Search Results (406)

Background/Objectives: Job stress negatively affects physical and psychological health and can lead to behavioral changes such as unhealthy eating. This study aimed to evaluate the relationship between job stress levels, adherence to the Mediterranean diet, and the phytochemical index (PI). Methods: The study included 200 healthy individuals aged 18–50 working at the Tuzla Gum Factory. Data were collected through demographic and dietary questionnaires, two-day 24-h food records, PI values, and anthropometric measurements. Job stress was assessed using the Job Stress Scale, and Mediterranean diet adherence was assessed with the Mediterranean Diet Adherence Questionnaire. Results: Waist and hip circumference, waist/hip ratio, and BMI were significantly higher in individuals with high levels of job stress (p < 0.01). Unskilled workers reported higher stress than professionals (p < 0.01). Significant differences were found in carbohydrate and fiber intake among males and in energy, protein, carbohydrate, and vitamin A intake among females with varying stress levels (p < 0.01). No significant difference in Mediterranean diet adherence was observed between medium and high stress groups. However, women had higher adherence and PI scores than men (p < 0.01). Diet adherence was better among managers than service-sales and technical staff (p < 0.01). PI scores were higher in medium stress than high stress individuals (p < 0.05) and in those with a higher BMI compared to a normal BMI (p < 0.01). Conclusions: Job stress influences both anthropometric parameters and dietary habits. Effective stress management may improve adherence to the Mediterranean diet and phytochemical intake. Workplace strategies supporting healthy eating behaviors are recommended. Full article

This study developed phospholipid-based liposomes loaded with extract from wild thyme (Thymus serpyllum L.) tea processing residues to enhance polyphenol stability and delivery. Liposomes were prepared with phospholipids alone or combined with 10–30 mol% cholesterol or β-sitosterol. The effect of different lipid compositions on encapsulation efficiency (EE), particle size, polydispersity index (PDI), zeta potential, stability, thermal properties, diffusion coefficient, and diffusion resistance of the liposomes was investigated. Liposomes with 10 mol% sterols (either cholesterol or β-sitosterol) exhibited the highest EE of polyphenols, while increasing sterol content to 30 mol% resulted in decreased EE. Particle size and PDI increased with sterol content, while liposomes prepared without sterols showed the smallest vesicle size. Encapsulation of the extract led to smaller liposomal diameters and slight increases in PDI values. Zeta potential measurements revealed that sterol incorporation enhanced the surface charge and stability of liposomes, with β-sitosterol showing the most pronounced effect. Stability testing demonstrated minimal changes in size, PDI, and zeta potential during storage. UV irradiation and lyophilization processes did not cause significant polyphenol leakage, although lyophilization slightly increased particle size and PDI. Differential scanning calorimetry revealed that polyphenols and sterols modified the lipid membrane transitions, indicating interactions between extract components and the liposomal bilayer. FT-IR spectra confirmed successful integration of the extract into the liposomes, while UV exposure did not significantly alter the spectral features. Thiobarbituric acid reactive substances (TBARS) assay demonstrated the extract’s efficacy in mitigating lipid peroxidation under UV-induced oxidative stress. In contrast, liposomes enriched with sterols showed enhanced peroxidation. Polyphenol diffusion studies showed that encapsulation significantly delayed release, particularly in sterol-containing liposomes. Release assays in simulated gastric and intestinal fluids confirmed controlled, pH-dependent polyphenol delivery, with slightly better retention in β-sitosterol-enriched systems. These findings support the use of β-sitosterol- and cholesterol-enriched liposomes as stable carriers for polyphenolic compounds from wild thyme extract, as bioactive antioxidants, for food and nutraceutical applications. Full article

Background/Objectives: Protecting students’ mental health during university is essential for their future quality of life. Therefore, diet should be emphasized as a complementary and preventive strategy in supporting and maintaining mental health. This study aimed to examine the association between dietary inflammatory and antioxidant indices and mental health indicators (depression, anxiety, stress, and well-being) in university students. Methods: This cross-sectional study included 907 university students. We collected dietary data using a 24 h recall. Based on this data, we used 33 food parameters to calculate the Dietary Inflammatory Index (DII) and 6 antioxidant nutrients to calculate the Dietary Antioxidant Index (DAI). We evaluated mental health using the Depression, Anxiety, and Stress Scales-21 (DASS-21), and assessed well-being using the World Health Organization Five Well-Being Index (WHO-5). Results: Overall, 62.4% of participants reported symptoms of depression, 56.2% anxiety, and 40.7% stress. Anxiety and stress levels were significantly higher among females compared to males (p < 0.001 and p = 0.005, respectively). In fully adjusted models, depression scores were significantly higher in the highest DII tertile compared to the lowest (B = 1.74; 95% CI: 0.24–3.26), while well-being was lower (B = −0.82; 95% CI: −1.65 to −0.00). For DAI, participants in tertile 2 had significantly lower anxiety (B = −1.38; 95% CI: −2.63 to −0.14), depression (B = −1.69; 95% CI: −3.19 to −0.19), and stress (B = −1.70; 95% CI: −3.22 to −0.18) scores compared to tertile 1. No significant association was found between DAI and well-being. Conclusions: In this study, university students’ pro-inflammatory diets were associated with poorer mental health profiles. Enhancing the diet’s anti-inflammatory potential may be a promising strategy to support mental health in this population. Full article

Background/Objectives: This study investigates the therapeutic potential of camel milk-derived extracellular vesicles (CM-EVs) for treating colonic damage caused by high-altitude hypoxia, supporting the WHO’s “Food as Medicine” initiative. Methods: Using a 5500 m mouse model, researchers induced colonic injury and treated it with oral CM-EVs for 15 days, comparing results to whole camel milk. Results: CM-EVs outperformed whole milk, significantly improving colon health by restoring barrier integrity and reducing disease activity index (DAI) (p < 0.01). They boosted beneficial bacteria like Lactobacillus and Bifidobacterium and decreased Enterobacteriaceae (p < 0.01). Metabolic analysis showed restored bile acid balance and amino acid modulation via the FXR/NF-κB pathway, reducing TLR4/MyD88-mediated inflammation and oxidative stress (p < 0.01). Fecal microbiota transplantation in the CM-EVs group notably decreased DAI and increased colon length (p < 0.05). Conclusions: CM-EVs repair mucosal damage, balance microbiota, and regulate metabolism to combat hypoxia-induced colonic damage, suggesting their potential as nutraceuticals and altitude-adaptive foods. This showcases nanotechnology’s role in enhancing traditional dietary benefits via precision nutrition. Full article

The utilization of saline–alkali land for rice cultivation is critical for global food security. However, most existing studies on rice salt tolerance focus on the seedling stage, with limited insights into tolerance mechanisms during reproductive growth, particularly at the panicle initiation stage (PI). Leveraging precision salinity-control facilities, this study imposed four salt stress gradients (0, 3, 5, and 7‰) to dissect the differential response mechanisms of six rice varieties (YXYZ: Yuxiangyouzhan, JLY3261: Jingliangyou3261, SLY91: Shuangliangyou91, SLY138: Shuangliangyou138, HLYYHSM: Hualiangyouyuehesimiao, and SLY11:Shuangliangyou111) during PI. The results revealed that increasing salinity significantly reduced tiller number (13.14–68.04%), leaf area index (18.58–57.99%), canopy light interception rate (11.91–44.08%), and net photosynthetic rate (2.63–52.42%) (p < 0.001), accompanied by reactive oxygen species (ROS)-induced membrane lipid peroxidation. Integrative analysis of field phenotypic and physiological indices revealed distinct adaptation strategies: JLY3261 rapidly activated antioxidant enzymes under 3‰ salinity, alleviating lipid peroxidation (no significant difference in H₂O₂ or malondialdehyde content compared to 0‰ salinity) and maintaining tillering and aboveground biomass. SLY91 tolerated 7‰ salinity via CAT/POD-mediated lipid peroxide degradation, with H₂O₂ and malondialdehyde contents increasing initially but decreasing with escalating stress. These findings highlight genotype-specific antioxidant strategies underlying salt-tolerance mechanisms and the critical need for integrating phenomics–physiological assessments at reproductive stages into salt-tolerance breeding pipelines. Full article

Accurate estimation of winter wheat yield under saline stress is crucial for addressing food security challenges and optimizing agricultural management in regional soils. This study proposed a method integrating Sentinel-2 data and field-measured soil salt content (SC) using a random forest (RF) method to improve yield estimation of winter wheat in Kenli County, a typical saline area in China’s Yellow River Delta. First, feature importance analysis of a temporal vegetation index (VI) and salinity index (SI) across all growth periods were achieved to select main parameters. Second, yield models of winter wheat were developed in VI-, SI-, VI + SI-, and VI + SI + SC-based groups. Furthermore, error assessment and spatial yield mapping were analyzed in detail. The results demonstrated that feature importance varied by growth periods. SI dominated in pre-jointing periods, while VI was better in the post-jointing phase. The VI + SI + SC-based model achieved better accuracy (R² = 0.78, RMSE = 720.16 kg/ha) than VI-based (R² = 0.71), SI-based (R² = 0.69), and VI + SI-based (R² = 0.77) models. Error analysis results suggested that the residuals were reduced as the input parameters increased, and the VI + SI + SC-based model showed a good consistency with the field-measured yields. The spatial distribution of winter wheat yield using the VI + SI + SC-based model showed significant differences, and average yields in no, slight, moderate, and severe salinity areas were 7945, 7258, 5217, and 4707 kg/ha, respectively. This study can provide a reference for winter wheat yield estimation and crop production improvement in saline regions. Full article

Climate change has intensified drought stress, threatening global food security by affecting sensitive crops like maize (Zea mays). This study evaluated the potential of Antarctic fungal endophytes (Penicillium chrysogenum and P. brevicompactum) to enhance maize drought tolerance under field conditions with different irrigation regimes. Drought stress reduced soil moisture to 59% of field capacity. UAV-based multispectral imagery monitored plant physiological status using vegetation indices (NDVI, NDRE, SIPI, GNDVI). Inoculated plants showed up to two-fold higher index values under drought, indicating improved stress resilience. Physiological analysis revealed increased photochemical efficiency (0.775), higher chlorophyll and carotenoid contents (45.54 mg/mL), and nearly 80% lower lipid peroxidation in inoculated plants. Lower proline accumulation suggested better water status and reduced osmotic stress. Secondary metabolites such as phenolics, flavonoids, and anthocyanins were elevated, particularly under well-watered conditions. Antioxidant enzyme activity shifted: SOD, CAT, and APX were suppressed, while POD activity increased, indicating reprogrammed oxidative stress responses. Yield components, including cob weight and length, improved significantly with inoculation under drought. These findings demonstrate the potential of Antarctic endophytes to enhance drought resilience in maize and underscore the value of integrating microbial biotechnology with UAV-based remote sensing for sustainable crop management under climate-induced water scarcity. Full article

North Africa faces significant challenges of food insecurity and environmental degradation, driven by rapid population growth, ongoing droughts, severe water stress, and increasing rates of undernourishment. Achieving food security and environmental sustainability requires a balanced evaluation of agricultural productivity, resource efficiency, ecosystem health, and climate resilience. Therefore, this study develops a composite food security and environmental sustainability index (FSESI) that encompasses complex relationships via multidimensional indicators. Data analysis was conducted for Egypt, Libya, Algeria, Tunisia, Morocco, Sudan, and Mauritania, covering the period from 2010 to 2022. A comprehensive methodology employing data envelopment analysis (DEA) for objective weighting and geometric mean aggregation was implemented. The findings indicate notable disparities; Sudan presented the highest undernourishment rate (21.8% in 2010, 11.4% in 2022), whereas Libya faced severe water stress (783.12% to 817.14%). Morocco recorded the highest FSESI score of 0.78, reflecting strong performance in both the food security and environmental dimensions, whereas Algeria and Libya each had scores of 0.48, indicating relatively modest outcomes. Finally, sensitivity analysis was employed to check the robustness of the results. This research highlights the need for immediate policy actions focused on equitable resource management, enhanced agricultural methods, and reinforced food security initiatives. This study directly supports Sustainable Development Goal (SDG) 2, Zero Hunger; SDG 6, Clean Water and Sanitation; SDG 13, Climate Action; and SDG 15, Life on Land, by addressing integrated challenges in food security and environmental sustainability in North Africa. The originality of this work lies in its thorough integration of environmental and food security dimensions through innovative aggregation methods, offering a replicable framework that policymakers and researchers can use to address complex environmental and food security issues in North Africa sustainably. Full article

Current tomato harvesters rely primarily on external color as the sole indicator of ripeness. However, this approach often results in premature harvesting, leading to insufficient lycopene accumulation and a suboptimal nutritional content for human consumption. Such limitations are especially critical in controlled-environment agriculture (CEA) systems, where maximizing fruit quality and nutrient density is essential for both the yield and consumer health. To address that challenge, this study introduces a novel, multimodal harvest readiness framework tailored to nutrient film technology (NFT)-based smart farms. The proposed approach integrates plant-level stress diagnostics and fruit-level phenotyping using wearable biosensors, AI-assisted computer vision, and non-invasive physiological sensing. Key physiological markers—including the volatile organic compound (VOC) methanol, phytohormones salicylic acid (SA) and indole-3-acetic acid (IAA), and nutrients nitrate and ammonium concentrations—are combined with phenomic traits such as fruit color (a*), size, chlorophyll index (rGb), and water status. The innovation lies in a four-stage decision-making pipeline that filters physiologically stressed plants before selecting ripened fruits based on internal and external quality indicators. Experimental validation across four plant conditions (control, water-stressed, light-stressed, and wounded) demonstrated the efficacy of VOC and hormone sensors in identifying optimal harvest candidates. Additionally, the integration of low-cost electrochemical ion sensors provides scalable nutrient monitoring within NFT systems. This research delivers a robust, sensor-driven framework for autonomous, data-informed harvesting decisions in smart indoor agriculture. By fusing real-time physiological feedback with AI-enhanced phenotyping, the system advances precision harvest timing, improves fruit nutritional quality, and sets the foundation for resilient, feedback-controlled farming platforms suited to meeting global food security and sustainability demands. Full article

Ensuring the security of agricultural systems is essential for achieving national food security and sustainable development. Given that agricultural systems are inherently complex and composed of coupled subsystems—such as water, land, and energy—a comprehensive and multidimensional assessment of system security is necessary. This study focuses on Northeast China, a major food-producing region, and introduces the concept of agricultural system coupling security, defined as the integrated performance of an agricultural system in terms of resource adequacy, internal coordination, and adaptive resilience under external stress. To operationalize this concept, a coupling security evaluation framework is constructed based on three key dimensions: reliability (Rel), coordination (Cor), and resilience (Res). An Agricultural System Coupling Security Index (AS-CSI) is developed using the entropy weight method, the Criteria Importance Through Intercriteria Correlation (CRITIC) method, and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method, while obstacle factor diagnosis is employed to identify key constraints. Furthermore, bivariate and trivariate Copula models are used to estimate joint risk probabilities. The results show that from 2001 to 2022, the AS-CSI in Northeast China increased from 0.38 to 0.62, indicating a transition from insecurity to relative security. Among the provinces, Jilin exhibited the highest CSI due to balanced performance across all Rel-Cor-Res dimensions, while Liaoning experienced lower Rel, hindering its overall security level. Five indicators, including area under soil erosion control, reservoir storage capacity per capita, pesticide application amount, rural electricity consumption per capita, and proportion of agricultural water use, were identified as critical threats to regional agricultural system security. Copula-based risk analysis revealed that the probability of Rel–Cor reaching the relatively secure threshold (0.8) was the highest at 0.7643, and the probabilities for Rel–Res and Cor–Res to reach the same threshold were lower, at 0.7164 and 0.7318, respectively. The probability of Rel–Cor-Res reaching the relatively secure threshold (0.8) exceeds 0.54, with Jilin exhibiting the highest probability at 0.5538. This study provides valuable insights for transitioning from static assessments to dynamic risk identification and offers a scientific basis for enhancing regional sustainability and economic resilience in agricultural systems. Full article

Drought significantly constrains common bean (Phaseolus vulgaris L.) production worldwide, and as climate change intensifies, projections indicate a subsequent reduction in yield. This study aimed to identify drought-resilient genotypes among twenty common bean lines in Chile under two water regimes: regular irrigation and terminal drought stress. The research was conducted over two seasons in south-central Chile. Drought significantly reduced grain yield (22.7%), aboveground biomass (37%), harvest index (19.5%), the number of grains per pod (61.3%), and hundred-grain weight (10.1%). Genotypes 452, 473, and 483 exhibited minimal yield reductions (<11%) and maintained stable physiological performance, including higher quantum yield of photosystem II and efficient photoprotective mechanisms (increased ΦNPQ) under stress. In contrast, sensitive genotypes like Blanco Español showed marked yield loss (54%) and lower photosynthetic efficiency. Chlorophyll retention emerged as a key trait for identifying high-yielding, drought-tolerant genotypes. Drought also accelerated crop maturation in susceptible genotypes, compromising yield potential. These findings highlight the importance of integrating agronomic, phenological, and physiological traits in breeding programs to develop drought-adapted varieties. The tolerant genotypes offer valuable genetic traits to improve drought resilience and contribute to food security in the face of climate change. Full article

This study highlights the significant environmental and health risks associated with heavy metal contamination (As, Cd, Cr, and Pb) in Oreochromis niloticus (Nile tilapia) from two locations: the Khon Kaen municipal landfill (study site) and the Thapra commercial fish farm (reference site). It also evaluates potential human health risks and investigates genotoxicity and oxidative stress markers, including malondialdehyde, hydrogen peroxide (H₂O₂), catalase (CAT), and superoxide dismutase (SOD) in fish. Heavy metal concentrations were analyzed using inductively coupled plasma optical emission spectrometry. To determine genetic differentiation, inter-simple sequence repeats with dendrogram construction and genomic template stability (%GTS) were applied. The results showed that the average concentrations of As, Cd, Cr, and Pb in water samples were 0.0848, 0.536, 1.23, and 0.73 mg/L, respectively. These values exceeded safety limits, and the average Cd in sediment (1.162 mg/kg) was above regulatory thresholds. In fish muscle, the average metal concentrations (mg/kg) followed the order Cr (1.83) > Pb (0.69) > Cd (0.096) > As (0.0758), with Pb exceeding food quality standards. The bioaccumulation factor ranked as Cr > Pb > As > Cd. Health risk assessments, including health risk index and carcinogenic risk, suggested Pb contamination poses significant health risks through fish consumption. From dendrogram results, the %GTS of O. niloticus from the landfill and reference sites were 46.34 to 71.67% and 87.34 to 96.00%, respectively. This suggests that fish from the landfill site exhibited greater genetic diversity compared to those from the reference site. Specific oxidative stress markers revealed higher levels of H₂O₂ and significantly lower activities of CAT and SOD in landfill O. niloticus than in the reference site. These results emphasize the urgent need for environmental monitoring, stricter pollution controls, and improved waste management strategies to protect aquatic ecosystems and human health. Full article

Climate change poses a significant threat to agricultural productivity, particularly in regions vulnerable to extreme temperatures and water scarcity, such as Irbid, Jordan. This study assesses the future impacts of projected shifts in precipitation and temperature on wheat yields, using the Decision Support System for Agrotechnology Transfer (DSSAT) model for calibrating and validating under local agro-environmental conditions. Two shared socioeconomic pathways (SSP3-7.0 and SSP5-8.5), representing high-emission and fossil-fuel-intensive futures, were evaluated across mid- and late-century periods (2030–2060 and 2070–2100). The DSSAT model was calibrated using local field data to simulate crop phenology, biomass accumulation, and nitrogen dynamics, showing strong agreement with observed grain yield and harvest index, thereby confirming its reliability for climate impact assessments. Yield projections under each scenario were further analyzed using machine learning algorithms—random forest and gradient boosting regression—to quantify the influence of individual climate variables. The results showed that under SSP5-8.5 (2030–2060), precipitation was the dominant factor influencing yield variability, underscoring the critical role of water availability. In contrast, under SSP3-7.0 (2070–2100), rising maximum temperatures became the primary constraint, highlighting the growing risk of heat stress. Predictive accuracy was higher in precipitation-dominated scenarios (R² = 0.81) than in temperature-dominated cases (R² = 0.65–0.73), reflecting greater complexity under extreme warming. These findings emphasize the value of integrating well-calibrated crop models with climate projections and machine learning tools to support climate-resilient agricultural planning. Moreover, practical adaptation strategies, such as adjusting planting dates, using heat-tolerant varieties, and optimizing irrigation, are recommended to enhance resilience. Emerging techniques such as seed priming show promise and merit integration into future crop models. The findings support SDG 2 and SDG 13 by informing climate-resilient food production strategies. Full article

Mindful eating (ME) has gained recognition in multidisciplinary weight management intervention and prevention programs for dysfunctional eating behaviors. This study aimed to evaluate the associations of mindful eating with body mass index, fat percentage, unhealthy food consumption, and emotional distress in Mexican college students. A cross-sectional study was performed. Anthropometry and body composition were evaluated. A self-reported Mindful Eating Questionnaire developed for the Mexican population, Food Frequency Questionnaire (frequency and serving size), and Depression, Anxiety and Stress Scale (DASS-21) were applied. Two separate analyses were conducted: the first used the whole Mexican Mindful Eating Questionnaire with 11 questions (ME-11), and the second excluded the emotional eating items (ME-8). A total of 224 students were included in the analysis. Lower levels of mindful eating were associated with higher body mass index (BMI) (p < 0.001), waist circumference (p < 0.001), and body fat percentage (p < 0.001), using ME-11 or ME-8. Significant associations were identified between lower levels of mindful eating (ME-11) and consumption of fried foods (p = 0.005), sweets and desserts (p = 0.003), and fast food (p = 0.003). Similar associations were observed using the ME-8 score. In both versions of the questionnaire, depression, anxiety, and stress scores were significantly associated with lower levels of mindful eating. Mindful eating was associated with BMI, body fat, eating habits, and emotional distress. Mindful eating could be used in multidisciplinary educational and intervention programs to promote a healthy lifestyle. Full article

Accurately extracting the phenology of maize, one of the three major staple crops, is crucial for assessing regional suitability under climate change, optimizing field management, predicting yield fluctuations, and ensuring food security. This study compares and validates the accuracy of various vegetation indices, including the normalized difference vegetation index (NDVI), the enhanced vegetation index (EVI), solar-induced chlorophyll fluorescence (SIF), and kernel NDVI (kNDVI), in extracting the phenological phases of summer maize at the sixth leaf (V6), tasseling (VT), and maturity (R6). Additionally, explainable machine learning methods were employed to elucidate how climate and stress factors influence the phenological sequences of summer maize. The results show that compared to NDVI and EVI, SIF and kNDVI are more suitable for extracting the summer maize phenological phase. SIF achieved the highest phenological extraction precision at the V6 and R6 phases, with root mean square errors (RMSEs) of 7.86 and 8.22 days, respectively. kNDVI provided the highest extraction accuracy for the VT phase, with an RMSE of 5 days. SHapley Additive exPlanations (SHAP) analysis revealed that temperature and radiation are the primary meteorological factors influencing maize phenology in the study area. Regarding stress factors, drought and heat stress delayed phenology at the V6 and VT phases, while heat stress prior to maturity accelerated summer maize maturation. In conclusion, this study reveals the potential of emerging vegetation indices for extracting maize phenology, offering both data and theoretical support for regional crop adaptability assessments. Full article