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In the modern era, the use of blockchain technology has been growing rapidly, where Ethereum smart contracts play an important role in securing decentralized application systems. However, these smart contracts are also susceptible to a large number of vulnerabilities, which pose significant threats to intelligent systems and IoT applications, leading to data breaches and financial losses. Traditional detection techniques, such as manual analysis and static automated tools, suffer from high false positives and undetected security vulnerabilities. To address these problems, this paper proposes an Artificial Intelligence (AI)-based security framework that integrates Generative Adversarial Network (GAN)-based feature selection and deep learning techniques to classify and detect malware attacks on smart contract execution in the blockchain decentralized network. After an exhaustive pre-processing phase yielding a dataset of 40,000 malware and benign samples, the proposed model is evaluated and compared with related studies on the basis of a number of performance metrics including training accuracy, training loss, and classification metrics (accuracy, precision, recall, and F1-score). Our combined approach achieved a remarkable accuracy of 97.6%, demonstrating its effectiveness in detecting malware and protecting blockchain systems. Full article

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16 pages, 1651 KiB

Open AccessArticle

Modular Pipeline for Text Recognition in Early Printed Books Using Kraken and ByT5

by Yahya Momtaz, Lorenza Laccetti and Guido Russo

Electronics 2025, 14(15), 3083; https://doi.org/10.3390/electronics14153083 (registering DOI) - 1 Aug 2025

Abstract

Early printed books, particularly incunabula, are invaluable archives of the beginnings of modern educational systems. However, their complex layouts, antique typefaces, and page degradation caused by bleed-through and ink fading pose significant challenges for automatic transcription. In this work, we present a modular pipeline that addresses these problems by combining modern layout analysis and language modeling techniques. The pipeline begins with historical layout-aware text segmentation using Kraken, a neural network-based tool tailored for early typographic structures. Initial optical character recognition (OCR) is then performed with Kraken’s recognition engine, followed by post-correction using a fine-tuned ByT5 transformer model trained on manually aligned line-level data. By learning to map noisy OCR outputs to verified transcriptions, the model substantially improves recognition quality. The pipeline also integrates a preprocessing stage based on our previous work on bleed-through removal using robust statistical filters, including non-local means, Gaussian mixtures, biweight estimation, and Gaussian blur. This step enhances the legibility of degraded pages prior to OCR. The entire solution is open, modular, and scalable, supporting long-term preservation and improved accessibility of cultural heritage materials. Experimental results on 15th-century incunabula show a reduction in the Character Error Rate (CER) from around 38% to around 15% and an increase in the Bilingual Evaluation Understudy (BLEU) score from 22 to 44, confirming the effectiveness of our approach. This work demonstrates the potential of integrating transformer-based correction with layout-aware segmentation to enhance OCR accuracy in digital humanities applications. Full article

(This article belongs to the Special Issue Electronics and Computer Science for Cultural Heritage: Advancements, Preservation, and Applications, 2nd Edition)

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24 pages, 3553 KiB

Open AccessArticle

A Hybrid Artificial Intelligence Framework for Melanoma Diagnosis Using Histopathological Images

by Alberto Nogales, María C. Garrido, Alfredo Guitian, Jose-Luis Rodriguez-Peralto, Carlos Prados Villanueva, Delia Díaz-Prieto and Álvaro J. García-Tejedor

Technologies 2025, 13(8), 330; https://doi.org/10.3390/technologies13080330 (registering DOI) - 1 Aug 2025

Abstract

Cancer remains one of the most significant global health challenges due to its high mortality rates and the limited understanding of its progression. Early diagnosis is critical to improving patient outcomes, especially in skin cancer, where timely detection can significantly enhance recovery rates. Histopathological analysis is a widely used diagnostic method, but it is a time-consuming process that heavily depends on the expertise of highly trained specialists. Recent advances in Artificial Intelligence have shown promising results in image classification, highlighting its potential as a supportive tool for medical diagnosis. In this study, we explore the application of hybrid Artificial Intelligence models for melanoma diagnosis using histopathological images. The dataset used consisted of 506 histopathological images, from which 313 curated images were selected after quality control and preprocessing. We propose a two-step framework that employs an Autoencoder for dimensionality reduction and feature extraction of the images, followed by a classification algorithm to distinguish between melanoma and nevus, trained on the extracted feature vectors from the bottleneck of the Autoencoder. We evaluated Support Vector Machines, Random Forest, Multilayer Perceptron, and K-Nearest Neighbours as classifiers. Among these, the combinations of Autoencoder with K-Nearest Neighbours achieved the best performance and inference time, reaching an average accuracy of approximately 97.95% on the test set and requiring 3.44 min per diagnosis. The baseline comparison results were consistent, demonstrating strong generalisation and outperforming the other models by 2 to 13 percentage points. Full article

(This article belongs to the Special Issue Application of Artificial Intelligence in Medical Image Analysis)

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20 pages, 3519 KiB

Open AccessArticle

Hylocereus polyrhizus Pulp Residues Polysaccharide Alleviates High-Fat Diet-Induced Obesity by Modulating Intestinal Mucus Secretion and Glycosylation

by Guanghui Li, Kit-Leong Cheong, Yunhua He, Ahluk Liew, Jiaxuan Huang, Chen Huang, Saiyi Zhong and Malairaj Sathuvan

Foods 2025, 14(15), 2708; https://doi.org/10.3390/foods14152708 (registering DOI) - 1 Aug 2025

Abstract

Although Hylocereus polyrhizus pulp residues polysaccharides (HPPP) have shown potential in improving metabolic disorders and intestinal barrier function, the mechanism by which they exert their effects through regulating O-glycosylation modifications in the mucus layer remains unclear. Therefore, this study established a HFD-induced obese colitis mouse model (n = 5 per group) and combined nano-capillary liquid chromatography-tandem mass spectrometry (nanoLC-MS/MS) technology to quantitatively analyze the dynamic changes in O-glycosylation. Additionally, through quantitative O-glycosylation proteomics and whole-proteome analysis, we identified 155 specifically altered O-glycosylation sites in colon tissue, with the glycosylation modification level of the MUC2 core protein increased by approximately 2.1-fold. The results indicate that HPPP alleviates colonic mucosal damage by regulating interactions between mucus O-glycosylation. Overall, we demonstrated that HPPP increases HFD-induced O-glycosylation sites, improves intestinal mucosal structure in obese mice, and provides protective effects against obesity-induced intestinal mucosal damage. Full article

(This article belongs to the Special Issue Exploring the Nutritional and Functional Properties of Plant Food-Derived Bioactive Components)

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36 pages, 1921 KiB

Open AccessArticle

Policy Synergies for Advancing Energy–Environmental Productivity and Sustainable Urban Development: Empirical Evidence from China’s Dual-Pilot Energy Policies

by Si Zhang and Xiaodong Zhu

Sustainability 2025, 17(15), 6992; https://doi.org/10.3390/su17156992 (registering DOI) - 1 Aug 2025

Abstract

Achieving synergies between government-led and market-based policy instruments is critical to advancing Energy–Environmental Productivity and Sustainable Urban Development. This study investigates the effects of China’s dual-pilot energy policies (New Energy Demonstration Cities (NEDCs) and Energy Consumption Permit Trading (ECPT)) on urban environmental productivity (UEP) across 279 prefecture-level cities from 2006 to 2023. Utilizing a Non-Radial Directional Distance Function (NDDF) approach, combined with Difference-in-Differences (DID) estimation and spatial econometric models, the analysis reveals that these synergistic policies significantly enhance both comprehensive and net measures of UEP. Mechanism analysis highlights the roles of industrial restructuring, technological innovation, and energy transition in driving these improvements, while heterogeneity analysis indicates varying effects across different city types. Spatial spillover analysis further demonstrates that policy impacts extend beyond targeted cities, contributing to broader regional gains in UEP. These findings offer important insights for the design of integrated energy and environmental policies and support progress toward key Sustainable Development Goals (SDG 7, SDG 11, and SDG 12). Full article

(This article belongs to the Special Issue Environmental Economics, Productivity and Efficiency: Synergies for Sustainable Development)

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25 pages, 5899 KiB

Open AccessReview

Non-Invasive Medical Imaging in the Evaluation of Composite Scaffolds in Tissue Engineering: Methods, Challenges, and Future Directions

by Samira Farjaminejad, Rosana Farjaminejad, Pedram Sotoudehbagha and Mehdi Razavi

J. Compos. Sci. 2025, 9(8), 400; https://doi.org/10.3390/jcs9080400 (registering DOI) - 1 Aug 2025

Abstract

Tissue-engineered scaffolds, particularly composite scaffolds composed of polymers combined with ceramics, bioactive glasses, or nanomaterials, play a vital role in regenerative medicine by providing structural and biological support for tissue repair. As scaffold designs grow increasingly complex, the need for non-invasive imaging modalities capable of monitoring scaffold integration, degradation, and tissue regeneration in real-time has become critical. This review summarizes current non-invasive imaging techniques used to evaluate tissue-engineered constructs, including optical methods such as near-infrared fluorescence imaging (NIR), optical coherence tomography (OCT), and photoacoustic imaging (PAI); magnetic resonance imaging (MRI); X-ray-based approaches like computed tomography (CT); and ultrasound-based modalities. It discusses the unique advantages and limitations of each modality. Finally, the review identifies major challenges—including limited imaging depth, resolution trade-offs, and regulatory hurdles—and proposes future directions to enhance translational readiness and clinical adoption of imaging-guided tissue engineering (TE). Emerging prospects such as multimodal platforms and artificial intelligence (AI) assisted image analysis hold promise for improving precision, scalability, and clinical relevance in scaffold monitoring. Full article

(This article belongs to the Special Issue Sustainable Biocomposites, 3rd Edition)

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20 pages, 1379 KiB

Open AccessArticle

Combined Effects of Polyethylene and Bordeaux Mixture on the Soil–Plant System: Phytotoxicity, Copper Accumulation and Changes in Microbial Abundance

by Silvia Romeo-Río, Huguette Meta Foguieng, Antía Gómez-Armesto, Manuel Conde-Cid, David Fernández-Calviño and Andrés Rodríguez-Seijo

Agriculture 2025, 15(15), 1657; https://doi.org/10.3390/agriculture15151657 - 1 Aug 2025

Abstract

Greenhouses have positively impacted plant production by allowing the cultivation of different crops per year. However, the accumulation of agricultural plastics, potentially contaminated with agrochemicals, raises environmental concerns. This work evaluates the combined effect of Bordeaux mixture and low-density polyethylene (LDPE) microplastics (<5 mm) on the growth of lettuce (Lactuca sativa L.) and soil microbial communities. Different levels of Bordeaux mixture (0, 100 and 500 mg kg⁻¹), equivalent to Cu(II) concentrations (0, 17 and 83 mg kg⁻¹), LDPE microplastics (0, 1% and 5%) and their combination were selected. After 28 days of growth, biometric and photosynthetic parameters, Cu uptake, and soil microbial responses were evaluated. Plant germination and growth were not significantly affected by the combination of Cu and plastics. However, individual Cu treatments influenced root and shoot length and biomass. Chlorophyll and carotenoid concentrations increased with Cu addition, although the differences were not statistically significant. Phospholipid fatty acid (PLFA) analysis revealed a reduction in microbial biomass at the highest Cu dose, whereas LDPE alone showed limited effects and may reduce Cu bioavailability. These results suggest that even at the highest concentration added, Cu can act as a plant nutrient, while the combination of Cu–plastics showed varying effects on plant growth and soil microbial communities. Full article

(This article belongs to the Special Issue Impacts of Emerging Agricultural Pollutants on Environmental Health)

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20 pages, 4765 KiB

Open AccessArticle

Ultrasonic EDM for External Cylindrical Surface Machining with Graphite Electrodes: Horn Design and Hybrid NSGA-II–AHP Optimization of MRR and Ra

by Van-Thanh Dinh, Thu-Quy Le, Duc-Binh Vu, Ngoc-Pi Vu and Tat-Loi Mai

Machines 2025, 13(8), 675; https://doi.org/10.3390/machines13080675 (registering DOI) - 1 Aug 2025

Abstract

This study presents the first investigation into the application of ultrasonic vibration-assisted electrical discharge machining (UV-EDM) using graphite electrodes for external cylindrical surface machining—an essential surface in the production of tablet punches and sheet metal-forming dies. A custom ultrasonic horn was designed and fabricated using 90CrSi material to operate effectively at a resonant frequency of 20 kHz, ensuring stable vibration transmission throughout the machining process. A Box–Behnken experimental design was employed to explore the effects of five process parameters—vibration amplitude (A), pulse-on time (Ton), pulse-off time (Toff), discharge current (Ip), and servo voltage (SV)—on two key performance indicators: material removal rate (MRR) and surface roughness (Ra). The optimization process was conducted in two stages: single-objective analysis to maximize MRR while ensuring Ra < 4 µm, followed by a hybrid multi-objective approach combining NSGA-II and the Analytic Hierarchy Process (AHP). The optimal solution achieved a high MRR of 9.28 g/h while maintaining Ra below the critical surface finish threshold, thus meeting the practical requirements for punch surface quality. The findings confirm the effectiveness of the proposed horn design and hybrid optimization strategy, offering a new direction for enhancing productivity and surface integrity in cylindrical EDM applications using graphite electrodes. Full article

(This article belongs to the Section Advanced Manufacturing)

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16 pages, 306 KiB

Open AccessArticle

Antibiotic Use in Pediatric Care in Ghana: A Call to Action for Stewardship in This Population

by Israel Abebrese Sefah, Dennis Komla Bosrotsi, Kwame Ohene Buabeng, Brian Godman and Varsha Bangalee

Antibiotics 2025, 14(8), 779; https://doi.org/10.3390/antibiotics14080779 (registering DOI) - 1 Aug 2025

Abstract

Background/Objectives: Antibiotic use is common among hospitalized pediatric patients. However, inappropriate use, including excessive use of Watch antibiotics, can contribute to antimicrobial resistance, adverse events, and increased healthcare costs. Consequently, there is a need to continually assess their usage among this vulnerable population. This was the objective behind this study. Methods: The medical records of all pediatric patients (under 12 years) admitted and treated with antibiotics at a Ghanaian Teaching Hospital between January 2022 and March 2022 were extracted from the hospital’s electronic database. The prevalence and appropriateness of antibiotic use were based on antibiotic choices compared with current guidelines. Influencing factors were also assessed. Results: Of the 410 admitted patients, 319 (77.80%) received at least one antibiotic. The majority (68.65%; n = 219/319) were between 0 and 2 years, and males (54.55%; n = 174/319). Ceftriaxone was the most commonly prescribed antibiotic (20.69%; n = 66/319), and most of the systemic antibiotics used belonged to the WHO Access and Watch groups, including a combination of Access and Watch groups (42.90%; n = 136/319). Neonatal sepsis (24.14%; n = 77/319) and pneumonia (14.42%; n = 46/319) were the most common diagnoses treated with antibiotics. Antibiotic appropriateness was 42.32% (n = 135/319). Multivariate analysis revealed ceftriaxone prescriptions (aOR = 0.12; CI = 0.02–0.95; p-value = 0.044) and surgical prophylaxis (aOR = 0.07; CI = 0.01–0.42; p-value = 0.004) were associated with reduced antibiotic appropriateness, while a pneumonia diagnosis appreciably increased this (aOR = 15.38; CI = 3.30–71.62; p-value < 0.001). Conclusions: There was high and suboptimal usage of antibiotics among hospitalized pediatric patients in this leading hospital. Antibiotic appropriateness was influenced by antibiotic type, diagnosis, and surgical prophylaxis. Targeted interventions, including education, are needed to improve antibiotic utilization in this setting in Ghana and, subsequently, in ambulatory care. Full article

(This article belongs to the Special Issue Knowledge, Attitudes and Practices of Antimicrobial Resistance and Stewardship in Primary Care Setting: From Understanding to Informing Interventions)

26 pages, 1263 KiB

Open AccessArticle

Identifying Key Digital Enablers for Urban Carbon Reduction: A Strategy-Focused Study of AI, Big Data, and Blockchain Technologies

by Rongyu Pei, Meiqi Chen and Ziyang Liu

Systems 2025, 13(8), 646; https://doi.org/10.3390/systems13080646 (registering DOI) - 1 Aug 2025

Abstract

The integration of artificial intelligence (AI), big data analytics, and blockchain technologies within the digital economy presents transformative opportunities for promoting low-carbon urban development. However, a systematic understanding of how these digital innovations influence urban carbon mitigation remains limited. This study addresses this gap by proposing two research questions (RQs): (1) What are the key success factors for artificial intelligence, big data, and blockchain in urban carbon emission reduction? (2) How do these technologies interact and support the transition to low-carbon cities? To answer these questions, the study employs a hybrid methodological framework combining the decision-making trial and evaluation laboratory (DEMATEL) and interpretive structural modeling (ISM) techniques. The data were collected through structured expert questionnaires, enabling the identification and hierarchical analysis of twelve critical success factors (CSFs). Grounded in sustainability transitions theory and institutional theory, the CSFs are categorized into three dimensions: (1) digital infrastructure and technological applications; (2) digital transformation of industry and economy; (3) sustainable urban governance. The results reveal that e-commerce and sustainable logistics, the adoption of the circular economy, and cross-sector collaboration are the most influential drivers of digital-enabled decarbonization, while foundational elements such as smart energy systems and digital infrastructure act as key enablers. The DEMATEL-ISM approach facilitates a system-level understanding of the causal relationships and strategic priorities among the CSFs, offering actionable insights for urban planners, policymakers, and stakeholders committed to sustainable digital transformation and carbon neutrality. Full article

(This article belongs to the Special Issue Operation Optimization and Performance Assessment of Complex Social-Economic Systems)

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33 pages, 870 KiB

Open AccessArticle

Decarbonizing Urban Transport: Policies and Challenges in Bucharest

by Adina-Petruța Pavel and Adina-Roxana Munteanu

Future Transp. 2025, 5(3), 99; https://doi.org/10.3390/futuretransp5030099 (registering DOI) - 1 Aug 2025

Abstract

Urban transport is a key driver of greenhouse gas emissions in Europe, making its decarbonization essential to achieving EU climate neutrality targets. This study examines how European strategies, such as the Green Deal, the Sustainable and Smart Mobility Strategy, and the Fit for 55 package, are reflected in Romania’s transport policies, with a focus on implementation challenges and urban outcomes in Bucharest. By combining policy analysis, stakeholder mapping, and comparative mobility indicators, the paper critically assesses Bucharest’s current reliance on private vehicles, underperforming public transport satisfaction, and limited progress on active mobility. The study develops a context-sensitive reform framework for the Romanian capital, grounded in transferable lessons from Western and Central European cities. It emphasizes coordinated metropolitan governance, public trust-building, phased car-restraint measures, and investment alignment as key levers. Rather than merely cataloguing policy intentions, the paper offers practical recommendations informed by systemic governance barriers and public attitudes. The findings will contribute to academic debates on urban mobility transitions in post-socialist cities and provide actionable insights for policymakers seeking to operationalize EU decarbonization goals at the metropolitan scale. Full article

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16 pages, 1947 KiB

Open AccessArticle

Benthic Macrofauna in the Loukkos Estuary, Morocco: Patterns and Environmental Drivers

by Feirouz Touhami

Ecologies 2025, 6(3), 53; https://doi.org/10.3390/ecologies6030053 (registering DOI) - 1 Aug 2025

Abstract

This study provides the first comprehensive characterization of benthic macrofaunal communities in the Loukkos estuary, highlighting their spatial and seasonal variability and the environmental factors shaping their structure. A total of 47 species were identified across 12 site–season combinations, dominated by molluscs (47%), polychaetes (23%), and crustaceans (21%). Species richness varied considerably along the estuarine gradient, ranging from fewer than five species in the upstream sector to up to 30 species downstream. Overall, higher diversity was observed in the downstream areas and during the dry season. Macrofaunal density also exhibited substantial variability, ranging from 95 ind.m⁻² to 14,852 ind.m⁻², with a mean density of 2535 ± 4058 ind.m⁻². Multivariate analyses identified four distinct benthic assemblages structured primarily by spatial factors (ANOSIM R = 0.86, p = 0.002), with negligible seasonal effect (R = −0.03, p = 0.6). Assemblages ranged from marine-influenced communities at the estuary mouth dominated by Cerastoderma edule, through rich and diverse seagrass-associated communities in the lower estuary dominated by Bittium reticulatum, and moderately enriched mid-estuary communities characterized by Scrobicularia plana and Hediste diversicolor, to species-poor upstream communities dominated by the tolerant species H. diversicolor. Canonical analysis showed that salinity and vegetation explain nearly 40% of the variation in benthic assemblages, highlighting the key role of Zostera seagrass beds as structuring habitats. Moreover, upstream anthropogenic pressures alter environmental conditions, reducing benthic diversity and favoring tolerant species. Full article

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20 pages, 6058 KiB

Open AccessArticle

The GPI-Anchored Aspartyl Proteases Encoded by the YPS1 and YPS7 Genes of Candidozyma auris and Their Role Under Stress Conditions

by Alvaro Vidal-Montiel, Daniel Clark-Flores, Eulogio Valentín-Gómez, Juan Pedro Luna-Arias, Erika Rosales-Cruz, César Hernández-Rodríguez, Lourdes Villa-Tanaca and Margarita Juárez-Montiel

J. Fungi 2025, 11(8), 573; https://doi.org/10.3390/jof11080573 (registering DOI) - 1 Aug 2025

Abstract

Candidozyma auris is a multidrug-resistant, thermo- and osmotolerant yeast capable of persisting on biotic and abiotic surfaces, attributes likely linked to its cell wall composition. Here, seven putative genes encoding yapsins, aspartyl proteases GPI-anchored to the membrane or cell wall, were identified in the genomes of C. auris CJ97 and 20-1498, from clades III and IV, respectively. The C. auris YPS1 gene is orthologous to the SAP9 of C. albicans. The YPS7 gene is orthologous to YPS7 in C. glabrata and S. cerevisiae, so that they may share similar roles. An in silico analysis suggested an interaction between pepstatin and the catalytic domain of Yps1 and Yps7. Although this inhibitor, when combined with caffeine, had a subtle effect on the growth of C. auris, it induced alterations in the cell wall. CauYPS1 and CauYPS7 expression increased under nutrient starvation and NaCl, and at 42 °C. The transcriptome of the 20-1498 strain suggests that autophagy may play a role in thermal stress, probably degrading deleterious proteins or maintaining cell wall and vacuolar homeostasis. Therefore, CauYps1 and CauYps7 may play a role in the cell wall integrity of C. auris in stress conditions, and they could be a target of new antifungal or antivirulence agents. Full article

(This article belongs to the Special Issue Candidiasis: Changes and Challenges in Its Epidemiology, Pathogenesis, Diagnosis, Treatment and Prevention)

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27 pages, 968 KiB

Open AccessArticle

Factors Influencing Generative AI Usage Intention in China: Extending the Acceptance–Avoidance Framework with Perceived AI Literacy

by Chenhui Liu, Libo Yang, Xinyu Dong and Xiaocui Li

Systems 2025, 13(8), 639; https://doi.org/10.3390/systems13080639 (registering DOI) - 1 Aug 2025

Abstract

In the digital era, understanding the intention to use generative AI is critical, as it enhances productivity, transforms workflows, and enables humans to focus on higher-value tasks. Drawing upon the unified theory of acceptance and use of technology (UTAUT) and the technology threat avoidance theory (TTAT), this research integrates perceived AI literacy into the AI acceptance–avoidance framework as a central variable. This study gathered 583 valid survey responses from China and validated its model using a dual-phase, combined method that integrates structural equation modeling and artificial neural networks. Research findings indicate that the model explains 51.6% of the variance in generative AI usage intention. Except for social influence, all variables within the extended framework significantly impact the usage intention, with perceived AI literacy being the strongest predictor (β = 0.33, p < 0.001). Additionally, perceived AI literacy mitigates the adverse effect of perceived threats on the intention to use AI. Practical implications suggest that enterprises adopt a tiered strategy, as follows: maximize perceived benefits by integrating AI skills into reward systems and providing task-automation training; minimize perceived costs through dedicated technical support and transparent risk mitigation plans; and cultivate AI literacy via progressive learning paths, advancing from data analysis to innovation. Full article

(This article belongs to the Topic Theories and Applications of Human-Computer Interaction)

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17 pages, 811 KiB

Open AccessArticle

Implementation of Polygenic Risk Stratification and Genomic Counseling in Colombia: An Embedded Mixed-Methods Study

by Cesar Augusto Buitrago, Melisa Naranjo Vanegas, Harvy Mauricio Velasco, Danny Styvens Cardona, Juan Pablo Valencia-Arango, Sofia Lorena Franco, Lina María Torres, Johana Cañaveral, Diana Patricia Silgado and Andrea López Cáceres

J. Pers. Med. 2025, 15(8), 335; https://doi.org/10.3390/jpm15080335 (registering DOI) - 1 Aug 2025

Abstract

Background: Breast cancer remains a major public health challenge in Latin America, where access to personalized risk assessment tools is still limited. This study aimed to evaluate the implementation of a polygenic risk score (PRS)-based stratification model combined with remote genomic counseling in Colombian women with sporadic breast cancer and healthy women. Methods: In 2023, an embedded mixed-methods observational study was conducted in Medellín involving 1997 women aged 40–75 years who underwent clinical PRS testing. The intervention integrated PRS-based risk categorization with individualized risk factor assessment and lifestyle recommendations delivered through a remote counseling platform. Results: PRS analysis classified 9.7% of women as high risk and 46% as low risk. Healthier lifestyle patterns were significantly associated with lower PRS categories (p = 0.034). Physical activity showed a protective effect (OR = 0.60, 95% CI: 0.5–0.8), while prior smoking, elevated BMI, and sedentary behavior were associated with higher risk. The counseling model achieved high delivery (93%) and satisfaction (85%) rates. Qualitative insights revealed improved understanding of genomic risk and greater engagement in preventive behaviors. Only one new case of breast cancer was detected among intermediate-risk participants, with a diagnostic lead time of 12 months. Conclusions: These findings support the feasibility, acceptability, and potential impact of integrating PRS and genomic counseling in cancer prevention strategies in middle-income settings. Full article

(This article belongs to the Special Issue Cancer Risk Assessment in Precision Medicine)

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