coefficient was 0.34, indicating moderate agreement with histopathological findings. Conclusions: ChatGPT demonstrated moderate accuracy in confirming complete excision but showed limited reliability in identifying incomplete margins. While promising, these findings emphasize the need for domain-specific training and further validation before such models can be implemented in clinical breast cancer workflows. Full article

This study introduces a novel approach to cyber defense exercises, emphasizing the emulation of technical tasks to create realistic incident response scenarios. Unlike traditional cyber ranges or tabletop exercises, this method enables both management and technical leaders to engage in decision-making processes without requiring a full technical setup. The initial observations indicate that exercises based on the emulation of technical tasks require less preparation time compared to conventional methods, addressing the growing demand for efficient training solutions. This study aims to assist organizations in developing their own cyber defense exercises by providing practical insights into the benefits and challenges of this approach. The key advantages observed include improved procedural compliance, inter-team communication, and a better understanding of the chain of command as participants navigate realistic, organization-wide scenarios. However, new challenges have also emerged, such as managing the simulation tempo and balancing technical complexity—particularly in offense–defense team configurations. This study proposes a structured and scalable approach as a practical alternative to the traditional training methods, aligning better with the evolving demands of modern cyber defense. Full article

The evolution of deepfake technology has the potential to reshape the threat landscape in corporate environments by enabling highly convincing digital impersonations. In this paper, we explore how artificial media produced by AI can be misused to assume authoritative personas, leaving traditional cybersecurity programs with significant vulnerabilities. Drawing from interviews with cybersecurity professionals across various industries, we find that the majority of organizations remain vulnerable due to their adoption of broad, vendor-centric security solutions that are not specifically designed to protect against deepfake attacks. In response to the evolving threat landscape, we introduce the PREDICT framework—a cyclical, iterative theoretical model. This model combines definitive policy direction, organizational preparedness, targeted employee training, and advanced AI detection tools. Additionally, it incorporates effective incident response plans with continuous improvement and simulations. Our findings underscore the need to revise the current security protocols and offer practical suggestions for strengthening corporate defenses against the increasingly dynamic threat landscape posed by deepfakes. Full article

The increase in malicious cyber activities has generated the need to produce effective tools for the field of digital forensics and incident response. Artificial intelligence (AI) and its fields, specifically machine learning (ML) and deep learning (DL), have shown great potential to aid the task of processing and analyzing large amounts of information. However, models generated by DL are often considered “black boxes”, a name derived due to the difficulties faced by users when trying to understand the decision-making process for obtaining results. This research seeks to address the challenges of transparency, explainability, and reliability posed by black-box models in digital forensics. To accomplish this, explainable artificial intelligence (XAI) is explored as a solution. This approach seeks to make DL models more interpretable and understandable by humans. The SHAP (SHapley Additive eXplanations) and LIME (Local Interpretable Model-agnostic Explanations) methods will be implemented and evaluated as a model-agnostic technique to explain predictions of the generated models for forensic analysis. By applying these methods to the XGBoost and TabNet models trained on the UNSW-NB15 dataset, the results indicated distinct global feature importance rankings between the model types and revealed greater consistency of local explanations for the tree-based XGBoost model compared to the deep learning-based TabNet. This study aims to make the decision-making process in these models transparent and to assess the confidence and consistency of XAI-generated explanations in a forensic context. Full article