Search Results (312)

Hypoxia-driven regulatory mechanisms play a critical role in tumor progression and therapeutic resistance in hepatocellular carcinoma (HCC), yet hypoxia-responsive microRNAs (HRMs) remain incompletely characterized. This study aimed to identify HRMs in canine HCC to evaluate their diagnostic potential and translational relevance to human disease. Next-generation sequencing of two canine HCC cell lines under normoxic and hypoxic conditions identified 332 and 321 differentially expressed miRNAs, respectively. Integrating these with tumor tissue data revealed 11 HRMs, featuring consistent upregulation of cfa-miR-210 and cfa-miR-34a, which was validated via RT-qPCR in hypoxic cells and clinical tissues. Both miRNAs were significantly elevated in plasma-derived extracellular vesicles (EVs), highlighting their value as promising circulating biomarkers (AUC 1.00 for miR-210; 0.98 for miR-34a). Target gene and pathway analyses identified shared regulatory nodes, including TGIF2 and SPRED1; and enrichment of MAPK, Ras, PI3K-Akt, and Rap1 signaling, broadly linked to hypoxia adaptation, cellular metabolism, and stress-response signaling. Cross-species comparison with human HCC datasets showed that, while miR-210 associates with poor prognosis in human HCC, miR-34a exhibits tumor-suppressive features. These findings define complementary HRM programs in canine HCC, reflecting conserved adaptive and stress-limiting regulatory mechanisms with potential diagnostic and translational relevance to human HCC. Full article

Soil liquefaction is a major seismic hazard that threatens coastal infrastructure. Yet, accurate prediction of the liquefaction factor of safety (FS) remains challenging because of the complex nonlinear interactions among geotechnical and stress-state variables. This study proposes a novel leakage-aware Hybrid Stacking Ensemble (HSE) framework for the direct prediction and geotechnical interpretation of FS in the Port Sudan coastal plain. The proposed model combines Random Forest, Extra Trees, and Gradient Boosting Regressors through a RidgeCV meta-learner while restricting the predictor set to independent spatial, geotechnical, and stress-state variables to prevent data leakage. A database comprising 534 Standard Penetration Test (SPT)-based observations from 61 boreholes was used to evaluate the proposed model against K-Nearest Neighbors (KNN), Artificial Neural Network (ANN), Random Forest, and XGBoost. The Hybrid Stacking Ensemble achieved the highest predictive performance, with a test RMSE of 0.058, an MAE of 0.032, and an R² of 0.985, demonstrating improved accuracy, robustness, and generalization over the benchmark models. SHAP-based interpretation identified N1(60)cs as the dominant predictor of FS, followed by relative density, depth, and stress-related variables, confirming that the proposed framework learned physically meaningful relationships consistent with established liquefaction mechanics. The contribution of this work lies in the development of a Hybrid Stacking Ensemble framework for the direct prediction of the liquefaction factor of safety (FS), integrated with a leakage-free modeling workflow and explainable machine learning techniques, rather than in the development of a new machine learning algorithm. Full article

Background/Objectives: Vitamin D has been implicated in lipid metabolism, but data regarding its association with atherogenic lipoproteins in elite athletes remain limited. Elite athletes represent a unique research model to investigate these associations with reduced confounding from obesity, chronic disease, smoking, and physical inactivity. Methods: This cross-sectional study included 773 male professional athletes from mixed sports disciplines (mean age 25.5 ± 5.0 years). Serum 25-hydroxyvitamin D [25(OH)D] concentrations and lipid parameters, including total cholesterol, low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), triglycerides, and lipoprotein(a) [Lp(a)], were assessed. Associations were analyzed using correlation analyses, subgroup comparisons according to predefined 25(OH)D categories (<30, 30–50, and >50 ng/mL), and multivariable linear regression models adjusted for age, body mass index, season, and training-related variables. Results: Higher serum 25(OH)D concentrations were independently associated with lower LDL-C (p = 0.028), triglyceride (p = 0.002), and Lp(a) concentrations (p = 0.036), whereas no independent association was observed with HDL-C (p = 0.559). Athletes with 25(OH)D concentrations ≥30 ng/mL demonstrated lower LDL-C, triglyceride, and Lp(a) levels compared with athletes below this threshold (all p < 0.05). Higher vitamin D status was additionally associated with greater peak exercise performance (4.29 ± 1.15 vs. 3.36 ± 0.68 W/kg; p < 0.001). Conclusions: Higher 25(OH)D concentrations were independently associated with a more favorable lipid profile in elite athletes, including lower LDL-C, triglyceride, and Lp(a) concentrations. Prospective studies are warranted to further investigate the relationship between vitamin D status and lipid metabolism in athletic populations. Full article

Cities preparing Sustainable Urban Mobility Plans (SUMPs) increasingly require practical tools capable of merging diverse mobility datasets and transforming them into planning-relevant indicators. This article introduces PRIORITY (Platform for the tRansition to sustaInable zerO-caRbon mobilITY), a prototype platform designed to support mobility analysis and decision-making in urban contexts. The platform integrates Floating Car Data, GTFS feeds describing public transport supply, and detailed land-use and zoning information. By relying on these heterogeneous data streams, PRIORITY generates indicators such as travel and stop times, trip distances, trip volumes, energy consumption, pollutant emissions, external costs, and electric-vehicle charging behavior. The platform is organized into two main components: a back end and a front end. The back end, which constitutes the operational core, manages all collected data and ensures their structured storage in a shared database capable of handling large volumes of information on urban form, individual mobility patterns, public transport services, and modeling outcomes. The front end provides an intuitive and versatile interface that dynamically presents the outputs generated by the platform’s analytical and modeling processes. A case application for the Metropolitan City of Rome (Italy) illustrates the operational use of the prototype and shows how PRIORITY can support transparent and reproducible evaluations during the preparation and monitoring of SUMPs. The demonstrated workflow highlights the prototype’s value for public authorities and planners seeking data-informed approaches to urban mobility assessment and decarbonization strategies. Full article

Landscape Ecological Risk (LER) reflects the potential adverse effects of landscape change on ecological structure, function, and stability. In rapidly urbanizing megacities such as Dhaka, vegetation loss and built-up expansion have intensified environmental pressure over recent decades. This study examines the spatiotemporal dynamics of LER in Dhaka from 2004 to 2024 under the combined influence of vegetation change and urban expansion. Multi-temporal remote sensing data were used to generate land cover maps, derive Fractional Vegetation Cover (FVC), and quantify urbanization intensity using Nighttime Light (NTL) data. The Landscape Ecological Risk Index (LERI) was calculated using landscape pattern metrics, while bivariate spatial autocorrelation and geographically weighted regression (GWR) were applied to examine spatial associations and local spatial heterogeneity. The results show that vegetation degradation affected 34.39% of the study area during 2004–2024, while high-risk zones increased from 24.36% in 2004 to 42.95% in 2024. Land cover analysis further indicates a substantial expansion of built-up areas, accompanied by the contraction and fragmentation of vegetation, agricultural land, and lowland classes. Spatial analyses reveal that the relationships among vegetation cover, urbanization intensity, and ecological risk vary across the city and became increasingly spatially differentiated over time. These findings suggest that vegetation loss and urban expansion are spatially associated with increasing ecological risk in Dhaka. However, the results should be interpreted with caution because of uncertainties related to remotely sensed data, unsupervised land cover classification, resampling procedures, and limited ground validation. Despite these limitations, the study provides a spatially explicit framework for understanding ecological risk dynamics and offers useful evidence for green-space conservation, ecological restoration, and sustainable urban planning in rapidly urbanizing regions. Full article

Objectives: Diminished ovarian reserve (DOR) significantly compromises in vitro fertilization (IVF) success. Although systemic markers such as anti-Müllerian hormone (AMH) serve as valuable clinical indicators of the ovarian reserve, they lack the sensitivity to reflect the qualitative deterioration of the follicular microenvironment. Therefore, in this study, we aimed to characterize the inflammatory proteome of follicular fluid (FF) to establish a high-performance auxiliary diagnostic model for DOR. Methods: Utilizing the ultra-sensitive Olink proximity extension assay, we quantified 92 inflammation-related proteins in the FF of 88 participants (67 with DOR and 21 normal controls). Differentially expressed proteins (DEPs) were identified, and their relationships with key clinical indices were evaluated. A robust predictive signature was refined through integrated Least Absolute Shrinkage and Selection Operator (LASSO) regression and Random Forest algorithms, with diagnostic performance assessed via 10-fold cross-validation. Results: Thirty-five DEPs were significantly dysregulated in the FF of patients with DOR, demonstrating strong associations with serum AMH and basal estradiol concentrations. A minimized diagnostic panel comprising four core proteins, adenosine deaminase (ADA), vascular endothelial growth factor A (VEGFA), eukaryotic translation initiation factor 4E-binding protein 1 (4E-BP1), and matrix metalloproteinase-1 (MMP-1), was established. This multivariable model achieved an excellent area under the receiver operating characteristic curve (AUC) of 0.953. Conclusions: The identified four-protein signature reflects localized chronic inflammation and early pathophysiological shifts in the DOR follicular microenvironment. As a high-performance molecular index, this panel could complement conventional systemic assessments, provide a reliable means of evaluating follicular viability, and optimize individualized therapeutic strategies. Full article

[Mn^III(pyrol)₃(tren)] {(Hpyrol)₃tren = tris(1-(2-azolyl)-2-azabuten-4-yl)amine)} is a mononuclear spin-transition compound switching between high spin (HS, S = 2) and low spin (LS, effective S = 1) around 47 K, preserving I$\overline{4}$3d symmetry. Its magnetic anisotropy is studied by calculating the atomic susceptibility tensor from the refinement of polarized neutron powder diffraction. The analysis reveals that the weakly prolate-type atomic magnetic anisotropy in the HS state abruptly switches to uniaxial needle-shaped/Ising-type anisotropy in the LS state. However, the overall magnetic anisotropy of the unit cell remains isotropic due to the cubic nature of the crystal symmetry. Irreversible coexistence of mixed spin states HS/LS is observed in the vicinity of the cooperative spin crossover, where the average magnetic moment of Mn³⁺ shows a hysteretic temperature variation. This hysteretic mixing of HS and LS at intermediate temperatures suggests complex growth and nucleation of HS and LS domains. The study demonstrates that polarized powder neutron diffraction is a unique and powerful tool for describing complex magnetic anisotropies and magneto-structural correlations in molecular-based magnetic materials. Full article

Self-efficacy—one’s belief in their ability to take the actions necessary to succeed—is a critical determinant of student success and retention, particularly during the transitional first two years of undergraduate study. Learning communities that incorporate peer mentors have been identified as promising structures for fostering self-efficacy, yet the mechanisms by which intentional peer mentoring within structured career development contexts shapes students’ self-efficacy beliefs remain underexplored. This study examined the following research questions: (1) How does participation in a career-focused Learning community course shape first- and second-year STEM students’ sense of self-efficacy regarding academic and career decision-making? (2) In what ways do peer mentors and peer support networks within the learning community contribute to students’ self-efficacy development? (3) How do students describe feeling empowered—or not—to pursue their career goals as a result of this experience? Using a mixed-methods design that combined pre- and post-course surveys, semi-structured focus groups, and phenomenological one-on-one interviews, we investigated the self-efficacy development of first- and second-year STEM students (N = 53) enrolled in a semester-long learning community course at a large, predominantly White public institution in the Midwest. Of these, 25 students completed both the pre- and post-course Career Self-Efficacy surveys and were included in matched statistical analyses. Three major findings emerged: (1) the learning community class environment created a space where self-efficacy was prioritized and developed; (2) peer support groups and peer mentors positively impacted students’ self-efficacy; and (3) students felt empowered by the experience in pursuing their chosen career goals. These findings have practical implications for the design of learning communities in STEM, highlighting the value of intentional peer mentoring structures and career-focused activities as tools for promoting student confidence, retention, and long-term academic success. Full article

Seed pre-treatment is imperative for breaking the seed dormancy of some perennial species. The addition of soil amendments might be helpful in supporting seed germination and growth by available essential plant nutrients. This research investigated the effects of different pre-treatment and soil amendments on the germination, growth, and physiological performance of radhachura (Caesalpinia pulcherrima L.), an important ornamental and multipurpose woody shrub. Four pre-treatments and five soil amendments were applied in a CRD (Completely Randomized Design) arrangement to evaluate their individual and combined impacts under controlled nursery conditions. The ANOVA result revealed that seed germination indices of radhachura were mostly influenced by soil amendment rather than the seed pre-treatment. Among the soil amendments, vermicompost had a more profound impact on germination speed, Timson’s index and peak value, which had a similar effect to NPK application. Soil organic amendments positively affected growth, with vermicompost exerting the greatest influence on multiple germination traits that may support the early growth of radhachura, while biochar and compost maximized certain root and plant-length traits. Pearson correlations and PCA (first seven PCs explaining 76.2% variation) revealed the strong integration of late biomass, plant length, and root development, identifying vermicompost as key enhancers of multivariate vigor in radhachura seedlings. It might be concluded that C. pulcherrima L. species germination and growth was mostly influenced by soil amendment rather than seed pre-treatment. The study highlights that integrated nursery practices combining appropriate pre-treatment and soil amendments can enhance the germination success of radhachura. Full article

The extensive use of agrochemicals and plastic materials has led to the accumulation of persistent pollutants in agricultural soils, raising concerns about agroecosystems through posing potential risks to soil and environmental health. This review synthesizes recent knowledge on these pollutant sources, including their distribution, fate, transformation pathways, and detection methods, as well as their impacts on soil physicochemical properties, microbial populations, plants, and ecosystems. Existing findings indicate that agrochemicals and micro-nano plastics (MPs-NPs) can significantly impede the stability of soil aggregation, increase soil water holding capacity (WHC) and porosity, reduce bulk density and infiltration, alter soil structure, and affect soil pH, cation exchange capacity (CEC), electrical conductivity (EC), and nutrient retention capacity. Moreover, exposure to these pollutants alters soil microbial communities, enzymatic activity, nitrification and denitrification processes, and arbuscular mycorrhizal fungi (AMF), thereby affecting carbon pools and fluxes as well as nutrient cycling. However, the magnitude and direction of these effects are strongly influenced by soil type, pollutant class, concentration, and physicochemical properties. Furthermore, terrestrial and aquatic ecosystems are negatively affected due to the presence of such persistent pollutants by impairing their physiological processes. Despite these findings, mechanistic understanding remains limited due to a lack of long-term field investigation and proper detection methods, particularly regarding NPs. A comprehensive understanding of agrochemical and MP-NP interactions is essential for developing sustainable soil management strategies and agroecosystems. Future studies should address the development of standardized NP detection methods and the conducting of long-term field studies to elucidate MP-NP and agrochemical interactions, soil impacts, and crop uptake mechanisms. Full article

Acute kidney injury (AKI) continues to pose a significant clinical challenge due to its high morbidity rates and limited therapeutic options. Recent evidence suggests that natural compounds may provide renoprotective benefits by modulating oxidative stress and inflammation. This study examines the protective effects of a novel polysaccharide peptide extracted from Ganoderma lucidum (GL-PPQ1) against renal ischemia–reperfusion (I/R) injury, with particular emphasis on the integrin β3/Fibronectin 1 (Fn1) signaling axis. A murine model of renal I/R injury was established, and GL-PPQ1 was administered orally for seven days before surgery. The assessment included renal function, histopathology, oxidative stress markers, and inflammatory cytokines. Additionally, transcriptomic profiling and protein expression analyses were conducted to elucidate the underlying mechanisms. The results revealed that GL-PPQ1 pretreatment significantly reduced renal tubular damage, lowered serum creatinine and blood urea nitrogen levels, and diminished oxidative stress and inflammatory responses. RNA sequencing revealed that GL-PPQ1 affected gene sets associated with extracellular matrix remodeling and cell adhesion. Western blot and immunohistochemistry further confirmed that GL-PPQ1 decreased the expression of integrin β3 and Fn1, suggesting a regulatory effect on their interaction during I/R injury. These findings demonstrate that GL-PPQ1 offers substantial kidney protection by mitigating oxidative stress, inflammation, and dysregulation of the integrin β3/Fn1 signaling pathway. Thus, this study supports that polysaccharide peptides derived from Ganoderma lucidum could have the potential to serve as both a dietary supplement and a therapeutic agent in the treatment of AKI. Full article

Background: Tumor neoantigens are key targets for personalized vaccines and T-cell therapies, yet most pipelines focus on neoantigens derived from SNV/small indel and often yield a limited number of high-quality candidates. SVs are prevalent in tumors and can generate novel chimeric sequences and neopeptides, making them a promising additional source of neoantigens. However, SV-derived neoantigen prediction remains challenging due to breakpoint uncertainty, isoform-dependent coding inference, and limited integration of multi-dimensional evidence and reproducibility. Methods: We developed SVNeoPP (Structural Variant Neoantigen Prediction and Prioritization), an end-to-end workflow for SV-derived neoantigen analysis. SVNeoPP takes WGS and RNA-seq as inputs, performs SV calling and annotation, and reconstructs altered transcripts and coding sequences in a traceable, isoform-aware manner to generate candidate peptides. Candidates are prescreened by integrating antigen-processing features with HLA binding prediction, and then hierarchically filtered and prioritized based on transcript expression, LC–MS/MS proteomics evidence, immunogenicity predictions, and sequence similarity to experimentally validated neoantigen databases. SVNeoPP is implemented in Snakemake to enable modular extension, checkpoint-based restarts, and end-to-end reproducibility. Results: Using a hepatocellular carcinoma (HCC) multi-omics dataset as a proof of concept, we demonstrated the performance of SVNeoPP and obtained a high-priority shortlist of candidate peptides. Compared with other methods, SVNeoPP substantially expanded the candidate search space for SV-derived neoantigens and showed more favorable distributions of antigen-processing and HLA binding features. Conclusions: SVNeoPP provides a reusable, traceable, and interpretable multi-dimensional evidence-driven framework for SV-derived neoantigens. As a complementary module to SNV/small-indel pipelines, it broadens the neoantigen candidate repertoire and generates ranked candidates with interpretable evidence to facilitate downstream prioritization and decision-making. Full article

The efficacy of immunotherapy in colorectal cancer (CRC) has long been considered to be closely associated with microsatellite instability (MSI) status. Patients with microsatellite stable (MSS) tumors typically exhibit poor responses to PD-1/PD-L1 inhibitors and a poor prognosis, often being categorized as immunologically ‘cold’ tumors. However, some MSS patients can still achieve favorable therapeutic responses, sometimes even surpassing those of certain MSI patients. Immune-cold and immune-hot tumor phenotypes are largely determined by the abundance, clonal expansion, and functional states of tumor-infiltrating T cells. This suggests that immunotherapy responses are driven by dynamic remodeling of T-cell clonality rather than by MSI status alone. To elucidate the underlying T cell clonal dynamics, integrated single-cell transcriptome (scRNA-seq) and T cell receptor sequencing (scTCR-seq) data analyses from 43 blood and tissue samples of MSI and MSS colorectal cancer patients before and after anti-PD-1 therapy were performed. Using our developed TCR reconstruction pipeline (TORBiT), we systematically analyzed the clonal architecture of the TCR repertoire, inter-tissue migration, and its association with T-cell functional state transitions. From a TCR clonal kinetic perspective, we revealed two distinct modes of immune Coercion that may further affect the immune response: a “high-fluctuation, deep-exhaustion” pattern in MSI tumors and a “high-baseline, strong-suppression” pattern in MSS tumors. These findings provide a novel theoretical foundation and research perspective for understanding the responsiveness and resistance mechanisms to immune checkpoint inhibitors. Full article

In the online shopping context, the proliferation of digital platforms has contributed to an increase in impulsive buying behavior (IBB), which can sometimes lead to regret. This study aims to explore the intrinsic and extrinsic stimuli that influence consumers’ online impulsive buying behavior, which subsequently affects their post-purchase cognitive dissonance, with the moderating role of price consideration (PC). The conceptual framework was formulated using the Stimulus–Organism–Response (S-O-R) model. A total of 813 responses were collected and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The findings revealed that perceived utilitarian value (PUV), perceived enjoyment (PE), fear of missing out (FOM), and green trust (GT) positively impact online impulsive buying behavior (IBB), which, in turn, positively impacts post-purchase cognitive dissonance (PCD). Moreover, a significant moderating role of PC is found in the relationship between IBB and PCD, suggesting that consumers with low price consideration tend to regret their impulsive buying more. The findings provide insights that guide online retail sellers and digital marketers to develop or implement customized strategies based on the intrinsic and extrinsic stimuli that influence customers’ impulsive buying and subsequent post-purchase cognitive dissonance. Full article

Phytocompounds undoubtedly are structurally diverse and play a crucial role in the development of novel therapeutic agents. 2-Hydroxy-4-methoxybenzaldehyde (HMB), from Hemidesmus indicus, is a potent antibacterial agent. Yet its pharmacophore has not been mechanistically defined. Here, we deconstructed HMB through a panel of structural derivatives to delineate the core structural determinants driving activity against foodborne pathogens. Structure–activity analysis revealed that the core benzaldehyde structure, rather than HMB itself, is the minimal active pharmacophore, with specific functional substitutions modulating antibacterial activity and membrane affinity. Integrating an experimental membrane assay with molecular dynamics simulations provided the first atomistic insight into how these derivatives interact with bacterial membrane lipids, demonstrating that substituent-driven modulation of hydrogen bonding dictates antibacterial potency. Specifically, electron-withdrawing groups enhanced membrane penetration and depolarization, particularly in Gram-positive pathogens. Time–kill kinetics and functional assays confirmed bactericidal action via membrane disruption rather than DNA interaction. Crucially, the active derivatives exhibited negligible cytotoxicity toward mammalian Vero cells, confirming their potential as selective and safe natural preservatives. This work provides a mechanistic blueprint for designing benzaldehyde-based antibacterials to combat antimicrobial resistance. Full article