Search Results (265,179)

Background: Folate is essential for one-carbon metabolism, yet deficiency remains common in non-fortified populations. Bitter-taste-receptor genetics may influence vegetable intake and thus folate status, but the cumulative impact of sensory genetics, diet, and sociodemographic factors is unclear. This study aimed to investigate how taste-related genetic variants, aggregated into a polygenic score (PGS), together with dietary behavior and sociodemographic factors, modulate serum folate levels in a Hungarian adult population, including Roma ethnic minority participants. Methods: In a cross-sectional sample of 626 adults (312 from the Hungarian general population and 314 from the Roma ethnic minority), serum folate was quantified by chemiluminescent immunoassay, and eight taste-related single-nucleotide polymorphisms (SNPs) were genotyped. A four-SNP PGS (TAS2R19 rs10772420, OR10G4 rs1527483, TRPV1 rs8065080, and CD36 rs1761667) was optimized via the stepwise method (ΔR² criterion, FDR q < 0.05). Multivariable linear regression was used to assess associations with continuous folate, and logistic models were used to evaluate deficiency risk (≤13 µmol/L; area under the curve, AUC). Interaction terms were tested for effect modification by education and vegetable intake, and mediation pathways were examined by structural equation modeling with 1000 bootstrap replications. Results: TAS2R19 rs10772420 was found to be the strongest predictor of serum folate level. This effect remained significant even after adjusting for vegetable intake (β = 1.12 nmol/L; p = 0.003), suggesting a persistent genetic association independent of vegetable intake. The taste-related PGS exhibited a significant dose–response relationship with folate levels (p < 0.001) but had only modest discriminatory power for deficiency (AUC = 0.569). Higher educational attainment amplified the associations between the PGS and folate levels (p for interaction < 0.05), whereas vegetable intake did not mediate genetic effects. The associations were consistent across Hungarian general and Roma population subgroups. Conclusions: Bitter-taste-receptor genetics are associated with serum folate levels in a pattern not substantially mediated by self-reported vegetable intake, and this influence is further modified by education. These findings support the development of genome-informed, culturally tailored nutrition strategies for non-fortified populations. Full article

The accurate monitoring and dynamic analysis of metal ions are of considerable practical significance in environmental toxicology and life sciences. Colorimetric analysis and surface-enhanced Raman scattering (SERS) sensing technologies, utilizing the aggregation effect of gold and silver nanoparticles (Au/Ag NPs), have emerged as prominent methods for rapid metal ion detection. While sharing a common plasmonic basis, these two techniques serve distinct yet complementary analytical roles: colorimetric assays offer rapid, instrument-free visual screening ideal for point-of-care testing (POCT), whereas SERS provides superior sensitivity and structural fingerprinting for precise quantification in complex matrices. Furthermore, the synergistic integration of these modalities facilitates the development of dual-mode sensing platforms, enabling mutual signal verification for enhanced reliability. This article evaluates contemporary optical sensing methodologies utilizing aggregation effects and their advancements in the detection of diverse metal ions. It comprehensively outlines methodological advancements from nanomaterial fabrication to signal transduction, encompassing approaches such as biomass-mediated green synthesis and functionalization, targeted surface ligand engineering, digital readout systems utilizing intelligent algorithms, and multimodal synergistic sensing. Recent studies demonstrate that these techniques have attained trace-level identification of target ions regarding analytical efficacy, with detection limits generally conforming to or beyond applicable environmental and health safety regulations. Moreover, pertinent research has enhanced detection linear ranges, anti-interference properties, and adaptability for POCT, validating the usefulness and developmental prospects of this technology for analysis in complicated matrices. Full article

Background/objectives: Grapevine (Vitis vinifera L.) is one of the most economically and culturally important fruit crops worldwide and hosts more than 100 viruses. Viral infections can cause severe yield losses, but plants can adapt to infection through changes in miRNA-mediated regulatory pathways. MicroRNAs are key regulators of plant development and stress responses. Several prediction tools are available for miRNA detection from small RNA sequencing data, each relying on different algorithms. The aim of this study was to compare miRNA predictions generated by three widely used tools (miRador, ShortStack, and miRDeep2) and to evaluate how viral coinfections influence miRNA expression in grapevine. Methods: Two grapevine cultivars, Refošk (“Terrano”) and Zeleni Sauvignon (“Sauvignon Vert”), were analyzed. Small RNA sequencing was performed on virus-free plants and plants coinfected with grapevine Pinot gris virus (GPGV), grapevine rupestris stem pitting-associated virus (GRSPaV), and grapevine rupestris vein feathering virus (GRVFV). Three miRNA prediction tools were used to identify miRNAs annotated in public databases. Differential expression analysis was performed separately for each tool and by using an integrated approach that combined all three datasets. The expression of selected miRNAs was further evaluated using stem-loop RT-qPCR. Results: The three prediction tools detected markedly different numbers of miRNAs, resulting in largely distinct sets of differentially expressed miRNAs and limited overlap between individual analyses. The integrated approach yielded a separate set of differentially expressed miRNAs, most of which overlapped with at least one individual dataset. Stem-loop RT-qPCR analysis supported the differential expression of several selected miRNAs. Conclusions: This study provides new insight into miRNA expression in grapevine under mixed-virus infection and demonstrates that miRNA profiling outcomes are strongly influenced by the choice of bioinformatic prediction tool. Our results highlight the importance of integrated analytical strategies combined with experimental validation to obtain robust and biologically meaningful interpretations of miRNA expression in plants. Full article

Objective: This retrospective study was conducted to evaluate long-term outcomes lcomplication rates of crown restorations supported by different types of endodontic posts and to determine the influence of post material on biological and technical outcomes. Materials and Methods: Clinical and radiographic data from 437 crowned teeth retained by fiber, metallic, or custom-made posts were collected at Qassim University Dental Hospital between August and November 2025. Biological (secondary caries, periapical lesions) and technical (debonding, fracture, chipping) complications were recorded. Kaplan–Meier and life-table analyses were used to estimate complication-free survival, and Cox regression was employed to identify significant predictors (α = 0.05). Results: The mean observation period was 6.76 ± 4.88 years. The overall complication rate was 56.8%. Crowns restored with fiber posts exhibited the lowest complication rate (40.0%) and the highest 15-year cumulative survival (52%), followed by custom-made (38%) and metallic posts (15%). Fiber posts demonstrated a significantly lower hazard of complications than metal posts (HR = 1.70, p = 0.009). Female sex (HR = 1.69, p = 0.001) and mandibular location (HR = 1.36, p = 0.048) were associated with increased risk. Metal–ceramic crowns showed a protective effect compared to ceramic crowns (HR = 0.56, p = 0.001). Conclusions: The type of post significantly affected long-term prognosis of crowned endodontically treated teeth. Fiber posts provided the most favorable outcomes by minimizing catastrophic root fractures, while metallic and custom-made posts demonstrated higher complication hazards. Crown material, arch location, and patient factors further influenced survival outcomes. Full article

Multidrug-resistant Pseudomonas aeruginosa is a major cause of healthcare-associated infections, particularly in high-burden clinical settings where rapid tools to capture clinically relevant resistance and virulence phenotypes are needed. In this study, we applied an integrated whole-genome sequencing (WGS) and Fourier-transform infrared (FTIR) spectroscopy approach to evaluate genotype–phenotype relationships in multidrug-resistant P. aeruginosa isolates collected during the COVID-19 pandemic. High-quality WGS data were used to characterize antimicrobial resistance determinants, mobile genetic elements, and virulence gene repertoires, while FTIR spectroscopy provided culture-based phenotypic fingerprints reflecting cell envelope composition. Genomic analyses revealed a conserved efflux-centered intrinsic resistance backbone, variably supplemented by acquired β-lactamases and aminoglycoside-modifying enzymes, alongside a largely conserved core virulome with heterogeneity driven primarily by type III secretion system effector profiles. Comparison of FTIR- and WGS-derived distance matrices revealed a weak but statistically significant global association, indicating a non-linear relationship between genomic relatedness and phenotypic similarity. Cluster-level concordance was strongly scale-dependent, with high agreement emerging only at finer clustering resolutions, consistent with FTIR capturing phenotypic variation linked to regulatory, metabolic, and cell envelope adaptations rather than deep phylogenetic structure. Together, these findings show that multidrug resistance and virulence in P. aeruginosa are shaped by a modular genomic architecture that manifests as distinct, measurable phenotypic states. The observed scale-dependent concordance supports FTIR spectroscopy as a rapid, cost-effective phenotypic screening tool for outbreak-oriented surveillance, complementing WGS in integrated antimicrobial resistance monitoring workflows. Full article

In lung cytology, screeners and pathologists examine many cells in cytological specimens and describe their corresponding imaging findings. To support this process, our previous study proposed an image-finding generation model based on convolutional neural networks and a transformer architecture. However, further improvements are required to enhance the accuracy of these findings. In this study, we developed a cytology-specific image-finding generation model using a vision transformer and open-source large language models. In the proposed method, a vision transformer pretrained on large-scale image datasets and multiple open-source large language models was introduced and connected through an original projection layer. Experimental validation using 1059 cytological images demonstrated that the proposed model achieved favorable scores on language-based evaluation metrics and good classification performance when cells were classified based on the generated findings. These results indicate that a task-specific model is an effective approach for generating imaging findings in lung cytology. Full article

Background/Objectives: Certain yeast species are recognized as significant opportunistic pathogens, capable of causing severe systemic infections, particularly in immunocompromised individuals or those with disrupted physiological barriers. The rising incidence of invasive candidiasis associated with vascular infections poses a significant clinical challenge due to the high mortality rates and the limited efficacy of conventional antifungal therapies. The formation of resilient biofilms on vascular catheters by species such as Candida albicans and Nakaseomyces glabratus further complicates treatment, often leading to persistent fungemia and necessitating device removal. With the emergence of multidrug-resistant (MDR) strains, there is a critical need for new therapeutic agents like rezafungin—a novel, long-acting echinocandin with potential enhanced antibiofilm activity. Methods: This study tested susceptibility to antimycotics available in Poland (fluconazole, voriconazole, posaconazole, amphotericin B, anidulafungin, caspofungin, and micafungin) using the commercial Micronaut-AM test (Bruker, Bremen, Germany). Susceptibility to rezafungin (Angene Chemical, Great Britain) was determined using the microdilution method in RPMI medium, recommended by European Committee on Antimicrobial Susceptibility Testing (EUCAST), with amphotericin B as a control compound. We evaluated the biofilm-forming capacity and the in vitro activity of rezafungin against 42 clinical isolates of Candida albicans and Nakaseomyces glabratus recovered from positive blood cultures. Results: The obtained minimum inhibitory concentration (MIC) values suggest rezafungin activity against all the tested isolates, with different susceptibility to echinocandins and other antifungal drugs (azoles, amphotericin B) currently registered and used in Poland. The MIC readings for rezafungin were in the range of 0.008–0.5, with MIC₅₀ = 0.016 and MIC₉₀ = 0.25. The isolates were categorized as low, moderate, or strong biofilm producers according to established Stepanović criteria (cut-off values OD₆₃₀ < 0.019, 0.19–0.38, >0.38, respectively). Furthermore, the higher minimum biofilm eradication concentrations (MBECs) compared to the minimum inhibitory concentrations (MICs) of planktonic cells confirm the reduced activity of rezafungin against biofilms. Conclusions: Critically, the high antibiofilm efficacy at clinically achievable concentrations suggests that rezafungin shows promise as a potential therapeutic option for catheter-related candidemia, though further clinical studies are needed. Furthermore, the high susceptibility of N. glabratus isolates—a species frequently associated with azole resistance—suggests rezafungin may be a valuable addition to the existing antifungal arsenal of multidrug-resistant (MDR) fungal infections in hospital settings. Future research should focus on in vivo models to confirm if these in vitro results translate into accelerated clearance of vascular biofilms. Full article

Donkey milk has been increasingly studied in recent years and has been proposed to be a functional food. However, its components undergo changes during lactation, including its lipid profile and redox-related properties. This study analyzed the fatty acid composition, antioxidant parameters, and redox-modulating properties of donkey milk from the Bulgarian local donkey breed at three lactation stages (0–30, 31–60, and 61–90 days postpartum). Milk samples from 40 clinically healthy donkeys were grouped by days postpartum. A cross-sectional design with three lactation stage groups was used; one-way ANOVA tested group differences with Tukey’s post hoc test, and associations with days postpartum were evaluated using regression models. Fatty acid methyl esters were analyzed by GC-FID, and the atherogenic (AI) and thrombogenic (TI) indices were calculated. Antioxidant enzymes (SOD, CAT, and GPx-1), GSH, MDA, TAC, and EPR-based redox markers (DPPH, Asc•, ROS, NO•, TEMPOL, and 5-MSL) were analyzed. During lactation, monounsaturated fatty acids decreased (approximately 32% in the first month to ~30% by the third month), while AI increased from ~1.9 to ~2.2, and TI increased to ~2.5. SOD and GPx-1 activities increased with advancing lactation, while total antioxidant capacity decreased (213.4 to 199.7 µmol). DPPH radical scavenging activity remained stable during lactation. EPR-detected ROS and NO• values increased with advancing lactation stage, while thiol-bound 5-MSL decreased, suggesting a shift in the balance between oxidative challenge and antioxidant defense during lactation. Regression modeling confirmed a significant effect of lactation period on multiple compositional and redox-related parameters. Therefore, the stage of lactation should be taken into account when interpreting the biological value, redox stability, and potential functional properties of milk, as well as when developing milk management and yield strategies. Full article

Green streets—streets that systematically integrate vegetation-based and nature-based solutions into the public right-of-way as part of contemporary urban green infrastructure and climate adaptation strategies—have become an increasingly important planning and design approach. While historical precedents of vegetated and tree-lined streets exist, modern green streets represent a more integrated and performance-oriented paradigm that combines stormwater management, ecosystem service provision, climate resilience, and social functions within coordinated policy and infrastructure frameworks. This review synthesizes current knowledge on green street management practices and their performance across environmental, hydrological, ecological, and socio-spatial dimensions. The analysis examines design strategies, maintenance regimes, governance arrangements, and performance assessment methods reported in the literature. Evidence indicates that well-managed green streets can significantly reduce stormwater runoff, improve water quality, mitigate urban heat, enhance biodiversity, and contribute to pedestrian comfort and neighborhood livability. However, reported outcomes vary widely depending on local climate, design specifications, maintenance intensity, and institutional capacity. Persistent research gaps include limited long-term monitoring, underrepresentation of cities in the Global South, insufficient integration of governance, economic, and social dimensions, and a lack of standardized performance metrics. Comparative and longitudinal studies remain scarce, constraining understanding of lifecycle performance and trade-offs. Future research should prioritize standardized evaluation frameworks, long-term empirical monitoring, socio-spatial equity assessments, and the integration of emerging digital technologies for real-time monitoring and decision support. Strengthening these areas is essential to support evidence-based planning and scalable implementation of green streets as a key component of sustainable urban development. Full article

This study evaluated airborne laser scanning (ALS) as a large-scale tool for forest carbon quantification by comparing ALS-derived estimates with traditional field sampling across multiple forest strata. Above-ground biomass was estimated using two different, commonly used equations, while below-ground biomass was derived from peer-reviewed root-to-shoot ratios. ALS and field estimates differed across forest strata and carbon pools: ALS detected higher live tree carbon in harvested areas—capturing residual trees often missed in traditional cruises—but underestimated dead wood carbon, relative to field-based methods. Consistent differences were also observed between biomass equations, with Woodall estimates being 12.8% and 16.7% lower than Jenkins estimates for ALS and field methods, respectively. The study further incorporated soil organic carbon (SOC) and carbon dating data, providing additional insight into subsurface carbon stocks and the temporal dynamics of forest carbon pools. Overall, ALS proved to be an efficient, repeatable, and scalable method for carbon assessment, offering clear advantages in monitoring carbon flux over time when integrated with forest management protocols. Although further research is needed to refine biomass equations and explore emerging technologies such as Geiger Mode LiDAR, ALS has strong potential to enhance forest carbon crediting processes and support climate change mitigation goals. Full article

Objectives: Glioblastoma multiforme (GBM) is considered the most aggressive primary brain tumor, which often exhibits tumor heterogeneity. Hypoxia is a key aspect of intratumoral heterogeneity that contributes to poor prognosis in GBM. In this study, we aimed to develop machine learning (ML) models using radiomics and clinical features for the prediction of one-year survival for GBM. Methods: Data from 35 patients in the ACRIN 6684 trial, including fluoromisonidazole (FMISO)-positron emission tomography (PET), magnetic resonance (MR) (T1, T2, and fluid-attenuated inversion recovery (FLAIR)) images, and clinical information, were retrieved from The Cancer Imaging Archive (TCIA). Three ML algorithms, namely, support vector machine (SVM), random forest (RF), and linear regression (LR), were utilized to analyze selected features. Receiver-operating characteristic (ROC) curves were utilized to evaluate the predictive performance of the models. Several statistical analyses, namely, the permutation test, the permutation importance of selected features, Fisher's exact test, and the unpaired t-test, were performed to analyze the models and features. Results: FMISO achieved the best performance in radiomics models, with an area under the curve (AUC) of 0.870. The clinical data model achieved the best performance of all models, with an AUC of 0.921, outperforming the combined all sequential forward selection (SFS) model (AUC: 0.862). Female sex (p = 0.030) and younger age (p = 0.0043) were significantly associated with better prognosis. Conclusions: Our proposed models have the potential to predict the one-year survival of GBM and facilitate personalized therapy. Future studies with a larger sample size are needed to confirm the generalizability of the models. Full article

The rapid assessment of building damage within a region after an earthquake is crucial for post-earthquake relief efforts. The current building damage assessment methods primarily employ remote sensing or structural equation modeling, which suffer from poor timeliness, are largely focused on individual buildings, and face difficulties in obtaining structural data. Furthermore, building assessment cases are often applicable only to a single earthquake, exhibiting poor generalization performance when the study area changes. This paper addresses the above issues by selecting historical earthquake cases from different geographical regions. The data includes hazard-causing factors, hazard-affected body factors, and hazard-formative environment factors captured by multi-sensors, as well as damage proxy map (DPM) data. In this study, we developed a technical approach to improve the generalization performance of building earthquake damage assessment using the light gradient boosting machine (LightGBM) and sequential least squares quadratic programming (SLSQP) methods. Among them, the LightGBM method is used to construct the evaluation model, while the SLSQP method is used to seek the optimal combination of single-earthquake-case models when constructing a multi-earthquake-case model. The analysis shows that the constructed multi-earthquake-case model is superior to the baseline model. Compared with the baseline model, the constructed multi-earthquake-case model has an mean absolute error (MAE) reduced by 0.015–0.037, an root mean squared error (RMSE) reduced by 0.021–0.056, and an coefficient of determination (R²) increased by 0.096–0.296. Furthermore, the availability of historical earthquake cases as prior data can improve the effectiveness of post-earthquake building damage assessments and is suitable for damage assessments lacking building structural data. Full article

Advances in medical care now enable significant functional recovery after traumatic limb injuries. The return-to-duty decision-making process is highly variable and dependent on multiple factors. To retain service members (SM) post-injury, there needs to be a robust method to inform the decision-making process. The collection of outcome data and decision tree analysis has the potential to assist in the development of an efficient decision support tool. Data were combined from two previous research studies on 31 injured SMs (26 with limb salvage wearing custom dynamic ankle–foot orthoses and 5 with varying levels of lower limb amputation wearing prostheses). Forty-two factors across military, demographic, injury, and outcome measures were used to develop categorical tree models to classify return to duty after injury. The feasibility of the final pruned model was evaluated using a 10-fold cross-validation to calculate sensitivity, specificity, and misclassification rate. The overall misclassification rate for the final pruned model was 29% (9/31). The model classified participants into successful return to duty: (1) Post Concussion Symptom Scale < 20 and (2) age at time of assessment ≥34. These preliminary results suggest that decision tree modeling could be an effective approach to augmenting the return-to-duty decision-making process. Full article

Background: The growing interest in developing new bioactive agents from natural sources led to medicinal and aromatic plants. These plants provide valuable phytochemicals that can serve as natural preservatives, food additives, and flavorings, with various applications. The aim of this study is to evaluate the potential of Juniperus oxycedrus berries’ supercritical extract through preliminary screenings related to in vitro antibacterial activity, as well as bioinformatics assessments of absorption and toxicity. Methods: Supercritical carbon dioxide (CO₂) was used to extract the bioactive phytochemical compounds from the berries. The extract was characterized using spectrophotometric methods and reverse-phase high-performance liquid chromatography (RP-HPLC). The antibacterial potential was tested against Staphylococcus aureus ATCC 25923, where the Minimal Inhibitory Concentration and the Minimal Bactericidal Concentration were determined. Additionally, the influence of the extract on the growth curve kinetics of S. aureus was assessed. For the bioinformatics analyses, SwissADME and ProTox-3.0 prediction software were utilized, focusing on the identified phenolic compounds as fingerprint molecules. Results: The results demonstrated that exposure to the juniper extract inhibited bacterial growth, resulting in a prolonged lag phase of 6 to 8 h, depending on the concentration of the extract. The software predictions revealed that the investigated phenolic compounds might exhibit high gastrointestinal absorption, along with potential interactions with metabolic mediators and pathways. Conclusions: The in vitro and in silico findings support the application of J. oxycedrus berries extract as an alternative or complementary strategy for pharmacological treatment and food applications aimed at targeting S. aureus. Full article

To enhance the hydrothermal performance of micro pin fin heat sinks (MPFHSs), this paper proposes five MPFHSs with different streamwise pin fin height variation modes and experimentally and numerically compares their hydrothermal performance, including pressure drop, maximum and average temperatures, and comprehensive performance evaluation criteria. The results indicate that, taking the uniform PFs height (UH) design as a reference, the designs with linearly increasing streamwise PFs height (LIH) and increasing streamwise PFs height with decelerating growth rate (DIH) demonstrate lower heat sink temperatures. Conversely, the designs with linearly decreasing streamwise PFs height (LDH) and decreasing streamwise PFs height with accelerating reduction rate (ADH) result in higher heat sink temperature. In addition, the comprehensive performance of LDH and ADH outperforms that of UH at low inlet flow rates, while the DIH surpasses that of UH at higher flow rates. As the inlet flow rate increases from 0.02 L/min to 0.5 L/min, our numerical study shows that the comprehensive performance of LDH and ADH decreases by 14.9% and 6.2%, respectively, whereas that of LIH and DIH increases by 17.4% and 10.2%, respectively. This finding provides insights to improve the hydrothermal performance of MPFHS. Full article