Search Results (295)

Excessive stress disrupts cardiac homeostasis via complex and multifactorial mechanisms, resulting in cardiac dysfunction, cardiovascular disease, or even sudden cardiac death, yet the underlying molecular mechanisms remain poorly understood. Accordingly, we aimed to elucidate how stress induces calcium dysregulation and contributes to cardiac dysfunction and injury through the nuclear receptor subfamily 3 group c member 1 (NR3C1)/Glomulin (GLMN)/FK506-binding protein 12.6 (FKBP12.6) signaling pathway. Using mouse models of acute and chronic restraint stress, we observed that stress-exposed mice exhibited reduced left ventricular ejection fraction, ventricular wall thickening, elevated serum and myocardial cTnI levels, along with pathological features of myocardial ischemia and hypoxia, through morphological, functional, and hormonal assessments. Using transmission electron microscopy and Western blotting, we found that stress disrupted mitochondrial quality control in cardiomyocytes, evidenced by progressive mitochondrial swelling, cristae rupture, decreased expression of fusion proteins (MFN1/OPA1) and biogenesis regulator PGC-1α, along with aberrant accumulation of fission protein (FIS1) and autophagy marker LC3. At the cellular level, ChIP-qPCR and siRNA knockdown confirmed that stress activates the glucocorticoid receptor NR3C1 to repress its downstream target GLMN, thereby preventing FKBP12.6 ubiquitination and degradation, resulting in calcium leakage and overload, which ultimately impairs mitochondrial quality control and damages cardiomyocytes. In conclusion, our findings reveal that stress induces myocardial damage through NR3C1/GLMN-mediated FKBP12.6 ubiquitination, disrupting calcium homeostasis and mitochondrial quality control, and lay a theoretical foundation for dissecting the intricate molecular network of stress-induced cardiomyopathy. Full article

Soybean (Glycine max L.) is one of the world’s most important sources of plant-based protein, with a protein content exceeding 35–40% (dry basis), along with other essential nutritional benefits. Ideally, soybeans are field-dried to approximately 13% moisture content (wet basis, wb); however, adverse weather conditions can necessitate harvesting at elevated moisture levels sometimes exceeding 20% (wb). In such cases, mechanized drying systems, particularly in northern U.S. regions, become essential for safe storage and quality preservation. This study investigated the effects of drying temperature, airflow rate, and initial moisture content on drying kinetics and kernel integrity using mathematical modeling. Drying behavior was modeled using fractional calculus and compared to the empirical Page model, while kernel cracking and breakage were analyzed using logistic regression. Both fractional and Page models exhibited strong agreement with experimental data (R² = 0.903–0.993). The fractional model achieved superior predictive accuracy, improving RMSE and MAE by 83.7% and 81.2%, respectively, compared to the Page model. Cracking and breakage were more strongly influenced by drying temperature than by initial moisture content, with the greatest quality degradation occurring at high temperatures. Optimal drying conditions were identified as temperatures below 27 °C and initial moisture contents between 19 and 20% (wb), which best preserved kernel quality. Logistic models more accurately predicted breakage than cracking, confirming their effectiveness in assessing mechanical damage during drying. The results affirm the suitability of fractional order models for accurately capturing drying kinetics, while logistic models offer robust performance for evaluating physical quality degradation. These modeling approaches provide a framework for efficient and quality-preserving soybean drying strategies in regions reliant on off-field drying systems. Full article

Aquaculture is recognized as a critical contributor to global high-quality protein provision and food security maintenance. As the world’s most extensively cultivated bivalve species, the Pacific oyster (Crassostrea gigas) holds significant ecological and socioeconomic value. However, environmental impacts associated with its supply chain remain inadequately quantified. In this study, a cradle-to-gate Life Cycle Assessment (LCA) framework was implemented to evaluate the oyster production supply chain in Dalian, China, encompassing breeding, aquaculture, and processing stages and eleven environmental impact categories were systematically quantified. The results demonstrate that the aquaculture stage dominates the life cycle environmental footprint, contributing 88.9% of the total impacts. Marine aquatic ecotoxicity potential (MAETP) was identified as the predominant category, representing 92% of impacts within this stage. To advance sustainable development, further quantification of environmental impact drivers is recommended. Additionally, the feasibility of renewable energy adoption must be assessed, intelligent aquaculture management systems developed, and integrated evaluation models established. This study provides a useful reference for LCA methodology advancement in China’s aquaculture sector while contributing to global aquatic Life Cycle Inventory databases. Full article

Oat is a dual-purpose crop valued for both grain and forage. The stay-green (SG) trait, which delays leaf senescence and prolongs photosynthesis, has been shown to increase yield and quality in several crop species, yet its performance across diverse environments in oats remains underexplored. In this study, multi-location field trials were conducted in Ledu, Huangzhong and Haiyan, Qinghai Province, China, to comprehensively evaluate the performance of stay-green oat lines. The traits evaluated included grain yield components, nutritional quality, and seedling establishment traits. A TOPSIS (technique for order preference by similarity to an ideal solution) model, coefficient of variation (CV) and G × E (genotype × environment) visualization were used to assess adaptability, stability, and genotype × environment interactions. On average, the stay-green lines exhibited an 16.00% increase in plot yield and a 22.93% increase in thousand-grain weight compared to controls. Notable improvements were also observed in the starch (7.58% LN_SG in HZ and HY) and protein (3.58%, QY5_SG all the sites) contents, as well as multiple seedling establishment indices, with the seedling vigor indices increasing by more than 50%. Stability analysis further showed that the stay-green lines were stable in spike length, thousand-grain weight, water-soluble carbohydrates, and seed and seedling vigor. TOPSIS analysis identified ‘LN_SG’ as the top-performing and most adaptable genotype across all environments. Overall, stay-green oat lines demonstrated superior performance in grain yield, nutritional quality, and seedling establishment. These findings highlight their potential for field application and their value as parental materials in oat breeding programs enhancing environmental adaptability and stability. Full article

Background/Objectives: Maternal nutrition and pregestational BMI are critical determinants of pregnancy outcomes. This prospective multicenter observational study investigated the interplay between prepregnancy BMI, dietary patterns, and oxidative/inflammatory status in 153 Italian healthy pregnant women with normal weight (NW), overweight (OW), or obesity (OB). Methods: Detailed clinical, biochemical, placental, and neonatal data were measured at third trimester and delivery. Dietary intake was assessed via a validated questionnaire, and dietary patterns were derived using principal component analysis. Results: OW and OB women had significantly higher levels of inflammatory (CRP, hepcidin) and oxidative stress biomarkers (DNA/RNA damage, catalase activity) than NW. Multivariate models confirmed independent associations between BMI and these biomarkers (CRP: β = 0.297, p = 0.000; hepcidin: β = 1.419, p = 0.006; DNA/RNA damage: β = 409.9, p = 0.000; catalase activity: β = 1.536, p = 0.000). Superoxide dismutase activity and total antioxidant capacity were not associated with BMI. Nutritional intake across BMI groups was largely suboptimal relative to national recommendations, with insufficient levels of polyunsaturated fats and key micronutrients. Four dietary patterns were identified, with adherence varying by BMI. A “prudent-style” pattern (high plant, low animal) was positively associated with gestational age (β = 0.243, p = 0.033) and inversely with neonatal head circumference (β = −0.414, p = 0.050). A “Western-like” pattern (high sugars, snacks, animal fats) was linked to reduced maternal ferritin (β = −2.093, p = 0.036) and increased neonatal head circumference (β = 0.403, p = 0.036). However, not all deviations from the “prudent-style” pattern were metabolically equivalent: while Pattern 3 (high-protein, carbohydrate) may offer partial protective effects, Pattern 4 (moderate protein/plant/sugar) displayed elements of nutritional imbalance with signs of placental inefficiency (β = −0.384, p = 0.023). Conclusions: These findings underscore the dual impact of maternal BMI and diet quality on oxidative-inflammatory balance and perinatal outcomes, supporting the need for early, individualized nutritional strategies in pregnancy. This is further emphasized by the variability in dietary adherence across BMI categories. Full article

Background: The regulation of oil and protein contents in cottonseed is governed by a complex genetic network. Gaining insight into the mechanisms controlling these traits is necessary for dissecting the formation patterns of cottonseed quality. Method: In this study, Xinluzhong 37 (P₁) and Xinluzhong 51 (P₂) were selected as parental lines for two reciprocal crosses: P₁ × P₂ (F₁) and its reciprocal P₂ × P₁ (F₁′). Each F1 was selfed and backcrossed to both parents to generate the F₂ (F₂′), B₁ (B₁′), and B₂ (B₂′) generations. To assess nutritional traits in hairy (non-delinted) and lint-free (delinted) seeds, two indicators, oil content and protein content, were measured in both seed types. Joint segregation analysis was employed to analyze the inheritance of these traits, based on a major gene plus polygene model. Results: In the orthogonal crosses, the CVs for the four nutritional traits ranged at 2.710–7.879%, 4.086–11.070%, 2.724–6.727%, and 3.717–9.602%. In the reciprocal crosses, CVs ranged at 2.710–8.053%, 4.086–9.572%, 2.724–6.376%, and 3.717–8.845%. All traits exhibited normal or skewed-normal distributions. For oil content in undelinted/delinted seeds, polygenic heritabilities in the orthogonal cross were 0.64/0.52, and 0.40/0.36 in the reciprocal cross. For protein content, major-gene heritabilities in the orthogonal cross were 0.79 (undelinted) and 0.78 (delinted), while those in the reciprocal cross were both 0.62. Conclusions: Oil and protein contents in cottonseeds are quantitative traits. In both orthogonal and reciprocal crosses, oil content is controlled by multiple genes and is shaped by additive, dominance, and epistatic effects. Protein content, in contrast, is largely controlled by two major genes along with minor genes. In the P₁ × P₂ combination, major genes act through additive, dominance, and epistatic effects, while in the P₂ × P₁ combination, their effects are additive only. In both combinations, minor genes contribute through additive and dominance effects. In summary, the oil content in cottonseed is mainly regulated by polygenes, whereas the protein content is primarily determined by major genes. These genetic features in both linted, and lint-free seeds may offer a theoretical foundation for molecular breeding aimed at improving cottonseed oil and protein quality. Full article

In response to the growing demand for sustainable protein sources, this study explores the valorization of sunflower meal—a by-product of oil extraction—as a protein-rich ingredient suitable for human nutrition. The aim was to optimize the extraction process and assess the nutritional and safety profile of the resulting protein flour. Mechanical stirring, ultrasound-assisted, and CO₂-assisted extraction methods were evaluated, with mechanical stirring selected for optimization due to its scalability and energy efficiency. A Taguchi L9 orthogonal array was employed to evaluate the effects of pH, temperature, and sample mass on protein content. A first-order regression model was developed and validated (R² = 0.86, p < 0.05), identifying optimal conditions at pH 10.0, 30 °C, and 60 g per 500 mL of distilled water. Under these conditions, protein content reached 49.87%. The extracted protein flour exhibited improved nutritional quality with high protein content, moderate solubility (53.4%), and favorable amino acid composition—particularly rich in glutamic acid, aspartic acid, and arginine. Safety analyses confirmed the absence of detectable aflatoxins and very low PAH levels. These results support the use of sunflower protein concentrate as a sustainable, nutritionally valuable, and safe ingredient for functional food applications. Further studies are recommended to improve functional properties and assess sensory acceptance. Full article

As consumer demand for quality fish products continues to rise, quality has become a key factor in market competition. Ecological aquaculture research is exploring various farming methods to balance high-quality demand with environmental protection. This study compared three aquaculture models—cage culture (CG), recirculating aquaculture (RAG), and rice–fish co-culture (RG)—by analyzing muscle quality (AOAC, GC-MS), intestinal microbiota (16S rRNA), and liver metabolism (LC-MS) to assess their effects on M. albus. In terms of muscle quality, the RG group showed increased levels of EPA and DHA, reduced muscle moisture and crude lipid content, and enhanced crude protein accumulation. The crude protein content was significantly higher in the RAG group than in the CG group (p < 0.05). The RG group also had the highest levels of total, essential, and umami amino acids, followed by the RAG and CG groups. In terms of intestinal microbiota, the RG group had the highest microbial diversity and stability, with increased abundance of Firmicutes and Bacteroidetes and decreased levels of Proteobacteria. Compared to the CG, the RAG group also showed increased microbial diversity and a reduction in pathogenic genera. Liver metabolomics analysis demonstrated that the RG group had significant advantages over the CG group in amino acid, lipid, and energy metabolism. The RAG group exhibited upregulation of glycerophospholipid metabolism and a decrease in oxidative stress marker levels. Overall, the RG group enhanced muscle quality and optimized intestinal and liver metabolism in M. albus. Full article

Background/Objectives: Deep learning (DL) has shown strong potential in analyzing food images, but few studies have directly predicted mass, energy, and macronutrient content from images. In addition to the importance of high-quality data, differences in country-specific food composition databases (FCDBs) can hinder model generalization. Methods: We assessed the performance of several standard DL models using four ground truth datasets derived from Nutrition5k—the largest image–nutrition dataset with ~5000 complex US cafeteria dishes. In light of developing an Italian dietary assessment tool, these datasets varied by FCDB alignment (Italian vs. US) and data curation (ingredient–mass correction and frame filtering on the test set). We evaluated combinations of four feature extractors [ResNet-50 (R50), ResNet-101 (R101), InceptionV3 (IncV3), and Vision Transformer-B-16 (ViT-B-16)] with two regression networks (2+1 and 2+2), using IncV3_2+2 as the benchmark. Descriptive statistics (percentages of agreement, unweighted Cohen’s kappa, and Bland–Altman plots) and standard regression metrics were used to compare predicted and ground truth nutritional composition. Dishes mispredicted by ≥7 algorithms were analyzed separately. Results: R50, R101, and ViT-B-16 consistently outperformed the benchmark across all datasets. Specifically, when replacing it with these top algorithms, reductions in median Mean Absolute Percentage Errors were 6.2% for mass, 6.4% for energy, 12.3% for fat, and 33.1% and 40.2% for protein and carbohydrates. Ingredient–mass correction substantially improved prediction metrics (6–42% when considering the top algorithms), while frame filtering had a more limited effect (<3%). Performance was consistently poor across most models for complex salads, chicken-based or eggs-based dishes, and Western-inspired breakfasts. Conclusions: The R101 and ViT-B-16 architectures will be prioritized in future analyses, where ingredient–mass correction and automated frame filtering methods will be considered. Full article

Objectives: The present study evaluated anthropometry Z-scores of exclusively breastfed infants aged 0~6 months and examined their associations with various parent–infant factors. Methods: This cross-sectional study included 383 mother–infant dyads from 10 Chinese cities in the final analyses, under strict inclusion and exclusion criteria. Data were collected by trained investigators using questionnaires covering demographic characteristics, perinatal health, maternal and infant factors during lactation. Nutrient intake was assessed and calculated by 24 h recall. Anthropometric measurements of parents and infants were taken using calibrated instruments, with infant growth assessed via Chinese growth standards. Statistical analyses included correlation and linear mixed-effect models accounting for regional clustering, with variable selection guided by backward elimination step regression. Nonlinear relationships were explored using spline and piecewise regression methods. Results: Over 60% of the mothers had inadequate energy and protein intake. Approximately two-thirds of the participants had fat intakes exceeding the upper limit. Inadequate or excessive gestational weight gain, poor maternal sleep quality, lactational mastitis, higher maternal fat intake and infant gastrointestinal symptoms were associated with lower infant anthropometry Z-scores. A threshold effect was detected between maternal fat intake and infant WAZ, BMI Z, and WLZ. Conclusions: This study found that anthropometry Z-scores of exclusively breastfed infants aged 0–6 months were significantly associated with certain maternal–infant factors and maternal fat intake, emphasizing the need for early intervention on adverse factors and balanced maternal diet nutrition during lactation. Full article

Background/Objectives: Vaccine immunogenicity is often suboptimal in vulnerable populations such as the elderly, infants, and individuals in low- and middle-income countries. One contributing factor may be pre-existing immunomodulatory conditions, including helminth infections. This study investigates the impact of Fasciola hepatica (F. hepatica) derived molecules on the early humoral response to the COVID-19 mRNA vaccine Comirnaty^® in a mouse model. Methods: BALB/c mice were pretreated with a F. hepatica protein extract (FH) or complete Freund’s adjuvant (CFA) prior to vaccination. Cytokine production and antibody responses were assessed at 0, 14, and 21 days post-vaccination (dpv) through serum analysis and ex vivo splenocyte stimulation with the SARS-CoV-2 receptor-binding domain (RBD) or LPS. Results: At 0 dpv, FH-treated mice showed increased serum IL-10, while CFA treatment induced IL-12. FH- but not CFA-treated splenocytes secreted IL-10 upon RBD or LPS stimulation. At 21 dpv, FH-treated mice lacked IFN-γ production but maintained IL-10 and showed elevated IL-4, consistent with a Th2-skewed profile. Although total anti-RBD IgG levels were similar between groups, FH-treated mice exhibited reduced IgG avidity and a higher IgG1/IgG2 ratio. CFA-treated mice showed delayed avidity maturation. Conclusions: Prior exposure to F. hepatica antigens can modulate the early immune response to Comirnaty^®, affecting both cellular activation and antibody quality. This altered response may reflect a reduced early protective capacity of the vaccine, which might need to be considered when designing or evaluating vaccination strategies using mRNA vaccines in helminth-endemic regions. Full article

This study aimed to explore innovative process technologies for producing milk liqueurs with balanced and stable formulations. Milk liqueurs are known to pose significant technological challenges due to phase separation, which compromises product stability and reduces shelf-life. Interactions between milk proteins, alcohol, carbohydrates, temperature, and ionic strength play a crucial role in such destabilization. Pectin, known for its stabilizing effect, can mitigate phase separation, enhancing both shelf-life and sensory quality. This research focused on developing stable formulations of liqueur milk based on fresh buffalo milk by incorporating the pectin extracted from lemon peels. Rheological properties, particularly viscosity, were assessed in formulations containing varying percentages of pectin. The most stable formulation was identified as the one containing 0.10% pectin. Accelerated shelf-life testing, modelled using the Arrhenius equation, predicted a shelf-life of 15 months at 25 °C under standard lighting. The findings demonstrate that lemon peel-derived pectin, obtained from agri-food waste, sustainably improves product stability. Further studies are needed to characterize the pectin structure and optimize extraction methods for industrial-scale applications. Full article

Background: Malaria remains a significant global health burden, particularly in sub-Saharan Africa, accounting for high rates of illness and death. The growing resistance to frontline antimalarial therapies underscores the urgent need for novel drug targets and therapeutics. Bromodomain-containing proteins, which regulate gene expression through chromatin remodeling, have gained attention as potential targets. Plasmodium falciparum bromodomain protein 1 (PfBDP1), a 55 kDa nuclear protein, plays a key role in recognizing acetylated lysine residues and facilitating transcription during parasite development. Methods: This study investigated ex vivo PfBDP1 gene mutations and identified potential small molecule inhibitors using computational approaches. Malaria-positive blood samples were collected. Genomic DNA was extracted, assessed for quality, and amplified using PfBDP1-specific primers. DNA sequencing and alignment were performed to determine single-nucleotide polymorphism (SNP). Structural modeling used the PfBDP1 crystal structure (PDB ID: 7M97), and active site identification was conducted using CASTp 3.0. Virtual screening and pharmacophore modeling were performed using Pharmit and AutoDock Vina, followed by ADME/toxicity evaluations with SwissADME, OSIRIS, and Discovery Studio. GROMACS was used for 100 ns molecular dynamics simulations. Results: The malaria prevalence rate stood at 12.24%, and the sample size was 165. Sequencing results revealed conserved PfBDP1 gene sequences compared to the 3D7 reference strain. Virtual screening identified nine lead compounds with binding affinities ranging from −9.8 to −10.7 kcal/mol. Of these, CHEMBL2216838 had a binding affinity of −9.9 kcal/mol, with post-screening predictions of favorable drug-likeness (8.60), a high drug score (0.78), superior pharmacokinetics, and a low toxicity profile compared to chloroquine. Molecular dynamics simulations confirmed its stable interaction within the PfBDP1 active site. Conclusions: Overall, this study makes a significant contribution to the ongoing search for novel antimalarial drug targets by providing both molecular and computational evidence for PfBDP1 as a promising therapeutic target. The prediction of CHEMBL2216838 as a lead compound with favorable binding affinity, drug-likeness, and safety profile, surpassing those of existing drugs like chloroquine, sets the stage for preclinical validation and further structure-based drug design efforts. These findings are supported by prior experimental evidence showing significant parasite inhibition and gene suppression capability of predicted hits. Full article

This study addresses the urgent need for the rapid, non-destructive assessment of key soybean components, including moisture, fat, and protein, using near-infrared (NIR) spectroscopy. This study provides technical and theoretical support for achieving the efficient and accurate detection of major soybean components and for the development of portable near-infrared (NIR) instruments. Thirty soybean samples from diverse sources were collected, and 360 spectral measurements were acquired using a 900–1700 nm NIR spectrometer after grinding and standardized sampling. To improve model robustness, preprocessing strategies such as standard normal variate (SNV), multiplicative scatter correction (MSC), and Savitzky–Golay derivatives were applied. Feature selection was conducted using competitive adaptive reweighted sampling (CARS), successive projections algorithm (SPA), and uninformative variable elimination (UVE), followed by model construction with partial least squares regression (PLSR), support vector regression (SVR), and random forest (RF). Comparative analysis revealed that the RF model consistently outperformed the others across most combinations. Specifically, the SPASNV + D₁–RF combination achieved an RPD of 14.7 for moisture, CARS–SNV + D₁–RF reached 5.9 for protein, and CARS–SG + D₂–RF attained 12.0 for fat, all significantly surpassing alternative methods and demonstrating a strong nonlinear learning capacity and predictive precision. These findings show that integrating optimal preprocessing and feature selection strategies can markedly enhance the predictive accuracy in NIR-based soybean analyses. The RF model offers exceptional stability and performance, providing both technical reference and theoretical support for the development of portable NIR devices and practical rapid-quality assessment systems for soybeans in industrial applications. Full article

Potatoes, as the world’s fourth-largest staple crop, are vital for global food security. Efficient methods for assessing yield and quality are essential for policy-making and optimizing production. Traditional yield assessment techniques remain destructive, labor-intensive, and unsuitable for large-scale monitoring. While remote sensing has offered a promising alternative, current approaches largely depend on empirical correlations rather than physiological mechanisms. This limitation arises because potato tubers grow underground, rendering their traits invisible to aboveground sensors. This study investigated the mechanisms underlying hyperspectral remote sensing for assessing belowground yield traits in potatoes. Field experiments with four cultivars and five nitrogen treatments were conducted to collect foliar biochemistries (chlorophyll, nitrogen, and water and dry matter content), yield traits (tuber yield, fresh/dry weight, starch, protein, and water content), and leaf spectra. Two approaches were developed for predicting belowground yield traits: (1) a direct method linking leaf spectra to yield via statistical models and (2) an indirect method using structural equation modeling (SEM) to link foliar biochemistry to yield. The SEM analysis revealed that foliar nitrogen exhibited negative effects on tuber fresh weight (path coefficient b = −0.57), yield (−0.37), and starch content (−0.30). Similarly, leaf water content negatively influenced tuber water content (0.52), protein (−0.27), and dry weight (−0.42). Conversely, chlorophyll content showed positive associations with both tuber protein (0.59) and dry weight (0.56). Direct models (PLSR, SVR, and RFR) achieved higher accuracy for yield (R² = 0.58–0.84) than indirect approaches (R² = 0.16–0.45), though the latter provided physiological insights. The reduced accuracy in indirect methods primarily stemmed from error propagation within the SEM framework. Future research should scale these leaf-level mechanisms to canopy observations and integrate crop growth models to improve robustness across environments. This work advances precision agriculture by clarifying spectral–yield linkages in potato systems, offering a framework for hyperspectral-based yield prediction. Full article