Search Results (447)

Immunotherapy has become one of the primary forms of cancer treatment. The inhibition of immune checkpoint molecules, including indoleamine 2,3-dioxygenase (IDO), is a promising approach for immunotherapy. Phosphatase and tensin homolog (PTEN) is well known as a tumor suppressor that antagonizes oncogenic signaling molecules/pathways and plays a key role in the prognosis and (immuno)therapy of the disease. In this study, twenty healthy and tumorous renal tissue pairs were investigated, and the mRNA (RT-qPCR) and protein (Western blot) expression of IDO and PTEN were analyzed. In two cancer cell lines (CAKI-2; A-498), the protein of IDO and PTEN was measured followed by IDO induction with interferon alpha-2 (IFN-α2). According to our results, a significantly higher mRNA expression of IDO and PTEN was found in tumorous tissues compared to the adjacent healthy kidney specimens. The mRNA expression of IDO and PTEN showed a positive correlation in 80% of the sample pairs. Western blot results confirmed the protein expression of both IDO and PTEN. In the cell lines, immunocytochemistry showed that IDO is inducible with IFN-α2. In summary, our results suggest that IDO expression may play a role in the development of renal cancer, and IDO as well as PTEN might be potential biomarkers for patients with RCC. Full article

Soil health is an important concept in promoting sustainable agriculture and food security, yet the absence of universally accepted benchmarks limits its utility in assessing soil function. This study explored the use of Conservation Reserve Program (CRP) soils as a potential benchmark to quantify the soil health gap (SHG) in Faribault County, Minnesota. Using the Soil Management Assessment Framework (SMAF), we evaluated physical, chemical, biological, and nutrient soil health indicators to derive a combined overall score that was used to quantify the SHG (i.e., benchmark soil minus test soil) between CRP and corn-based agricultural production (AP). Three paired farms were assessed, each consisting of CRP tall grass prairie established in 2001 and adjacent long-term AP. The results showed higher overall SMAF scores in CRP soils, with a mean SHG of 0.09. Land use had a strong influence on overall scores, largely driven by biological indicators such as soil organic carbon, microbial biomass carbon, and β-glucosidase activity. However, the SMAF demonstrated limited applicability in CRP systems, potentially under-representing their soil health status due to the SMAF’s agricultural emphasis and lack of ecosystem-specific factors such as pH. Full article

Herein, a three-dimensional mathematical model was established to investigate the metallurgical behavior of liquid steel in a funnel-shaped mold equipped with single-ruler electromagnetic braking (EMBr). The effects of mold thicknesses, electromagnetic intensity, and casting speed in flow behavior were investigated. The results indicate that with EMBr, multiple pairs of induced current loops are present in the horizontal section of the magnetic pole center, distributed in pairs between the jets and broad faces. The Lorentz force acting on the main jet, which impacts the downward and upward flow at adjacent broad faces, is opposite in direction. Increasing mold thickness results in a larger jet penetration depth, leading to a higher meniscus temperature near the narrow faces accompanied by elevated velocity and turbulent kinetic energy. EMBr can lead to a decrease in shell thickness and an improvement in its uniformity at mold exit. For the thickened mold, as the magnetic flux density increases and the casting speed decreases, the penetration depth of jets and velocity near the narrow faces and meniscus decreases. The shell thickness decreases as the casting speed increases, with the lowest non-uniformity coefficient of 6.78% observed at a casting speed of 5.0 m/min. Full article

Background/Objectives: Colorectal cancer (CRC) is a significant global health issue. The identification of methylated Septin 9 (mSEPT9) as a biomarker for CRC represents a significant advancement in cancer diagnostics. On the other hand, Fusobacterium nucleatum (FN) is one of the most studied cancer-related microbes in CRC. This study provided cohort evidence on the association of mSEPT9 with clinicopathologic characteristics and FN infection in CRC patients. Methods: Paired formalin-fixed paraffin-embedded (FFPE) tissue DNA (cancerous and adjacent non-cancer tissues) of eighty-three CRC patients was collected. Methylation-specific qPCR targeting the v2 promoter region of mSEPT9 was carried out on bisulfite-converted FFPE DNA. For FN detection, a TaqMan probe-based method targeting the 16S rRNA gene was used. The differences in mSEPT9 levels and FN expression between cancer and non-cancer tissues were evaluated. Association studies between mSEPT9 in the tumor and relative mSEPT9 levels with FN infection and available clinical data were conducted. Results: Higher mSEPT9 levels were found in the cancerous tissue compared to non-cancerous tissue (p < 0.0001). High mSEPT9 levels in the tumor were significantly associated with older patients (p < 0.001) and larger tumor size (p = 0.048) but not with other clinicopathologic variables. In double-positive patients where mSEPT9 was detected in both cancerous and non-cancerous tissue, the expression fold-change in mSEPT9, calculated using the 2^−ΔΔCT formula, was significantly higher in patients with tumor size equal to or greater than 5 cm (p = 0.042). High levels of mSEPT9 in tumor were not associated with FN infection. However, high levels of FN infection were associated with mSEPT9 (p < 0.021). Conclusions: High levels of mSEPT9 are found in CRC tumor tissue and are associated with older age and larger tumor size, while high levels of FN infection are associated with mSEPT9 in this single-center cohort study. Full article

Traditional methods for urban power grid design have struggled to meet the demands of multi-energy integration and high resilience scenarios due to issues such as delayed updates of terminology and semantic ambiguity. Current techniques for constructing domain-specific lexicons face challenges like the insufficient coverage of specialized vocabulary and imprecise synonym mining, which restrict the semantic parsing capabilities of intelligent design systems. To address these challenges, this study proposes a framework for constructing a domain-specific lexicon for urban power grid design based on Large Language Models (LLMs). The aim is to enhance the accuracy and practicality of the lexicon through multi-level term extraction and synonym expansion. Initially, a structured corpus covering national and industry standards in the field of power was constructed. An improved Term Frequency–Inverse Document Frequency (TF-IDF) algorithm, combined with mutual information and adjacency entropy filtering mechanisms, was utilized to extract high-quality seed vocabulary from 3426 candidate terms. Leveraging LLMs, multi-level prompt templates were designed to guide synonym mining, incorporating a self-correction mechanism for semantic verification to mitigate errors caused by model hallucinations. This approach successfully built a domain-specific lexicon comprising 3426 core seed words and 10,745 synonyms. The average cosine similarity of synonym pairs reached 0.86, and expert validation confirmed an accuracy rate of 89.3%; text classification experiments showed that integrating the domain-specific dictionary improved the classifier’s F1-score by 9.2%, demonstrating the effectiveness of the method. This research innovatively constructs a high-precision terminology dictionary in the field of power design for the first time through embedding domain-driven constraints and validation workflows, solving the problems of insufficient coverage and imprecise expansion of traditional methods, and supporting the development of semantically intelligent systems for smart urban power grid design, with significant practical application value. Full article

The aim of this study was to compare the interproximal contact tightness of lateral teeth after restoring adjacent proximal walls with four types of direct adherent biomaterials. Distal and mesial boxes were prepared on 160 artificial right first and second upper molars. Each set of 40 pairs of boxes was restored using one bulk biomaterial: Equia Forte Fil HT (GC), Cention^® Forte (IVOCLAR VIVADENT), Admira Fusion x-tra (VOCO), or 3M^TMFiltek^TM One Bulk Fill. The mean difference in the passing-through force varied from sound to restored surfaces immediately after application, as well as at 7 and 14 days after: Equia Forte Fil HT—4.07 ± 0.01, 4.08 ± 0.01, and 4.11 ± 0.01; Cention^® Forte—3.30 ± 0.01, 3.50 ± 0.01, and 3.56 ± 0.01; Admira Fusion x-tra—4.10 ± 0.01, 4.13 ± 0.01, and 4.13 ± 0.01; 3M^TMFiltek^TM One Bulk Fill—4.08 ± 0.01, 4.09 ± 0.01, and 4.07 ± 0.01 (p < 0.05). The passing-through force of the restored contact areas showed significantly higher values when compared to those for the sound surfaces, and among them, all biomaterials presented similar values, except for Cention^® Forte. The potential clinical relevance of this study relates to better knowing the most appropriate restorative material for large proximal caries on adjacent surfaces from the outset of the treatment protocol. Full article

Pancreatic cancer is one of the most lethal and challenging malignancies. Its severity is primarily linked to the constitutively activated mitogen-activated protein kinase (MAPK)/extracellular signal-regulated kinase (ERK) pathway. Aquaporins (AQPs) are frequently overexpressed in pancreatic cancer, playing crucial roles in cell signaling, and consequently promoting cell migration, proliferation, and invasion. Here, we investigate the transcriptomics of key players in epithelial–mesenchymal transition (EMT) and the MAPK/ERK signaling pathway in pancreatic cancer tissues, correlating them with tumor AQP expression to highlight their potential as diagnostic or prognostic tools. The transcriptomics analysis was conducted in 24 paired pancreatic tumors and adjacent healthy tissues, and analyses were performed considering the patients’ age and gender, as well as tumor invasiveness and aggressiveness. Our results revealed strong positive Pearson correlation coefficients between AQP3 and c-Jun, and between AQP5 and CDH1/EGFR in pancreatic tumors but not in healthy tissues, with posterior in vitro confirmation in pancreatic cancer BxPC3 cells, suggesting a shift in the regulatory mechanisms of gene expression that certainly affect the physiology of the tissue, influencing cancer initiation and progression. This study underscores the interplay between AQPs and cancer signaling pathways, opening new avenues for defining novel clinical biomarkers and improving the early detection of pancreatic cancer. Full article

Background/Objectives: Gastric cancer (GC) is a prevalent malignant tumor worldwide, with its pathological mechanisms largely unknown. Understanding the metabolic reprogramming associated with GC is crucial for the prevention and treatment of this disease. This study aims to identify significant alterations in metabolites and pathways related to the development of GC. Methods: A liquid chromatography–mass spectrometry-based non-targeted metabolomics data acquisition was performed on paired tissues from 80 GC patients. Differences in metabolic profiles between tumor and adjacent normal tissues were first investigated through univariate and multivariate statistical analyses. Additionally, differential correlation network analysis and a newly proposed network analysis method (NAM) were employed to explore significant metabolite pathways and subnetworks related to tumorigenesis and various TNM stages of GC. Results: Over half of the annotated metabolites exhibited significant alterations. Phosphatidylcholine (PC)_30_0 and fatty acid C20_3 demonstrated strong diagnostic performance for GC, with AUCs of 0.911 and 0.934 in the discovery and validation sets, respectively. Differential correlation network analysis revealed significant fatty acid-related metabolic reprogramming in GC with elevated levels of medium-chain acylcarnitines and increased activity of medium-chain acyl-CoA dehydrogenase, firstly observed in clinical GC tissues. Of note, using NAM, two correlation subnetworks were identified as having significant alterations across different TNM stages, centered with choline and carnitine C4_0-OH, respectively. Conclusions: The identified significant alterations in fatty acid metabolism and TNM-related metabolic subnetworks in GC tissues will facilitate future investigations into the metabolic reprogramming associated with gastric cancer. Full article

In addressing the issue of node classification with imbalanced data distribution, traditional models exhibit significant limitations. Conventional improvement methods, such as node replication or weight adjustment, often focus solely on nodes, neglecting connection relationships. However, numerous studies have demonstrated that optimizing edge distribution can improve the quality of node embeddings. In this paper, we propose the Edge and Node Collaborative Enhancement method (ENE-GCN). This method identifies potentially associated node pairs by similarity measures and constructs a hybrid adjacency matrix, which enlarges the fitting space of node embedding. Subsequently, an adversarial generation strategy is employed to augment the minority class nodes, thereby constructing a balanced sample set. Compared to existing methods, our approach achieves collaborative enhancement of both edges and nodes in a concise manner, improving embedding quality and balancing the training scenario. Experimental comparisons on four public graph datasets reveal that, compared to baseline methods, our proposed method achieves notable improvements in Recall and AUC metrics, particularly in sparsely connected datasets. Full article

Although it has been shown that the tumor extracellular matrix (ECM) may sustain the cancer stem cell (CSC) niche, its role in the modulation of CSC properties remains poorly characterized. To elucidate this, paired tumor and adjacent normal mucosa, derived from colon cancer patients’ surgical resections, were decellularized and recellularized with two distinct colon cancer cells, HT-29 or HCT-15. Methods: The matrix impact on cancer stem cell marker expression was evaluated by flow cytometry and qRT-PCR, while transforming growth factor-β (TGF-β) secretion and matrix metalloprotease (MMP) activity were quantified by ELISA and zymography. Results: In contrast to their paired normal counterparts, the tumor decellularized matrices enhanced HT-29 expression of the pluripotency and stemness genes NANOG (p = 0.0117), SOX2 (p = 0.0156), and OCT4 (p = 0.0312) and of the epithelial-to-mesenchymal transition (EMT)-associated transcription factor SNAI1 (p = 0.0156). Notably, no significant differences were found in the expression of SLUG or TGFB on HT-29 or of the six transcripts on HCT-15 cells. HT-29 mRNA alterations were followed by enhanced expression of the stemness-associated receptors cluster of differentiation 44 (CD44), CD133, and CD166 (p = 0.0078), the secretion of TGF-β (p = 0.0286), and MMP-2 (p = 0.0081) and MMP-9 (p = 0.0402) proteolysis. To infer the clinical relevance of these findings, we assessed cohort databases and evidenced that patients expressing higher levels of the four stemness-associated genes (NANOG/SOX2/OCT4/SNAI1) had worse overall survival. This study demonstrates that normal and tumor matrices harbor different stemness potential and suggest patient-derived decellularized matrices as an excellent three-dimensional (3D) model to unveil stemness signatures, appointing candidates for future therapeutic strategies. Full article

A new family of graph operations based on multipartite graph with an arbitrary number of parts is defined and their applications are explored in this paper. The complete spectra of graphs derived from multipartite graphs are determined. Because the adjacency matrix of the multipartite graph is symmetric, we can use it to generate an unlimited number of special symmetric graphs. Methods for generating countless new families of integral graphs using these multipartite graph operations have been presented. By applying these multipartite graph operations, we can construct infinitely many orderenergetic graphs from orderenergetic or non-orderenergetic graphs. Additionally, infinite pairs of equienergetic and non-cospectral graphs can be generated through these new operations. Moreover, this kind of graph operation can also be used to construct other special graphs related to eigenvalues and energy. Full article

Current research on the transition mechanisms induced by moderate-height roughness elements remains insufficiently explored. Hence, direct numerical simulation (DNS) and BiGlobal stability analysis are employed in this study to investigate boundary layer transition from laminar to turbulent flow induced by moderate-height isolated roughness elements and roughness strips under a supersonic freestream at Mach 3.5. Analysis of DNS results reveals that the isolated roughness element induces transition within the boundary layer, characterized by two high-speed streaks in the wake. This transition is attributed to the coupling between the separated shear layer at the roughness apex and the downstream counter-rotating vortex pair (CVP). BiGlobal stability analysis further identifies that symmetric eigenmodes dominate the transition process in the wake, actively promoting flow destabilization. Conversely, the roughness strip configuration suppresses transition, with only attenuated high-speed streaks persisting in the near wake before complete dissipation. The wake flow exhibits multiple CVPs and adjacent horseshoe vortex pairs interacting with the shear layer, with antisymmetric modes dominating this process. These findings provide technical foundations and theoretical frameworks for predicting and controlling roughness-induced transition. Full article

Corrosion is considered a leading cause of failure in pipeline systems. Therefore, frequent inspection and monitoring are essential to maintain structural integrity. Feature matching based on in-line inspections (ILIs) aligns corrosion data across inspections, facilitating the observation of corrosion progression. Nonetheless, the uncertainties of inspection tools and corrosion processes present in ILI data influence feature matching accuracy. This study proposes a new extensible feature matching model based on consecutive ILIs and data clustering. By dynamically segmenting the data into spatially localized clusters, this framework enables feature matching of isolated pairs and merging defects, as well as facilitating more precise localized transformations. Moreover, a new clustering technique—directional epsilon neighborhood clustering (DENC)—is proposed. DENC utilizes spatial graph structures and directional proximity thresholds to address the directional variability in ILI data while effectively identifying outliers. The model is evaluated on six pipeline segments with varying ILI data complexities, achieving high recall and precision of 91.5% and 98.0%, respectively. In comparison to exclusively point matching models, this work demonstrates significant improvements in terms of accuracy, stability, and managing the spatial variability and interactions of adjacent defects. These advancements establish a new framework for automated feature matching and contribute to enhanced pipeline integrity management. Full article

Background/Objectives: Laryngeal squamous cell carcinoma represents one of the most common head and neck cancers with a five-year survival rate that, despite diagnostic and therapeutic advances, has not shown any significant improvement in recent decades. Oxidative stress, generated by an imbalance between reactive oxygen species and cellular antioxidant systems, is considered a central mechanism in the carcinogenesis of laryngeal squamous cell carcinoma, causing DNA damage and genomic alterations. Methods: This prospective observational paired case–control study focused on the evaluation of antioxidant proteins, such as superoxide dismutase, catalase, heme-oxygenase 1, vimentin, metallothionein, and nuclear factor erythroid 2-related factor 2, in cancer tissues from fifteen patients with laryngeal squamous cell carcinoma, using adjacent healthy tissues as controls. Results: The results show a statistically significant overexpression of all proteins analyzed in cancer tissues compared to controls, with relevant correlations between specific biomarkers and clinical characteristics, age, sex, smoking habits, and degree of tumor differentiation. Conclusions: These preliminary studies, while limited by sample size and the complexity of molecular regulation, indicate that the overexpression of antioxidant enzymes in laryngeal squamous cell carcinoma tissues, along with their correlations with key clinical parameters, underscores a context-dependent role of oxidative stress in tumor progression. A deeper understanding of oxidative stress mechanisms could contribute to advance personalized management strategies for laryngeal squamous cell carcinoma, potentially improving treatment outcomes and patient prognosis. Full article

In this paper, we propose Sound Source Localization (SSL) using deep learning for Human–Robot Interaction (HRI) under intelligent robot environments. The proposed SSL method consists of three steps. The first step preprocesses the sound source to minimize noise and reverberation in the robotic environment. Excitation source information (ESI), which contains only the original components of the sound source, is extracted from a sound source in a microphone array mounted on a robot to minimize background influence. Here, the linear prediction residual is used as the ESI. Subsequently, the cross-correlation signal between each adjacent microphone pair is calculated by using the ESI signal of each sound source. To minimize the influence of noise, a Generalized Cross-Correlation with the phase transform (GCC-PHAT) algorithm is used. In the second step, we design a single-channel, multi-input convolutional neural network that can independently learn the calculated cross-correlation signal between each adjacent microphone pair and the location of the sound source using the time difference of arrival. The third step classifies the location of the sound source after training with the proposed network. Previous studies have primarily used various features as inputs and stacked them into multiple channels, which made the algorithm complex. Furthermore, multi-channel inputs may not be sufficient to clearly train the interrelationship between each sound source. To address this issue, the cross-correlation signal between each sound source alone is used as the network input. The proposed method was verified on the Electronics and Telecommunications Research Institute-Sound Source Localization (ETRI-SSL) database acquired from the robotic environment. The experimental results revealed that the proposed method showed an 8.75% higher performance in comparison to the previous works. Full article