Search Results (498)

Fusarium wilt, caused by Fusarium oxysporum f. sp. ciceris (Foc), is a devastating disease of chickpea (Cicer arietinum L.), leading to vascular necrosis and plant death. This study evaluated 120 chickpea genotypes under natural infection field conditions during spring sowing in southeastern Kazakhstan, assessing disease incidence (DI) and severity (DS) to identify resistant germplasm. Molecular screening using eight SSR markers linked to Foc-1, Foc-2, Foc-3, and Foc-5 loci detected resistant alleles in 18, 26, 19, and 42 genotypes, respectively. The correlation between molecular marker data and phenotypic resistance evaluations confirmed UBC-170 (Foc-2) and TA-194 (Foc-5) as the most predictive diagnostic markers (p < 0.01). Ten genotypes showed complete disease resistance (DI < 5%, R), corresponding to the resistant control (cultivar “WR-315”), with confirmed presence of multiple Foc resistance genes. The results of this study revealed valuable genetic resources for marker-assisted breeding programs aimed at developing Fusarium wilt-resistant chickpea cultivars adapted to Central Asian agroclimatic conditions. Full article

Beyond sensing bitter-tasting compounds, bitter taste receptors (TAS2Rs) have been demonstrated to play a functional role in proton secretion as a key mechanism of gastric acid secretion (GAS) and the cellular uptake of the zinc metal ion. Given its chemical similarity and comparable effects in GAS, we focused this work on cadmium and hypothesized that gastric TAS2Rs are involved in (i) cadmium-induced inhibition of proton secretion and (ii) in its cellular uptake. To test this hypothesis, immortalized human parietal HGT-1 cells were exposed to 62.5–1000 µM CdCl₂ for 30 min to elucidate TAS2R-mediated proton secretory activity (PSA) using a fluorescence-based pH cell assay and to quantitate cellular cadmium uptake by ICP-MS. HGT-1 cells exposed to CdCl₂ exhibited a dose-dependent decrease in PSA, accompanied by a corresponding increase in intracellular cadmium concentrations. Following a TAS2R RT-qPCR screening, the functional roles of TAS2R8 and TAS2R10 were clarified using a siRNA knockdown approach, demonstrating that TAS2R8 promotes and TAS2R10 mediates protection against excessive cellular cadmium accumulation. An additional cDNA microarray screening revealed, via gene ontology analysis, a distinct gene association of TAS2R8 and TAS2R10 with several metal ion transporters. These results provide the first evidence for a specific role of individual TAS2Rs beyond taste perception, particularly in metal ion homeostasis and gastric physiology. Full article

Cold stress impedes the growth and development of wheat (Triticum aestivum) and other crops, ultimately reducing both yields and quality. Research indicates that non-coding RNAs (ncRNAs) play a crucial role in regulating plant stress responses and resistance. In a previous study, we observed that the expression of tae-miR164 was inversely correlated with the expression of TaNAC6A in Dongnongdongmai 1 (Dn1), a winter wheat variety with high cold resistance, under cold-stress conditions. However, the molecular mechanism governing the cold responsiveness of the tae-miR164-TaNAC6A module was not fully understood. We found that tae-miR164 and TaNAC6A were both induced to express in opposite trends, and TaNAC6A was located in the nucleus. We also discovered that the expression of tae-miR164 and its target gene, TaNAC6A, was responsive to short-term freezing stress in transgenic Arabidopsis plants. Compared to wild-type (WT) Arabidopsis plants, OE-tae-miR164 plants showed decreased cold tolerance, whereas those overexpressing TaNAC6A demonstrated increased tolerance. On average, the OE-TaNAC6A and STTM-tea-miR164 plants exhibited fewer morphological abnormalities in response to cold stress, higher antioxidant enzyme activities and gene expression levels, lower levels of reactive oxygen species (ROS) and malondialdehyde (MDA), and higher expressions of AtDREB1, AtDREB2, and AtABI5 in the cold-signaling pathway. Thus, the biological functions of tae-miR164 and TaNAC6A were initially confirmed through heterologous expression strategies, and we have made the first attempt to elucidate its associated tae-miR164-TaNAC6A module of cold resistance. The findings of this research will support further investigations into the regulation of plant stress resistance by ncRNAs and will inform molecular module breeding strategies aimed at enhancing the cold tolerance of crop plants. Molecular module design breeding, as a significant breakthrough in modern biotechnology, is transforming traditional breeding models. Conventional hybrid breeding relies on empirical screening, which is time-consuming and subject to randomness. In contrast, molecular module breeding directly targets key genes and achieves precise regulation through technologies such as gene editing and synthetic biology. Full article

Legumes contribute to sustainable agriculture by reducing fertilizer use, enhancing nitrogen fixation, and with high species diversity (~20,000 species). Spain is a leading EU producer, yielding up to 30,000 tons of different legume varieties annually. The Mediterranean climate, particularly in regions like Andalusia, is under increasing pressure from climate change, with extreme temperature variations and drought becoming more frequent. While these changes may jeopardize crop yields, limited information is available on their effects on the nutritional profile of legumes. From 2017 to 2019, six faba bean (Vicia faba) varieties were monitored in two climatically distinct areas of Andalusia to assess the impact of temperature (T) and rainfall (R) on key nutrients and bioactive compounds, including protein, minerals (K, Ca, Mg, Zn, P, Fe, Mn, B), total polyphenol content (TPC), tannins (TA), and saponins (S). Spearman correlations showed that higher T negatively impacted TPC (r = −0.40) and Mg (r = −0.33), while positively influencing Zn (r = 0.27) and Ca (r = 0.22). Rainfall increased TPC and Mg but reduced TA, Zn, and Ca. Canonical correspondence analysis (CCA) and PERMANOVA (p < 0.001) confirmed T, R, and yield as significant factors. These insights support breeding strategies for climate-adapted, nutrient-rich faba beans and the development of more resilient food systems. Full article

Background/Objectives: Obesity is increasing worldwide, driven by unhealthy dietary habits and sedentary lifestyles. Genetic variations in taste receptor genes, particularly TAS1R2 and TAS2R38, may influence taste preferences, dietary intake, and obesity risk. This study examined associations between TAS1R2 and TAS2R38 polymorphisms, sugar-sweetened beverage (SSB) intake, and obesity risk in Kuwaiti adolescents. Methods: A cross-sectional study was conducted among 260 adolescents aged 11–14 years recruited from public schools in Kuwait. Genotyping of five single-nucleotide polymorphisms (SNPs) was performed using TaqMan assays. Associations between SNPs, SSB intake, and obesity parameters were evaluated using multinomial logistic regression and non-parametric tests, adjusted for age, sex, nationality, BMI z-scores, basal metabolic rate, and physical activity. p-values were corrected using the Benjamini–Hochberg method. Results: The rs713598 SNP in TAS2R38 showed a marginal association with BMI percentiles and z-scores. Adolescents carrying the CC genotype of rs10246939 SNP in TAS2R38 had significantly lower odds of high SSB consumption (>3 servings/week) compared with T-allele carriers (p = 0.018, OR= 0.24, 95% CI = 0.08–0.79). No significant associations were detected for TAS1R2 SNPs. Conclusions: Variations in TAS2R38 were linked to obesity measures and beverage intake in Kuwaiti adolescents, supporting a potential genetic contribution to dietary behaviors. These findings highlight the importance of taste receptor genetics in obesity research, though replication in larger and more diverse populations is required. Full article

Background: Autonomic nervous system (ANS) and vascular factors are associated with glaucoma. However, the association between systemic comorbidity burden and ANS and hemodynamic function in patients with glaucoma remains unclear. This study aimed to examine the association between heart rate variability (HRV) and acceleration plethysmography (APG) parameters and the age-adjusted Charlson Comorbidity Index (ACCI) in patients with glaucoma. Methods: A total of 260 subjects (260 eyes), including 186 with primary open-angle glaucoma (PG) and 74 with exfoliation glaucoma (EG), were enrolled at Shimane University Hospital from June 2023 to July 2024. HRV and APG were assessed using a sphygmograph (TAS9 Pulse Analyzer Plus View). HRV parameters included time-domain measures (SDNN, RMSSD, CVRR) and frequency-domain measures (TP, VLF, LF, HF, LF/HF). APG parameters included the a, b, c, d, and e components of the accelerated pulse wave, and the following vascular types: Type A, Type B, and Type C. The association between ACCI and HRV and APG parameters was evaluated using Spearman’s rank correlation and multivariate regression adjusted for sex, body mass index, pulse rate, systolic and diastolic blood pressure, intraocular pressure, medication score, mean deviation, and glaucoma type. Results: By univariate analysis, against ACCI, significant inverse correlations were observed for several parameters: LnLF (R = −0.17, p = 0.0062); LnLF/LnHF (R = −0.24, p = 0.00012); b peak (R = −0.14, p = 0.031); d peak (R = −0.17, p = 0.0072); and e peak (R = −0.15, p = 0.015). Regarding HRV parameters, multivariate linear regression models showed that ACCI was significantly positively associated with RMSSD (coefficient: 2.861; 95% CI: 0.447 to 5.274) and significantly negatively associated with the frequency-domain parameters LnLF (coefficient: −0.127; 95% CI: −0.245 to −0.009) and LnLF/LnHF (coefficient: −0.038; 95% CI: −0.062 to −0.014). In APG parameters, the c peak was significant associated with ACCI (coefficient: −12.6; 95% CI: −22.5 to −2.69). ACCI was significantly associated with Type B (coefficient: 0.305; 95% CI: 0.057 to 0.552). Conclusions: Greater systemic comorbidity burden may be related to impaired ANS regulation and increased vascular stiffness in glaucoma patients. Full article

Background/Objectives: Emotional competence is a crucial skill in nursing education, yet it remains underdeveloped in academic curricula. This study aims to (1) propose and preliminarily apply the TRI-COM model as a conceptual and educational framework to operationalize the definition of emotions within nursing contexts, and (2) explore the multidimensional structure of emotional competence among nursing students. Methods: A cross-sectional study was conducted with 233 nursing students (M_age = 23.79, SD = 5.19) from various Italian universities, with 82.8% identifying as female. The participants completed an online questionnaire including the Toronto Alexithymia Scale (TAS-20) and the Jefferson Scale of Empathy—Health Profession Student (JSE-HPS) version. Descriptive statistics, t-test, ANOVA, and Pearson’s correlation analyses were performed. Results: The overall mean TAS-20 score was 60.36 (SD = 11.22), which is close to the clinical threshold (cut-off = 61). The EOT subscale showed the highest mean (M = 26.48, SD = 3.16), suggesting a tendency toward externally oriented thinking. The mean JSE-HPS total score was 87.05 (SD = 7.88), with higher scores for Perspective Taking (M = 60.95, SD = 7.42) than Compassionate Care (M = 18.92, SD = 6.40). A significant gender difference was found in Perspective Taking (female: M = 61.54, male: M = 58.10; p = 0.007). The ANOVA results showed no significant differences in empathy across academic years, but the DIF subscale of TAS-20 showed a trend near significance (p = 0.053). Significant negative correlations were observed between age and TAS-20 scores (e.g., age–TAS-20 total: r = −0.23, p < 0.001). Conclusions: The findings suggest a general tendency toward rationalization and emotional detachment among students, possibly as a coping strategy in emotionally demanding contexts. The TRI-COM model—an original pedagogical framework inspired by tripartite theories of emotion—was used as a conceptual lens, providing a preliminary framework to interpret emotional competence in nursing education. Further research is needed to validate its educational relevance and explore practical applications within curricula. Full article

The quality control of fruit purée products such as mango and mangosteen is crucial for maintaining consumer satisfaction and meeting industry standards. Traditional destructive techniques for assessing key quality parameters like the soluble solid content (SSC) and titratable acidity (TA) are labor-intensive and time-consuming; prompting the need for rapid, nondestructive alternatives. This study investigated the use of deep learning (DL) models including Simple-CNN, AlexNet, EfficientNetB0, MobileNetV2, and ResNeXt for predicting SSC and TA in mango and mangosteen purée and compared their performance with the conventional chemometric method partial least squares regression (PLSR). Spectral data were preprocessed and evaluated using 10-fold cross-validation. For mango purée, the Simple-CNN model achieved the highest predictive accuracy for both SSC (coefficient of determination of cross-validation (${\mathrm{R}}_{\mathrm{CV}}^2$) = 0.914, root mean square error of cross-validation (RMSE_CV) = 0.688, the ratio of prediction to deviation of cross-validation (RPD_CV) = 3.367) and TA (${\mathrm{R}}_{\mathrm{CV}}^2$ = 0.762, RMSE_CV = 0.037, RPD_CV = 2.864), demonstrating a statistically significant improvement over PLSR. For the mangosteen purée, AlexNet exhibited the best SSC prediction performance (${\mathrm{R}}_{\mathrm{CV}}^2$ = 0.702, RMSE_CV = 0.471, RPD_CV = 1.666), though the RPD_CV values (<2.0) indicated limited applicability for precise quantification. TA prediction in mangosteen purée showed low variance in the reference values (standard deviation (SD) = 0.048), which may have restricted model performance. These results highlight the potential of DL for improving NIR-based quality evaluation of fruit purée, while also pointing to the need for further refinement to ensure interpretability, robustness, and practical deployment in industrial quality control. Full article

Background/Objectives: The application of predictive and explainable artificial intelligence to bioinformatics data such as single nucleotide polymorphism (SNP) information is attracting rising attention in the diagnosis of various diseases. However, there are few reviews available on the recent progress of genetic artificial intelligence for the early diagnosis of gastrointestinal disease (GID). The purpose of this study is to complete a systematic review on the recent progress of genetic artificial intelligence in GID. Methods: The source of data was ten original studies from PubMed. The ten original studies were eligible according to the following criteria: (participants) the dependent variable of GID or associated disease; (interventions/comparisons) artificial intelligence; (outcomes) accuracy, the area under the curve (AUC), and/or variable importance; a publication year of 2010 or later; and the publication language of English. Results: The performance outcomes reported varied within 79–100 for accuracy (%) and 63–98 for the AUC (%). Random forest was the best approach (AUC 98%) for the classification of inflammatory bowel disease with 13 single nucleotide polymorphisms (SNPs). Similarly, random forest was the best method (R-square 99%) for the regression of the gut microbiome SNP saturation number. The following SNPs were discovered to be major variables for the prediction of GID or associated disease: rs2295778, rs13337626, rs2296188, rs2114039 (esophageal adenocarcinoma); rs28785174, rs60532570, rs13056955, rs7660164 (Crohn’s disease early intestinal resection); rs4945943 (Crohn’s disease); rs316115020, rs316420452 (calcium metabolism); rs738409_G, rs2642438_A, rs58542926_T, rs72613567_TA (steatotic liver disease); rs148710154, rs75146099 (esophageal squamous cell carcinoma). The following demographic and health-related variables were found to be important predictors of GID or associated disease besides SNPs: age, body mass index, disease behavior, immune cell type, intestinal microbiome, MARCKS protein, smoking, and SNP density/number. No deep learning study was found even though deep learning was used as a search term together with machine learning. Conclusions: Genetic artificial intelligence is effective and non-invasive as a decision support system for GID. Full article

Background: Binge eating disorder (BED) frequently arises during the transitional age (18–25 years), a critical developmental period characterized by challenges in autonomy, identity formation, and interpersonal functioning. This study investigated psychopathological predictors of BED risk in this age group, with particular focus on parental bonding, attachment style, body dissatisfaction, alexithymia, and depressive symptoms. Methods: A total of 287 participants aged 18–25 years completed the Binge Eating Scale (BES), Beck Depression Inventory-II (BDI-II), Body Shape Questionnaire (BSQ), Toronto Alexithymia Scale (TAS-20), Attachment Style Questionnaire (ASQ), and Parental Bonding Instrument (PBI). Sociodemographic information and body mass index (BMI) were also collected. Results: Compared with non-BED risk groups, individuals at risk of BED exhibited significantly higher BMI, greater alexithymia, higher body dissatisfaction, more insecure attachment patterns, and lower recalled paternal and maternal care. Hierarchical binary logistic regression revealed that the final model explained 56.1% of the variance (Nagelkerke R²) and correctly classified 92.1% of cases. Significant predictors of BED included body dissatisfaction, elevated BMI, low maternal care, and low maternal protection. Conclusions: This study is the first to examine BED risk factors specifically during the transitional age. Findings indicate that body image dissatisfaction, higher BMI, and inadequate maternal emotional care and protection are salient predictors at this life stage. Preventive interventions should integrate parental psychoeducation, nutritional guidance, and therapeutic strategies addressing both eating disorder symptoms and attachment-related difficulties to reduce BED onset and improve psychosocial outcomes in emerging adults. Full article

Regional extreme climate change remains a major environmental issue of global concern. However, in the context of the joint effects of urban expansion and the urban ecological environment, the responses of the normalized difference vegetation index (NDVI) to regional climate change and its driving mechanism remain unclear. This study takes Chengdu as an example, selects the air temperature (Ta), precipitation (P), wind speed (WS), and soil water content (SWC) within the period from 2001 to 2023 as influencing factors, and uses Theil-Sen median trend analysis and interpretable machine learning models (random forest (RF), BP neural network, support vector machine (SVM), and extreme gradient boosting (XG-Boost) models). The average absolute value of Shapley additive explanations (SHAPs) is adopted as an indicator to explore the key mechanism driving regional climate change in Chengdu in terms of NDVI changes. The analysis results reveal that the NDVI exhibited an extremely significant increasing trend during the study period (p = 8.6 × 10⁻⁶ < 0.001), and that precipitation showed a significant increasing trend (p = 1.2 × 10⁻⁴ < 0.001); however, the air temperature, wind speed, and soil-relative volumetric water content all showed insignificant increasing trends. A simulation of interpretable machine learning models revealed that the random forest (RF) model performed exceptionally well in terms of simulating the dynamics of the urban NDVI (R² = 0.746), indicating that the RF model has an excellent ability to capture the complex ecological interactions of a city without prior assumptions. The dependence relationship between the simulation results and the main driving factors indicates that the Ta and P are the main factors affecting the NDVI changes. In contrast, the SWC and WS had relatively small influences on the NDVI changes. The prediction analysis results reveal that a monthly average temperature of 25 °C and a monthly average precipitation of approximately 130 mm are conducive to the stability of the NDVI in the study area. This study provides a reference for exploring the responses of NDVI changes to regional climate change in the context of urban expansion and urban ecological construction. Full article

Quality control of fresh Lycium barbarum during storage presents significant challenges, particularly regarding the unclear relationship between quality characteristics and storage conditions. This study analyzes the changes in qualitative and structural characteristics, including fruit hardness, soluble solid content (SSC), titratable acidity (TA), and vitamin C (Vc), under various storage conditions (temperature, duration, and initial maturity). We employed optimized Latin hypercubic sampling to develop radial basis function neural networks (RBFNNs) and Elman neural networks to establish predictive models for the quality characteristics of fresh wolfberry. Additionally, we applied the Particle Swarm Optimization (PSO) algorithm to determine the optimal solution for the constructed models. The results indicate a significant variation in how different storage conditions affect the quality characteristics. The established RBFNN predictive model exhibited the highest accuracy for TA and Vc during the storage of fresh wolfberry (R² = 0.99, RMSE = 0.21 for TA; R² = 0.99, RMSE = 0.19 for Vc), while the predictive performance for hardness and SSC was slightly lower (R² = 0.98, RMSE = 385.78 for hardness; R² = 0.94, RMSE = 2.611 for SSC). Multi-objective optimization led to the conclusion that the optimal storage conditions involve harvesting Lycium barbarum fruits at an initial maturity of 60% or greater and storing them for approximately 10 days at a temperature of 10 °C. Under these conditions, the fruit hardness was observed to be 15 N, with SSC at 17.5%, TA at 1.22%, and Vc at 18.5 mg/100 g. The validity of the prediction model was confirmed through multi-batch experimental verification. This study provides theoretical insights for predicting nutritional quality and informing storage condition decisions for other fresh fruits, including wolfberries. Full article

Mixing enthalpy (ΔH_mix), mixing entropy (ΔS_mix), atomic-size difference (δ), and valence electron concentration (VEC) are the indicators determining the phase structures of multi-principal element alloys. Exploring the relationships between the structures and properties of multi-principal element films is a fundamental study. TiZrHf films with a ΔH_mix of 0.00 kJ/mol, ΔS_mix of 9.11 J/mol·K (1.10R), δ of 3.79%, and VEC of 4.00 formed a hexagonal close-packed (HCP) solid solution. Exploring the characterization of TiZrHf films after solving Ta, Y, and Cr atoms with distinct atomic radii is crucial for realizing multi-principal element alloys. This study fabricated TiZrHf, TiZrHfTa, TiZrHfY, and TiZrHfCr films through co-sputtering. The results indicated that TiZrHfTa films formed a single body-centered cubic (BCC) solid solution. In contrast, TiZrHfY films formed a single HCP solid solution, and TiZrHfCr films formed a nanocrystalline BCC solid solution. The crystallization of TiZrHf(Ta, Y, Cr) films and the four indicators mentioned above for multi-principal element alloy structures were correlated. The mechanical properties and thermal stability of the TiZrHf(Ta, Y, Cr) films were investigated. Full article

Ion-conductive-hydrogel strain sensors demonstrate broad application prospects in the fields of flexible sensing and bioelectric signal monitoring due to their excellent skin conformability and efficient signal transmission characteristics. However, traditional preparation methods face significant challenges in enhancing adhesion strength, conductivity, and mechanical stability. To address this issue, this study employed a freeze–thaw cycling method, using polyvinyl alcohol (PVA) as the matrix material, tannic acid (TA) as the adhesion reinforcement material, and lithium chloride (LiCl) as the conductive medium, successfully developing an ion-conductive hydrogel with superior comprehensive performance. Experimental data confirm that the PVA-TA-0.5/LiCl-1 hydrogel achieves optimal levels of adhesion strength (2.32 kPa on pigskin) and conductivity (0.64 S/m), while also exhibiting good tensile strength (0.1 MPa). Therefore, this hydrogel shows great potential for use in strain sensors, demonstrating excellent sensitivity (GF = 1.15), reliable operational stability, as the ΔR/R₀ signal remains virtually unchanged after 2500 cycles of stretching, and outstanding strain sensing and electromyographic signal acquisition capabilities, fully highlighting its practical value in the fields of flexible sensing and bioelectric monitoring. Full article

Background: Seiza, a traditional sitting posture requiring deep ankle plantarflexion and knee flexion, often becomes difficult after ankle fracture surgery because of restricted mobility. Increased stiffness of the tibialis anterior (TA) muscle, particularly in its deep and superficial fibres, may limit plantarflexion and affect functional recovery. This study aimed to investigate the relationship between TA muscle stiffness, assessed using shear wave elastography (SWE), and the ability to assume the seiza posture after ankle fracture surgery. We also sought to determine whether the stiffness in the deep or superficial TA fibres was more strongly correlated with seiza ability. Methods: In this cross-sectional study, 38 patients who underwent open reduction and internal fixation for ankle fractures were evaluated 3 months postoperatively. Seiza ability was assessed using the ankle plantarflexion angle and heel–buttock distance. The shear moduli of the superficial and deep TA fibres were measured using SWE. Ankle range of motion, muscle strength, and self-reported seiza pain were also measured. Multiple linear regression was used to identify the predictors of seiza performance. Results: The shear moduli of both deep (β = −0.454, p < 0.001) and superficial (β = −0.339, p = 0.017) TA fibres independently predicted ankle plantarflexion angle during seiza (adjusted R², 0.624). Pain during seiza was significantly associated with reduced plantarflexion, whereas muscle strength was not a significant predictor. Conclusions: TA muscle stiffness, especially in the deep fibres, was significantly associated with limited postoperative seiza performance. Targeted interventions that reduce deep TA stiffness may enhance functional outcomes. Full article