Search Results (58)

Fibrotic disorders pose a significant global health burden due to limited treatment options, creating an urgent need for novel therapeutic strategies. Amphiregulin (AREG), a low-affinity ligand for the epidermal growth factor receptor (EGFR), has emerged as a key mediator of fibrogenesis through dual signaling pathways. Unlike high-affinity EGFR ligands, AREG induces sustained signaling that activates downstream effectors and promotes the integrin-mediated activation of transforming growth factor (TGF)-β. This enables both canonical and non-canonical EGFR signaling pathways that contribute to fibrosis. Elevated AREG expression correlates with disease severity across multiple organs, including the lungs, kidneys, liver, and heart. The therapeutic targeting of AREG has shown promising antifibrotic and anticancer effects, suggesting a dual-benefit strategy. The increasing recognition of the shared mechanisms between fibrosis and cancer further supports the development of unified treatment approaches. The inhibition of AREG has been shown to sensitize fibrotic tumor microenvironments to chemotherapy, enhancing combination therapy efficacy. Targeted therapies, such as Self-Assembled-Micelle inhibitory RNA (SAMiRNA)-AREG, have demonstrated enhanced specificity and favorable safety profiles in preclinical studies and early clinical trials. Personalized treatment based on AREG expression may improve clinical outcomes, establishing AREG as a promising precision medicine target for both fibrotic and malignant diseases. This review aims to provide a comprehensive understanding of AREG biology and evaluate its therapeutic potential in fibrosis and cancer. Full article

T cells equipped with chimeric antigen receptors (CARs) have evolved into an essential pillar of lymphoma therapy, reaching second-line treatment. In solid cancers, however, a dearth of lasting CAR T cell activation poses the major obstacle to achieving a substantial and durable anti-tumor response. To extend T cell cytotoxic capacities, we engineered CAR T cells to constitutively release an immunostimulatory variant of soluble SLAMF6. While wild-type SLAMF6 induces T cell exhaustion, CAR T cells with the soluble Δ17-65 SLAMF6 variant exhibited refined, CAR redirected functionality compared to canonical CAR T cells. CD28-ζ CAR T cells releasing soluble SLAMF6 increased IFN-γ secretion and augmented CD25 upregulation on CD4⁺ CAR T cells upon CAR engagement by pancreatic carcinoma and melanoma cells. Moreover, under conditions of repetitive antigen encounter, SLAMF6-secreting CAR T cells evinced superior cytotoxic capacity in the long term. Mechanistically, SLAMF6-secreting CAR T cells showed predominantly a central memory phenotype, a PD-1^- TIGIT^- double negative profile, and reduced expression of exhaustion-related transcription factors IRF-4 and TOX with augmented amplification and persistence capacities. Overall, CAR T cells engineered with the release isoform 2 SLAMF6 establish an auto-stimulatory loop with the potential to boost the cytolytic attack against solid tumors. Full article

Malignant Triton Tumors (MTTs) are rare, high-grade malignant peripheral nerve sheath tumors (MPNSTs) frequently associated with Type 1 Neurofibromatosis (NF1). NF1, an autosomal dominant disorder, predisposes approximately 10% of affected individuals to developing MPNSTs, with 50% of these tumors occurring in NF1 patients, while others arise sporadically or in association with radiation exposure. MTTs predominantly affect anatomical regions rich in large nerves, such as the limbs, spinal root, and cranial nerves. Mediastinal presentations are exceedingly rare, posing significant diagnostic and therapeutic challenges. Current treatment strategies include surgical resection, chemotherapy, radiotherapy, and lung-sparing procedures for metastatic disease. Molecular studies of MPNSTs have revealed that NF1 mutations lead to dysregulation of the RAS signalling pathway, while epigenetic alterations (e.g., SUZ12/EED mutations) further contribute to tumor progression. Dysregulated phylogenetically conserved pathways, including Wnt/beta-catenin and non-canonical SHH signalling, play a role in sarcoma progression and Schwann cell transformation. Recent advances in miRNA research highlight their involvement in tumor invasion and progression, with dysregulated miRNA expression and chromatin remodeling contributing to the pathogenesis of these neoplasms. However, the distinct molecular profiles for MTTs remain incompletely understood. Further investigation of the genetic and epigenetic landscape is essential for improving our understanding and identifying potential therapies. Herein, we present a single-center retrospective case series of three patients with an intrathoracic triton tumor treated at our University Hospital between 2000 and 2024, serving as a starting point for future insights into MPNST pathobiology. Full article

By introducing the concept of nanoreaction-based fluctuating mixing entropy, the challenge posed by the smallness of a closed molecular system is addressed through equilibrium statistical–mechanical averaging over fluctuating reaction extent. Based on the canonical partition function, the interplay between the mixing entropy and fluctuations in the reaction extent in nanoscale environments is unraveled while maintaining consistency with macroscopic behavior. The nanosystem size dependence of the mixing entropy, the reaction extent, and a concept termed the “reaction extent entropy” are modeled for the combination reactions $A+B\leftrightarrow 2C$ and the specific case of $H_2+I_2\leftrightarrow 2HI$. A distinct inverse correlation is found between the first two properties, revealing consistency with the nanoconfinement entropic effect on chemical equilibrium (NCECE). To obtain the time dependence of the instantaneous mixing entropy following equilibration, the Stochastic Simulation (Gillespie) Algorithm is employed. In particular, the smallest nanosystems exhibit a step-like behavior that deviates significantly from the smooth mean values and is associated with the discrete probability distribution of the reaction extent. As illustrated further for molecular adsorption and spin polarization, the current approach can be extended beyond nanoreactions to other confined systems with a limited number of species. Full article

Agriculture plays a dual role in shaping biodiversity, providing secondary habitats while posing significant threats to ecological systems through habitat fragmentation and land-use intensification. This study aims to assess the relationship between bird species composition and land-use types in Apulia, Italy. Specifically, we investigate how different agricultural and semi-natural landscapes influence avian biodiversity and which agricultural models can have a positive impact on biodiversity. Biodiversity indices were calculated for each bird community observed. The abundance curves showed a geometric series pattern for the AGR communities, indicative of ecosystems at an early stage of ecological succession, and a lognormal distribution for the MIX and NAT communities, typical of mature communities with a more even distribution of species. Analysis of variance showed significant differences in richness and diversity between AGR and NAT sites, but not between NAT and MIX, which had the highest values. Logistic regression estimated the probability of sites belonging to the three ecosystem categories as a function of biodiversity, confirming a strong similarity between NAT and MIX. Finally, linear discriminant analysis confirmed a clear separation from AGR areas, as evidenced by the canonical components. The results highlight the importance of integrating high-diversity landscape elements and appropriate agricultural practices to mitigate biodiversity loss. Even a small increase in the naturalness of agricultural land would be sufficient to convert it from the AGR to the MIX ecosystem category, with significant biodiversity benefits. Full article

In the challenges that the digital era currently poses, the Church can demonstrate how canon law, both in its cognitive and regulatory dimensions, can innovate by protecting Tradition, guaranteeing the depositum fidei, respecting the intangibility of the principles of divine law in the adaptability of its content, and through a change of paradigm constituted by a plurality of techniques and shared methods that evolve, replace themselves, complement each other, and are integrated for the attainment of salus animarum, supreme lex Ecclesiae. Full article

Smart manufacturing systems aim to enhance the efficiency, adaptability, and reliability of industrial operations through advanced data-driven approaches. Achieving these objectives hinges on accurate fault detection and timely maintenance, especially in highly dynamic industrial environments. However, capturing nonlinear and temporal dependencies in dynamic nonlinear industrial processes poses significant challenges for traditional data-driven fault detection methods. To address these limitations, this study presents an Incremental Pyraformer–Deep Canonical Correlation Analysis (DCCA) framework that integrates the Pyramidal Attention Mechanism of the Pyraformer with the Broad Learning System for incremental learning in a DCCA basis. The Pyraformer model effectively captures multi-scale temporal features, while the BLS-based incremental learning mechanism adapts to evolving data without full retraining. The proposed framework enhances both spatial and temporal representation, enabling robust fault detection in high-dimensional and continuously changing industrial environments. Experimental validation on the Tennessee Eastman (TE) process, Continuous Stirred-Tank Reactor (CSTR) system, and injection molding process demonstrated superior detection performance. In the TE scenario, our framework achieved a 100% Fault Detection Rate with a 4.35% False Alarm Rate, surpassing DCCA variants. Similarly, in the CSTR case, the approach reached a perfect 100% Fault Detection Rate (FDR) and 3.48% False Alarm Rate (FAR), while in the injection molding process, it delivered a 97.02% FDR with 0% FAR. The findings underline the framework’s effectiveness in handling complex and dynamic data streams, thereby providing a powerful approach for real-time monitoring and proactive maintenance. Full article

The estimation of robot pose and pipe diameter is an essential task for reliable in-line inspection (ILI) operations and the accurate assessment of pipeline attributes. This paper addresses the problem of robot pose and pipe diameter estimation for natural gas pipelines based on 3D time-of-flight (ToF) sensors. To tackle this challenge, we model the problem as a non-linear least-squares optimization that fits 3D ToF sensor measurements in its local coordinates to an elliptic cylindrical model of the pipe inner surface. We identify and prove that the canonical ellipse-based estimation method (C-EPD), which uses a canonical residual function, suffers from bias in diameter estimation due to its asymmetry to depth errors. To overcome this limitation, we propose the robust and unbiased estimation of pose and diameter (RU-EPD) approach, which employs a novel error-based residual function. The proposed function is symmetric to depth errors, effectively reducing estimation bias. Extensive numerical simulations and prototype pipeline experiments demonstrate that RU-EPD outperforms C-EPD, achieving an at least six times lower estimation bias and a 2.5 times smaller estimation error range in pipe diameter and about a 2 times smaller estimation error range in pose estimation. Full article

The automated and precise classification of lung and colon cancer from histopathological photos continues to pose a significant challenge in medical diagnosis, as current computer-aided diagnosis (CAD) systems are frequently constrained by their dependence on singular deep learning architectures, elevated computational complexity, and their ineffectiveness in utilising multiscale features. To this end, the present research introduces a CAD system that integrates several lightweight convolutional neural networks (CNNs) with dual-layer feature extraction and feature selection to overcome the aforementioned constraints. Initially, it extracts deep attributes from two separate layers (pooling and fully connected) of three pre-trained CNNs (MobileNet, ResNet-18, and EfficientNetB0). Second, the system uses the benefits of canonical correlation analysis for dimensionality reduction in pooling layer attributes to reduce complexity. In addition, it integrates the dual-layer features to encapsulate both high- and low-level representations. Finally, to benefit from multiple deep network architectures while reducing classification complexity, the proposed CAD merges dual deep layer variables of the three CNNs and then applies the analysis of variance (ANOVA) and Chi-Squared for the selection of the most discriminative features from the integrated CNN architectures. The CAD is assessed on the LC25000 dataset leveraging eight distinct classifiers, encompassing various Support Vector Machine (SVM) variants, Decision Trees, Linear Discriminant Analysis, and k-nearest neighbours. The experimental results exhibited outstanding performance, attaining 99.8% classification accuracy with cubic SVM classifiers employing merely 50 ANOVA-selected features, exceeding the performance of individual CNNs while markedly diminishing computational complexity. The framework’s capacity to sustain exceptional accuracy with a limited feature set renders it especially advantageous for clinical applications where diagnostic precision and efficiency are critical. These findings confirm the efficacy of the multi-CNN, multi-layer methodology in enhancing cancer classification precision while mitigating the computational constraints of current systems. Full article

The emergence of the “Three Caverns” 三洞 thought was a critical step in the formation of medieval Daoism. It proposed the first viable approach to integrating emerging Daoist scriptural traditions, enabling the creation of the first canonical Daoist catalog, and laying the foundation for the compilation of the Daozang and the establishment of the Ordination Ranks 法位 system. Scholars generally agree that the Shangqing taiji yinzhu yujing baojue 上清太極隱注玉經寶訣 played a significant role in the development of the Three Caverns thought. However, research on the formation of this scripture remains lacking. This study fills this gap by confirming the composition of the scripture through two independent lines of evidence. Then, based on new insights into its composition, this study discusses the historical context of the Three Caverns concept in this scripture and its direct impact on Lu Xiujing 陸修靜 (406–477)’s cataloging work. These discussions illustrate that, when confronted with the challenge posed by the newly composed Shangqing scriptures, the authors of the Shangqing taiji yinzhu yujing baojue employed the integrative approach commonly found in the Ancient Lingbao Scriptures to propose a more inclusive scriptural system. This approach played a crucial role in providing a theoretical foundation for the formation of medieval Daoism. Full article

The tripartite-motif protein 56 (TRIM56) is a RING-type E3 ubiquitin ligase whose functions were recently beginning to be unveiled. While the physiological role(s) of TRIM56 remains unclear, emerging evidence suggests this protein participates in host innate defense mechanisms that guard against viral infections. Interestingly, TRIM56 has been shown to pose a barrier to viruses of distinct families by utilizing its different domains. Apart from exerting direct, restrictive effects on viral propagation, TRIM56 is implicated in regulating innate immune signaling pathways that orchestrate type I interferon response or autophagy, through which it indirectly impacts viral fitness. Remarkably, depending on viral infection settings, TRIM56 either operates in a canonical, E3 ligase-dependent fashion or adopts an enzymatically independent, non-canonical mechanism to bolster innate immune signaling. Moreover, the recent revelation that TRIM56 is an RNA-binding protein sheds new light on its antiviral mechanisms against RNA viruses. This review summarizes recent advances in the emerging roles of TRIM56 in innate antiviral immunity. We focus on its direct virus-restricting effects and its influence on innate immune signaling through two critical pathways: the endolysosome-initiated, double-stranded RNA-sensing TLR3-TRIF pathway and the cytosolic DNA-sensing, cGAS-STING pathway. We discuss the underpinning mechanisms of action and the questions that remain. Further studies understanding the complexity of TRIM56 involvement in innate immunity will add to critical knowledge that could be leveraged for developing antiviral therapeutics. Full article

This study explores the long-run relationship among the environmental footprint (EnF), renewable energy consumption, energy use, industrial growth, and urbanization in Saudi Arabia from 1990 to 2023, employing the Autoregressive Distributed Lag (ARDL) model, alongside Fully Modified Ordinary Least Squares (FMOLS), Dynamic Ordinary Least Squares (DOLS), and Canonical Cointegrating Regression (CCR) for robustness checks. Results indicate a significant long-term relationship among the variables, with renewable energy adoption emerging as a crucial factor in reducing carbon emissions. The ARDL bounds test confirms the existence of cointegration, revealing the dynamic interplay among renewable energy, economic growth, and environmental sustainability. The findings show that renewable energy consumption significantly reduces the environmental footprint (CO₂ emissions), supporting Saudi Arabia’s Vision 2030 goals for economic diversification and sustainable development. However, industrial expansion, while critical for economic growth, still contributes to increased emissions, underscoring the need for further investment in clean technologies. The study also highlights the role of urbanization, which, while essential for development, poses challenges for environmental sustainability. Short-term dynamics, represented by the Error Correction Model, indicate a fast adjustment speed toward equilibrium, with deviations corrected by approximately 52% each period. The study offers valuable insights for policymakers aiming to balance industrial growth with environmental protection, emphasizing the need for strategic investments in renewable energy and energy efficiency. This research contributes to the understanding of energy–economy–environment interactions in oil-rich economies, providing a foundation for future studies to explore the impact of advanced technologies and policy interventions on sustainable development Full article

Modern industrial processes are characterized by increasing complexity, often exhibiting pronounced dynamic behaviors and significant nonlinearity. Addressing these dynamic and nonlinear characteristics is essential for effective process monitoring. However, many existing methods for process monitoring and fault diagnosis are insufficient in handling these challenges. In this article, we present a novel process monitoring approach, CVA-DisDAE, which integrates an improved Denoising Autoencoder (DAE) with Canonical Variate Analysis (CVA) to address the challenges posed by dynamic behaviors and nonlinear relationships in industrial processes. First, CVA is employed to reduce data dimensionality and minimize information redundancy by maximizing correlations between past and future observations, thereby effectively capturing process dynamics. Following this, we introduce a discriminative DAE model (DisDAE) designed to serve as a semi-supervised denoising autoencoder for precise feature extraction. This is achieved by incorporating both between-class separability and within-class variability into the traditional DAE framework. The key distinction between the proposed DisDAE and the conventional DAE lies in the integration of a linear discriminant analysis (LDA) penalty into the DAE’s loss function, resulting in extracted features that are more conducive to fault classification. Finally, we validate the effectiveness of the proposed semi-supervised dynamic process monitoring approach through its application to the Tennessee Eastman benchmark process, demonstrating its superior performance. Full article

Apple core browning not only affects the nutritional quality of apples, but also poses a health risk to consumers. Therefore, there is an urgent need to develop a fast and reliable non-destructive detection method for apple core browning. To deal with the challenges of the long incubation period, strong infectivity, and difficulty in the prevention and control of apple core browning, a novel non-destructive detection method for apple core browning has been developed through combining hyperspectral imaging and dielectric techniques. To reduce the computational complexity of high-dimensional multi-view data, canonical correlation analysis is employed for feature dimensionality reduction. Then, the two low-dimensional vectors extracted from two different sensors are concatenated into one united feature vector; therefore, the information contained in the hyperspectral and dielectric data is fused to improve the detection accuracy of the non-destructive method. At last, five traditional classifiers, such as k-Nearest Neighbors, a support vector machine with radial basis function kernel and polynomial kernel, Decision Tree, and neural network, are trained on the fused feature vectors to discriminate apple core browning. The experimental results on our own constructed dataset have shown that the sensitivity, specificity, and precision of SVM with RBF kernel based on concatenated 70-dimensional feature vectors extracted via canonical correlation analysis reached 99.98%, 99.70%, and 99.70%, respectively, which achieved better results than other models. This study can provide theoretical assurance and technical support for further development of higher accuracy and lower-cost non-destructive detection devices for apple core browning. Full article

Nematodes pose significant challenges for the fish processing industry, particularly in white fish. Despite technological advances, the industry still depends on manual labor for the detection and extraction of nematodes. This study addresses the initial steps of automatic nematode detection and differentiation from other common defects in fish fillets, such as skin remnants and blood spots. VideometerLab 4, an advanced Multispectral Imaging (MSI) System, was used to acquire 270 images of 50 Atlantic cod fillets under controlled conditions. In total, 173 nematodes were labeled using the Segment Anything Model (SAM), which is trained to automatically segment objects of interest from only few representative pixels. With the acquired dataset, we study the potential of identifying nematodes through their spectral signature. We incorporated normalized Canonical Discriminant Analysis (nCDA) to develop segmentation models trained to distinguish between different components within the fish fillets. By incorporating multiple segmentation models, we aimed to achieve a satisfactory balance between false negatives and false positives. This resulted in 88% precision and 79% recall for our annotated test data. This approach could improve process control by accurately identifying fillets with nematodes. Using MSI minimizes unnecessary inspection of fillets in good condition and concurrently boosts product safety and quality. Full article