Search Results (262)

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

In recent years, sustainable biopolymers have attracted increasing attention in environmental engineering as alternatives to conventional synthetic materials due to their renewable origins, biodegradability, and functional versatility. However, their performance and technological viability are strongly influenced by structural design, modification strategies, and behavior under realistic environmental conditions. This review critically analyzes recent advances in biopolymers for environmental remediation, covering their main application formats such as hydrogels, membranes, beads, aerogels, and composites, their interaction mechanisms with contaminants, and their performance relative to conventional adsorbents. Particular emphasis is placed on emerging approaches, including advanced functionalization, integration with inorganic phases, and green synthesis technologies, which have significantly improved efficiency, selectivity, and operational stability. Despite these advances, key limitations persist, particularly regarding mechanical robustness, regenerability, reproducibility, and scalability, underscoring the need for standardized evaluation protocols in complex matrices. The role of biopolymers within circular economy frameworks is also examined, emphasizing their capacity to integrate material sustainability, resource recovery, and multifunctional environmental applications. Overall, sustainable biopolymers are positioned not only as substitutes for traditional materials but also as strategic platforms for the development of next-generation regenerative environmental technologies. Full article

Parastrephia quadrangularis (tola) is a native plant of the Chilean Andean Altiplano that is traditionally used for its anti-inflammatory properties. In this study, the aerial parts of the plant were analysed to determine their fatty acid (FA) profile and to identify bioactive compounds using gas chromatography–mass spectrometry (GC–MS). Both conventional extraction methods and Supercritical Fluid Extraction (SFE) were employed, using a 2³ factorial design with centre-point replicates. The variables included temperature (30–60 °C), pressure (15–45 MPa), and ethanol as a cosolvent (0–30% v/v). Extraction kinetics were evaluated using a linear spline model under central conditions (45 °C, 30 MPa, 15% ethanol). Response variables included extraction yield, Total Phenolic Content (TPC), antioxidant activity measured by Trolox Equivalent Antioxidant Capacity (TEAC), and FA composition. A factorial design identified pressure and ethanol concentration as key drivers of phenolic content and antioxidant activity, as supported by confocal autofluorescence microscopy. Multi-response optimisation based on the desirability function was applied to simultaneously maximise all response variables, yielding predicted optimal extraction conditions at 60 °C, 45 MPa, and 30% v/v ethanol for P. quadrangularis. The FA profile highlighted polyunsaturated FAs such as oleic, linoleic, and linolenic acids, as well as saturated FAs including palmitic and lignoceric acids, and short-chain non-volatile FAs. GC–MS analysis revealed metabolites potentially responsible for the plant’s traditionally reported therapeutic effects. Overall, these results highlight ethanol-based SFE as a sustainable strategy for recovering phenolic compounds and antioxidant-related fractions from ancestral medicinal plants. Full article

Extracellular matrix (ECM) stiffening is a defining biophysical feature of solid tumors that reshape gene regulation through mechanotransduction. Increased collagen crosslinking and stromal remodeling enhance integrin engagement, focal-adhesion signaling and force transmission to the nucleus, where key hubs such as lysyl oxidase (LOX), focal adhesion kinase (FAK) and the Hippo co-activators YAP1 and TAZ (WWTR1) promote proliferation, invasion, stemness and therapy resistance. Here, we synthesize evidence that quantitative changes in matrix stiffness remodel the miRNome and lncRNome in both tumor and stromal compartments, including extracellular vesicle cargo that reprograms metastatic niches. To address heterogeneity in experimental support, we classify mechanosensitive ncRNAs into studies directly validated by stiffness manipulation (e.g., tunable hydrogels/AFM) versus indirect associations based on mechanosensitive signaling, and we summarize physiological versus pathophysiological stiffness ranges across tissues discussed. We further review competing endogenous RNA (ceRNA) networks converging on mechanotransduction nodes and ECM remodeling enzymes, and discuss translational opportunities and challenges, including targeting mechanosensitive ncRNAs, combining ncRNA modulation with anti-stiffening strategies, delivery barriers in dense tumors, and the potential of circulating/exosomal ncRNAs as biomarkers. Overall, integrating ECM mechanics with ncRNA regulatory circuits provides a framework to identify feed-forward loops sustaining aggressive phenotypes in rigid microenvironments and highlights priorities for validation in physiologically relevant models. Full article

To improve crop yields and incomes, farmers consistently adapt their practices to climate and market fluctuations, resulting in highly variable crop field distribution and coverage in space and time. As these dynamics illustrate farmers’ challenges, up-to-date crop-type mapping is essential for understanding farmers’ needs and supporting their adoption of sustainable practices. With global coverage and frequent temporal observations, remote sensing data are generally integrated into machine learning models to monitor crop dynamics. Unlike physical-based models that rely on straightforward use, implementing machine learning models requires extensive user interaction. In this context, this study assesses how sensitive the models’ outputs are to feature selection and hyperparameter tuning, as both processes rely on user judgment. To achieve this, Sentinel-1 (S1) and Sentinel-2 (S2) features are integrated into five distinct models (Random Forest (RF), Support Vector Machine (SVM), Light Gradient Boosting (LGB), Histogram-based Gradient Boosting (HGB), and Extreme Gradient Boosting (XGB)), considering several features selection (Variance Inflation Factor (VIF) and Sequential Feature Selector (SFS)) and hyperparameter tuning (Grid-Search) setup. Results show that the preprocess modeling feature selection (VIF) discards the features that the wrapped method (SFS) keeps, resulting in less reliable crop-type mapping. Additionally, hyperparameter tuning appears to be sensitive to the input features, and considering it after any feature selection improved the crop-type mapping. In this context a three-step nested modeling setup, including first hyperparameter tuning, followed by a wrapped feature selection (SFS) and additional hyperparameter tuning, leads to the most reliable model outputs. For the study region, LGB and XGB (SVM) are the most (least) suitable models for crop-type mapping, and model reliability improves when integrating S1 and S2 features rather than considering S1 or S2 alone. Finally, crop-type maps are derived across different regions and time periods to highlight the benefits of the proposed method for monitoring crop dynamics in space and time. Full article