Search Results (266)

Pumpkin (Cucurbita maxima) is an important vegetable in traditional agricultural systems worldwide, including Mesoamerica. Recently, pumpkins have gained global attention for their substantial nutritional value and bioactive compounds; however, their seeds are frequently discarded as waste. These underutilized seeds represent a promising alternative flour source for functional baked goods, such as pancakes. We aimed to optimize the formulation of pancakes made from pumpkin seed flour using a mixture experimental design (MED) and to evaluate the physicochemical, bromatological, and sensory characteristics of the final product. The experimental design investigated various formulations comprising three flour types: shell-less pumpkin seed flour (SL), pumpkin seed flour with shell (WS), and wheat flour (WF). The results indicated that pancakes formulated with WS and SL contained higher levels of protein, amino acids, and fatty acids compared to pancakes control made only with WF. Furthermore, these formulations achieved higher consumer acceptability in sensory evaluations. These findings demonstrate that formulations incorporating SL (moisture content = 21; protein = 19; sensory acceptance = 7) and WS water activity = 0.88; protein = 19; fat = 26; and fiber = 10), optimized through MED, provide superior nutritional quality and sensory acceptance. The formulation with 0.5 of WF and 0.5 of SL exhibited the best health attributes and functional characteristics. Full article

Native Andean potatoes (Solanum tuberosum subsp. andigenum) are a valuable phytogenetic resource due to their compositional diversity and adaptation to high-altitude environments. Their starch is a key functional polysaccharide widely used in food systems; however, information on the kinematic viscosity of dilute gels under moderate thermal conditions remains limited. This study evaluated the effects of temperature (26, 36, and 46 °C) and starch concentration (1–3% w/v) on the kinematic viscosity of gels from three Andean potato varieties: Imilla Negra, Compis, and Peruanita. Starch was extracted from fresh tubers (Puno, Peru) using a wet extraction method, and gels were prepared by heating dispersions at 85 °C for 5 min under controlled conditions. Viscosity (0.61–34.47 cSt) decreased with increasing temperature and increased with concentration, confirming the sensitivity of these systems to thermal and compositional factors. The Arrhenius model adequately described temperature dependence, with activation energies of 15.19–29.75 kJ·mol⁻¹, showing an increasing trend with concentration. At 3% and 26 °C, viscosity followed Compis > Imilla Negra > Peruanita, indicating varietal differences in thickening capacity. These results provide useful rheological data for the design and optimisation of food processes involving dilute Andean potato starch dispersions. Full article

The preservation of hydrobiological diversity is essential to ensuring the stability of the food chain and the sustainable development of high-Andean basins, which face increasing vulnerability to anthropogenic factors such as the construction of dams and reservoirs. In this study, multiple regression models, both linear and nonlinear, were developed to predict the Shannon–Wiener (H′) and Pielou (J′) indices of periphyton and macrobenthos using 21 water quality parameters and concentrations of nine metals in sediments. Samples of macrobenthos and periphyton were collected at seven monitoring stations during the dry and wet seasons between 2014 and 2025. For the analysis, linear regression models were compared with nonlinear machine learning models, specifically Gradient Boosting and Random Forest. Principal component analysis (PCA) revealed that variability of the basin’s ecosystem is dominated by geogenic factors (conductivity, boron, chlorides, and arsenic) and thermal influence. The Gradient Boosting model demonstrated superior predictive capacity (R² = 0.768 for macrobenthos) compared to linear models (R² = 0.354), successfully capturing the nonlinear responses of biota to stressors such as arsenic in sediments and temperature. It is concluded that natural chemical anomalies in the Titire River act as severe ecological filters, and that artificial intelligence shows promising results in the exploration of new applied tools for environmental management in extreme altoandine ecosystems. Full article

Various tubers are cultivated in the Peruvian Andes. Olluco (Ullucus tuberosus) is consumed locally for its culinary qualities and nutritional value. In addition to its resistance to pests and extreme climatic conditions, this Andean tuber is an important source of starch. In this study, the extraction and characterization of the physical, chemical, technofunctional, and thermal properties of olluco starches from the Puka cheqche papalisa (PCP), Bela api chuqcha lisa (BACL), and Q’ello muro lisa (QML) varieties were conducted, with samples collected in 2024. Extraction yields ranged from 3.00 to 4.45%, viscosities from 6443.17 to 6892.77 cP, a high whiteness index from 90.43 to 93.52, water activity less than 0.55, and a heterogeneous particle size distribution. Amylose content ranged from 31.00 to 33.33%. FTIR analysis revealed similar functional groups and structural bonds across the varieties. For technofunctional properties, the QML variety exhibited greater water absorption, a higher solubility index, and greater swelling power. Pasting temperatures ranged from 68.70 to 71.10 °C, with low retrogradation. Thermal analysis showed good thermal stability from 104.46 to 268.42 °C, a low gelatinization temperature from 59.37 to 60.19 °C, and an enthalpy of up to 5.5757 J/g. Olluco starches have high potential for industrial applications, and their ease of cultivation makes them ideal for starch extraction. Full article

Background/Objectives: Colorectal cancer (CRC) is a leading cause of cancer-related mortality worldwide. Emerging evidence highlights the role of the gut microbiota in the development and progression of CRC. Microbial dysbiosis is hypothesized to contribute to chronic inflammation through a variety of mechanisms, such as the production of free radicals, which induce mutagenesis and immune dysregulation in the host, ultimately leading to diseases such as cancer. Methods: Tumor tissue samples or healthy mucosa tissue were collected for bacterial DNA extraction. The V3–V4 region of the 16S rRNA gene was amplified and sequenced using the Illumina MiSeq platform. Bioinformatics analysis was performed with QIIME2, including quality control, DADA2 denoising, alpha and beta diversity calculation, and taxonomic classification using the SILVA database. Results: Differences in microbial composition were observed between groups. The healthy controls exhibited high relative abundances of beneficial genera such as Faecalibacterium, Bacteroides, and Asteroleplasma, whereas the patients with CRC showed enrichment of atypical genera including Novosphingobium, Bradyrhizobium, and Undibacterium. Alpha diversity was lower in the CRC group, and clear clustering by group was observed in the beta diversity analysis. LEfSe analysis identified potential bacterial biomarkers associated with CRC at both the species and genus levels. Conclusions: The findings of this study support the hypothesis that colorectal cancer is associated with distinct alterations in gut microbiota composition, such as an increase in the Novosphingobium genus and a decrease in the Bacteroides genus. An exploratory description of these microbial profiles may aid in the development of microbiome-based diagnostic and therapeutic strategies and contribute to current knowledge of the role of the gut microbiota in CRC in southern Peru. Full article

The aim of this study was to assess the effect of sertraline on the gene expression of placental transporters for hormones, folates, nutrients and drugs over the course of pregnancy in rats. The studies were conducted on gestational days (GDs) 16 and 20 following oral treatment with 10 mg/kg/day sertraline or the vehicle, administered from weaning onward. The weight and area of the fetuses and placentas were analyzed, and maternal plasma sertraline concentrations were measured. Gene expression of ATP-binding cassette transporter b1a and b1b (Abcb1a and Abcb1b), organic anion-transporting polypeptide 4a1(Slco4A1/Oatp4a1), folate receptor-α (Folr1), reduced folate carrier (Slc19A1/Rfc), and L-type amino acid transporter (Slc7A5/Lat1) was evaluated in the placenta. Sertraline reduced fetal weight (p < 0.001) and fetal area (p < 0.01) at GD 16, while no significant differences were observed in placental weight or area between exposed and unexposed groups. Sertraline concentration was significantly lower at GD20 than at GD16 (p < 0.001). At GD 16, sertraline reduced the expression of Abcb1a (p = 0.027), Abcb1b (p < 0.01), and Oatp4a1 (p = 0.037) compared with controls. Conversely, sertraline induced Folr1 expression in both GDs and increased Rfc expression at GD 20, while Lat1 was not affected. These findings indicate that sertraline alters placental drug transporter gene expression and may impair nutrient transfer to the fetus. Full article

Modeling milk yield in dairy cows is essential for improving management decisions, but traditional lactation curve models often fail to capture individual variability. Machine learning approaches offer greater flexibility; however, their performance in small, within-herd datasets and their reliance on explicit cow identification remain unclear, particularly in grazing systems. This study aimed to evaluate whether routinely measured biological traits can substitute for cow identification in machine learning models for predicting daily milk yield within a herd under limited data conditions. The dataset comprised 62 lactations from 48 Holstein–Friesian cows in a grazing system. Two machine learning models were developed: one including cow identification (With ID) and another excluding cow identification but incorporating milk quality traits, body weight, and body condition score (Without ID). Both models were compared with the Wood lactation model fitted to individual cows. The With ID and Without ID models achieved $R^2$ values of 0.97 and 0.93 and RMSE values of 1.2 and 1.6 ${\mathrm{kg} \mathrm{d}}^{-1}$, respectively. Both machine learning models outperformed the Wood model fitted individually to each cow ($R^2$ < 0.90; RMSE > 2.03 ${\mathrm{kg} \mathrm{d}}^{-1}$), which represents an implicitly cow-specific approach. The model including cow identification therefore served as a machine learning analogue to this benchmark. Importantly, the trait-based model closely matched the performance of the cow-specific model. These results demonstrate that machine learning models based on routinely measured traits provide a practical approach for predicting within-herd milk yield from small datasets, while retaining much of the accuracy of cow-specific models. Full article

While convolutional neural networks (CNNs) excel at image classification, their generalization across domains and robustness to nonlinear degradation remain challenges in art media classification (AMC). To address these challenges, this article presents a dual-stage analytical framework: first, an evaluation of seven discrete CNN architectures—ranging from VGG16 to ConvNeXt—subjected to domain shift using the New Spain (Mexico) Art Media Dataset; and second, a formal robustness analysis using an artistic corruption benchmark (Art-C). This benchmark simulates nonlinear degradations, including cracking, oxidized varnish, and pictorial abstraction. Our results demonstrate that while deep convolutional representations maintain acceptable transferability (accuracy $>70\%$), significant variability exists in architectural stability (mean 0.0607) under progressive stochastic degradation. Notably, Xception exhibited the highest robustness (Art-mCE = 0.8039), whereas VGG16 showed the greatest relative performance decay. Severity analysis further indicates that structural perturbations induce higher error rates than chromatic shifts, suggesting that CNNs are more sensitive to topological features (depth and residual connections) than color-space distributions. We provide quantitative evidence characterizing the relationship between architectural topology and empirical stability in non-natural image domains. Full article

Background: Adolescent insomnia is a public health concern associated with affective disturbances, poor academic performance, and cardiometabolic risk. In Peru, nighttime screen use, psychosocial stressors, and social inequalities intensify its impact; however, brief, validated screening instruments remain limited. Objective: To translate, culturally adapt, and evaluate the psychometric properties of the Youth Self-rated Insomnia Scale (YSIS) in Peruvian adolescents, examining its internal structure, reliability, and invariance across sex. Methods: An instrumental study was conducted with 300 students aged 13 to 17 years (M = 15.02; SD = 1.07). Descriptive statistics were calculated, and confirmatory factor analysis (CFA) was performed using a robust estimator. Reliability was assessed through Cronbach’s alpha (α), McDonald’s omega (ω), and average variance extracted (AVE). Factorial invariance by sex was examined at the configural, metric, scalar, and strict levels. Results: The unidimensional model demonstrated adequate fit (χ² = 44.55, df = 18, p < 0.001; CFI = 0.97; TLI = 0.95; RMSEA = 0.07; SRMR = 0.04), with factor loadings ranging from 0.47 to 0.76, high internal consistency (α = 0.86; ω = 0.81), and AVE = 0.51. Although the two-factor model showed acceptable global fit indices, it revealed insufficient AVE in one factor (AVE = 0.40) and a high inter-factor correlation (r = 0.93), suggesting a lack of discriminant validity. Factorial invariance across sex was supported at all evaluated levels. Conclusions: The Spanish version of the YSIS demonstrates a unidimensional structure, adequate internal consistency, and factorial invariance across sex, supporting its use as a brief screening tool in clinical and school settings, as well as in epidemiological studies among Peruvian and Latin American adolescents. Full article

This study examines female microenterprise entrepreneurship in the city of Juliaca, Peru, as a response to structural conditions of poverty, informality, and limited inclusion in public policies. The research aims to understand and interpret the dynamics of women-led entrepreneurship and its relationship with sustainable local socioeconomic development. A qualitative methodological approach based on an interpretive phenomenological design was adopted. Data was collected through in-depth interviews, direct observation, and document analysis with sixteen microentrepreneurs selected through purposive and snowball sampling. The findings reveal that intrinsic motivations (resilience, leadership, and self-fulfillment) and extrinsic motivations (economic independence, access to financing, and education) are key factors in the entrepreneurial process. In addition, entrepreneurial social capital, expressed through family, community, and institutional networks, plays a strategic role in the sustainability of businesses. The results also show that women entrepreneurs actively and significantly contribute to sustainable local socioeconomic development by strengthening local development ecosystems, generating employment, and promoting socially, fiscally, and ethically responsible practices. Despite their role as agents of change and transformation, women entrepreneurs continue to face structural barriers, highlighting the need for public policies with territorial and gender-sensitive approaches to strengthen their impact and sustainability. Full article

Yucca decipiens is a native species from arid and semi-arid regions with emerging nutritional and biotechnological potential. This study evaluated its proximate composition, elemental profile determined by inductively coupled plasma mass spectrometry (ICP-MS), and growth performance under nursery conditions. Proximate analysis revealed a high dietary fiber content in leaves (58.93%) and higher carbohydrate levels in stems (28.83%). Free amino acid content was significantly higher in stems (2.75 g histidine equivalents kg⁻¹) than in leaves (1.76 g kg⁻¹). Multi-elemental profiling (63 elements) showed organ-specific accumulation patterns, with essential macro- and micronutrients predominantly concentrated in leaves, including potassium (28,334 ppm) and calcium (15,345 ppm), while iron was the most abundant trace element in stems (1253 ppm). Principal component analysis (PCA) revealed clear organ-specific mineral partitioning between leaves and stems, indicating differentiated physiological roles and potential selective biomass utilization. Growth assessment conducted over a two-year period demonstrated steady biomass accumulation and good adaptive performance under nursery conditions. Overall, the results highlight the emerging nutritional and agroindustrial relevance of Yucca decipiens for applications in semi-arid environments. Full article

The pronounced heterogeneity of high-Andean soils constitutes a critical constraint to the sustainable productivity of quinoa in southern Peru, where current yields (1.6 t ha⁻¹) remain well below potential (>5 t ha⁻¹). This study aimed to develop a spatially predictive model of a weighted soil quality index (SQIw), the edaphic supply of nitrogen (N), phosphorus (P) and potassium (K), and the agricultural gypsum requirement by integrating edaphoclimatic covariates through regression–kriging. A total of 198 quinoa-cultivated soil samples were analysed; a minimum data set (MDS) was defined using correlation and principal component analyses, and regression–kriging was applied to map SQIw and the variables of interest. The MDS comprised electrical conductivity (EC), organic matter (OM), available P, exchangeable Na, sand, clay, and effective cation exchange capacity (ECEC); exchangeable Na (Wi = 0.160) and available P (Wi = 0.158) received the largest weights in the SQIw. SQIw values ranged from 0.22 to 0.84 and supported a five-class soil quality taxonomy; spatial modelling revealed a dominance of moderate-quality soils across the territory (85.21% of the agricultural area, 13,461.19 ha). The model achieved R² = 0.56, RMSE = 0.05, and MAE = 0.04 for SQIw. Most of the area (12,175.65 ha; 77%) exhibited an intermediate gypsum requirement (9.73–14.33 t ha⁻¹). Nitrogen and phosphorus showed the greatest territorial limitations, whereas potassium was largely non-limiting (84.82–570.17 kg ha⁻¹). These results indicate that sodicity and N–P deficiencies are the primary functional constraints; the generated maps enable prioritisation of gypsum amendments and targeted variable-rate fertilisation strategies to optimise the sustainability of quinoa production in the Altiplano. Full article

This study evaluates the incidence of food and non-food social programs in function of income inequality in households in Peru during 2022–2024 in a context of persistent distributive gaps, despite social interventions aimed at promoting equity. Data from the National Household Survey (ENAHO) were used, with 93,148 observations corresponding to beneficiary and non-beneficiary households, and Recentered Influence Function (RIF) regressions were estimated to decompose the marginal effect of both types of programs on the Gini and Atkinson indices (ε = 0.5; 1.0 and 1.5). Food programs reduced inequality by 2.14% according to the RIF of the Gini and by −1.23%, −2.84% and −4.82% according to the RIF of the Atkinson. Non-food programs generated a greater reduction in the RIF of the Gini (−4.06%) and decreases of −2.52%, −3.51% and −3.06% in the Atkinson. Both types of programs positively influenced the decrease in inequality, highlighting the importance of incorporating structural determinants and household characteristics in redistributive policies. Social programs have positive redistributive effects, although insufficient in the face of structural and territorial inequalities. Strengthening their targeting and territorial articulation is recommended, especially in Andean and Amazon regions. Full article

The design, production, and study of new poly[ADP-ribose] polymerase 1 (PARP-1) inhibitors have emerged as an interesting exploration area, since PARP-1 is an overexpressed enzyme in several carcinomas. In this sense, menadione, or vitamin K3, is well known for its use in correct blood clotting, and for the generation of reactive oxygen species, but it is important to mention that it has been used as an antineoplastic agent against several cell lines. Related to the last commentary, in this work, four novel molecules (2–5) were produced from menadione through a Michael addition protocol, using 1,2-ethanedithiol, cysteamine, benzene-1,4-dithiol, and 4-aminobenzenethiol as nucleophiles, and menadione (1) as substrate, to evaluate them as plausible candidates to inhibit PARP-1. It is convenient to note that after their production and spectroscopic characterization, both docking and theoretical studies for each compound were conducted, using density functional theory (DFT) with the hybrid method B3LYP with the 6-311G(d,p) basis set. As a complement, the reactivity properties determined by DFT calculations were obtained for all compounds; the results revealed that 2 has the best properties to bind with PARP-1, and 3 offered good results. Hence, the target compounds were evaluated in vitro, determining their activity against PARP-1, using olaparib as a reference. Molecules 2 and 3 displayed the free binding energy values −7.97 and −9.35 kcal/mol, respectively, but 2 has the best IC₅₀ value, 13.76 µM. It is important to highlight that 2 and 3 must be considered as potential new inhibitor agents against PARP-1, exhibiting competitive IC₅₀ values with olaparib. Full article

Sustainable water management in high Andean ecosystems involves identifying and protecting recharge areas, integrating both biophysical and social knowledge. The purpose of this study was to conduct a participatory analysis of the recharge zone in the Totorani micro-basin, with a total area of 61.39 km², located in Puno District, Peru, which supplies water to more than 21,000 people. A hierarchical multicriteria analysis in a GIS environment was used, considering five variables (vegetation cover, slope, soil type, geology, and land use), complemented by participatory workshops. The results indicate that moderate recharge predominates in 56.01% of the area, followed by high (39.91%) and very high (3.81%) recharge, associated with the high-altitude Andean wetlands and alluvial plains. Areas of low recharge comprised 0.28% and were found on slopes >30%, with thin soils and low infiltration. The participatory validation process confirmed the alignment between the maps and local knowledge, emphasizing the wetlands and springs as essential areas for water regulation. The stakeholder analysis identified three key groups as direct users: farmers and livestock breeders, public or educational institutions, and social organizations. The stakeholders highlighted threats, such as agricultural expansion, overgrazing, and climate variability, while also emphasizing the importance of traditional conservation practices. Water recharge in Totorani is both a biophysical and social process, requiring the integration of technical methodologies with community participation for effective management. These findings represent a strategic contribution to water governance and offer a replicable model for other high Andean micro-basins. Full article