Abstract: Background: Epidemiological studies have established a positive association between water hardness, chlorine content, and the prevalence or severity of atopic dermatitis (AD). These environmental factors are known to exacerbate skin barrier dysfunction and increase discomfort in individuals with atopy-prone skin. Objectives: This research aimed to objectify the detrimental effects of hard and chlorinated water on atopic skin, both under controlled experimental conditions and in real-life settings. The studies assessed the efficacy of a daily dermocosmetic routine (comprising a cleanser and moisturizer adapted for atopy-prone skin) for reducing water-induced discomfort and improving the quality of life. Methods: Three clinical studies were conducted: one experimental comparative study of repeated washing or immersion with hard, chlorinated, and soft water, and two intra-individual 21-day studies on hard water and swimming pool water (chlorinated) exposure in real-life conditions. Results: Cumulative exposure to hard water (HW) and chlorinated water (CW) increased TEWL, while soft water (SW) had no significant effect on barrier function. The dermocosmetic routine significantly improved skin hydration and barrier function, with TEWL significantly decreasing by 25% (HW), 17% (CW), and 20% (SW) compared to untreated areas. In real-life studies, 21-day use of the products significantly reduced skin discomfort and improved quality of life. Conclusion: Repeated exposure to hard and chlorinated water can exacerbate skin discomfort and clinical symptoms of atopic dermatitis. An adapted daily dermocosmetic routine can significantly mitigate these effects, improving barrier function, skin comfort, and daily quality of life.

Abstract: Amid COVID-19-related in-person school closures in 2021, an agent-based simulation grounded in social impact theory was implemented and documeted to investigate the effects of in-person school closure on nonmedical prescription opioid use among adolescents in Ontario, Canada. The results of model simulations forecasted an alarming rebound effect in the opioid use prevalence after the lifting of in-person school closures and identified secure medication storage in households as an effective strategy for mitigating associated risks. This study evaluates this result by comparing the baseline projection from the previously published study with newly released 2023 data from the Ontario Student Drug Use and Health Survey. Furthermore, it employs the developed agent-based model to simulate the projection through 2030 and assesses the efficacy of secure medication storage in households for the coming years. The study confirms that the previously published simulation projection for 2023 closely aligns with observed data, showing nonmedical prescription opioid use prevalence among Ontario adolescents nearly doubling from 2021 to 2023. Additionally, the results show that nonmedical prescription opioid use prevalence among youth is projected to remain at these elevated levels. Critically, the findings suggest that the temporal window for effective secure medication storage interventions has elapsed, and these interventions are now expected to have minimal impact on reducing this increase, even when applied extensively. The agreement between reported predictions and observed data demonstrates that a simulation model with relevant conceptual foundation can accurately predict future trends and provide sufficient lead time for policymakers to implement interventions within critical time-sensitive windows to alter undesirable trajectories before public health crises escalate.

Abstract: High construction costs have plagued recent nuclear projects, and they hamper the widespread deployment of nuclear technology. The Nuclear Cost Reduction Tool is a reactor economic framework that quantifies the impact that various reactor design and construction attributes have on construction costs and cost overruns and shows the positive effects of learning over a series of deployments. However, a downside of the current model is that all model output and capabilities are deterministic. To provide a more comprehensive view, this study evaluated the impact of model parameter uncertainty through sensitivity analysis applied to 18 model parameters. This approach quantified the impact of model uncertainty on the output variables of Net Overnight Capital Cost (Net OCC), Construction Duration (CD), and Levelized Cost of Electricity (LCOE). Monte Carlo analysis revealed uncertainty distributions for these variables, showing that absolute uncertainty decreases over a series of deployments. A local sensitivity analysis showed that even small parameter perturbations (5%) can have a significant impact on project execution, highlighting areas that could reduce costs by billions across an order book of reactors. The results of this study have improved the understanding of the model and identified the most impactful model parameters and construction attributes.

Abstract: This study investigates managerial perceptions of artificial intelligence (AI) adoption in decision-making, focusing on understanding how managers interpret, evaluate, and integrate AI systems into their organizational processes. As organizations increasingly rely on AI-driven tools for data analysis, forecasting, and decision support, managers play a critical role in determining the effectiveness and ethical deployment of these technologies. The research employed a qualitative approach, utilizing in-depth semi-structured interviews with managers from diverse industries to capture their experiences, insights, and concerns regarding AI adoption. Thematic analysis was conducted to identify patterns and themes that illustrate how managerial perceptions shape both the adoption process and the outcomes of AI-assisted decision-making. The findings reveal that managers perceive AI as a valuable tool for enhancing efficiency, analytical accuracy, and strategic focus, allowing them to shift attention from routine operational tasks to higher-order decision-making activities. At the same time, managers reported challenges associated with AI complexity, resistance to change, data quality, trust, transparency, and accountability, highlighting the socio-technical nature of AI adoption. Ethical considerations, including fairness, bias, and data privacy, were emphasized as critical factors influencing managerial confidence and willingness to rely on AI outputs. Organizational support, leadership endorsement, and continuous skill development were identified as essential enablers for successful integration. The study underscores the importance of balancing human judgment with machine-generated insights, reflecting the concept of collaborative intelligence, where AI augments rather than replaces managerial decision-making. This research provides a nuanced understanding of the factors shaping managerial engagement with AI, offering practical and strategic insights for organizations seeking to implement AI responsibly and effectively in decision-making processes.

Abstract: Around 1637, Pierre de Fermat famously wrote in the margin of a book that he had a proof showing the equation $a^n + b^n = c^n$ has no positive integer solutions for exponents $n$ greater than 2. This statement, known as Fermat's Last Theorem, remained unproven for over three centuries despite efforts by countless mathematicians. In 1994, Andrew Wiles finally provided a rigorous proof using advanced techniques from elliptic curves and modular forms—methods far beyond those available in Fermat's era. Wiles was awarded the Abel Prize in 2016, with the citation describing his work as a ``stunning advance'' in mathematics. The Beal conjecture, formulated in 1993, generalizes Fermat's Last Theorem. It states that if $A^{x} + B^{y} = C^{z}$ holds for positive integers $A, B, C, x, y, z$ with $x, y, z > 2$, then $A$, $B$, and $C$ must share a common prime factor. In this paper, we prove the Beal conjecture using elementary methods involving parametrization of quadratic Diophantine equations, divisibility properties, and congruence relations. Our approach potentially offers a solution closer in spirit to the mathematical tools available in Fermat's time.

Abstract:

Background: The middle meningeal artery (MMA) plays a central role in migraine pathophysiology as a vascular and neuroimmune interface driving the throbbing pain. Inhibition of this cascade has been explored as a therapeutic approach, yet fewer than a dozen centers worldwide have published procedural or mechanistic data. Given the nascency of this field and the need for standardization, this review synthesizes the mechanistic and clinical evidence supporting intra-arterial pharmacologic modulation of the MMA for migraine treatment. Methods: A focused narrative review was conducted using limited but high-impact studies from pioneering groups exploring intra-arterial approaches to the MMA. Literature was arranged thematically and organized by the sites of cascade interruption and associated outcomes. Results: Since 2009, the use of intra-arterial therapies for severe headache syndromes has evolved from nimodipine for vasospasm-related headaches to verapamil for reversible cerebral vasoconstriction and, more recently, lidocaine for refractory or status migrainosus cases, sometimes with MMA embolization. Current research reframes migraines as an immunologically mediated neurovascular process, rather than purely a vascular or neuronal phenomenon. Recent studies have identified interleukins such as IL-1β, TNF-α, and IL-6 as key amplifiers of trigeminovascular activation, while emerging evidence implicates purinergic (P2X3, P2Y13) and PACAP/VIP pathways in modulating MMA excitability and neuropeptide release. Novel CGRP receptor antagonists, including zavegepant further reinforce the artery’s role as a therapeutic target. Conclusion: Our findings highlight a transition toward immune-modulating intra-arterial strategies, suggesting that future migraine therapies may increasingly focus on cytokine and neuroimmune signaling within the MMA rather than traditional vasodilatory control.

Abstract: This paper proposes an integrated numerical–experimental methodology for the durability assessment and optimisation of a passenger-car rear axle. A dedicated rear-suspension durability test bench was designed to impose a controlled cyclic vertical excitation on a dependent axle, reproducing service-like translational and rotational amplitudes of the beam and stabilizer bar. A detailed flexible multibody model of the bench–axle system was developed in MSC ADAMS and used to tune the kinematic excitation and determine an equivalent design load at the wheel spindles, consistent with the stiffness of the suspension assembly. Experimental strain measurements at nine locations on the axle, acquired with strain-gauge instrumentation on the bench, were converted into stresses and used to validate an explicit dynamic finite element model in ANSYS. The FE predictions agree with the experiments within about 10% at the beam mid-span and correctly identify a critical region at the junction between the side plate and the arm, where peak von Mises stresses of about 104 MPa occur. The validated model then supports a response-surface-based optimisation of the safety-critical wheel spindle, yielding a geometry that lowers spindle-fillet stresses to around 180–185 MPa under the maximum admissible wheel load, with only a modest mass penalty.

Abstract: Emergent properties are properties of a whole that cannot be reduced to the properties of its parts. Properties of a system can be defined as relations between a particular input given to a system and its corresponding output. From this perspective, whole systems formed by coupling component systems have properties different from the properties of their components. Wholes tend to arise spontaneously through a process of self-organization, in which components randomly interact until they settle in a stable configuration that in general cannot be predicted from the properties of the components. That configuration constrains the relations between the components, thus defining emergent “laws” that downwardly cause the further behavior of the components. Thus, emergent wholes and their properties arise in a simple and natural manner.

Abstract: This paper examines the limitations of using self-reported health status (SRHS) as a direct measure of true health in dynamic economic models. Motivated by empirical evidence from major panel datasets (MEPS, HRS, and PSID) showing duration dependence and violations of the Markov property in SRHS transitions, we introduce a latent health model that accounts for transitory reporting error and individual heterogeneity. The model treats health as a continuous latent variable following an autoregressive process with mixture-distributed shocks, mapped to discrete SRHS outcomes through individual-specific reporting thresholds. Estimation results reveal strong persistence in latent health (ρ ≈ 0.9) and systematic reporting heterogeneity: older and less-educated individuals report worse SRHS for identical latent health levels. The model outperforms standard Markov specifications in capturing observed transition dynamics and demonstrates strong predictive validity through external validation using mortality data and labor force outcomes. Our framework enables more accurate policy simulations for social insurance programs and provides a template for handling noisy ordinal outcomes in other domains of applied microeconomics.

Abstract: A retrospective study on 135 cases of teat and udder surgical conditions in 129 small ru-minants is described. On 19 repairs of teat lacerations, a primary and a secondary inten-tion healing in 13 (68%) and in 4 (21%) cases, respectively, was observed; 2 (11%) had poor response and consequent mastitis. Good outcome and first intention healing in 100% of the fistula repairs (2 cases), thelectomies (5 cases), teat neoplasm removals (14), and mas-tectomies (2 cases) were observed. Among 26 teat curettage cases, all 18 (69%) unilateral lesions treatment had a good outcome versus the 8 (31%) with bilateral lesion that suffered definitive relapse. On 67 skin udder neoplasms removal, a primary and a secondary in-tention healing in 59 (88%) and in 8 (12%) cases, respectively, was observed; however, 2 of the latter suffered mastitis. These procedures are associated with a good prognosis, and the percentage of favourable outcomes was high. Wound infections and dehiscence were the main complications observed. More interest in teat and udder surgery on small ruminants should be encouraged, and farmers should be made aware that the animal can often return into production at a reasonable cost, however, their post-operative care is the key to success.

Abstract:

The expansion of the Internet of Things (IoT) devices in domestic smart homes has created new conveniences but also significant security risks. Insecure firmware, weak authentication and encryption leave households exposed to privacy breaches, data leakage, and systemic attacks. Although research has addressed several challenges contributions remain fragmented and difficult for non-technical users to apply. This work addresses the research question: How can a theoretical framework be developed to enable automated vulnerability scanning and prioritisation for non-technical users in domestic IoT environments? A Systematic Literature Review of 40 peer-reviewed studies, conducted under PRISMA 2020 guidelines, identified four structural gaps: dispersed vulnerability knowledge, fragmented scanning approaches, over-reliance on technical severity in prioritisation and weak protocol standardisation. The paper introduces a four-module framework: a Vulnerability Knowledge Base, an Automated Scanning Engine, a Context-Aware Prioritisation Module and a Standardisation and Interoperability Layer. The framework advances knowledge by integrating previously siloed approaches into a layered and iterative artefact tailored to households. While limited to conceptual evaluation, the framework establishes a foundation for future work in prototype development, household usability studies and empirical validation. By addressing fragmented evidence with a coherent and adaptive design, the study contributes to both academic understanding and practical resilience, offering a pathway toward more secure and trustworthy domestic IoT ecosystems.

Abstract: The tourism industry faces increasing pressure for agile, personalized services, yet travel agencies struggle with fragmented knowledge scattered across isolated systems and legacy formats. While Large Language Models (LLMs) are widely applied in customer-facing roles, their potential to enhance internal operational efficiency remains largely underexplored. This study presents the design and evaluation of an intelligent assistant specifically for travel agency operations, built upon a Retrieval-Augmented Generation (RAG) architecture using Gemini 2.0 Flash. The system integrates heterogeneous data sources, including structured product catalogs and unstructured documentation processed via Optical Character Recognition (OCR), into a unified interface comprising work assistance, interactive training, and evaluation modules. Results demonstrate information retrieval times not greater than 45 seconds, ensuring its daily usability, while maintaining 95\% accuracy. Furthermore, the system democratizes tacit senior expertise and accelerates new employee onboarding. This research validates RAG architectures as a powerful solution to knowledge fragmentation, shifting the strategic AI focus from customer automation to employee empowerment and operational optimization.

Abstract: High-grade serous ovarian cancer (HGSOC) is the most prevalent and aggressive form of epithelial ovarian cancer, characterized by high recurrence rates and poor clinical outcomes. In this study, we identify molecular signatures associated with recurrence by conducting integrative transcriptomic and proteomic analyses on paired primary and recurrent HGSOC tissues from 34 patients. RNA sequencing and proteomic profiling revealed 185 differentially expressed genes (DEGs) and 36 differentially expressed proteins (DEPs) linked to recurrence. Pathway Enrichment and Ingenuity Pathway Analysis highlighted the involvement of immune cell trafficking, cell signaling, and MAPK pathway activation in recurrent tumors. Survival analysis identified seven DEGs significantly correlated with recurrence-free survival; among these, IL7R, IRF8, and PTPRC were upregulated in recurrent tumors and associated with poor prognosis, while NSG1 was downregulated and linked to favorable outcomes. Immunohistochemistry validated the differential expression of these markers at the protein level. Proteomic analysis demonstrated that recurrent tumor-specific DEGs are functionally linked to MAPK signaling. Co-expression analyses revealed dynamic regulatory interactions between DEGs and DEPs, suggesting context-dependent molecular shifts during recurrence. This integrative multi-omics approach reveals key molecular alterations underlying HGSOC recurrence and identifies IL7R, IRF8, PTPRC, and NSG1 as potential prognostic biomarkers and therapeutic targets. Our findings provide a foundation for targeted strategies to improve outcomes for patients with recurrent HGSOC.

Abstract: Background: Vitamin D deficiency is common in hospitalized COVID-19 patients and as-sociates with increased severity. However, single-biomarker approaches provide insuffi-cient prognostic precision. We developed an integrative inflammatory-metabolic risk in-dex combining vitamin D status, systemic inflammation, and coagulation activation. Methods: Prospective cohort study of 512 hospitalized COVID-19 patients (September 2022–December 2023) with serum 25(OH)D3 measurement at admission. Primary analy-sis (N=301) included patients with complete inflammatory marker data (CRP, ferritin, D-dimer, LDH). The Vitamin D Inflammatory Burden Score (VDIBS) integrated: (1) vita-min D tier (deficient 75: 0), (2) inflammation score (CRP ≥100, ferritin ≥1000, IL-6 ≥50 each +1 point; 0–3 total), and (3) coagulation score (D-dimer ≥1000, LDH ≥6 each +1 point; 0–2 total). IL-6 measurement was available in 48 patients (9.4% of cohort); all other components were measured in the primary analysis population (N=301). Outcomes were severe COVID-19, ICU admission, and mortality. Predictive performance was compared across four multi-variate models. Results: Mean vitamin D was 63.4±33.2 nmol/L (68.1% deficient). Severe disease occurred in 386 patients (75.4%), ICU admission in 30 (5.9%), and mortality in 14 (2.7%). VDIBS risk stratification showed: low-risk (VDIBS 0–2) n=178, 8.4% severe; moderate-risk (3–5) n=245, 45.7% severe; high-risk (6–8) n=89, 78.6% severe (χ²=142.3, p< 0.001). VDIBS predicted se-vere disease with AUC 0.78 (95% CI 0.74–0.82), equivalent to more complex multivariate models (AUC 0.82, p=0.08) but with superior clinical simplicity. In stratified analyses, VDIBS showed robust discrimination independent of season, age, or sex (all interaction p>0.05), supporting generalizability. Conclusions: VDIBS provides bedside-implementable risk stratification integrating vita-min D-dependent immune regulation, systemic inflammation, and coagulation activa-tion. This composite approach offers practical tool for treatment intensity escalation and potential therapeutic target for vitamin D repletion in severe COVID-19.

Abstract: Path integral Monte Carlo simulations and closure computations of quantum fluid triplet structures in the diffraction regime are presented. The systems selected are helium-3 under supercritical conditions and the quantum hard-sphere fluid on its crystallization line. The fourth-order propagator in the form given by Voth et al (helium-3) and Cao-Berne’s pair action (hard spheres) are employed in the path integral simulations; helium-3 interactions are described with Janzen-Aziz’s pair potential. The closures used are Kirkwood superposition, Jackson-Feenberg convolution, the intermediate AV3, and the symmetrized form of Denton-Ashcroft approximation. The centroid and instantaneous triplet structures, in the real and the Fourier spaces, are investigated by focusing on salient equilateral and isosceles features. To accomplish this goal, complementary simulations and closure calculations at the structural pair level are also carried out. The basic theoretical and technical points are described in some detail, the obtained results complete the structural properties reported by this author elsewhere for the abovementioned systems, and a meaningful comparison between the path integral and the closure results is made. The present works intends to shed some more light on the incipient general knowledge of this topic (e.g., the very slow convergence of path integral calculations, the behavior of certain salient Fourier components, such as the double-zero momentum transfers or the equilateral maxima, etc.). Also, closures are proven to provide valuable information on these computationally demanding quantum problems, over a wide range of conditions and at a much lower cost. Thus, the study with and the further development of closure approaches, in the real and the Fourier spaces, do appear as targets well-worth pursuing in this context.

Abstract: Large Reasoning Models (LRMs) often exhibit an efficiency-accuracy trade-off, leading to errors from insufficient self-diagnosis and correction during inference. Existing reasoning methods frequently lack internal feedback for refining generated steps. To address this, we propose the Reflective Reasoning System (RRS), an inference-time framework integrating explicit self-diagnosis and self-correction loops into LRM reasoning. RRS strategically employs meta-cognitive tokens to guide the model through initial reasoning, critical self-assessment of potential flaws, and subsequent revision, all without requiring additional training or fine-tuning. Our extensive experiments across diverse open-source models and challenging benchmarks spanning mathematics, code generation, and scientific reasoning demonstrate that RRS consistently achieves significant accuracy improvements compared to baseline models and competitive inference-time enhancement methods. Human evaluations and ablation studies further confirm the efficacy of these distinct self-diagnosis and self-correction phases, highlighting RRS's ability to unlock LRMs' latent reflective capabilities for more robust and accurate solutions.

Abstract: Surface plasmon resonance (SPR) sensors based on nanostructured metasurfaces offer enhanced sensitivity through engineered electromagnetic responses. In this study, we present an analytical–numerical investigation of the plasmonic behavior of gold nanopillar (Au-NP) and nanohole (Au-NH) arrays under both p- and s-polarized illumination, employing the Effective Medium Theory (EMT) in combination with the Transfer Matrix Method (TMM). This framework provides a consistent and computationally efficient description of the macroscopic optical response of multilayer plasmonic systems. For p-polarization, the nanostructure geometry strongly modulates the real and imaginary parts of the effective permittivity, with nanoholes supporting stronger SPR coupling and reduced optical losses compared to nanopillars. Under s-polarization, the effective permittivity remains largely invariant, driven mainly by filling fraction. The analysis reveals that polarization-dependent effects arise from variations in boundary-condition coupling rather than distinct localized resonances, aligning with classical plasmonic theory. Benchmarking against analytical dispersion relations and published experimental data for Au/BK7 systems shows close agreement within ±2°, confirming the physical consistency of EMT–TMM predictions. No full-wave simulations or experiments are presented; all results derive from analytical-numerical modeling. Rather than proposing new excitation mechanisms, this study provides a validated theoretical framework for understanding how polarization and nanostructural filling fraction jointly modulate SPR coupling in thin-film metasurfaces. The results offer a foundation for rational design and optimization of plasmonic coatings and SPR sensors with tunable surface sensitivity.

Abstract:

Brain health monitoring is increasingly essential as modern cognitive load, stress, and lifestyle pressures contribute to widespread neural instability. The paper introduces BrainTwin, a next-generation cognitive digital twin that integrates advanced MRI analytics for comprehensive neuro-oncological assessment with real-time EEG–based brain health intelligence.Structural analysis is driven by an Enhanced Vision Transformer (ViT++), which improves spatial representation and boundary localization, achieving more accurate tumor prediction than conventional models. Extracted tumor volume forms the baseline for short-horizon tumor progression modeling. Parallel to MRI analysis, continuous EEG signals are captured through an in-house wearable skullcap, preprocessed using Edge AI on a Hailo Toolkit–enabled Raspberry Pi 5 for low-latency denoising and secure cloud transmission. Pre-processed EEG packets are authenticated at the fog layer ensuring secure and reliable cloud transfer, enabling significant load reduction in the edge and cloud nodes. In the digital twin, EEG characteristics offer real-time functional monitoring through dynamic brain-wave analysis,while a BiLSTM classifier distinguishes relaxed, stress, and fatigue states. Unlike static MRI imaging, EEG provides real-time brain health monitoring. The Brain-Twin performs EEG–MRI fusion, co-relating functional EEG metrics with ViT++ structural embeddings to produce a single risk score that can be interpreted by clinicians to determine brain vulnerability to future diseases. Explainable artificial intelligence (XAI) provides clinical interpretability through Gradient weighted class activation mapping (Grad-CAM) heatmaps, which are used to interpret ViT++ decisions and are visualized on a 3D interactive brain model to allow more in-depth inspection of spatial details. The evaluation metrics demonstrate a BiLSTM macro-F1 of 0.94 (Precision/ Recall/ F1: Relaxed 0.96, Stress 0.93, Fatigue 0.92) and ViT++ MRI accuracy of 96% outperforming baseline architectures. These results demonstrate BrainTwin’s reliability, interpretability, and clinical utility as an integrated digital companion for tumor assessment and real-time functional brain monitoring.

Abstract: Choledochal cyst (CC), a congenital biliary anomaly, is associated with recurrent infections, chronic inflammation, and an increased risk of malignancy. Although emerging evidence implicates the biliary microbiome in disease pathophysiology, its developmental dynamics in pediatric CC remain unclear. Using deep metagenomic sequencing and comprehensive functional annotation, this study characterized age-dependent changes in the biliary microbiome of 201 pediatric CC patients stratified into infancy (&lt;1 year), early childhood (1-5 years), and later childhood (5-12 years). We found that while the taxonomic composition and alpha diversity of the microbiota remained conserved across age groups, profound functional remodeling occurred with host development. A core set of microbial species and functional pathways was shared across all ages; however, early childhood (1-5 years) exhibited the greatest number of unique functional genes, metabolic pathways, and carbohydrate-active enzymes, identifying this period as a critical window for microbial metabolic adaptation. Age-specific patterns were also evident in clinically relevant traits: infants (&lt;1 year) harbored the most unique antibiotic resistance and virulence factor genes, whereas the resistome and virulome became more streamlined in older children. These findings establish a paradigm of “taxonomic conservation coupled with functional remodeling” in the CC microbiome and highlight age as a key determinant of microbial community function. This study offers novel insights into the microbial dynamics underlying CC progression and suggests potential age-specific targets for future therapeutic strategies.

Abstract: Despite significant advancement in cancer treatments, therapies with minimal toxicity to healthy cells are still limited. One targetable weakness of cancer cells is their sensitivity to oxidative stress. We find that the combination of two antioxidants—the common food additive tert-butylhydroquinone (tBHQ) and a manganese porphyrin in clinical trials, MnTnBuOE-2-PyP5+—increases oxidative stress and causes apoptotic death in several cancer cell lines, but not in mouse primary fibroblasts. Investigating the mechanism of cell death, MnTnBuOE-2-PyP5+ catalyzes the oxidation of tBHQ, producing the electrophilic quinone tert-butylquinone (tBQ). A critical role for tBQ and its electrophilic character was revealed with the observation that di-tert-butylhydroquinone (dtBHQ) in combination with MnTnBuOE-2-PyP5+ causes no observable oxidative stress and is non-toxic, despite rapid oxidation to di-tert-butylquinone (dtBQ), a non-electrophilic quinone. Cell death from the combination of tBHQ and MnTnBuOE-2-PyP5+ is completely dependent on the generation of hydrogen peroxide, as shown by the inclusion of catalase. This system, in which two non-toxic molecules in combination cause specific toxicity to cancer cells, is a potential means to kill cancer cells in a targeted manner.