Search Results (1,215)

Clear cell renal cell carcinoma (ccRCC) is characterized by marked biological heterogeneity, which limits the prognostic accuracy of conventional clinicopathological models. Increasing attention has therefore focused on identification of biomarkers that can enhance risk stratification throughout all stages of the disease. Starting from the current state of the art, this narrative review summarizes and critically appraises the evidence published over the past decade regarding prognostic biomarkers in ccRCC. The analysis is structured into four overarching domains: (i) genomic biomarkers, covering somatic alterations and transcriptomic signatures; (ii) tissue-based biomarkers, including immunohistochemical surrogates and immune microenvironment features; (iii) circulating biomarkers, such as systemic inflammation parameters and indices; and (iv) integrated predictive models, represented by emerging multi-omic approaches. Going through the broad framework of potential prognostic biomarkers, emphasis is placed on their individual and integrative value in relation to classic clinical-pathological factors and survival parameters. At the tissue level, chromosome 3p-related alterations constitute a central molecular feature of ccRCC. Among these, BAP1 loss has emerged as one of the most consistently validated indicators of aggressive tumor behavior. Disruption of the SETD2/H3K36me3 axis and immune-related biomarkers, including PD-L1 expression, have demonstrated prognostic associations in selected settings, although with variable and context-dependent performance. In the circulating compartment, plasma KIM-1 has shown prognostic relevance following nephrectomy, while postoperative detection of circulating tumor DNA (ctDNA) may identify patients at increased risk of recurrence. However, limited analytical sensitivity and methodological heterogeneity currently restrict the broader clinical applicability of ctDNA-based strategies. Systemic inflammatory indices, such as the neutrophil-to-lymphocyte ratio, show reproducible associations with outcomes but largely reflect host inflammatory status rather than tumor-specific biology. However, no single biomarker currently supports routine prognostic implementation in ccRCC. Future progress will likely depend on integrative models combining genomic, tissue-based, immune, and circulating parameters with established clinical variables. Prospective validation and clear demonstration of incremental clinical utility will be essential before such strategies can meaningfully inform therapeutic decision-making. Full article

Surface and interface engineering has become a decisive factor in determining the performance and reliability of integrated photonic sensors. As photonic device architectures advance and geometric optimization strategies approach their fundamental performance limits, the nanoscale interface region where confined optical modes interact with the surrounding environment progressively becomes the dominant factor governing sensitivity, noise characteristics, and long-term operational stability. This review critically examines recent advances in these strategies applied to integrated photonic sensing platforms, including waveguide, interferometric, and resonant architectures. Emphasis is placed on how functional layers, nanomaterials, and hybrid interfaces modify light–matter interactions, while simultaneously introducing optical loss, spectral distortion, and stability constraints. Beyond summarizing reported sensitivity enhancements, this review analyzes performance benchmarking methodologies and highlights the limitations of conventional metrics such as bulk sensitivity and nominal limit of detection. Normalized figures of merit are discussed as essential tools for isolating genuine interface contributions across diverse platforms. Experimentally documented trade-offs between enhanced surface interaction, optical degradation, and temporal drift are examined in detail, alongside challenges related to reproducibility, wafer-scale variability, and long-term interface stability. By synthesizing insights from photonics, surface chemistry, and materials science, this review outlines key open questions and identifies design principles necessary for translating surface-engineered photonic sensors from laboratory demonstrations to robust and scalable sensing technologies. Full article

Functionally graded multi-material thermoplastic architectures provide a promising route for tailoring viscoelastic energy dissipation through controlled phase contrast and interfacial interactions. However, rational selection of optimal material compositions remains challenging due to competing requirements among stiffness, damping efficiency, thermal stability, and processability. The absence of a quantitative decision framework often limits systematic design of architected polymer systems. This study proposes an Analytic Hierarchy Process (AHP)-based multi-criteria decision model to identify the optimal rigid–elastic thermoplastic composition for enhanced damping performance. Nine performance criteria were considered, including storage modulus, loss factor, damping bandwidth, interfacial adhesion strength, elongation at break, impact resistance, glass transition temperature, thermal stability, and printability. Fourteen alternative material configurations combining different rigid phases, elastomeric interlayers, filler contents, and layer thickness ratios were evaluated. Pairwise comparison matrices were constructed based on experimentally measured thermomechanical data and literature-reported values, and consistency ratios were maintained below 0.1 to ensure decision reliability. Numerical results indicate that a graded PLA/soft-TPU/PLA architecture with optimized layer thickness ratio achieved the highest global priority weight (0.431), outperforming the baseline PLA/TPU system by approximately ~25–30% in overall performance index. Sensitivity analysis confirmed ranking robustness across variations in damping and stiffness weighting factors. The proposed framework establishes a systematic methodology for polymer material selection and multi-material architectural optimization, enabling data-driven design of thermoplastic systems with tunable viscoelastic performance. Full article

Detecting changes in the permittivities of materials has important applications in electronic information, materials science, biomedicine, and many other fields. However, existing detection methods are limited by factors such as sample thickness and resonance intensity, making it difficult to achieve sensitive dielectric constant detection at desired frequency bands. This paper proposes a method for detecting the dielectric constant changes in samples based on destructive interference of spoof surface plasmon polaritons (SSPPs) in a dual-path transmission structure, which forms a characteristic absorption peak at the SSPPs’ cutoff frequency. Specifically, by utilizing the dependence of the SSPPs’ phase on the periodic unit, a constant π phase difference is formed at the cutoff frequency through the periodic unit number difference between the two paths, resulting in a cutoff frequency absorption peak. When the sample is coated on the SSPPs’ dual-path structure, the boundary conditions are altered, leading to a cutoff frequency shift, thereby enabling dielectric constant detection at the specified frequency. Simulation results show that, with proper structural design, the normalized characteristic frequency shift reaches 10.8%/${\epsilon }_S$ and further demonstrates dramatic robustness against initial phase difference, sample thickness and sample loss. In summary, this work provides a novel high-precision and high-robustness method for detecting dielectric constant changes in samples at specified frequencies. Full article

Research on low-concentration CO₂ capture technology is of great significance for China to achieve “carbon peak and carbon neutrality”. However, there are currently two challenges in low-concentration CO₂ capture technology: high energy consumption and significant methane loss. Therefore, in this study, a numerical simulation method is used to establish the reaction model of the piperazine-activated N-methyl diethanolamine (abbreviated as MDEA-PZ) system with CO₂ using Aspen HYSYS industrial software, taking shale gas from a production area as the raw material gas. Single-factor sensitivity analysis is conducted to study the impact of N-methyl diethanolamine (abbreviated as MDEA) mass fraction, piperazine (abbreviated as PZ) mass fraction, CO₂ absorption temperature, and amine liquid regeneration temperature on the process. The results show that when the N-methyl diethanolamine mass fraction is between 37% and 42% and the piperazine mass fraction is between 2.5% and 5%, the regeneration energy consumption is lower and methane loss is smaller. For the raw material gas in this study, the recommended optimal amine liquid mass ratio is 40% of N-methyl diethanolamine + 3% of piperazine. Under this condition, the preferred absorption temperature is 46 °C, the amine liquid circulation rate is 45 m³/h, and the regeneration temperature is 118 °C, resulting in a significant reduction in the total energy consumption by 8.9% compared with the initial value. Full article

Recent studies increasingly leverage pre-trained language models (PLMs) for patent relevance assessment. In practice, whether a query patent and the candidate patent share the technical domain is a critical factor in relevance assessment. Existing PLM-based rerankers usually ignore domain information, leading to domain-insensitive semantic modeling. In particular, distributional discrepancies between a query and the candidate with different domain labels introduce domain bias. To address these issues, we propose a Domain-Sensitive Semantic Decomposition Network ($DSSDNet$), including native semantic decomposition, multi-field fusion regression and pairwise ranking with hard negative mining, for patent relevance assessment. It takes a query and the candidate to generate the technical representation which is decomposed into domain-sensitive and domain-insensitive parts via the gating mechanism with three constraints. And a domain-balanced focal loss is designed to remove domain bias existing in the domain-sensitive part. In addition, multi-field fusion regression is introduced to model the overall technical semantics by incorporating both domain-sensitive and domain-insensitive parts, along with domain information. As for pairwise ranking with hard negative mining, it optimizes the re-ranking objective from a holistic ranking perspective through increasing the margin between positive and negative instances. Experiments on the public CLEF-IP 2011 demonstrate that $DSSDNet$ consistently outperforms strong baselines, achieving gains of 2.5–17% in Recall, 2–7% in MAP, and 3–15% in PRES at different cut-off levels. These results indicate that explicitly modeling domain-sensitive and domain-insensitive semantics is an effective way to mitigate domain bias and enhance patent re-ranking performance. Full article

Dye-sensitized solar cells (DSSCs) remain attractive as low-cost photovoltaic devices; however, their practical efficiency is still constrained by electron-transport losses, interfacial recombination, and incomplete light harvesting in conventional titanium dioxide (TiO₂) photoanodes. The effects of TiO₂ film thickness, multi-walled carbon nanotube (MWCNT) incorporation, and multilayer oxide interface engineering on DSSC performance were examined. Degussa P25-TiO₂ photoanodes were first optimized with respect to thickness, after which controlled MWCNT loadings and sequential compact sol–gel TiO₂ and tin dioxide (SnO₂) sublayers were introduced. The optimum pristine P25-TiO₂ photoanode thickness was 9.11 μm, yielding an open-circuit voltage of 0.74 ± 0.01 V, a short-circuit current density of 14.10 ± 0.40 mA/cm², a fill factor of 56.24 ± 1.00%, and a power-conversion efficiency of 5.93 ± 0.20%. The incorporation of 0.025 wt% MWCNTs increased the efficiency to 6.04 ± 0.20%, corresponding to an absolute gain of 0.11 percentage points. The best performance was obtained with the sol–gel SnO₂/sol–gel TiO₂/P25-CNT multilayer photoanode, which delivered 0.74 ± 0.02 V, 16.22 ± 0.40 mA/cm², 57.59 ± 1.00%, and 6.89 ± 0.30%, respectively. FE-SEM, EIS, XRD, Heated Ultrasonic Cleaner and UV–visible analyses indicate that the multilayer architecture preserves porosity, enhances light harvesting, and suppresses interfacial recombination, while the CNT network facilitates charge transport. Full article

In the process of extracting and transporting waxy crude oil, pipeline blockages resulting from wax deposition significantly impede production efficiency and lead to substantial economic losses. Ethylene vinyl acetate copolymer (EVA) is a widely used chemical wax inhibitor; however, its performance is influenced by multiple factors, including its molecular structure, concentration, and the carbon number distribution of the wax system. A systematic elucidation of its mechanism of action and associated phase changes is therefore necessary. In this study, differential scanning calorimetry (DSC) was employed to systematically investigate the thermal behavior of a wax system with a broad carbon number distribution (C5–C50). The objectives were to analyze the influence of EVA concentration, vinyl acetate (VA) content, and molecular weight on the phase transition parameters, to elucidate the wax inhibition mechanism, and to construct a phase prediction model based on the Flory–Huggins theory. The results demonstrate that the wax appearance temperature (WAT), phase transition temperature, and phase transition enthalpy of the wax systems increase monotonically with carbon number. Furthermore, the addition of EVA was found to significantly reduce both the WAT and the amount of wax precipitated. The optimal structural parameters were identified as a VA content of 10%, a number average molecular weight of 20,000, and an optimal concentration of 800 ppm. The medium-carbon wax system (C16–C30) was found to be the most sensitive to the EVA response. The established phase model exhibited high predictive accuracy, with a mean relative error of less than 4%, a root mean square error (RMSE) of 0.32%, and a coefficient of determination (R²) of 0.987, thereby providing preliminary insights and a practical tool for optimizing EVA wax inhibitor formulations under simplified conditions and guiding their potential engineering applications. Full article

The abnormal activation of the Nlrp3 (Nod-like receptor pyrin 3) inflammasome, in response to oxidative stress or impaired antioxidant defense, is linked to aging-related diseases. Previously, we have shown that Peroxiredoxin (Prdx)6 deficiency triggers reactive oxygen species (ROS)-dependent activation of Kruppel-like factor (Klf)9/Nlrp3 inflammasome in aging lens epithelial cells (LECs). Herein, we test the therapeutic efficacy of Prdx6 delivery in abating the oxidative stress-induced aberrant activation of the Klf9/NF-ĸB/Nlrp3 pathway and subsequent pyroptotic cell death in LECs and Prdx6-deficient (Prdx6^−/−) LECs. Similar to aged LECs, Prdx6-depleted LECs exhibited activation of Nlrp3 inflammasome components—including ASC, Caspase-1, IL-1β, IL-18, GSDMD—and displayed heightened sensitivity to H₂O₂/ UVB-induced oxidative damage. The delivery of TAT-HA-Prdx6 or the overexpression of Prdx6 in Prdx6^−/− mLECs significantly suppressed the aberrant activation of these inflammatory components and restored redox balance by eliminating ROS levels during oxidative stress. Similarly, TAT-HA-Prdx6 effectively internalized into SRA-hLECs and suppressed the H₂O₂- and/or UVB-induced upregulation of Nlrp3 and its components. Furthermore, the oxidative stress or Prdx6 deficiency led to increased Nlrp3 promoter activity and NF-ĸB activation, accompanied by decreased cytosolic IĸBα and increased phosphorylation of IĸBα; these alterations were reversed by Prdx6 overexpression. The elevated Klf9 transcription observed in aging and Prdx6^−/− mLECs or under oxidative stress was also inhibited by Prdx6 delivery. Additionally, Prdx6^−/− mLECs and aging LECs displayed increased TXNIP and reduced TRX levels, which were normalized by Prdx6 restoration. Collectively, this study provides the first evidence that the loss of Prdx6 drives aberrant activation of Klf9/NF-ĸB/Nlrp3 inflammasome axis, leading to pyroptotic cell death. Prdx6 delivery represents a promising therapeutic strategy to rescue cells from pyroptosis (oxidative stress-induced inflammatory cell death). Full article

Synthetic Aperture Radar (SAR) ship target detection is crucial for marine monitoring, offering vital support for maritime security, navigation safety, and environmental surveillance. However, deploying advanced deep learning models on resource-constrained edge devices like UAVs and spaceborne platforms is challenging due to the high computational complexity and large parameter counts, hindering real-time performance. To address this, we propose the HGXES model, a lightweight SAR ship detection network. This model integrates efficient structural design, feature enhancement mechanisms, and an attention mechanism to reduce computational costs while preserving feature extraction capabilities. It employs factorized convolutions, a cross-level feature reuse module, and an attention mechanism to dynamically adjust feature weights, enhancing sensitivity to ship targets. A lightweight detection head ensures rapid and accurate target classification and localization. Experiments on benchmark SAR datasets show that based on the lightweight HGNetV2 backbone, our incremental designs (Xfeat, ELA, LWDetect) further compress the model and achieve a 70% reduction in parameters compared with traditional models, with a model size of just 1.9 MB, 2.3 M parameters, and 3.9 G FLOPs, achieving 49.7 fps detection speed. Comparative analyses reveal the superiority of the ELA attention mechanism and ShapeIoU loss function in enhancing performance. Thus, the HGXES model successfully achieves lightweight SAR ship detection, supporting real-time marine monitoring on resource-limited platforms with high accuracy and reduced computational costs. Full article

To address the actuator fault problem faced by underactuated surface vessels (USVs), this study develops an active fault-tolerant control scheme based on finite-time output feedback. First, a finite-time neural terminal homogeneous state observer with a portional-integral structure is established. High-precision pose reconstruction enables finite-time synchronous reconstruction of unmeasured states. This allows unknown nonlinearities to be explicitly expressed online and incorporated into the compensation channel, significantly reducing the sensitivity of modeling errors to control performance. A neural damping mechanism is used to structurally reconstruct uncertain dynamics and loss-of-effectiveness (LOE) fault factors within the system, thereby constructing an online approximator to achieve real-time identification and compensation of composite uncertainties. This integrates the unknown nonlinearities and fault effects of the original system into an online-updatable estimation channel. Adopting a backstepping-based design methodology, a finite-time hybrid event-triggered control (ETC) architecture is further constructed. By introducing an event-triggered update mechanism at the control layer, the real-time continuous control signal is transformed into a discrete update. Based on Lyapunov stability theory, a comprehensive analysis is carried out to verify the stability of the proposed control scheme. Numerical simulations are finally carried out to validate the effectiveness of the scheme. Simulation results show that the tracking error is reduced by about $93\%$ and $60\%$ compared to the comparison scheme. Full article

Understanding dry-out-induced weakening of reservoir and caprock rocks driven by gas displacement is critical for ensuring the operational safety and efficiency of underground gas storage (UGS). Using core samples from the Xiangguosi UGS collected from different regions and stratigraphic intervals, we quantify the evolution of dynamic elastic parameters during simulated downhole dry-out with a joint ultrasonic and nuclear magnetic resonance (NMR) monitoring system. The results show that as water saturation (S_w) decreases, the dynamic bulk modulus (K_d) and P-wave velocity (V_p) decline by varying degrees across specimens, with reductions ranging from 3.0% to 50.48% and from 1.34% to 17.56%, respectively, whereas the dynamic shear modulus (G_d) and S-wave velocity (V_s) show only minor variations throughout the process. These findings demonstrate that the sensitivity of dynamic parameters to dry-out is strongly specimen-dependent. Further analysis indicates that the dry-out response is highly variable and depends on a combination of petrophysical properties. Among these, the heterogeneity of the initial pore structure acts as an important factor, with its influence shaped by mineralogy and bulk frame rigidity. Cores with multimodal pore size distributions and well-developed macropores (long T₂ components) respond more strongly to dry-out, whereas higher clay mineral contents tend to mitigate modulus degradation by retaining water under stronger capillary confinement. Based on these observations, we propose a conceptual model of pore support and skeleton constraint. The model suggests that dry-out weakening arises from a progressive loss of pore fluid volumetric support to the rock skeleton as free water is preferentially displaced from meso- and macropores. These findings provide key experimental evidence and mechanistic insights for using geophysical methods to monitor dry-out zone expansion and to assess long-term formation stability in UGS. Full article

Seismic traveltime tomography is essential for constructing subsurface velocity models that underpin high-resolution imaging and inversion. Traditional ray- and eikonal-based methods are sensitive to the starting model and lack a unified, physically consistent framework to integrate seismic data with high-confidence prior information. PINN-based approaches offer flexible, grid-free inversion but often suffer from training instability and limited use of prior constraints. We propose a Multi-Source Prior-Guided Residual Physics-Informed Neural Network (MSP-ResPINNs) to address these limitations through two key technical advancements. First, MSP-ResPINNs integrates a Residual Network (ResNet) architecture with Sinusoidal Representation (SIREN) activation to replace standard MLPs, ensuring the robust capture of high-frequency velocity gradients. Second, the framework implements a unified loss function that rigorously enforces multi-source constraints, including well-logs and geological horizons. Numerical experiments demonstrate that MSP-ResPINNs accurately reconstructs sharp velocity contrasts and complex geological features compared with conventional PINN-based approaches. Among the tested variants, the multiplicative factorization consistently provides the most stable and physically consistent results, outperforming the additive factorization. Full article

Temporomandibular disorders (TMDs) are the prevalent causes of orofacial pain and dysfunction of the temporomandibular joint (TMJ) and masticatory muscles. Previous studies have revealed that proinflammatory cytokines play a key role in promoting inflammation, pain, and degeneration within the TMJ. In this context, the present systematic review synthesizes current evidence on various cytokines involved in the pathophysiology of TMDs and evaluates their associations with clinical signs and structural TMJ damage. A PRISMA-guided search (PROSPERO: CRD420251163290) was conducted in PubMed/MEDLINE, Embase, Scopus, and the Cochrane Library to identify human-based, in vivo, and in vitro studies (January 2014 to September 2025) that assessed the roles of proinflammatory cytokines in TMDs. The following data were extracted from the identified studies: cytokine profiles, sampling methods, clinical outcomes, and TMJ structural changes. Study quality and risk of bias were systematically evaluated. A total of 15 studies (clinical, animal, and mechanistic) were included in the review. Tumor necrosis factor-alpha (TNF-α), interleukin-1β (IL-1β), interleukin-6 (IL-6), and interleukin-17 (IL-17) consistently emerged as the major contributors to synovitis, cartilage degradation, nociceptive sensitization, and bone resorption. Human studies showed that high levels of TNF-α, IL-1β, and IL-6 and chemokines such as C-C motif chemokine ligand 2 (CCL2) and regulated on activation, normal T-cell expressed and secreted (RANTES) were associated with TMJ pain, restricted mandibular motion, crepitus, malocclusion, and erosive changes on imaging. An increased ratio of TNF to soluble TNF receptor in synovial fluid correlated with both pain and condylar damage, suggesting that loss of cytokine control contributes to progressive joint destruction. TMDs, particularly inflammatory and degenerative subtypes, are cytokine-driven pathologies rather than purely mechanical disorders. TNF-α, IL-1β, and IL-6 are the promising candidate biomarkers of local inflammation and structural joint pathology. Standardized longitudinal studies are required to validate cytokine-based diagnostics and develop anti-cytokine therapeutics. Full article

Aging is a complex physiological process influenced by various factors, including individuals’ mental attitude. This interaction between biological vulnerability and psychological resources characterizes the entire life course; however, in older age, it becomes particularly salient due to the higher prevalence of multimorbidity, frailty, functional decline, and existential transitions (e.g., retirement, bereavement, loss of social roles), which intensify the impact of mental outlook on adaptation and quality of survival. Optimism has gained growing attention in clinical practice as a psychological asset associated with better health. This has also encouraged the incorporation of optimism-enhancing strategies into geriatric care. However, encouraging optimism in older patients, although well intentioned, can create ethical tensions in clinical communication, decision-making, and care planning. Sensitivity should be paid to aspects such as education, cultural background and religion within interactions with older adult patients. Uncritical promotion of optimism can undermine autonomy, foster unrealistic expectations, or place emotional burdens on patients who may already feel vulnerable. The appeal of optimism should therefore be balanced with careful ethical consideration. Full article