Abstract: Digital sustainable marketing is used by firms to communicate environmental efforts through social media and e-commerce platforms, yet its effectiveness in shaping green choices remains unclear in emerging markets like Saudi Arabia. This study investigates how digital sustainable marketing influences green consumer choices directly and indirectly through green perceived value and green skepticism. A cross-sectional survey was administered to 400 Saudi consumers who use digital channels and purchase offerings promoted as green or sustainable. Data were analyzed using partial least squares structural equation modelling. The results show that digital sustainable marketing has a significant positive effect on green consumer choices and on green perceived value, while it reduces green skepticism. Green perceived value increases, and green skepticism decreases, green consumer choices. Both mediators partially transmit the impact of digital sustainable marketing on green consumer choices, revealing a value-enhancing path and a skepticism-reducing path operating in parallel. The study contributes by integrating positive and negative psychological mechanisms into a single dual-path model of digital sustainable marketing and by providing evidence from the Saudi market. The findings offer guidance for designing digital sustainability campaigns that enhance perceived value while limiting skepticism to accelerate green consumption.

Abstract: We propose a novel mathematical framework for understanding conscious experience based on the topology of 4-manifolds and the theory of Painlevé transcendents, with deep connections to quantum field theory and topological quantum field theory (TQFT). We conjecture that consciousness emerges through a \emph{two-stage quantum-to-classical transition}: pre-conscious processing corresponds to the $I_0^*$ fiber (dual graph $\tilde{D}_4$) of Painlevé VI (PVI); an intermediate quantum ``bipolar'' state corresponds to the $I_1^*$ ``fishtail'' fiber ($\tilde{D}_5$) of Painlevé V (PV), characterized by two bordered cusps representing coexisting quantum modes; and full classical consciousness corresponds to the $I_2^*$ fiber ($\tilde{D}_6$) of PVdeg (equivalent to $\text{PIII}^{D_6}$), with a single cusp representing unified percept. Each stage is modeled as a coalescence of punctures or cusp-removal on a Riemann sphere: symmetry-breaking transitions analogous to phase transitions in gauge theories on 4-manifolds. This topological structure is not arbitrary: 4-manifolds play a central role in quantum field theory, Painlevé equations arise naturally in quantum integrable systems, and the monodromy groups in our framework are mathematically identical to gauge holonomy in Yang-Mills theory. We demonstrate through WKB (semiclassical) analysis that the fishtail fiber ($I_1^*$) of PV naturally generates gamma-band oscillations (30-80 Hz) with temporal characteristics matching empirical observations of neural gamma bursts. The key insight is that gamma oscillations emerge at the \emph{quantum intermediate stage} (PV, fishtail): the PVI $\to$ PV transition initiates coherent oscillations, while the subsequent PV $\to$ PVdeg transition (cusp removal) represents the classical collapse from bipolar quantum superposition to unified classical percept. This provides a potential mathematical realization of Penrose-Hameroff Orch-OR theory while making testable predictions about observable neural activity. Our framework unifies concepts from Seiberg-Witten theory, topological quantum computation, and neuroscience, suggesting that consciousness may be fundamentally describable as a quantum-to-classical phase transition on a 4-dimensional spatiotemporal manifold with singularity structure governed by integrable systems.

Abstract:

Tau protein is a nonspecific marker of neurodegeneration, and its phosphorylated form, ptau-181, is specifically associated with Alzheimer's disease (AD). Calculating the ratio of the phosphorylated form to total tau protein can help distinguish AD from other tauopathies or neurodegeneration, as well as reduce the impact of individual differences in total tau protein levels. This also allows for monitoring and comparing the dynamics of changes within the same patient. For this purpose, two SPRi biosensors were constructed, sensitive to the proteins described: total tau and ptau-181 for plasma determinations. The use of these biosensors requires prior sensor validation, during which specific parameters of the analytical method are established. A study of the optimal concentration of the receptor layer in which particular antibodies were immobilized found that the optimal concentration for total tau protein determinations was 1000 ng/mL. For ptau-181, it was 90 ng/mL. Biosensor layer formation was confirmed by analysis over a wide angle range, which enabled the generation of SPR curves. The dynamic range of the sensors is 1–50 pg/mL for total tau and 1–100 pg/mL for ptau-181. The limits of detection are 0.18 pg/mL and 0.037 pg/mL, respectively. Low standard deviation (SD) and coefficient of variation (CV) values indicate good precision and accuracy of the results obtained using the SPRi biosensors. Specificity testing confirmed that no interferents influenced the assay. The method is therefore suitable for researching biological materials, such as blood plasma. Proteins were thus measured in the blood plasma of AD patients and controls. Statistical analysis revealed significant differences in the concentrations of tau and ptau-181 protein in both groups. The calculated ptau/total tau ratio for both sample groups also demonstrated high statistical significance. This suggests that a high ratio may be characteristic of AD. However, more extensive analysis is needed to obtain cutoff values. The ROC curves indicate that both biosensors have good diagnostic utility, with lower specificity for total tau.

Abstract: Muscle regeneration following injury reveals a striking paradox: the same phenomenon, fiber branching, can serve as both a beneficial adaptation in healthy muscle and a pathological hallmark in disease. In healthy muscle, branched fibers emerge as an adaptive response to extreme mechanical loading, redistributing stress, enhancing hypertrophy, and protecting against injury. Conversely, in conditions such as Duchenne Muscular Dystrophy, excessive and complex branching contributes to mechanical weakness, increased susceptibility to damage, and progressive functional decline. This review explores the dichotomy of fiber branching in muscle physiology, synthesizing current research on its molecular and cellular mechanisms. By understanding the paradoxical nature of fiber branching, we aim to uncover new perspectives for therapeutic strategies that balance its adaptive and pathological roles to improve outcomes for muscle diseases.

Abstract:

Rapid phylogenomic analysis is essential for outbreak surveillance and large-scale viral comparative genomics, yet conventional alignment-based workflows remain computationally intensive and difficult to deploy at scale. Covary is a computational framework designed for large-scale biological sequence analysis. It is a translation-aware, alignment-free machine learning framework that encodes genomic information into biologically informed vector representations, enabling efficient genome-scale comparison without multiple sequence alignment (MSA). Here, Covary was applied to thousands-scale analysis of outbreak-causing viral genomes to assess its scalability and biological resolution. A total of 4,000 complete genomes of SARS-CoV-2, dengue virus, measles virus, and alphainfluenza virus were retrieved from the NCBI Viral Genomes Resource, of which 3,831 passed quality filtering and were analyzed using Covary. Results showed that Covary rapidly processed all genomes and consistently grouped sequences according to expected taxonomic assignments and known ingroup structure, including SARS-CoV-2 Pango lineages, dengue virus subtypes, measles virus geographic origin, and alphainfluenza virus clades. Covary completed the analysis in 45 minutes on free-tier Google Colab, inferring genome-wide relationships using modest computational resources. These results demonstrate that Covary enables rapid, alignment-free phylogenomic analysis of thousands of outbreak-causing viral genomes without requiring advanced computational infrastructure. In conclusion, Covary represents a scalable, deploy-ready machine learning pipeline for genome-informed outbreak surveillance and monitoring systems.

Abstract: The ongoing miniaturization of electronic systems and the increasing demand for sustainable, autonomous technologies driven by the Internet of Things (IoT) highlight the importance of efficient, ultra-low-power energy harvesting devices. This study evaluates fifteen such devices manufactured by five of the eight industry leaders. The study assesses the technological suitability of these devices for small-scale, intelligent, autonomous seed germination systems. The evaluation is based on a flexible, practical, multicriteria analysis framework that incorporates a broad set of criteria related to the context of the case study system. The framework also considers the functional and operational limitations of the low-power energy harvesters under analysis. The findings suggest that a comprehensive and transparent methodological approach can generate a prioritized list of energy harvesters aligned with the case study system. This list facilitates selecting the most suitable energy harvesters for IoT-based, small-scale seed germination systems. The analysis demonstrates the feasibility of systematically and structurally selecting the analyzed energy harvesting devices while considering conflicting technical, economic, and environmental priorities. Finally, the study emphasizes that distinct device prioritization lists can emerge when the scope or objective of the project changes because these alterations impact the set of evaluation criteria, their ranking, and weighting. This study outlines a structured evaluation framework that can be adapted to different contexts to facilitate technology selection. Technology researchers and practitioners can use this replicable, auditable tool to identify the advantages and disadvantages of incorporating technology into specific projects.

Abstract: This paper is dedicated to long term atmospheric corrosion behaviour of magnesium alloys. Five different magnesium alloys namely AZ31, AM60, AZ61, AZ80 and AZ91 were exposed for 4 years under harsh conditions at the marine corrosion site of Brest (France). From the results, the corrosion performance increased in the following order: AZ31<AM60<AZ91<AZ61<AZ80. The corrosion was highly localised during the first year of exposure, but more general corrosion prevailed after long term of exposure. All materials followed a power law with rather similar kinetics of corrosion. The observed difference in the corrosion performance of the alloys was explained by the amount of secondary phases as well as that of the Al-content in the α-Mg phase.

Abstract: Faizal et al. (2025) argue that Gödel–Tarski–Chaitin limits render a purely algorithmic Theory of Everything impossible, concluding that the universe cannot be a computer simulation. We demonstrate that this conclusion commits a quantifier overreach by conflating two distinct notions: (i) algorithmic simulation, which attempts to compute all truths about the fundamental layer, and (ii) projection simulation, which approximates observables on a well-posed shadow manifold. Within the Timeless Counterspace and Shadow Gravity (TCGS-SEQUENTION) framework, we show that the 4-D counterspace C functions as the Tarskian “semantic truth” (the Territory), while the 3-D shadow Σ constitutes “syntactic provability” (the Map). Undecidability theorems constrain the Map, not the Territory. Crucially, the TCGS framework provides a concrete geometric instantiation of the “Meta-Theory of Everything” (MToE) that Faizal et al. invoke abstractly: the projection map X : Σ → C plays the role of their external truth predicate T(x), grounding non-algorithmic truths in geometric structure rather than meta-logical assertion. We prove three main results: (A) the undecidability-based no-go theorem applies only to algorithmic simulations targeting the Territory; (B) the shadow manifold Σ admits well-posed dynamics under a single extrinsic constitutive law, rendering all empirical observables computably approximable to arbitrary accuracy; (C) the inference from “no algorithmic simulation of C” to “no simulation whatsoever” is a formal quantifier error. We conclude that non-algorithmicity at the source is fully compatible with deterministic, simulable shadow phenomenology—and that quantum complementarity, dark-sector phenomenology, and biological convergence all manifest as projection artifacts of this same geometric architecture.

Abstract: In this study, we investigated the effect of annealing ultrathin silver (Ag) films of varying thicknesses (1–6 nm) on both their optical absorption and the performance of poly(3-hexylthiophene-2,5-diyl) (P3HT) and [6,6]-phenyl-C61-butyric acid methyl ester (PCBM) organic solar cells (OSCs). The Ag films were deposited on indium tin oxide (ITO) anodes and annealed at 300 °C for 1–2 hours to modify the anodic interface. The optical and electrical properties of the resulting devices were systematically characterized and optimized. The results revealed that a 1 nm AgO layer annealed for 2 hours significantly enhanced the device performance, yielding a 6% increase in power conversion efficiency compared to the standard configuration. This improvement is attributed to two main factors: (i) a 25% increase in light absorption of the AgO/P3HT:PCBM film due to localized surface plasmon resonance of Ag nanoparticles, and (ii) an 11% reduction in series resistance resulting from the favorable alignment of the Ag work function with the ITO anode and the polymer HOMO, which facilitates efficient hole extraction. These findings highlight the potential of ultrathin, annealed Ag/AgO interfacial layers as an effective strategy to enhance light absorption and charge transport in OSCs.

Abstract: The Greater Caribbean manatee is classified as vulnerable, yet the lack of data related to population status in the Costa Rican Caribbean severely hinders conservation policy due to limited ecological knowledge. This study aims to address this challenge by refining a pipeline for the automated manatee count method to enhance classification robustness and efficiency for accurate spatial and temporal density estimation. The bioacoustics analysis consists of a deep learning manatee call detector and an unsupervised individual manatee counting. Methodologically, we implemented an offline feature extraction strategy to avoid a substantial initial computational bottleneck, measured at almost 13h, required to convert 43,031 audio samples into labeled images. To mitigate the high risk of overfitting associated with class imbalance, common in bioacoustic databases, a bootstrapping method was applied post-data splitting, generating a labeled dataset of 100,000 spectrograms. Transfer learning with the VGG-16 architecture yielded superior results, achieving a robust mean 10-fold cross-validation accuracy of 98.94% (±0.10%) and normalized F1-scores of 0.99. Furthermore, this optimized fine-tuning was rapidly executed in just 22min and 36s. Subsequently, the unsupervised individual manatee counting utilized k-means clustering on the top three music information retrieval descriptors along with dimensionality reduction, successfully segregating detected calls into three acoustically distinct clusters, likely representing three individuals. This performance was validated by a silhouette coefficient of 79.03%. These validated results confirm the refined automatic manatee count method as a robust and scalable framework ready for deployment on Costa Rican passive acoustic monitoring data to generate crucial scientific evidence for species conservation.

Abstract: We present a maximum-entropy (MaxEnt) derivation of spacetime geometry starting from a quantum thermal ensemble of local displacement fluctuations. The sole constraint imposed is the expectation value of a quadratic line-element observable. Maximization of entropy yields a Gaussian displacement kernel whose second moments encode an emergent metric structure. Beginning in a locally inertial (flattened Minkowski) frame, we show how curved spacetime geometry and field-space metrics arise through pushforward of the same MaxEnt measure, performed entirely inside the defining integrals. We demonstrate the equivalence of this formulation with the quantum thermal (Matsubara) density-matrix description, without assuming a prior Hilbert-space structure. The resulting geometry is expectation-valued and information-theoretic in origin. This framework provides a unified statistical foundation for spacetime geometry consistent with information geometry, quantum statistical mechanics, and covariant field theory.

Abstract: Drones have long been explored for supply. While several systems offering small pay-loads in drone delivery have seen operational use, large-scale supply drones have yet to be adopted. A range of setbacks cause this, including technological and operational challenges that hinder their adoption. Here, these challenges are evaluated from a conceptual modelling perspective to forecast their applicability once these barriers are overcome. The study uses technology trend modelling and bibliometric activity map-ping methodologies to predict the applicability of specific technologies that are cur-rently identified as operational challenges. Specifically for supply drones, trends in technological improvements of battery technology and aircraft control are modelled to project effects and focus on landing zone autonomy and powertrain. The prediction also focuses on the current state of hybrid power and higher levels of automation required for landing zone operations. These models are validated through several published case studies of small delivery drones and then applied to assess the feasibility and con-straints of larger supply drones. A case study, conceptual design of a supply drone large enough to move a shipping container, is presented to illustrate the critical technologies required to transition large supply drones from concept to operational reality. Key technologies required for large-scale supply drones have yet to build up a critical mass of research activity, particularly on landing zone autonomy and powertrain. Moreover, additional constraints beyond technological and operational challenges could include limitations in autonomy, certification hurdles, regulatory complexity, and the need for greater social trust and acceptance.

Abstract: Neurons have exceptionally high energy demands, sustained by thousands to millions of mitochondria per cell. Each mitochondrion depends on the integrity of its mitochondrial DNA (mtDNA), which encodes essential electron transport chain (ETC) subunits required for oxidative phosphorylation (OXPHOS). The continuous, high-level production of ATP through OXPHOS generates reactive oxygen species (ROS), posing a significant threat to the highly exposed mtDNA. To counter these insults, neurons rely on base excision repair (BER), the principal mechanism for removing oxidative and other small, non-bulky base lesions in nuclear and mtDNA. BER involves a coordinated enzymatic pathway that excises damaged bases and restores DNA integrity, helping maintain mitochondrial genome stability, which is vital for neuronal bioenergetics and survival. When mitochondrial BER is impaired, mtDNA becomes unstable, leading to ETC dysfunction and a self-perpetuating cycle of bioenergetic failure, elevated ROS levels, and continued mtDNA damage. Damaged mtDNA fragments can escape into the cytosol or extracellular space, where they act as damage-associated molecular patterns (DAMPs) that activate innate immune pathways and inflammasome complexes. Chronic activation of these pathways drives sustained neuroinflammation, exacerbating mitochondrial dysfunction and neuronal loss, and functionally links genome instability to innate immune signaling in neurodegenerative diseases. This review summarizes recent advancements in understanding how BER preserves mitochondrial genome stability, affects neuronal health when dysfunctional, and contributes to damage-driven neuroinflammation and neurodegenerative disease progression. We also explore emerging therapeutic strategies to enhance mtDNA repair, optimize its mitochondrial environment, and modulate neuroimmune pathways to counteract neurodegeneration.

Abstract: The impact of climate change on marine bivalves, particularly on their reproductive processes, is a current issue of concern. The aim of this study was to investigate how seawater temperatures influenced the gonadal development and overall condition of the grooved carpet shell clam population in the Baldaio Lagoon (NW Spain) over the last 20 years. Adult clams were collected and biometric, histological and biochemical analyses were performed. Gonadal development phases were assessed, several condition indices were calculated, water temperatures were recorded and statistical analyses were carried out. Results indicated variations in reproductive timing, including changes in gonadal maturation, an earlier spawning period and prolonged maturation phases which contrasted with previous reproductive patterns described for this species. These findings coincided with thermal changes in the lagoon, where mean minimum temperatures increased and maximum temperatures decreased, and the annual thermal range was reduced in comparison with historical data (1998-1999). Biochemical composition and condition indices also reflected variations linked to temperature fluctuations, suggesting that warmer water temperatures may alter energy storage and reproduction. This highlights the importance of continuous environmental monitoring to better understand the effects of climate change on marine invertebrate populations and to improve management strategies that could help to restore natural populations.

Abstract: Intensifying urbanisation in the Arctic, particularly in spatially constrained coastal and island cities, requires reliable information on long-term land use/land cover (LULC) change to assess environmental impacts and support urban planning. However, multi-decadal, high-resolution LULC datasets for Arctic cities remain limited. In this study, we quantify LULC change on Tromsøya (Tromsø, Norway) from 1984 to 2024 using multispectral satellite imagery based on Landsat and PlanetScope, complemented by LiDAR-derived canopy height models (CHM) and building footprints. We mapped LULC change trajectories and examined how these shifts relate to district-level population redistribution using gridded population data. The integration of a LiDAR-derived CHM was found to substantially improve the accuracy of Landsat-based LULC mapping and to represent the dominant source of classification gains, particularly for spectrally similar urban classes such as residential areas, roads, and other paved surfaces. Landsat augmented with CHM was shown to achieve practical equivalence to PlanetScope when the latter was modelled using spectral features only, supporting the feasibility of scalable and cost-effective long-term monitoring of urbanisation in Arctic cities. Based on the best-performing Landsat configuration, the proportions of artificial and green surfaces were estimated, indicating that approximately 20% of green areas were transformed into artificial classes. Spatially, population growth was concentrated in a small number of districts and broadly coincided with hotspots of green to artificial conversion The workflow provides a reproducible basis for long-term, district-scale LULC monitoring in small Arctic cities where data constraints limit consistent use of high-resolution image.

Abstract: Ultrasound (US) imaging is crucial for breast anomaly detection, but its interpretation is subjective and suffers from data scarcity and domain generalization issues. Existing deep learning models struggle to achieve both precise pixel-level localization and fine-grained image-level classification simultaneously, especially in few-shot and cross-domain settings. To address these challenges, we propose ContextualCLIP, a novel few-shot adaptation framework built upon CLIP. ContextualCLIP introduces three core enhancements: (1) a Contextualized Adaptive Prompting (CAP) generator that dynamically creates clinically relevant text prompts by integrating high-order semantic contextual information; (2) a Multi-Grained Feature Fusion Adapter (MGFA) that extracts and adaptively fuses features from different CLIP visual encoder layers using gated attention for multi-scale lesion analysis; and (3) a Domain-Enhanced Memory Bank (DEMB) that improves cross-domain generalization by learning domain-invariant embeddings through a lightweight domain-aware module and contrastive learning. Jointly optimized for localization and classification, ContextualCLIP is evaluated on BUS-UCLM for adaptation and BUSI/BUSZS for zero-extra-adaptation. Results demonstrate that ContextualCLIP consistently achieves superior performance over state-of-the-art baselines across various few-shot settings, yielding substantially higher classification and localization metrics. Ablation studies validate the efficacy of each module, and human evaluation suggests significant augmentation of radiologists' diagnostic accuracy and confidence. ContextualCLIP provides a robust and efficient solution for comprehensive ultrasound anomaly analysis in data-scarce and diverse clinical environments.

Abstract: Metachromatic leukodystrophy (MLD) results from arylsulfatase A (ARSA) deficiency and progressive demyelination. This study evaluates the safety and therapeutic potential of intravenously administered allogeneic mesenchymal stem cells transduced with AAV9 encoding human ARSA in a porcine in vivo study. While ARSA activity in plasma and cerebrospinal fluid did not significantly change, CNS tissues showed a marked increase in ARSA activity, indicating successful CNS targeting and local enzyme expression. Biochemical parameters and cytokine profiles remained within physiological ranges, demonstrating good tolerability and absence of systemic inflammation. These findings suggest that MSC-based delivery of AAV9-ARSA is a safe approach capable of enhancing ARSA activity in the CNS and may represent a promising therapeutic strategy for MLD.

Abstract: his study investigates the application of causal representation learning in financial auditing risk identification, aiming to address problems in traditional methods such as spurious correlations, limited interpretability, and unstable recognition. The proposed framework is built around causal-driven latent representations, where nonlinear mapping is used to obtain deep feature representations of financial data, and structural equation models are employed to establish causal dependencies, thereby removing the interference of non-causal features in risk modeling. On this basis, causal regularization constraints are introduced, and the joint optimization of the objective function enhances the consistency and robustness of representations, improving the reliability and interpretability of the model in complex scenarios. Furthermore, in the risk scoring stage, causal representation is combined with intervention effect calculation, which enables risk identification to provide not only outcome judgments but also insights into the underlying driving mechanisms, thereby improving traceability of risk sources. To verify effectiveness, a dataset closely related to financial auditing tasks was constructed, and comparative experiments under an alignment robustness benchmark were conducted. The results show that the proposed method outperforms existing models in ACC, Precision, Recall, and F1-Score, with notable advantages in robustness and interpretability. In addition, hyperparameter sensitivity experiments analyzed the impact of the causal regularization coefficient on model performance, and the results indicate that appropriate causal constraints can significantly improve stability while maintaining predictive accuracy. Overall, the proposed causal representation learning framework enables more precise and reliable risk identification in financial auditing and provides strong support for building intelligent and data-driven auditing systems.

Abstract: In this paper, we investigate the lexicographic and colexicographic orderings of m-ary vectors of length n, as well as the mirror (left-recursive) reflected Gray code, complementing the classical m-ary reflected Gray code. We present efficient algorithms for generating vectors in each of these orders, each achieving constant amortized time per vector. Additionally, we propose algorithms implementing the four fundamental functions in generating combinatorial objects—successor, predecessor, rank, and unrank—each with time complexity Θ(n). The properties and the relationships between these orderings and the set of integers {0,1,…,mn−1} are examined in detail. We define explicit transformations between the different orders and illustrate them as a digraph very close to the complete symmetric digraph. In this way, we provide a unified framework for understanding ranking, unranking, and order conversion. Our approach, based on emulating the execution of nested loops, proves to be powerful and flexible, leading to elegant and efficient algorithms that can be extended to other combinatorial generation problems. The mirror m-ary Gray code introduced here has potential applications in coding theory and related areas. By providing an alternative perspective on m-ary Gray codes, we aim to inspire further research and applications in combinatorial generation and coding.

Abstract: Type 2 diabetes mellitus (T2DM) is strongly associated with cardiovascular mortality, with coronary artery disease (CAD) being the main manifestation. The pathophysiology of this condition is exacerbated by the accumulation of advanced glycation end-products (AGEs), specifically carboxymethyllysine (CML), which intensifies inflammation, vascular dysfunction, and the progression of atherosclerosis. Considering that diet is the primary exogenous source of these compounds and a modifiable risk factor, this study aimed to evaluate the effect of a low-CML diet on reducing serum levels in patients with T2DM and CAD. This was a randomized clinical trial involving 36 overweight elderly patients, divided into an intervention group (n=19, assigned to a low-CML diet) and a control group (n=17), over a period of 15 days. The intervention reduced CML intake by approximately 56% (p<0.001), resulting in a 30% decrease in serum CML (from 2.90 to 2.03 µg/g; p=0.015). The proposed diet also increased fiber intake and significantly reduced the consumption of trans fatty acids, polyunsaturated fatty acids, and cholesterol. A positive correlation was observed between serum CML and lipid peroxidation (r=0.33; p=0.045), body water (r=0.35; p=0.03), and dietary AGEs (r=0.52; p<0.01), indicating a relationship with oxidative stress and osmolarity. We conclude that reducing CML consumption for 15 days, through temperature control in food preparation, proved to be an effective nutritional strategy. The intervention promoted vascular and metabolic protection, suggesting potential for ameliorating diabetes complications. Future studies with a longer duration and the development of Brazilian food composition tables are recommended to expand upon these findings.