Search Results (151,988)

Dickkopf-1 (DKK1) is a 266-amino-acid secreted glycoprotein originally identified as a high-affinity antagonist of the canonical Wnt/β-catenin signaling pathway and has emerged as a complex regulator in oncology. While historically considered as a tumor suppressor due to its ability to abrogate Wnt-driven proliferation, recent discoveries highlight a paradoxical pro-oncogenic role across various malignancies. The molecular mechanisms by which DKK1 promotes tumor progression, metastasis, and immune evasion are driven by its interaction with cell-surface receptors, specifically LRP5/6 and CKAP4. The DKK1-CKAP4 axis independently activates PI3K/AKT signaling, facilitating epithelial–mesenchymal transition (EMT), chemoresistance, and the formation of osteolytic bone lesions. Furthermore, DKK1 serves as a critical orchestrator of the tumor microenvironment (TME) by driving comprehensive immune reprogramming. It mediates the recruitment of myeloid-derived suppressor cells (MDSCs) and inactivates cytotoxic CD8⁺ T cells and natural killer (NK) cells, thereby fostering an immunosuppressive tumor microenvironment and resistance to checkpoint inhibitors. Interestingly, cancer-associated fibroblasts (CAFs) are a primary source of DKK1 in the stroma, where they facilitate immune evasion. Clinically, elevated circulating DKK1 levels correlate with advanced disease stages, increased metastatic potential, and poor overall survival in solid and hematological tumors. When used in combination with established biomarkers, serum DKK1 levels demonstrate significant utility for early detection and therapeutic monitoring. Given its intricate impact on malignancy, DKK1 has become a promising therapeutic target, with ongoing clinical trials investigating neutralizing antibodies such as DKN-01 to disrupt its oncogenic and immunosuppressive signaling. Understanding the context-dependent nature of DKK1 signaling remains essential for refining its application as both a biomarker and a component of emerging precision immunotherapy strategies. By prioritizing the literature from the last decade, this review characterizes DKK1 as a key mediator of tumor progression and immune reprogramming, while assessing its clinical potential as a biomarker and therapeutic target. Full article

Carotid artery disease, including atherosclerotic stenosis and non-atherosclerotic abnormalities, substantially increases ischemic stroke risk and motivates accessible tools for early screening. Current diagnostic pathways rely on clinic-based imaging and skilled operators, creating barriers to frequent monitoring and scalable deployment. We present a non-invasive diagnostic approach using a wearable MEMS accelerometer patch to capture mechano-acoustic vibrations generated by carotid blood flow at the neck. The miniature device integrates a hermetically sealed wideband accelerometer with out-of-plane sensitivity and micro-g resolution to detect subtle flow-induced vibrations. We validated the approach in a carotid flow phantom and a clinical study of 74 patients. Time–frequency representations were computed using the continuous wavelet transform (CWT), from which interpretable spectral and scalogram-derived candidate biomarkers were extracted. Six non-redundant features were then selected for multivariate classification, distinguishing pathology, defined as 50% or greater stenosis or a non-atherosclerotic abnormality, from non-pathology, defined as less than 50% stenosis. Finally, model interpretability was assessed using SHapley Additive exPlanations (SHAP) to quantify the contribution of each biomarker to predicted disease probability. These findings resulted in an AUROC of 0.97 and AUPR of 0.947, with 81.7% sensitivity and 93.6% specificity at the prespecified threshold (precision 85.4%, F1 83.5%, accuracy 89.8%), highlighting the potential of wearable seismic sensing combined with interpretable machine learning for fast screening and longitudinal monitoring of the right and left carotid arteries. Full article

Contact–impact phenomena caused by joint clearances can significantly alter the dynamic response of high-speed mechanical systems, yet fewer studies combine analytical impact-force modeling, virtual prototyping, and experimental observations for multi-cylinder internal combustion engine mechanisms within a unified framework. This problem is scientifically important because the piston–connecting rod–crankshaft chain is subjected to rapid motion reversals, high transmitted loads, and local clearances that may generate shocks, force amplification, and vibration growth. The objective of this study is to evaluate the influence of mechanical impact on the dynamic response of a three-cylinder inline engine mechanism by combining analytical modeling, MSC Adams virtual prototyping, and experimental investigation. The mechanism was analyzed in two operating conditions: under load, using an experimentally derived gas pressure input, and without load at low speed imposed on the crankshaft, using a sectioned engine test bench. The loaded virtual model was studied at a crankshaft speed of 6000 rpm, with cylinder gas pressure peaks above 90 bar and engine torque oscillating around 170 Nm. A radial clearance of 0.03 mm was introduced in the connecting rod–piston joint to evaluate clearance-induced impacts. The results showed that the damping coefficient strongly influences the amplitude and harmonic content of the impact force. For the analyzed no-load case at low speed, the simulated impact force reached a maximum value of 3000 N. Experimentally, the worn connecting rod with 0.03 mm clearance exhibited markedly higher dynamic response than the clearance-free case, with the maximum longitudinal acceleration increasing from 17.77 to 48.26 m/s² at 1.341 Hz. The novelty of the study lies in the integrated analytical–virtual–experimental investigation of clearance-induced impact in a three-cylinder inline engine mechanism and in the comparative evaluation of its effects on joint forces and vibration signatures. In addition, compared to other models, the novelty lies in introducing and adapting the impact force damping component for mechanisms with rapid motion and high dynamic loads. Full article

There are no unique and universally accepted procedures for the determination of the maximum and minimum void ratios, e_max and e_min. This issue is particularly pertinent in the characterisation of the alternative sustainable materials examined in this study: well-graded tyre-derived aggregate (TDA), recycled concrete aggregate (RCA) and their mixtures (RCA-TDA), with a rubber content by weight of Χ_M = 11, 23 and 55%. Uniformly graded TDA–sand mixtures with Χ_M = 0, 15, 27, 42, and 100% were also considered. The results from dry and moist samples were compared with void ratios obtained after Proctor compaction and static loading. It was found that, in contrast to vibration for sand and sand–TDA mixtures, the most efficient densification techniques involve impact compaction at the optimum water content for RCA and RCA-TDA and static loading for TDA. Inversion of dry RCA, TDA and RCA-TDA samples in a graduated cylinder was the most effective to consistently achieve e_max but induced visible segregation. Unlike sand–rubber mixtures, well-graded RCA-TDA did not exhibit a threshold rubber content at which e_max and e_min fell below those of RCA and TDA alone, suggesting reduced segregation. The findings offer practical guidance for improving specimen preparation reproducibility in the laboratory. Full article

Traditional satellite-derived bathymetry (SDB) often suffers from systematic optical path distortions due to the neglect of seafloor slope effects, leading to significant accuracy degradation in high-gradient coastal areas. This study proposes a Slope-Aware Physics-Informed Neural Network (SA-PINN) framework that synergistically utilizes ICESat-2 bathymetric photons and Sentinel-2 multispectral imagery. The core innovation involves a slope-aware operator, integrated into the radiative transfer-based physics loss function, which explicitly rectifies directional optical path deviations induced by seafloor inclination. By fusing physical mechanisms with data-driven features, the model utilizes a seven-dimensional feature space comprising four spectral bands, two directional slope components, and prior depth. Applications at Culebra, Maui, and Molokai demonstrate that SA-PINN significantly outperforms the Stumpf model, Random Forest, and standard CNNs, achieving root mean square errors (RMSE) of 1.36 m, 2.91 m, and 1.34 m, respectively. Ablation studies confirm that SA-PINN reduces RMSE by up to 37% compared to CNN in complex regions with slopes exceeding 10°, ensuring superior physical consistency and spatial continuity. This research provides a robust, in situ-free automated solution for high-resolution bathymetric mapping in remote and steep coastal environments globally. Full article

Background: Emotional distress (ED) is common among patients with gynecological malignancies and is associated with reduced quality of life and suboptimal health outcomes. Total-score approaches may overlook the complex interrelationships among individual emotional symptoms. Objective: This study provides a theory-informed contextual application and empirical boundary test of symptom network analysis, organized by the Stress Process Model (SPM), to examine not only how ED symptoms cluster and connect with psychosocial correlates and quality-of-life domains, but also whether psychosocial stratification is reflected in altered symptom topology or primarily in differences in distress burden. Methods: A cross-sectional study was conducted among 415 patients with gynecological malignancies recruited from a tertiary hospital in China. ED was assessed using the Brief Profile of Mood States-Short Form (BPOMS-SF30). An exploratory three-track screening strategy was used to derive a focused 16-node set of frequent negative mood symptoms. Gaussian graphical models with EBICglasso regularization were estimated for the symptom network and for extended networks including demographic/clinical variables, SPM-related psychosocial variables, and quality-of-life indicators. Results: The ED network showed dense positive connectivity, with strong within-domain clustering and several cross-domain associations. Exhaustion, restlessness, and irritability were relatively more relationally prominent in the primary network, although centrality stability was low to moderate across models. Fatigue-related symptoms were closely connected with anxiety, depressive symptoms, and impaired quality of life. Among psychosocial variables, self-perceived burden showed the strongest conditional association with fatigue. Adjusting for demographic and clinical variables did not materially alter the core symptom network, and no significant subgroup differences in global strength or overall structure were observed across psychosocial strata. Conclusions: In this sample, psychosocial risk stratification appeared to relate more to the overall severity and burden of distress than to major reorganization of symptom topology. The study therefore contributes primarily as a theory-informed contextual application of network methods and as an empirical boundary test showing that several psychosocial strata did not exhibit major topological differences. Because the retained nodes were selected for prevalence, association strength, and selection stability, the observed prominence of fatigue- and activation-related symptoms should be interpreted as conditional on this focused symptom subset. Overall, the findings are correlational, exploratory, and hypothesis-generating. Full article

Tuberculosis (TB) remains a major global cause of morbidity and mortality. Current tools for monitoring treatment response rely on sputum-based microscopy and culture, which are often insensitive, time-consuming, and impractical in extrapulmonary or pediatric TB and in individuals unable to produce sputum. Metabolomics has emerged as a promising approach for identifying host-derived biomarkers that reflect treatment-associated immunometabolic changes; however, the available evidence remains heterogeneous and has not been comprehensively synthesized. We conducted a comprehensive literature review of human studies evaluating metabolomic biomarkers in relation to TB treatment response or outcomes. PubMed, Scopus, and EMBASE were searched for human studies evaluating targeted or untargeted metabolomics (NMR, LC-MS, GC-MS, CE-MS) in relation to treatment response or outcomes. Two reviewers independently screened studies, extracted data, and assessed risk of bias using QUIPS and PROBAST. Findings were synthesized using a structured framework organized across treatment stages and outcomes. Of 218 records identified, 139 titles and abstracts were screened and 42 full texts assessed; 15 studies met the inclusion criteria. Recurrent treatment-associated signals involved amino acid metabolism, particularly the tryptophan–kynurenine pathway, as well as vitamin and cofactor metabolites (pyridoxate, nicotinamide, trigonelline). Plasma studies frequently reported lipid remodeling and bile acid perturbations, whereas urine studies highlighted polyamine metabolism (e.g., N¹,N¹²-diacetylspermine) and fatty acid β-oxidation markers. Common limitations included inadequate adjustment for confounders and, in prediction models, small sample sizes and limited external validation. Metabolomics reveals reproducible but heterogeneous immunometabolic changes during TB therapy. Key pathways include tryptophan–kynurenine metabolism, vitamin and cofactor metabolism, lipid remodeling, and urine polyamine pathways. Standardization and prospective multicenter validation are needed for clinical translation. Full article

Impulsive alpha (α)-stable noise, characterized by heavy tails and intense outliers, is a key ingredient in simulating financial, medical, seismic, and digital communication technologies. It poses versatile challenges to conventional machine learning (ML) algorithms in predicting noise parameters for multidisciplinary artificial intelligence (AI)-embedded devices. In this study, we adopted a two-phase methodology to investigate the complexity and performance of supervised ML algorithms while classifying impulsive noise parameters. We generated synthetic datasets of α-stable noise distributions for experimentation in a controlled environment. It was followed by experimental evaluation to derive the complexity and performance of ML classifiers—k-nearest neighbors (KNN), Support Vector Machine (SVM), Naïve Bayes (NB), Decision Tree (DT), and Random Forest (RF). Moreover, we employed a very high channel noise level of −15 dB in the test datasets to ensure that the derived analysis applies to real-world devices. The results demonstrate the high performance of DT and RF in structured binary classification of the α regime and the sign of skewness, while incurring satisfactory computational costs. However, SVM and kNN are comparatively more robust for multi-class classification, albeit with higher memory and training costs. On the contrary, NB fails to address the skewed and impulsive behavior of α-stable noise. We observed that even the most effective classifiers struggle to achieve perfect accuracy in multi-class classification. Overall, the experimental results reveal significant trade-off relationships between the complexity and performance of ML classifiers. Conclusively, simple models are well-suited for coarse-grained tasks, such as α-approximation and sign-of-skewness classification. In contrast, sophisticated models can be deployed to predict noise parameters to some extent. Our study provides a clear set of trade-offs for future applied AI devices that address adversarial and impulsive noise. Full article

Circular RNAs (circRNAs) exhibit significant sex- and development stage-specific expression patterns in the gonads of various fish species, yet their functions and regulatory mechanisms in male reproductive development remain largely unexplored in crucian carp (Carassius auratus). In this study, we characterized the expression features and biological functions of circSPEF2, a circular RNA derived from the reproduction-related gene spef2. Our results showed that circSPEF2 expression was markedly elevated in mature testes and progressively upregulated during gonadal maturation. Functional studies suggested that circSPEF2 likely does not act through a ceRNA-dependent mechanism. Transcriptome sequencing following circSPEF2 overexpression identified 45 upregulated and 70 downregulated differentially expressed genes, with GO and KEGG enrichment analyses revealing significant alterations in multiple gonadal development-related genes and signaling pathways. Subsequent siRNA-mediated knockdown of circSPEF2, combined with qRT-PCR validation, confirmed that circSPEF2 positively regulates the expression of genes associated with cell maturation and differentiation, including prdm1a, lamc2, and slc25a27, while concurrently suppressing that of proliferation- and apoptosis-related genes such as wnt8b, cpeb3, and bcl2l11. Furthermore, RNA pull-down combined with mass spectrometry identified three candidate circSPEF2-binding proteins, namely, hnRNP A/B, SRSF2, and CFAP263. Collectively, these findings indicate that circSPEF2 plays an important role in male gonadal development in fish and provide new insights into the post-transcriptional regulatory mechanisms underlying vertebrate male reproduction. Full article

Accurate, high-resolution emission inventories are essential for air quality modeling and policy evaluation, yet national-scale inventories for Thailand remain limited in spatial and temporal detail. This study develops a comprehensive national emission inventory for Thailand in 2019 (EI–TH 2019), covering 12 major air pollutants and greenhouse gases across key sectors, including energy, transport, industry, agriculture, waste, and residential activities. The inventory is constructed using country-specific activity data from official statistics and sectoral surveys, combined with GAINS-consistent emission factors and control assumptions. Emissions are resolved at 1 × 1 km spatial resolution and monthly temporal resolution to capture Thailand-specific emission dynamics. The results show that emissions across major pollutants are dominated by a limited number of source groups, with biomass burning and residential solid-fuel use driving particulate matter, transport dominating NO_x and CO emissions, large-scale combustion and industry controlling SO₂ emissions, and agriculture contributing the majority of NH₃ emissions. Strong seasonal variability is observed in PM_2.5, CO, and NH₃, primarily driven by dry-season biomass burning, whereas NO_x and SO₂ exhibit relatively stable temporal patterns. The reliability of EI–TH 2019 is supported by a multi-dimensional evaluation framework. Temporal consistency is demonstrated through strong agreement between modeled PM_2.5 emissions and ground-based observations, as well as between NO_x emissions and satellite-derived TROPOMI NO₂ (r = 0.93; ρ = 0.96). Biomass burning timing is further validated using satellite fire activity (VIIRS), showing consistent seasonal patterns. Comparisons with global inventories (EDGAR v8.1, HTAP v3.2, and GFED5.1) reveal systematic differences in sectoral contributions, temporal profiles, and emission magnitudes, particularly for biomass burning, reflecting the importance of country-specific data and assumptions. Overall, EI–TH 2019 provides a robust, high-resolution, and policy-relevant emission dataset that improves the representation of emission processes in Thailand. The results highlight key priority sectors—biomass burning, transport, industry, and agriculture—for targeted emission-reduction strategies and support applications in chemical transport modeling, exposure assessment, and integrated air-quality and climate-policy analysis. Full article

Background: Non-small cell lung cancer (NSCLC) remains a leading cause of cancer-related mortality, and although PD-1/PD-L1 immune checkpoint blockade has improved outcomes in some patients, therapeutic responses remain heterogeneous. Tumor-intrinsic heterogeneity within malignant epithelial populations is increasingly recognized as a critical determinant of disease progression and therapy response. Methods: Here, we constructed a comprehensive single-cell atlas of NSCLC by integrating 650,461 cells from 216 tumor and normal samples. Tumor-derived epithelial cells were reclustered to identify transcriptionally distinct subpopulations. Pseudotime analysis, functional experiments, and in vivo validation using a humanized xenograft model were performed to investigate the role of COL3A1. Results: Reclustering of tumor-derived epithelial cells revealed 25 transcriptionally distinct subpopulations. Among these, a high-risk cluster exhibited coordinated activation of epithelial–mesenchymal transition (EMT) and angiogenesis programs and was associated with poor patient survival. Within this aggressive subpopulation, Collagen type III alpha 1 (COL3A1) emerged as a tumor-intrinsic gene associated with extracellular matrix remodeling and angiogenic signaling. Pseudotime analysis indicated that COL3A1⁺ cells represent a late-stage, poorly differentiated malignant state. Functional experiments demonstrated that COL3A1 knockdown impaired NSCLC cell proliferation, migration, and invasion. Virtual knockout further suggested that COL3A1 may be associated with transcriptional programs involved in PD-L1 upstream signaling pathways, indicating a potential indirect link between tumor-intrinsic states and immune regulatory networks. Consistently, in vivo silencing of COL3A1 enhanced the antitumor efficacy of PD-L1 blockade. Conclusions: Collectively, our study identifies COL3A1 as a tumor-intrinsic gene enriched in malignant epithelial cells with mesenchymal features and a potential therapeutic target. These findings provide a rationale for exploring combinatorial strategies integrating tumor-intrinsic pathway inhibition with immune checkpoint blockade in NSCLC. Full article

Idiopathic pulmonary fibrosis (IPF) and autoimmune usual interstitial pneumonia (aUIP) share overlapping clinico-radiological features, complicating differential diagnosis. Electronic nose (eNose) technology characterizes exhaled breath profiles (“breathprints”) and may offer a non-invasive diagnostic approach in fibrotic interstitial lung diseases. To evaluate whether eNose breathprint analysis can discriminate between IPF and aUIP. In this cross-sectional study of 60 patients (34 IPF, 26 aUIP), breathprints were analyzed using principal component analysis (PCA, retaining eigenvalues > 1). Group differences were assessed via independent t-tests. Linear discriminant analysis (LDA) with leave-one-out cross-validation evaluated the discriminatory performance of PC combinations. PCA identified four principal components, with PC1 explaining 96% of the total variance. PC1 scores were significantly higher in aUIP compared to IPF (mean difference −0.53; 95% CI −1.04 to −0.02; p = 0.04); PC2-PC4 showed no significant differences (p > 0.3). LDA utilizing PC1 and PC3 achieved a cross-validated classification accuracy of 73.3% (95% CI 60.7–84.4, p < 0.05). eNose-derived breathprints showed preliminary discriminatory potential between IPF and autoimmune UIP, supporting further validation of this non-invasive adjunctive approach. Breathomics represents a promising non-invasive adjunctive tool for phenotyping fibrotic interstitial lung diseases, though larger validation studies integrating clinical and biological data are warranted. Full article

As global food security challenges intensify, edible crickets are recognized as sustainable protein alternatives; however, genomic resources for commercially important species remain limited, restricting evolutionary inference and the development of robust tools for farm management. We sequenced and assembled the complete mitochondrial genomes of Gryllus bimaculatus and provided the first report for Teleogryllus mitratus, both derived from commercial farms in Thailand, using high-throughput Illumina sequencing, achieving high coverage depths of 32,391× and 63,258×, respectively. The circular mitochondrial genomes were 15,955 bp and 16,046 bp and exhibited the typical insect mitochondrial gene complement of 37 genes, with a strong AT bias. Selective pressure analyses indicated pervasive purifying selection across all protein-coding genes (PCGs) (ω < 1), while episodic diversifying selection was detected in cox1, cox3, cytb, and nad5; additionally, atp8 displayed a comparatively elevated ω. Codon usage analyses revealed a strong preference for AT-ending codons, with leucine codons showing the highest bias. Phylogenetic analyses using concatenated protein-coding and ribosomal RNA genes recovered well-supported relationships within Gryllidae. These farm-derived mitogenomes provide practical foundations for molecular species authentication, population monitoring, and comparative analyses relevant to breeding and traceability. Furthermore, they provide candidate loci for future investigations into mitochondrial evolutionary dynamics and the potential development of molecular markers for commercial breeding management. Full article

Understanding the impact of ash on nanopore heterogeneity is crucial for evaluating deep coalbed methane (CBM) reservoirs. This study investigates the Benxi Formation coal Seam 8 in the Nalinhe Block, Ordos Basin. Based on proximate analysis, samples were categorized by ash yield (A_ad%). Pore structures were characterized using low-temperature nitrogen adsorption (<2 nm) and carbon dioxide adsorption (2–100 nm). Fractal theory was employed to quantitatively assess pore heterogeneity across different scales. The results indicate that ash content significantly constrains the development of both micropores (<2 nm) and mesopores (2–100 nm), with the most pronounced effect on micropores in the 0.3–0.6 nm range. Ash, primarily derived from kaolinite, occludes pores, reducing pore volume and specific surface area, thereby diminishing methane adsorption capacity. Notably, pore heterogeneity is found to decrease with increasing pore volume. These findings provide valuable insights for the efficient exploration and development of deep CBM resources in the Nalinhe and Suide blocks. Full article

Substituted p-phenylenediamines (PPDs) and their quinone derivatives (PPD-Qs) are emerging contaminants associated with tire-related antioxidants and antiozonants and have raised increasing concern because of their potential environmental effects. However, information on their occurrence in estuarine systems, particularly their combined distribution in water and sediment, remains limited in the Pearl River Estuary. In this study, 30 water samples and five sediment samples collected from the Pearl River Estuary were analyzed for selected PPDs and PPD-Qs. Four target compounds were detected in water, whereas nine were found in sediments, indicating broader occurrence in the sediment phase. The total concentration of PPDs ranged from below the detection limit to 17.6 ng/L in water and from 0.140 to 1.37 ng/g in sediments. In water, 6PPD and 6PPD-Q showed relatively high detection frequencies, while elevated IPPD concentrations were observed at several sites near urbanized coastal areas. In sediments, the highest ΣPPDs level was recorded in Shenzhen Bay. The observed spatial patterns suggest that mixed anthropogenic inputs may influence the occurrence of these compounds in the estuary, although direct source attribution requires further investigation. Overall, this study provides preliminary baseline information on the occurrence and phase-specific distribution of PPDs and PPD-Qs in the Pearl River Estuary and supports future investigations into their environmental fate and ecological relevance. Full article