Peptides as Therapeutic Agents: Challenges and Opportunities in the Green Transition Era.

Peptides are at the cutting edge of contemporary research for new potent, selective, and safe therapeutical agents. Their rise has reshaped the pharmaceutical landscape, providing solutions to challenges that traditional small molecules often cannot address. A wide variety of natural and modified peptides have been obtained and studied, and many others are advancing in clinical trials, covering multiple therapeutic areas. As the demand for peptide-based therapies grows, so does the need for sustainable and environmentally friendly synthesis methods. Traditional peptide synthesis, while effective, often involves environmentally draining processes, generating significant waste and consuming vast resources. The integration of green chemistry offers sustainable alternatives, prioritizing eco-friendly processes, waste reduction, and energy conservation. This review delves into the transformative potential of applying green chemistry principles to peptide synthesis by discussing relevant examples of the application of such approaches to the production of active pharmaceutical ingredients (APIs) with a peptide structure and how these efforts are critical for an effective green transition era in the pharmaceutical field.

Genome-wide association analysis of hyperspectral reflectance data to dissect the genetic architecture of growth-related traits in maize under plant growth-promoting bacteria inoculation.

Plant growth-promoting bacteria (PGPB) may be of use for increasing crop yield and plant resilience to biotic and abiotic stressors. Using hyperspectral reflectance data to assess growth-related traits may shed light on the underlying genetics as such data can help assess biochemical and physiological traits. This study aimed to integrate hyperspectral reflectance data with genome-wide association analyses to examine maize growth-related traits under PGPB inoculation. A total of 360 inbred maize lines with 13,826 single nucleotide polymorphisms (SNPs) were evaluated with and without PGPB inoculation; 150 hyperspectral wavelength reflectances at 386-1021 nm and 131 hyperspectral indices were used in the analysis. Plant height, stalk diameter, and shoot dry mass were measured manually. Overall, hyperspectral signatures produced similar or higher genomic heritability estimates than those of manually measured phenotypes, and they were genetically correlated with manually measured phenotypes. Furthermore, several hyperspectral reflectance values and spectral indices were identified by genome-wide association analysis as potential markers for growth-related traits under PGPB inoculation. Eight SNPs were detected, which were commonly associated with manually measured and hyperspectral phenotypes. Different genomic regions were found for plant growth and hyperspectral phenotypes between with and without PGPB inoculation. Moreover, the hyperspectral phenotypes were associated with genes previously reported as candidates for nitrogen uptake efficiency, tolerance to abiotic stressors, and kernel size. In addition, a Shiny web application was developed to explore multiphenotype genome-wide association results interactively. Taken together, our results demonstrate the usefulness of hyperspectral-based phenotyping for studying maize growth-related traits in response to PGPB inoculation.

Mussel power: Scoping a nature-based solution to microplastic debris.

Microplastics are a prolific environmental contaminant. Curbing microplastic pollution requires an array of globally relevant interventions, including source-reduction and curative measures. A novel, nature-based solution to microplastics is proposed, in which mussels are deployed in aquatic ecosystems to act as microplastic biofilters, removing waterborne microplastics and repackaging them into biodeposits that are subsequently captured and removed. Blue mussels (Mytilus edulis) were used to establish the feasibility of such an approach. In the laboratory, mussels were exposed to representative microplastics in a flume tank; at an initial concentration of 1000 microplastics L-1, mussels reduced waterborne microplastic concentrations at an average rate of 40,146 microplastics kg-1 h-1. Mussel faeces sank irrespective of microplastic content, with average sinking velocities of 223-266 m day-1. Modelling predicts ∼3 × 109 mussels deployed on ropes at the mouths of estuaries could remove 4% of waterborne microplastics discharged from rivers. Mussels were successfully deployed in a prototype biodeposit collection system in an urban marina, with 5.0 kg of mussels removing and repackaging 239.9 ± 145.9 microplastics and anthropogenic particles day-1 into their faeces. These results provide impetus for further development of nature-based solutions targeting plastic debris.

Potato protein: current review of structure, technological properties, and potential application on spray drying microencapsulation.

Studies regarding spray drying microencapsulation are aplenty available; especially focusing on processing parameters, microparticle characteristics and encapsulation efficiency. Hence, there is a rising interest in tailoring wall materials aiming to improve the process's effectiveness. Reflecting a market trend in the food industry, plant-based proteins are emerging as alternative protein sources, and their application adaptability is an increasing research of interest related to consumers' demand for healthy food, product innovation, and sustainability. This review presents a perspective on the investigation of potato protein as a technological ingredient, considering it a nonconventional source obtained as by-product from starch industry. Furthermore, this piece emphasizes the potential application of potato protein as wall material in spray drying encapsulation, considering that this purpose is still limited for this ingredient. The literature reports that vegetal-based proteins might present compromised functionality due to processing conditions, impairing its technological application. Structural modification can offer a potential approach to modify potato protein configuration aiming to improve its utilization. Studies reported that modified proteins can perform as better emulsifiers and antioxidant agents compared to intact proteins. Hence, it is expected that their use in microencapsulation would improve process efficiency and protection of the core material, consequently delivering superior encapsulation performance.

Automated Machine Learning: A Case Study of Genomic "Image-Based" Prediction in Maize Hybrids.

Machine learning methods such as multilayer perceptrons (MLP) and Convolutional Neural Networks (CNN) have emerged as promising methods for genomic prediction (GP). In this context, we assess the performance of MLP and CNN on regression and classification tasks in a case study with maize hybrids. The genomic information was provided to the MLP as a relationship matrix and to the CNN as "genomic images." In the regression task, the machine learning models were compared along with GBLUP. Under the classification task, MLP and CNN were compared. In this case, the traits (plant height and grain yield) were discretized in such a way to create balanced (moderate selection intensity) and unbalanced (extreme selection intensity) datasets for further evaluations. An automatic hyperparameter search for MLP and CNN was performed, and the best models were reported. For both task types, several metrics were calculated under a validation scheme to assess the effect of the prediction method and other variables. Overall, MLP and CNN presented competitive results to GBLUP. Also, we bring new insights on automated machine learning for genomic prediction and its implications to plant breeding.

Population-tailored mock genome enables genomic studies in species without a reference genome.

Based on molecular markers, genomic prediction enables us to speed up breeding schemes and increase the response to selection. There are several high-throughput genotyping platforms able to deliver thousands of molecular markers for genomic study purposes. However, even though its widely applied in plant breeding, species without a reference genome cannot fully benefit from genomic tools and modern breeding schemes. We used a method to assemble a population-tailored mock genome to call single-nucleotide polymorphism (SNP) markers without an available reference genome, and for the first time, we compared the results with standard genotyping platforms (array and genotyping-by-sequencing (GBS) using a reference genome) for performance in genomic prediction models. Our results indicate that using a population-tailored mock genome to call SNP delivers reliable estimates for the genomic relationship between genotypes. Furthermore, genomic prediction estimates were comparable to standard approaches, especially when considering only additive effects. However, mock genomes were slightly worse than arrays at predicting traits influenced by dominance effects, but still performed as well as standard GBS methods that use a reference genome. Nevertheless, the array-based SNP markers methods achieved the best predictive ability and reliability to estimate variance components. Overall, the mock genomes can be a worthy alternative for genomic selection studies, especially for those species where the reference genome is not available.

Introgression of Maize Diversity for Drought Tolerance: Subtropical Maize Landraces as Source of New Positive Variants.

Current climate change models predict an increased frequency and intensity of drought for much of the developing world within the next 30 years. These events will negatively affect maize yields, potentially leading to economic and social instability in many smallholder farming communities. Knowledge about the genetic resources available for traits related to drought tolerance has great importance in developing breeding program strategies. The aim of this research was to study a maize landrace introgression panel to identify chromosomal regions associated with a drought tolerance index. For that, we performed Genome-Wide Association Study (GWAS) on 1326 landrace progenies developed by the CIMMYT Genetic Resources Program, originating from 20 landraces populations collected in arid regions. Phenotypic data were obtained from early testcross trials conducted in three sites and two contrasting irrigation environments, full irrigation (well-watered) and reduced irrigation (drought). The populations were genotyped using the DArTSeq® platform, and a final set of 5,695 SNPs markers was used. The genotypic values were estimated using spatial adjustment in a two-stage analysis. First, we performed the individual analysis for each site/irrigation treatment combination. The best linear unbiased estimates (BLUEs) were used to calculate the Harmonic Mean of Relative Performance (HMRP) as a drought tolerance index for each testcross. The second stage was a joint analysis, which was performed using the HMRP to obtain the best linear unbiased predictions (BLUPs) of the index for each genotype. Then, GWAS was performed to determine the marker-index associations and the marker-Grain Yield (GY) associations for the two irrigation treatments. We detected two significant markers associated with the drought-tolerance index, four associated with GY in drought condition, and other four associated with GY in irrigated conditions each. Although each of these markers explained less than 0.1% of the phenotypic variation for the index and GY, we found two genes likely related to the plant response to drought stress. For these markers, alleles from landraces provide a slightly higher yield under drought conditions. Our results indicate that the positive diversity delivered by landraces are still present on the backcrosses and this is a potential breeding strategy for improving maize for drought tolerance and for trait introgression bringing new superior allelic diversity from landraces to breeding populations.

Optimizing Genomic-Enabled Prediction in Small-Scale Maize Hybrid Breeding Programs: A Roadmap Review.

The usefulness of genomic prediction (GP) for many animal and plant breeding programs has been highlighted for many studies in the last 20 years. In maize breeding programs, mostly dedicated to delivering more highly adapted and productive hybrids, this approach has been proved successful for both large- and small-scale breeding programs worldwide. Here, we present some of the strategies developed to improve the accuracy of GP in tropical maize, focusing on its use under low budget and small-scale conditions achieved for most of the hybrid breeding programs in developing countries. We highlight the most important outcomes obtained by the University of São Paulo (USP, Brazil) and how they can improve the accuracy of prediction in tropical maize hybrids. Our roadmap starts with the efforts for germplasm characterization, moving on to the practices for mating design, and the selection of the genotypes that are used to compose the training population in field phenotyping trials. Factors including population structure and the importance of non-additive effects (dominance and epistasis) controlling the desired trait are also outlined. Finally, we explain how the source of the molecular markers, environmental, and the modeling of genotype-environment interaction can affect the accuracy of GP. Results of 7 years of research in a public maize hybrid breeding program under tropical conditions are discussed, and with the great advances that have been made, we find that what is yet to come is exciting. The use of open-source software for the quality control of molecular markers, implementing GP, and envirotyping pipelines may reduce costs in an efficient computational manner. We conclude that exploring new models/tools using high-throughput phenotyping data along with large-scale envirotyping may bring more resolution and realism when predicting genotype performances. Despite the initial costs, mostly for genotyping, the GP platforms in combination with these other data sources can be a cost-effective approach for predicting the performance of maize hybrids for a large set of growing conditions.

EnvRtype: a software to interplay enviromics and quantitative genomics in agriculture.

Envirotyping is an essential technique used to unfold the nongenetic drivers associated with the phenotypic adaptation of living organisms. Here, we introduce the EnvRtype R package, a novel toolkit developed to interplay large-scale envirotyping data (enviromics) into quantitative genomics. To start a user-friendly envirotyping pipeline, this package offers: (1) remote sensing tools for collecting (get_weather and extract_GIS functions) and processing ecophysiological variables (processWTH function) from raw environmental data at single locations or worldwide; (2) environmental characterization by typing environments and profiling descriptors of environmental quality (env_typing function), in addition to gathering environmental covariables as quantitative descriptors for predictive purposes (W_matrix function); and (3) identification of environmental similarity that can be used as an enviromic-based kernel (env_typing function) in whole-genome prediction (GP), aimed at increasing ecophysiological knowledge in genomic best-unbiased predictions (GBLUP) and emulating reaction norm effects (get_kernel and kernel_model functions). We highlight literature mining concepts in fine-tuning envirotyping parameters for each plant species and target growing environments. We show that envirotyping for predictive breeding collects raw data and processes it in an eco-physiologically smart way. Examples of its use for creating global-scale envirotyping networks and integrating reaction-norm modeling in GP are also outlined. We conclude that EnvRtype provides a cost-effective envirotyping pipeline capable of providing high quality enviromic data for a diverse set of genomic-based studies, especially for increasing accuracy in GP across untested growing environments.

A novel way to validate UAS-based high-throughput phenotyping protocols using in silico experiments for plant breeding purposes.

KEY MESSAGE: It is possible to make inferences regarding the feasibility and applicability of plant high-throughput phenotyping via computer simulations. Protocol validation has been a key challenge to the establishment of high-throughput phenotyping (HTP) in breeding programs. We add to this matter by proposing an innovative way for designing and validating aerial imagery-based HTP approaches with in silico 3D experiments for plant breeding purposes. The algorithm is constructed following a pipeline composed of the simulation of phenotypic values, three-dimensional modeling of trials, and image rendering. Our tool is exemplified by testing a set of experimental setups that are of interest in the context of maize breeding using a comprehensive case study. We report on how the choice of (percentile of) points in dense clouds, the experimental repeatability (heritability), the treatment variance (genetic variability), and the flight altitude affect the accuracy of high-throughput plant height estimation based on conventional structure-from-motion (SfM) and multi-view stereo (MVS) pipelines. The evaluation of both the algorithm and the case study was driven by comparisons of the computer-simulated (ground truth) and the HTP-estimated values using correlations, regressions, and similarity indices. Our results showed that the 3D experiments can be adequately reconstructed, enabling inference-making. Moreover, it suggests that treatment variance, repeatability, and the choice of the percentile of points are highly influential over the accuracy of HTP. Conversely, flight altitude influenced the quality of reconstruction but not the accuracy of plant height estimation. Therefore, we believe that our tool can be of high value, enabling the promotion of new insights and further understanding of the events underlying the practice of high-throughput phenotyping.

On the usefulness of parental lines GWAS for predicting low heritability traits in tropical maize hybrids.

Genome-wide association studies (GWAS) is one of the most popular methods of studying the genetic control of traits. This methodology has been intensely performed on inbred genotypes to identify causal variants. Nonetheless, the lack of covariance between the phenotype of inbred lines and their offspring in cross-pollinated species (such as maize) raises questions on the applicability of these findings in a hybrid breeding context. To address this topic, we incorporated previously reported parental lines GWAS information into the prediction of a low heritability trait in hybrids. This was done by marker-assisted selection based on significant markers identified in the lines and by genomic prediction having these markers as fixed effects. Additive-dominance GWAS of hybrids, a non-conventional procedure, was also performed for comparison purposes. Our results suggest that incorporating information from parental inbred lines GWAS led to decreases in the predictive ability of hybrids. Correspondingly, inbred lines and hybrids-based GWAS yielded different results. These findings do not invalidate GWAS on inbred lines for selection purposes, but mean that it may not be directly useful for hybrid breeding.

Bayesian analysis and prediction of hybrid performance.

BACKGROUND: The selection of hybrids is an essential step in maize breeding. However, evaluating a large number of hybrids in field trials can be extremely costly. However, genomic models can be used to predict the expected performance of un-tested genotypes. Bayesian models offer a very flexible framework for hybrid prediction. The Bayesian methodology can be used with parametric and semi-parametric assumptions for additive and non-additive effects. Furthermore, samples from the posterior distribution of Bayesian models can be used to estimate the variance due to general and specific combining abilities even in cases where additive and non-additive effects are not mutually orthogonal. Also, the use of Bayesian models for analysis and prediction of hybrid performance has remained fairly limited. RESULTS: We provided an overview of Bayesian parametric and semi-parametric genomic models for prediction of agronomic traits in maize hybrids and discussed how these models can be used to decompose the genotypic variance into components due to general and specific combining ability. We applied the methodology to data from 906 single cross tropical maize hybrids derived from a convergent population. Our results show that: (1) non-additive effects make a sizable contribution to the genetic variance of grain yield; however, the relative importance of non-additive effects was much smaller for ear and plant height; (2) genomic prediction can achieve relatively high accuracy in predicting phenotypes of un-tested hybrids and in pre-screening. CONCLUSIONS: Genomic prediction can be a useful tool in pre-screening of hybrids and could contribute to the improvement of the efficiency and efficacy of maize hybrids breeding programs. The Bayesian framework offers a great deal of flexibility in modeling hybrid performance. The methodology can be used to estimate important genetic parameters and render predictions of the expected hybrid performance as well measures of uncertainty about such predictions.

Modeling copy number variation in the genomic prediction of maize hybrids.

KEY MESSAGE: Our study indicates that copy variants may play an essential role in the phenotypic variation of complex traits in maize hybrids. Moreover, predicting hybrid phenotypes by combining additive-dominance effects with copy variants has the potential to be a viable predictive model. Non-additive effects resulting from the actions of multiple loci may influence trait variation in single-cross hybrids. In addition, complementation of allelic variation could be a valuable contributor to hybrid genetic variation, especially when crossing inbred lines with higher contents of copy gains. With this in mind, we aimed (1) to study the association between copy number variation (CNV) and hybrid phenotype, and (2) to compare the predictive ability (PA) of additive and additive-dominance genomic best linear unbiased prediction model when combined with the effects of CNV in two datasets of maize hybrids (USP and HELIX). In the USP dataset, we observed a significant negative phenotypic correlation of low magnitude between copy number loss and plant height, revealing a tendency that more copy losses lead to lower plants. In the same set, when CNV was combined with the additive plus dominance effects, the PA significantly increased only for plant height under low nitrogen. In this case, CNV effects explicitly capture relatedness between individuals and add extra information to the model. In the HELIX dataset, we observed a pronounced difference in PA between additive (0.50) and additive-dominance (0.71) models for predicting grain yield, suggesting a significant contribution of dominance. We conclude that copy variants may play an essential role in the phenotypic variation of complex traits in maize hybrids, although the inclusion of CNVs into datasets does not return significant gains concerning PA.

Calcareous Bio-Concretions in the Northern Adriatic Sea: Habitat Types, Environmental Factors that Influence Habitat Distributions, and Predictive Modeling.

Habitat classifications provide guidelines for mapping and comparing marine resources across geographic regions. Calcareous bio-concretions and their associated biota have not been exhaustively categorized. Furthermore, for management and conservation purposes, species and habitat mapping is critical. Recently, several developments have occurred in the field of predictive habitat modeling, and multiple methods are available. In this study, we defined the habitats constituting northern Adriatic biogenic reefs and created a predictive habitat distribution model. We used an updated dataset of the epibenthic assemblages to define the habitats, which we verified using the fuzzy k-means (FKM) clustering method. Redundancy analysis was employed to model the relationships between the environmental descriptors and the FKM membership grades. Predictive modelling was carried out to map habitats across the basin. Habitat A (opportunistic macroalgae, encrusting Porifera, bioeroders) characterizes reefs closest to the coastline, which are affected by coastal currents and river inputs. Habitat B is distinguished by massive Porifera, erect Tunicata, and non-calcareous encrusting algae (Peyssonnelia spp.). Habitat C (non-articulated coralline, Polycitor adriaticus) is predicted in deeper areas. The onshore-offshore gradient explains the variability of the assemblages because of the influence of coastal freshwater, which is the main driver of nutrient dynamics. This model supports the interpretation of Habitat A and C as the extremes of a gradient that characterizes the epibenthic assemblages, while Habitat B demonstrates intermediate characteristics. Areas of transition are a natural feature of the marine environment and may include a mixture of habitats and species. The habitats proposed are easy to identify in the field, are related to different environmental features, and may be suitable for application in studies focused on other geographic areas. The habitat model outputs provide insight into the environmental drivers that control the distribution of the habitat and can be used to guide future research efforts and cost-effective management and conservation plans.

Role of components of frailty on quality of life in dialysis patients: a cross-sectional study.

BACKGROUND: Many people on dialysis suffer a variety of conditions that can affect frailty (the condition or quality of being frail), such as comorbidities, disabilities, dependence, malnutrition, cognitive impairment and poor social conditions. Frailty is suspected to affect quality of life (QoL). OBJECTIVES: The study aimed to evaluate the effect of the different components of frailty on the QoL of people on dialysis. METHODS: We enrolled 203 out of 233 prevalent patients on dialysis in the Trieste area of Italy. We applied the Short-Form 36 (SF-36) questionnaire, Activities of Daily Living, Instrumental Activities of Daily Living, Subjective Global Assessment scales and Karnofsky Index. In addition we analysed their social conditions. RESULTS: Dependence, malnutrition and disability had a negative role on QoL. Living with family and good social-economic conditions were significantly related to a better QoL. CONCLUSIONS: Dependence, malnutrition, disability, poor social and economic conditions have a significant effect on life quality. The role of comorbidities appears to be less important. Screening of patients, nutritional and functional rehabilitation and prevention of social isolation appear to be indispensable in guaranteeing a satisfactory life quality.

Clinical, radiological, and biochemical features of a bilateral buttock amyloidoma emerging after 27 years of hemodialysis.

Deposition of amyloid in the buttock is a rare complication of dialysis related amyloidosis (DRA), but this localization is even rarer in other types of amyloidoses. We report here the clinical, radiological, and biochemical features of a patient who incurred into this complication after 27 years of hemodialysis. Imaging of the amyloid deposition by magnetic resonance imaging (MRI) documents the amyloid infiltration in the muscles of the buttock region and highlights a peculiar feature of amyloid fibrils deposition in the subcutaneous fat. The amyloid deposition is confirmed by biochemical and microscopic analysis of fibrils extracted from a biopsy specimen. Review of literature and the features of this case lead to speculation that the peculiar involvement of the buttock region including muscles and subcutaneous fat in DRA might derive from the propagation of amyloid initially deposited in the hip joint.

Characterization of molecular motions in biomolecular systems by elastic incoherent neutron scattering.

In the present work the role played by the instrumental resolution function in elastic incoherent neutron scattering (EINS) experiment is discussed. An important result consists in the definition of an equivalent time t(*), which depends both on the characteristic system time and on the resolution time, for which the spatial Fourier transform of EINS intensity profile and the self-distribution function (SDF) evaluated at t=t(*) are proportional. Then the equivalent time t(*) is introduced in the SDF procedure, an operational recipe for the mean square displacement determination. The new revised procedure is applied on data of myoglobin in trehalose dry environment and of hydrated homologous disaccharides (sucrose and trehalose).

Determination of aloesin and aloeresin A for the detection of aloe in beverages.

This work describes a sensitive high-performance liquid chromatography (HPLC) method for the quantification of aloesin and aloeresin A in alcoholic beverages containing aloe as a flavoring agent. The compounds were prepared from Aloe ferox juice. Sephadex LH20 and ion-exchange resin AG1X2 column chromatography were used for aloesin. Aloeresin A was obtained by Sephadex LH20 and silica gel column chromatography followed by purification on Discovery DSC-18 solid-phase extraction tubes. A 98 mg amount of aloesin (>99% purity) and 34 mg of aloeresin A (>98% purity) were recovered from 2.5 g of aloe juice. The HPLC method was validated, and intra- and interday performances were established. In-house validation was carried out by analyzing samples of beverages with and without aloe as a flavoring agent.

A minimally invasive technique for the evaluation of the regulatory steps of the two major pathways of bile acid synthesis.

BACKGROUND: Bile acid synthesis accounts for more than 95% of total cholesterol catabolism per day. We have developed a minimally invasive technique in humans that quantifies the rates of plasma appearance of 7alpha- and 27-hydroxycholesterol, representing the first steps of the "classical" and "alternative" pathways of bile acid synthesis, respectively. METHODS: For this purpose, during the intravenous infusion of synthetic deuterated isotopomers of 7alpha-hydroxycholesterol and 27-hydroxycholesterol plasma samples are collected and analysed by a GC-MS based method that allows to quantify the exogenous/natural isotopomer ratio of the two sterols. From this data, the rates of plasma appearance of 7alpha- and 27-hydroxycholesterol are calculated. RESULTS: In a group of healthy individuals steady state kinetics are obtained during a 2 h period yielding mean values of 2.0+/-0.8 and 3.7+/-0.6 mg/h for 7alpha- and 27-hydroxycholesterol, respectively. The data are consistent with findings using older techniques that require studies over several days. CONCLUSION: Considering that at steady state of the exogenous/natural isotopomer ratio the plasma appearance of the two regulatory hydroxysterols are related to the rate of bile acid synthesis via the "classical" and the "alternative" pathways, respectively, the proposed method could be used to evaluate the immediate effects of different diets and drugs and other determinants on cholesterol catabolism.

Virgin Olive Oil Study (VOLOS): vasoprotective potential of extra virgin olive oil in mildly dyslipidemic patients.

BACKGROUND: In vitro, olive phenols exert potent antioxidant and enzyme-modulating activities. AIM OF THE STUDY: We comparatively evaluate, in mildly dyslipidemic patients, the vasoprotective potential of extra virgin olive oil. METHODS: 22 patients were administered 40 mL/day of either extra-virgin, i. e. phenol rich, or refined, i. e. phenol poor, olive oils (EVOO or ROO, respectively, with nearly identical fatty acid composition), with a crossover design. Each treatment was carried out for seven weeks, with four weeks of washout in between. Plasma antioxidant capacity, serum thromboxane B2 (TXB2) formation, and urinary isoprostane excretion were evaluated as surrogate markers of cardioprotective potential and vascular function. RESULTS: No effects on plasma lipid/lipoprotein profile were observed. Conversely, EVOO consumption was associated with favorable effects on circulating markers. Namely, decreased serum TXB2 production and increased plasma antioxidant capacity were observed when EVOO was administered in both treatment arms. Neither treatment had any significant effect on isoprostane excretion. CONCLUSIONS: EVOO consumption by mildly dyslipidemic patients is associated with favorable changes in circulating markers of cardiovascular condition. Based on current knowledge, these effects may be associated with cardioprotection.