Abstract: This study presents a fundamental validation of an AI-based impact analysis framework for wooden structures, aiming to support efficient and automated engineering judgment in seismic design. Focusing on a single-story residential building, the proposed method quantitatively evaluates the influence of individual seismic elements and their spatial lo-cations on structural response. Numerical time-history analyses were conducted using a detailed three-dimensional nonlinear model, and parametric variations of stiffness and strength were systematically generated using an orthogonal array. Machine learning models were then trained to capture the relationship between these parameters and seis-mic responses, and explainable artificial intelligence (XAI) techniques were applied to in-terpret parameter influence. The results demonstrated that wall elements oriented parallel to the target inter-story drift consistently exhibited dominant influence, which is consistent with structural engineer-ing knowledge. In addition, model comparison revealed that linear regression achieved high accuracy in the elastic response range, while Gradient Boosting outperformed other models under strong excitation conditions involving plastic behavior. This difference re-flects the transition from approximately linear to highly nonlinear structural response. These findings suggest that a hybrid modeling strategy combining interpretable linear models and flexible nonlinear models is effective for impact analysis. Overall, this fundamental study demonstrates that the proposed AI-based framework provides a transparent, rational, and time-efficient tool for seismic performance evalua-tion of wooden structures, bridging data-driven analysis and practical engineering deci-sion-making.

Abstract: Background/Objectives: Many products that claim to have anti-aging effects have been reported, but their relative potency is not clear. In this study, in vitro replicative lifespan extension (RLE) activity of various groups of physiologically active substances was compared by updated “overlay method”. Methods: Human dermal and periodontal ligament fibroblasts (HDFa, HPLF) were inoculated into the inner 60 wells of 96-well microplate, surround by sterile water to prevent the water evaporation. At Day 1 and Day 8, the cells were overlayed with wide ranges of concentrations (0.01~100 µM) of samples without medium change. Viable cell number was measured by MTT method at Day 15, and then corrected for the variation of cell growth due to the location of inoculated cells. RLE value was calculated as the maximum cell proliferation rate relative to the control. Results: Cell density of HDFa and HPLF at subculture decreased with the passage number, and their growth was stopped at 56 or 85 population doubling levels (PDLs), respectively. Hydrocortisone showed the highest RIE values among six hormones, followed by 3 plant extracts, sodium ascorbate and quercetin. On the other hand, other antioxidants, chlorogenic acid, phenylpropanoids, vanilloids, bacterial products showed little or no RLE effects. However, for HPLF cells, hydrocortisone did not show RLE effects while oxytocin showed slight stimulation. Conclusions: When differences in proliferation due to cell seeding position were corrected, biphasic dose response curve of most of the compounds significantly reduced. The present study suggests the significant role of hormone for the regulation of long-term aging process.

Abstract: Measurement models that have a chemical composition as one of the arguments require special attention when used with the law of propagation of uncertainty from the Guide to the expression of uncertainty in measurement. The constraint that the amount fractions in a composition add exactly to unity does not only affect the covariance matrix associated with the composition, but also impacts the differentiation of the measurement model to obtain the expressions and values of the sensitivity coefficients. Differentiating the measurement model with respect to each variable individually is not possible as it involves evaluating the model for infeasible inputs, leading to an undefined output. In this work, a numerical method for constrained partial derivatives is presented, enabling using the law of propagation of uncertainty for measurement models with compositions as one of their arguments. The numerical method enables treating the measurement model as a black box and using it with measurement models in the form an algorithm. The numerical method is demonstrated by showing how the uncertainty associated with composition, temperature and pressure can be propagated through an equation of state, in this case the GERG-2008 equation of state. It is shown that this propagation can be done in a few simple steps, requiring only a valid implementation of the measurement model that provides an output value for given input quantities. The numerical differentiation method applies in principle to all differentiable functions of a composition.

Abstract: Self-compacting concrete (SCC) offers significant advantages in construction due to its superior workability; however, optimizing SCC mixture design remains challenging because of complex nonlinear material interactions and increasing sustainability requirements. This study proposes an integrated, sustainability-oriented framework that combines machine learning (ML), SHapley Additive exPlanations (SHAP), and multi-objective optimization to improve SCC mixture design. A large and heterogeneous global dataset, compiled from 156 peer-reviewed studies and enhanced through a structured three-stage data augmentation strategy, was used to develop robust predictive models for key fresh-state properties. An optimized XGBoost model demonstrated high predictive performance, achieving coefficients of determination of R² = 0.835 for slump flow and R² = 0.828 for T50 time, with strong generalization to industrial SCC datasets. SHAP-based interpretability analysis identified the water-to-binder ratio and superplasticizer dosage as the dominant factors governing fresh-state behavior, providing physically meaningful insights into mixture performance. A cradle-to-gate life cycle assessment was integrated within a multi-objective genetic algorithm to simultaneously minimize embodied CO₂ emissions and material costs while satisfying workability constraints. The resulting Pareto-optimal mixtures achieved up to 3.9% reduction in embodied CO₂ emissions compared to conventional SCC designs without compromising performance. External validation using industrial data confirms the practical reliability and transferability of the proposed framework. Overall, this study presents an interpretable and scalable AI-driven approach for the sustainable optimization of SCC mixture design.

Abstract: Climate change and altered hydrological regimes are restructuring wetland habitats globally, triggering cascading effects on colonial waterbirds. This study investigates how environmental drivers, including thermal anomalies, water level fluctuations, and aqueous surface extent influence the distribution, size, and diversity of waterbird colonies in the Danube Delta Biosphere Reserve. We integrated colony census data (2016–2023) with remote sensing-derived habitat metrics, in-situ meteorological and hydrological measurements to model site occupancy dynamics using Generalized Linear Mixed Models. Our results indicate that elevated early spring temperatures and water level variability are the primary determinants of nesting success. Spatial analysis revealed a heterogeneous response to hydrological stress: while the westernmost colony exhibited high site fidelity due to its proximity to persistent aquatic surfaces, the central colonies suffered severe declines or local extirpation during extreme drought periods (2020–2022). A discernible eastward shift in bird assemblages was observed toward zones with superior hydrological connectivity and proximity to anthropogenic hubs, suggesting remarkable behavioral plasticity. We propose prioritizing the investigation of sustainable solutions for lacustrine threshold regulation to maintain critical shallow-water foraging zones. This integrative framework provides essential insights for the long-term conservation of deltaic ecosystems, highlighting the pivotal role of remote sensing.

Abstract:

b-thalassemia and sickle cell disease are two inherited hematological diseases due to defective hemoglobin synthesis or to the production of hemoglobin with altered properties. These two conditions have prolonged survival with modern support therapies, albeit life-long, complex, expensive and resources-consuming. Studies carried out in the last three decades have shown that allogeneic hematopoietic stem cell transplantation (allo-HSCT) and gene therapy may offer a curative approach for these diseases. Allo-HSCT should be performed early in life to reduce disease-related complications like irreversible tissue damage due to iron overload in patients with transfusion-dependent b-thalassemia (TDT) and systemic vasculopathy in patients with sickle cell disease (SCD). HSCTs from a matched-sibling donor or a matched-unrelated donor represent the best therapeutic option; however, haplo-identical HSCT in both TDT and SCD is now increasingly performed as a valuable and viable option for a larger number of these patients. An alternative curative strategy is based on gene therapy. These curative approaches, particularly those of gene therapy, are available only in a part of the world. Gene therapy diffusion is strongly limited by its high technological and infrastructure requirements and its very high cost. Criteria must be defined for the optimal selection of TDT and SCD patients for allo-HSCT or gene therapy.

Abstract:

Background/Objectives: Calcium and magnesium are essential minerals involved in neuromuscular function, bone metabolism, and fall prevention. Deficiency of these minerals contributes to musculoskeletal fragility, including osteoporosis, sarcopenia, and falls. While dairy products are the traditional source of dietary calcium, mineral waters naturally rich in calcium and magnesium offer a highly bioavailable alternative. This study evaluated the efficacy of daily intake of a naturally calcium‑ and magnesium‑rich mineral water compared with low-mineral water on fall risk, muscle mass, and muscle function in adults aged ≥50 years, with or without osteosarcopenia. Methods: In this 12‑month, randomized, double‑blind, controlled trial, 98 participants were assigned to consume 1 L/day of either SG9 (mineral water with high calcium and magnesium content) or J66 (low-mineral water). Outcomes included incidence of falls (primary endpoint), appendicular skeletal muscle mass (ASMM), muscle strength, physical performance, biochemical markers, and patient-reported measures. Assessments were performed at baseline, 6 months, and 12 months following CONSORT guidelines. Results: Ninety-eight participants (mean age ~63 years) were randomized and completed the 12-month follow-up. At 6 months, the incidence of falls was significantly lower in the SG9 group compared with the J66 group (relative risk reduction 82%; RR = 0.18, 95% CI 0.04–0.88; p < 0.05), whereas no significant between-group difference was observed at 12 months. Appendicular skeletal muscle mass indexed to height (ASMM/h²) was significantly higher in the SG9 group at 12 months (p = 0.005). In participants with osteosarcopenia, SG9 intake was associated with a consistent improvement in ASMM/h² at 6 and 12 months (p = 0.012 and p = 0.005, respectively). No significant between-group differences were detected in physical performance scores, biochemical markers, or quality-of-life measures. Conclusions: Daily consumption of calcium- and magnesium rich mineral water reduced fall risk and improved muscle mass in adults over 50 years, with or without osteosarcopenia. These findings support the role of calcium- and magnesium-rich mineral water as a complementary nutritional strategy for musculoskeletal health and fragility prevention.

Abstract: The aim of this study was to synthesise qualitative evidence from family members’ experiences of long-term home care for older adults provided by live-in migrant caregivers. We conducted a systematic literature review with meta-synthesis using four online databases. The search included articles published between January 2015 and November 2025 on the CINAHL, PubMed, SCOPUS and WOS databases. Thematic synthesis of qualitative data was conducted. Results: eleven papers from six different countries fulfilled the criteria and were included in the thematic synthesis. Four main themes were identified: 1. Not an easy decision. 2. A stranger at the heart of family life. 3. Two worlds that meet and need each other. 4. Improving the integration of migrant caregivers into family life. Hiring migrant caregivers to provide long-term home care to older adults can ease the burden on family caregivers, but it is an additional source of stress and worry. The family members of older adults call for greater financial and institutional support, as well as the involvement of social and health services in the training and education of families and migrant caregivers. La capacidad de negociación y llegar a consensos entre OAs, familiares y cuidadores migrantes residents, son claves para mejorar la convivencia y cuidados a OAs. Negotiation skills and the ability to reach consensus between older adults (OAs), family members and resident migrant caregivers are key to improving cohabitation and care for OAs. The primary goal is the well-being of the OAs, which involves overcoming cultural prejudices, learning together in response to the new situation, improving caregivers’ training, and ensuring continuity of care.

Abstract: Introduction: Post-Orgasmic Illness Syndrome (POIS) is a rare, debilitating condition characterized by a constellation of systemic, allergic, cognitive, and emotional symptoms occurring shortly after orgasm. These symptoms severely impact sexual functioning and quality of life. Despite its profound effects, POIS remains poorly understood, underrecognized, and without a definitive treatment.Case Presentation: We report two cases observed in a specialized sexology consultation. The first case concerns a 49-year-old married man presenting with secondary-onset POIS. His symptoms, including muscle rigidity, genital burning, and profound fatigue, consistently emerged after ejaculation, whether during intercourse or masturbation. Extensive investigations revealed no abnormalities. Management included pharmacotherapy (fluvoxamine 50 mg daily and cyclobenzaprine 10 mg daily), psychological support, and sexual counseling. The patient adapted sexual practices, including adopting a lateral coital position to minimize exposure to seminal fluid. Although symptoms persisted after each orgasm, these interventions led to an overall improvement in quality of life.The second case involves an 18-year-old male experiencing primary-onset POIS since puberty. He developed severe fatigue, cognitive dysfunction, low-grade fever sensations, irritability, and abdominal discomfort following ejaculation. Comprehensive evaluations excluded infectious and allergic causes. Notably, the patient required psychiatric hospitalization after a severe behavioral disturbance characterized by agitation and hetero-aggressiveness post-orgasm. Treatment involved antipsychotic and antidepressant therapy, psychological support and psychoeducation.Conclusion: These cases highlight the heterogeneous and distressing nature of POIS and its profound impact on sexual health and psychosocial functioning. Multidisciplinary management combining pharmacological, psychological, and sexual interventions may yield partial symptom control and improve patients' quality of life. Increased clinician awareness and further research are urgently needed to advance understanding, diagnosis, and therapeutic strategies for POIS.

Abstract: In Candida species, including Candidozyma auris (formerly Candida auris), overexpression of efflux pumps is a well-established mechanism of antifungal resistance. However, accumulating evidence indicates that impaired drug import may also significantly contribute to reduced antifungal susceptibility. Sugar importers, historically viewed solely as hexose transporters (HGTs), are now emerging as potential indirect modulators of antifungal uptake. Here, we performed a comprehensive inventory and functional analysis of the HGT family in C. auris to assess its contribution to antifungal import. Phylogenetic analyses revealed that C. auris HGTs are more closely related to those of Candida albicans (C. albicans) than Saccharomyces cerevisiae (S. cerevisiae). All HGT genes showed basal expression, with several significantly downregulated upon fluconazole (FLC) exposure. To establish functional relevance, we generated a mini-library of HGT deletion mutants. Notably, the Δhgt13 strain exhibited markedly increased FLC resistance, concomitant with reduced intracellular FLC accumulation and decreased membrane permeability. Consistently, molecular docking and molecular dynamics simulations demonstrated strong and stable interactions between FLC and Hgt13p. Together, these findings implicate Hgt13p as a key determinant of FLC import and membrane permeability, revealing reduced FLC import could also contribute to antifungal resistance in C. auris.

Abstract: Individual phishing URLs are often short-lived, but underlying infrastructure such as domains, IP addresses, and certificates exhibits recurring patterns. We propose a graph-based detection framework that models a heterogeneous network comprising domains, IP addresses, TLS certificates, and registrars. Node embeddings are learned using a relational graph convolutional network (R-GCN) trained on 3.1 million domains, of which 210,000 are labeled as phishing-related. Structural features such as shared-IP communities, certificate reuse, and registrar clusters are incorporated into the model. The graph-based detector is capable of flagging suspicious domains before they are widely used in attacks; in a retrospective study, it identifies 73% of phishing domains at least 24 hours prior to first appearance in blacklists. Compared with domain-lexical baselines, the method improves precision at 90% recall by 15.6 percentage points. These findings demonstrate that infrastructure-level graph modeling provides complementary signals to content-based phishing detection and can enhance proactive defense.

Abstract: The growing emphasis on environmental sustainability and the need for advanced manufacturing methods have accelerated progress in materials processing. Aluminum powder metallurgy (APM) is particularly promising due to aluminum’s low density, high strength-to-weight ratio, and the inherent benefits of the powder metallurgy (PM) process. However, the corrosion resistance of sintered aluminum components remains a significant concern. In this study, shot peening (SP) was employed as a surface modification technique to improve the corrosion behavior of Alumix 321 PM alloy. Sampleas of the as-sintered and shot peened Alumix 321 PM alloy, together with the wrought alloy counterpart AA6061, were characterized using non-contact optical profilometry, optical microscopy (OM), and scanning electron microscopy (SEM). Corrosion performance was evaluated in 3.5 wt.% NaCl solution using Tafel extrapolation (TE), cyclic polarization (CP), stair-step polarization (SSP), and electrochemical impedance spectroscopy (EIS). The results revealed that shot peening increased surface roughness and significantly reduced the corrosion rate from 0.079 mmpy to 0.004 mmpy for the unpeened and peened samples, respectively. While pitting was the dominant corrosion mechanism in the wrought alloy, the PM alloy exhibited a combination of pitting, crevice, and intergranular corrosion. These findings highlight the potential of SP in enhancing the durability of aluminum-based PM components, offering valuable insights for industrial applications.

Abstract: Background: Achilles tendinopathy (AT) is a debilitating condition with limited therapeutic options in patients contraindicated for corticosteroids. Injective collagen has emerged as a promising alternative, yet evidence in fragile populations such as diabetics remains scarce. Objective: Aim of present work was to evaluate the clinical effectiveness of Porcine collagen injections in diabetic patients with chronic AT, looking for the pain reduction and increase of functionality. Methods: Twenty-two diabetic patients with degenerative Achilles tendinopathy unresponsive to treatment were included were splited in two groups, according the type of AT pathology: Insertional (I) and non-insertional (NI). Patients received five weekly peritendinous injections of porcine collagen. Outcome measures included VAS (baseline, post-2nd injection, 1 month, 6 months), VISA-A at 6 months, return-to-activity time and adverse events monitoring. Additional variables included BMI, HbA1c, symptom duration, and previous treatments. Analyses included descriptive statistics, paired t-tests, regression models, and ANOVA tests. Results: All patients completed the protocol with no adverse events. Mean VAS decreased significantly from baseline to 6 months in both AT-I and AT-NI patients (mean delta VAS: 5.1 and 4.4, respectively; p=0.001). Mean delta VISA-A scores were 32.78 and 38.97 in AT-I and AT-NI (p&lt;0.0001) and median return to work (RTW) were 37 and 35 days in AT-I and AT-NI, respectively (p=ns). No significant differences were discovered comparing AT-I vs AT-NI, in terms both VAS and VISA-A variation per ml of injected product (p=ns). Conclusions: Porcine collagen is a safe, effective, and sustainable treatment for Achilles tendinopathy in diabetic patients with both AT-I and AT-NI conditions. This study supports its adoption as a first-line of conservative approach for pain reduction and functionality improvement.

Abstract: Autoimmune rheumatic diseases arise when the immune system transitions from a flexible, self-regulating network into a metabolically and epigenetically fixed inflammatory attractor state. This review synthesizes emerging evidence that immune tolerance is governed by a coupled epigenetic–metabolic axis integrating mitochondrial fitness, chromatin accessibility, redox balance, and nutrient flux across lymphoid, myeloid, and stromal compartments. We examine how chronic cytokine signaling, hypoxia, and oxidative stress destabilize regulatory programs, imprint glycolytic effector states, and remodel enhancer landscapes, thereby sustaining autoreactive circuits even after inflammatory pathways are pharmacologically suppressed. Multi-omic and spatial analyses reveal that pathogenic chromatin architectures, persistent mitochondrial dysfunction, and intercellular metabolite exchange cooperate to establish self-sustaining inflammatory ecosystems in rheumatoid arthritis, systemic lupus erythematosus, systemic sclerosis, and Sjögren’s syndrome. We further highlight therapeutic strategies aimed at tolerance reprogramming, including metabolic correction, chromatin-targeted agents, CAR-Tregs, tolerogenic dendritic cells, and integrative biomarkers that quantify metabolic–epigenetic coherence. By reframing autoimmunity as a disorder of energetic and chromatin desynchronization rather than isolated immune activation, this review outlines a mechanistic path toward durable, drug-free remission through deliberate restoration of the molecular architecture that maintains immune self-recognition.

Abstract: Human action recognition (HAR) remains challenging, particularly for skeleton-based methods due to issues like domain shift and limited deep semantic understanding. Traditional Graph Convolutional Networks often struggle with effective cross-domain adaptation and inferring complex semantic relationships. To address these limitations, we propose CD-SEAFNet, a novel framework meticulously designed to significantly enhance robustness and cross-domain generalization for skeleton-based action recognition. CD-SEAFNet integrates three core modules: an Adaptive Spatio-Temporal Graph Feature Extractor that dynamically learns and adjusts graph structures to capture nuanced spatio-temporal dynamics; a Semantic Context Encoder and Fusion Module which leverages natural language descriptions to inject high-level semantic understanding via a cross-modal adaptive fusion mechanism; and a Domain Alignment and Classification Module that employs adversarial training and contrastive learning to generate domain-invariant, yet discriminative, features. Extensive experiments on the challenging NTU RGB+D datasets demonstrate that CD-SEAFNet consistently outperforms state-of-the-art methods across various evaluation protocols, unequivocally validating the synergistic effectiveness of our adaptive graph structure, semantic enhancement, and robust domain alignment strategies.

Abstract: This paper introduces an enhanced version of Dr.Scratch, a web-based tool for automatically assessing Compu-tational Thinking (CT) skills evident in visual programmingprojects. We propose a fuzzy logic-based scoring framework toaddress limitations in existing rule-based assessment systems.The paper reviews relevant prior initiatives, details the analyticalframework employed to interpret Scratch code, and elucidatesthe computational aspects considered when deriving a CT scorefrom user-created artifacts. Our methodology integrates fuzzyinference to generate continuous, explainable scores across coreCT dimensions. Experimental analysis of over 250 Scratchprojects demonstrates that the fuzzy scoring model providesfiner granularity, better alignment with educator evaluation, andimproved interpretability compared to deterministic approaches.We present preliminary findings from our investigation, discussfuture directions, and address current limitations in automatededucational assessment. The contributions of this work advancethe field of Educational AI toward more nuanced, pedagogicallygrounded computational thinking assessment.

Abstract: This study presents a drone-based method for assessing the condition of road markings from high-resolution imagery acquired by an unmanned aerial vehicle (UAV). A DJI Matrice 300 RTK equipped with a Zenmuse P1 camera is flown over urban road corridors to capture images with centimeter-level ground sampling distance. In contrast to common approaches that rely on vehicle-mounted or street-view cameras, using a UAV reduces survey time and deployment effort while still providing views that are suitable for marking. The flight altitude, overlap, and corridor pattern are chosen to limit occlusions from traffic and building shadows while preserving the resolution required for condition assessment.From these images, the method locates individual markings, assigns a class to each marking, and estimates its level of deterioration. Candidate markings are first detected with YOLOv9 on the UAV imagery. The detections are cropped and segmented, which refines marking boundaries and thin structures. The condition is then estimated at the pixel level by modeling gray-level statistics with kernel density estimation (KDE) and a two-component Gaussian mixture model (GMM) to separate intact and distressed material. Subsequently, we compute a per-instance damage ratio that summarizes the proportion of degraded pixels within each marking. All results are georeferenced to map coordinates using a 3D reference model, allowing visualization on base maps and integration into road asset inventories. Experiments on unseen urban areas report detection performance (precision, recall, mean average precision) and segmentation performance (intersection over union), and analyze the stability of the damage ratio and processing time. The findings indicate that the drone-based method can identify road markings, estimate their condition, and attach each record to geographic space in a way that is useful for inspection scheduling and maintenance planning.

Abstract: Background: Arterial hypertension and increased blood pressure variability (BPV) are major prognostic determinants in patients with ischemic heart disease (IHD). While exercise training is known to improve blood pressure (BP) control, the effects of different combined exercise modalities on BPV in IHD remain poorly defined. This randomized pilot study compared the effects of continuous combined training (CCT; moderate-intensity continuous aerobic exercise plus resistance training) and interval combined training (ICT; high-intensity interval aerobic exercise plus resistance training) on BPV and BP parameters in hypertensive patients with IHD. Methods: Thirty-six clinically stable patients with IHD and hypertension were randomized to CCT or ICT for 12 weeks. Outcomes included short-term BPV assessed by 24-hour ambulatory BP monitoring, resting and 24-hour BP, and exercise capacity. Results: Short-term systolic BPV significantly decreased in the CCT group but remained unchanged in the ICT group: [adjusted between-group difference −2.1 mmHg (95% CI: −4.1 to −0.1; p 0.029]. Resting systolic BP decreased similarly in both groups, whereas no significant changes were observed in 24-hour BP values. Peak oxygen uptake improved in both groups with a greater increase in the ICT group [adjusted between-groups difference +1.7 mL·kg⁻¹·min⁻¹ (95% CI: 0.7 to 2.8); p = 0.032). Conclusion: These findings suggest that, in patients with IHD, continuous combined training may be more effective than interval combined training in reducing short-term BPV, whereas interval training may confer greater improvements in aerobic capacity. Further adequately powered studies are warranted to confirm these results.

Abstract: Growing global populations and the rapid increase in older adults are driving healthcare costs upward. In response, the healthcare system is shifting toward models that allow for continuous monitoring of individuals without requiring hospital ad-mission. Advances in sensing technologies, embedded systems, wireless communica-tion, nanotechnology, and device miniaturization have made it possible to develop smart systems that continuously track human activity. Wearable sensors can monitor physiological indicators and other symptoms, helping to detect unusual or unexpected events. This enables timely assistance when it is needed most. This paper outlines these challenges and reviews recent developments in wearable sensor–based human activity monitoring systems. The focus is on health monitoring applications, including relevant biomarkers, wearable and implantable sensors, estab-lished sensor technologies currently used in healthcare, and the future prospects and challenges involved in researching, developing, and applying these sensors to support widespread use in human health monitoring.

Abstract: Sulforaphane (SFN), an aliphatic isothiocyanate derived from cruciferous vegetables such as broccoli, has emerged as a chemopreventive dietary agent. SFN exerts multifaceted anticancer effects by the activation of the nuclear factor erythroid 2-related factor 2 (Nrf2)–antioxidant response element (ARE) pathways, inhibition of histone deacetylases (HDACs) and hypoxia-inducible factor-1α (HIF-1α), and regulation of apoptosis and autophagy. Epidemiological studies have consistently associated cruciferous vegetable intake with reduced cancer risk, while mechanistic research has elucidated the capacity of SFN to modulate redox balance, detoxification pathways, and epigenetic processes. Recent clinical trials have further demonstrated its potential to reduce carcinogenic biomarker levels and support metabolic detoxification. This review integrates evidence from epidemiological observations, molecular mechanisms, and clinical studies to provide a comprehensive understanding of the role of SFN in cancer prevention and therapy. Finally, translational challenges, including limited bioavailability, dose optimization, and standardization of broccoli-derived preparations, are discussed as critical factors for advancing SFN from bench to bedside.