Search Results (3,034)

Recent preclinical and clinical studies have confirmed the essential role of interferons in the host’s immune response against malignant cells. Merkel cell carcinoma (MCC) is a rare, aggressive skin cancer strongly associated with Merkel cell polyomavirus (MCPyV). Despite progress in understanding MCC pathogenesis, the role of innate immune signaling, particularly interferon-γ (IFN γ) and its downstream pathways, remains underexplored. This review summarizes recent findings on IFN-γ in MCC, highlighting its dual role in promoting both antitumor immunity and immune evasion. IFN-γ enhances cytotoxic T cell responses, upregulates MHC class I/II expression, and induces tumor cell apoptosis. Transcriptomic studies have shown that IFN-γ treatment upregulates immune-regulatory genes including PD-L1, HLA-A/B/C, and IDO1 by over threefold; it also activates APOBEC3B and 3G, contributing to antiviral defense and tumor editing. Clinically, immune checkpoint inhibitors (ICIs) such as pembrolizumab and avelumab yield objective response rates of 30–56% and two-year overall survival rates exceeding 60% in advanced MCC. However, approximately 50% of patients do not respond, in part due to IFN-γ signaling deficiencies. This review further discusses IFN-γ’s crosstalk with the STAT1/3/5 pathways and emerging combination strategies aimed at restoring immune sensitivity. Understanding these mechanisms may inform personalized immunotherapeutic approaches and guide the development of IFN-γ–based interventions in MCC. Full article

Inflammatory arthritis (IA) is a chronic condition marked by joint dysfunction and pain, posing significant challenges for effective drug delivery. This study separated Perilla frutescens leaf-derived exosome-like nanovesicles (PFE) to effectively penetrate the stratum corneum barrier. These nanovesicles and indomethacin (IND) were subsequently developed into a nanogel designed for topical drug delivery systems (PFE-IND-GEL). PFE exhibited a typical vesicular structure with a mean diameter of 98.4 ± 1.3 nm. The hydrodynamic size and zeta potential of PFE-IND-GEL were 129.6 ± 5.9 nm and −17.4 ± 1.9 mV, respectively. Mechanistic investigations in HaCaT keratinocytes showed that PFE significantly downregulated tight junction proteins (ZO-1 and Occludin, p < 0.01) via modulation of the IL-17 signaling pathway, as evidenced by transcriptomic analysis. In a sodium urea crystal-induced rat IA model, the topical application of PFE-IND-GEL significantly reduced joint swelling (p < 0.05) and serum levels of inflammatory cytokines (IL-6, IL-1α, TNF-α) compared to control groups. Histopathological analysis confirmed the marked attenuation of synovial inflammation and cartilage preservation in treated animals. These findings underscore the dual role of PFE as both a topical permeation enhancer and an anti-inflammatory agent, presenting a promising strategy for managing IA. Full article

Rapid screening of foodborne pathogens is critical for food safety, yet current detection techniques often suffer from low efficiency and complexity. In this study, we developed a sliding microfluidic colorimetric biosensor for the fast, sensitive, and multiplex detection of Salmonella. First, the target bacteria were specifically captured by antibody-functionalized magnetic nanoparticles in the microfluidic chip, forming magnetic bead–bacteria complexes. Then, through motor-assisted sliding of the chip, manganese dioxide (MnO₂) nanoflowers conjugated with secondary antibodies were introduced to bind the captured bacteria, generating a dual-antibody sandwich structure. Finally, a second sliding step brought the complexes into contact with a chromogenic substrate, where the MnO₂ nanoflowers catalyzed a colorimetric reaction, and the resulting signal was used to quantify the Salmonella concentration. Under optimized conditions, the biosensor achieved a detection limit of 10 CFU/mL within 20 min. In spiked pork samples, the average recovery rate of Salmonella ranged from 94.9% to 125.4%, with a coefficient of variation between 4.0% and 6.8%. By integrating mixing, separation, washing, catalysis, and detection into a single chip, this microfluidic biosensor offers a user-friendly, time-efficient, and highly sensitive platform, showing great potential for the on-site detection of foodborne pathogens. Full article

Reactive oxygen species (ROS) play a dual role as both essential signaling molecules and harmful mediators of damage. Imbalances in the redox state of the liver can overwhelm antioxidant defenses and promote mitochondrial dysfunction, oxidative damage, and inflammation. Complex feedback loops between ROS and immune signaling pathways are a hallmark of pathological liver conditions, such as hepatic ischemia–reperfusion injury (IRI). This is a major cause of liver transplant failure and is of increasing significance due to the increased use of marginally discarded livers for transplantation. This review outlines the major enzymatic and metabolic sources of ROS in hepatic IRI, including mitochondrial reverse electron transport, NADPH oxidases, cytochrome P450 enzymes, and endoplasmic reticulum stress. Hepatocyte injury activates redox feedback loops that initiate immune cascades through DAMP release, toll-like receptor signaling, and cytokine production. Emerging regulatory mechanisms, such as succinate accumulation and cytosolic calcium–CAMKII signaling, further shape oxidative dynamics. Pharmacological therapies and the use of antioxidant and immunomodulatory approaches, including nanoparticles and redox-sensitive therapeutics, are discussed as protective strategies. A deeper understanding of how redox and immune feedback loops interact is an exciting and active area of research that warrants further clinical investigation. Full article

A novel reflective metasurface (RMS) is proposed in this paper. The MS measures 128 × 128 × 2.794 mm³ and consists of a six-layer vertically stacked structure, with a liquid crystal (LC) cavity in the middle layer. A dual fan-shaped direct current (DC) bias circuit is designed to minimize the interaction between the radio frequency (RF) signal and the DC source, allowing control of the LC dielectric constant via bias voltage. This enables multi-band operation to improve communication capacity and quality for x-band devices. The polarization conversion (PC) structure employs an orthogonal anisotropic design, utilizing logarithmic functions to create two pairs of bowtie microstrip patches for linear-to-circular polarization conversion (LCPC). Simulation results show that for x-polarized incident waves, with an LC dielectric constant of ε_r = 2.8, left- and right-handed circularly polarized (LHCP and RHCP) waves are achieved in the frequency ranges of 8.15–8.46 GHz and 9.84–12.52 GHz, respectively. For ε_r = 3.9, LHCP and RHCP are achieved in 9–9.11 GHz and 9.86–11.81 GHz, respectively, and for ε_r = 4.6, they are in 8.96–9.11 GHz and 9.95–11.51 GHz. In the case of y-polarized incident waves, the MS reflects the reverse CP waves within the same frequency ranges. Measured results show that at ε_r = 2.8, the axial ratio (AR) is below 3 dB in the frequency ranges 8.16–8.46 GHz and 9.86–12.48 GHz, with 3 dB AR relative bandwidth (ARBW) of 3.61% and 23.46%, respectively. For ε_r = 4.6, the AR < 3 dB in the frequency range of 9.78–11.34 GHz, with a 3 dB ARBW of 14.77%. Finally, the measured and simulated results are compared to validate the proposed design, which can be applied to various applications within the corresponding operating frequency band. Full article

The stable operation of electrical equipment is critical for industrial safety, yet traditional anomaly detection methods often suffer from limitations, such as high resource demands, dependency on expert knowledge, and lack of real-world capabilities. To address these challenges, this article proposes an unsupervised anomaly detection method for electrical equipment, utilizing audio latent representation and a parallel attention mechanism. The framework employs an autoencoder to extract low-dimensional features from audio signals and introduces a phase-aware parallel attention block to dynamically weight these features for an improved anomaly sensitivity. With adversarial training and a dual-encoding mechanism, the proposed method demonstrates robust performance in complex scenarios. Using public datasets (MIMII and ToyADMOS) and our collected real-world wind turbine data, it achieves high AUC scores, surpassing the best baselines, which demonstrates our framework design is suitable for industrial applications. Full article

To address the issue of low accuracy in identifying the transition states of rolling bearing performance degradation when relying solely on vibration signals, this study proposed a vibration–temperature fusion-based adaptive method for bearing performance degradation assessments. First, a multidimensional time–frequency feature set was constructed by integrating vibration acceleration and temperature signals. Second, a novel composite sensitivity index (CSI) was introduced, incorporating the trend persistence, monotonicity, and signal complexity to perform preliminary feature screening. Mutual information clustering and regularized entropy weight optimization were then combined to reselect highly sensitive parameters from the initially screened features. Subsequently, an adaptive feature fusion method based on auto-associative kernel regression (AFF-AAKR) was introduced to compress the data in the spatial dimension while enhancing the degradation trend characterization capability of the health indicator (HI) through a temporal residual analysis. Furthermore, the entropy weight method was employed to quantify the information entropy differences between the vibration and temperature signals, enabling dynamic weight allocation to construct a comprehensive HI. Finally, a dual-criteria adaptive bottom-up merging algorithm (DC-ABUM) was proposed, which achieves bearing life-stage identification through error threshold constraints and the adaptive optimization of segmentation quantities. The experimental results demonstrated that the proposed method outperformed traditional vibration-based life-stage identification approaches. Full article

Background/Objectives: Increasing evidence suggests that lncRNAs are core regulators in the field of tumor progression, with context-specific functions in oncogenic tumorigenesis. LINC01133, a lncRNA that has been identified as both an oncogene and a tumor suppressor, remains largely unexplored in terms of its molecular mechanisms. The purpose of this study was to conduct an in silico analysis, incorporating literature research on various cancer types, to investigate the structural and functional duality of LINC01133. This analysis aimed to identify pathways influenced by LINC01133 and evaluate its mechanism of action as a potential therapeutic target and diagnostic biomarker. Methods: In silico analyses and a narrative review of the literature were performed to predict conserved structural elements, functional internal loops, and overall conservation of the LINC01133 sequence among different vertebrate organisms, summarizing the empirical evidence regarding its roles as a tumor suppressor and tumor-promoting roles in various types of tumors. Results: LINC01133 harbors the evolutionarily conserved structural regions that might allow for binding to relevant driver signaling pathways, substantiating its specific functionality. Its action extends beyond classical tumor mechanisms, affecting proliferation, migration, invasion, and epigenetic pathways in various types of tumors, as indicated by the in silico results and narrative review of the literature we present here. Clinical outcome associations pointed to its potential as a biomarker. Conclusions: The dual character of LINC01133 in tumor biology further demonstrates its prospective therapeutic value, but complete elucidation of its mechanisms of action requires further investigation. This study establishes LINC01133 as a multifaceted lncRNA, supporting context-specific strategies in targeting its pathways, and calls for expanded research to harness its full potential in oncology. Full article

This paper presents the design, simulation, and experimental validation of a dual-Circularly Polarized (CP) array antenna to be used as single element for a bistatic radar system, aimed at detecting and tracking objects in Low Earth Orbit (LEO). The antenna operates at 412 MHz in reception mode and consists of an array of 19 slotted-patch radiating elements with a cavity-based metallic superstrate, designed to support dual circular polarization. These elements are arranged in a hexagonal configuration, enabling the array structure to achieve a maximum realized gain of 17 dBi and a Side Lobe Level (SLL) below −17 dB while maintaining high polarization purity. Two identical analog feeding networks enable the precise control of phase and amplitude, allowing the independent reception of Right-Hand and Left-Hand Circularly Polarized (RHCP and LHCP) signals. Full-wave simulations and experimental measurements confirm the high performance and robustness of the system, demonstrating its suitability for integration into large-scale Space Situational Awareness (SSA) sensor networks. Full article

As the core device for protecting the safety of the pressure-bearing system, the spring full-open safety valve is prone to various forms of valve seat sealing surface damage after long-term opening and closing impact, corrosion, and medium erosion, which may lead to internal leakage. In view of the problems that the high-frequency acoustic emission signal of the internal leakage of the safety valve has, namely, a large number of energy-overlapping areas in the frequency domain, the overall signal presents broadband characteristics, large noise content, and no obvious time–frequency characteristics. A composite denoising method, IWTD, improved wavelet threshold function with dual adjustable factors, and the improved VMD algorithm is proposed. In view of the problem that the optimal values of the dual adjustment factors a and b of the function are difficult to determine manually, an improved dung beetle optimization algorithm is proposed, with the maximum Pearson coefficient as the optimization target; the optimization is performed within the value range of the dual adjustable factors a and b, so as to obtain the optimal value. In view of the problem that the key parameters K and α in VMD decomposition are difficult to determine manually, the maximum Pearson coefficient is taken as the optimization target, and the improved dung beetle algorithm is used to optimize within the value range of K and α, so as to obtain the IVMD algorithm. Based on the IVMD algorithm, the characteristic decomposition of the internal leakage acoustic emission signal occurs after the denoising of the IWTD function is performed to further improve the denoising effect. The results show that the Pearson coefficients of all types of internal leakage acoustic emission signals after IWTD-IVMD composite noise reduction are greater than 0.9, which is much higher than traditional noise reduction methods such as soft and hard threshold functions. Therefore, the IWTD-IVMD composite noise reduction method can extract more main features out of the measured spring full-open safety valve internal leakage acoustic emission signals, and has a good noise reduction effect. Feature recognition after noise reduction can provide a good evaluation for the safe operation of the safety valve. Full article

We present mmHSE, a two-stage framework for human skeleton estimation using dual millimeter-Wave (mmWave) Frequency-Modulated Continuous-Wave (FMCW) radar signals. To enable data-driven model design and evaluation, we collect and process over 30,000 range–angle maps from 12 users across three representative indoor environments using a dual-node radar acquisition platform. Leveraging the collected data, we develop a two-stage neural architecture for human skeleton estimation. The first stage employs a dual-branch network with depthwise separable convolutions and self-attention to extract multi-scale spatiotemporal features from dual-view radar inputs. A cross-modal attention fusion module is then used to generate initial estimates of 21 skeletal keypoints. The second stage refines these estimates using a skeletal topology module based on graph convolutional networks, which captures spatial dependencies among joints to enhance localization accuracy. Experiments show that mmHSE achieves a Mean Absolute Error (MAE) of 2.78 cm. In cross-domain evaluations, the MAE remains at 3.14 cm, demonstrating the method’s generalization ability and robustness for non-intrusive human pose estimation from mmWave FMCW radar signals. Full article

Time domain reflectometry (TDR) has been validated for monitoring water level evolution and riverbed scouring in the laboratory. Previous studies have also validated the feasibility of field-based single hydrological parameter monitoring using TDR. However, the current research focuses on developing separated TDR sensing systems, and integrated measurements of multiple hydrological parameters from a single reflected waveform have not been reported. This study presents an improved helical probe sensor specifically designed for implementation in geologically hard soils, together with an improved data interpreting methodology to simultaneously determine water surface level, bed elevation, and suspended sediment concentration from a single reflection signal. Experimental comparisons were conducted in the laboratory to evaluate the measuring performance between the traditional dual-needle probe and the novel spiral probe under the same scouring conditions. The experiments confirmed the reliability and superior performance of spiral probe in accurately capturing multiple hydrological parameters. The measurement errors for the spiral probe across multiple hydrological parameters were all within ±10%, and the accuracy further improved with increased probe embedding depth in the sand medium. Across all tested parameters, the spiral probe showed enhanced measurement precision with a particularly significant improvement in suspended sediment concentration detection. Full article

Accurate assessment of the state of health (SOH) of lithium-ion batteries (LIBs) is fundamental to ensuring safe operation. However, due to the complex electrochemical processes during battery operation and the limited availability of training data, accurate estimation of the state of health remains challenging. To address this, this paper proposes a prediction framework based on dual-view voltage signal features and an improved Long Short-Term Memory (LSTM) neural network. By relying solely on readily obtainable voltage signals, the data requirement is greatly reduced; dual-view features, comprising kinetic and aggregated aspects, are extracted based on the underlying reaction mechanisms. To fully leverage the extracted feature information, Scaled Dot-Product Attention (SDPA) is employed to dynamically score all hidden states of the long short-term memory network, adaptively capturing key temporal information. The experimental results based on the NASA PCoE battery dataset indicate that, under various operating conditions, the proposed method achieves an average absolute error below 0.51% and a root mean square error not exceeding 0.58% in state-of-health estimation, demonstrating high predictive accuracy. Full article

Background: Protein kinase R (PKR) inhibits general mRNA translation by phosphorylating the alpha subunit of eukaryotic translation initiation factor 2 (eIF2). PKR also modulates NF-κB signaling during viral infections, but comparative studies of PKR-mediated NF-κB responses across mammalian species and their regulation by viral inhibitors remain largely unexplored. This study aimed to characterize the conserved antiviral and inflammatory roles of mammalian PKR orthologs and investigate their modulation by poxviral inhibitors. Methods: Using reporter gene assays and quantitative RT-PCR, we assessed the impact of 17 mammalian PKR orthologs on general translation inhibition, stress-responsive translation, and NF-κB-dependent induction of target genes. Congenic human and rabbit cell lines infected with a myxoma virus strain lacking PKR inhibitors were used to compare the effects of human and rabbit PKR on viral replication and inflammatory responses. Site-directed mutagenesis was employed to determine key residues responsible for differential sensitivity to the viral inhibitor M156. Results: All 17 mammalian PKR orthologs significantly inhibited general translation, strongly activated stress-responsive ATF4 translation, and robustly induced NF-κB target genes. Inhibition of these responses was specifically mediated by poxviral K3 orthologs that effectively suppressed PKR activation. Comparative analyses showed human and rabbit PKRs similarly inhibited virus replication and induced cytokine transcripts. Amino acid swaps between rabbit PKRs reversed their sensitivity to viral inhibitor M156 and NF-κB activation. Conclusions: Our data show that the tested PKR orthologs exhibit conserved dual antiviral and inflammatory regulatory roles, which can be antagonized by poxviral K3 orthologs that exploit eIF2α mimicry to modulate the PKR-NF-κB axis. Full article

In space gravitational wave detection, various physical effects in the spacecraft, such as self-gravity, electricity, and magnetism, will introduce undesirable parasitic stiffness. The coupling noise between stiffness and the motion states of the test mass critically affects the performance of scientific detection, making accurate stiffness identification crucial. In response to the question, this paper proposes a method to optimize the test mass motion state for enhancing stiffness identification performance. First, the dynamics of the test mass are studied and a recursive least squares algorithm is applied for the implementation of on-orbit stiffness identification. Then, the motion state of the test mass is parametrically characterized by multi-frequency sinusoidal signals as the variable to be optimized, with the optimization objectives and constraints of stiffness identification defined based on convergence time, convergence accuracy, and engineering requirements. To tackle the dual-objective, computationally expensive nature of the problem, a multigranularity surrogate-assisted evolutionary algorithm with individual progressive constraints (MGSAEA-IPC) is proposed. A fuzzy radial basis function neural network PID (FRBF-PID) controller is also designed to address complex control needs under varying motion states. Numerical simulations demonstrate that the convergence time after optimization is less than 2 min, and the convergence accuracy is less than 1.5 × 10⁻¹⁰ s⁻². This study can provide ideas and design references for subsequent related identification and control missions. Full article