Search Results (289)

Background: Rheumatoid arthritis (RA) is an autoimmune disease that remains incurable. An increasing number of proteomic genome-wide association studies (GWASs) are emerging, offering immense potential for identifying novel therapeutic targets for diseases. This study aims to identify potential therapeutic targets for RA based on human plasma proteome. Methods: Protein quantitative trait loci were extracted and integrated from eight large-scale proteomic GWASs. Proteome-wide Mendelian randomization (Pro-MR) was performed to prioritize proteins causally associated with RA. Further validation of the reliability and stratification of prioritized proteins was performed using MR meta-analysis, colocalization, and transcriptome-wide summary-data-based MR. Subsequently, prioritized proteins were characterized through protein–protein interaction and enrichment analyses, pleiotropy assessment, genetically engineered mouse models, cell-type-specific expression analysis, and druggability evaluation. Phenotypic expansion analyses were also conducted to explore the effects of the prioritized proteins on phenotypes such as endocrine disorders, cardiovascular diseases, and other immune-related diseases. Results: Pro-MR prioritized 32 unique proteins associated with RA risk. After validation, prioritized proteins were stratified into four reliability tiers. Prioritized proteins showed interactions with established RA drug targets and were enriched in an immune-related functional profile. Four trans-associated proteins exhibited vertical or horizontal pleiotropy with specific genes or proteins. Genetically engineered mouse models for 18 prioritized protein-coding genes displayed abnormal immune phenotypes. Single-cell RNA sequencing data were used to validate the enriched expression of several prioritized proteins in specific synovial cell types. Nine prioritized proteins were identified as targets of existing drugs in clinical trials or were already approved. Further phenome-wide MR and mediation analyses revealed the effects and potential mediating roles of some prioritized proteins on other phenotypes. Conclusions: This study identified 32 plasma proteins as potential therapeutic targets for RA, expanding the prospects for drug discovery and deepening insights into RA pathogenesis. Full article

This study develops a comprehensive artificial neural network (ANN) model for predicting the mechanical properties of carbon–manganese cast steel, specifically, the yield strength (YS), tensile strength (TS), elongation (El), and reduction of area (RA), based on the chemical composition (16 alloying elements) and heat treatment parameters. The neural network model, employing a 20-44-44-4 architecture and trained on 400 samples from an industrial dataset of 500 samples, achieved 90% of test predictions within a 5% deviation from actual values, with mean prediction errors of 3.45% for YS and 4.9% for %EL. A user-friendly graphical interface was developed to make these predictive capabilities accessible, without requiring programming expertise. Sensitivity analyses revealed that increasing the copper content from 0.05% to 0.2% enhanced the yield strength from 320 to 360 MPa while reducing the ductility, whereas niobium functioned as an effective grain refiner, improving both the strength and ductility. The combined effects of carbon and manganese demonstrated complex synergistic behavior, with the yield strength varying between 280 and 460 MPa and the tensile strength ranging from 460 to 740 MPa across the composition space. Optimal strength–ductility balance was achieved at moderate compositions of 1.0–1.2 wt% Mn and 0.20–0.24 wt% C. The model provides an efficient alternative to costly experimental trials for optimizing C-Mn steels, with prediction errors consistently below 6% compared with 8–20% for traditional empirical methods. This approach establishes quantitative guidelines for designing complex multi-element alloys with targeted mechanical properties, representing a significant advancement in computational material engineering for industrial applications. Full article

This study presents a numerical comparison between Direct numerical simulation (DNS) and the standard κ-ε turbulence model to evaluate natural convection in a two-dimensional, differentially heated, air-filled cavity over the Rayleigh number range 10³ to 10¹⁰. The objective is to assess the predictive capabilities of both methods across laminar and turbulent regimes, with a particular emphasis on the quantitative comparison of thermal characteristics under high Rayleigh number conditions. The Navier–Stokes and energy equations were solved using the finite element method with Boussinesq approximation, employing refined meshes near the hot and cold walls to resolve thermal and velocity boundary layers. The results indicate that for Ra ≤ 10⁶, the κ-ε model significantly underestimates temperature gradients, maximum velocities, and average Nusselt numbers, with errors up to 19.39%, due to isotropic assumptions and empirical formulation. DNS, in contrast, achieves global energy balance errors of less than 0.0018% across the entire range. As Ra increases, the κ-ε model predictions converge to DNS, with Nusselt number deviations dropping below 1.2% at Ra = 10¹⁰. Streamlines, temperature profiles, and velocity distributions confirm that DNS captures flow dynamics more accurately, particularly near the wall vortices. These findings validate DNS as a reference solution for high-Ra natural convection and establish benchmark data for assessing turbulence models in confined geometries Full article

Background: Understanding interactions between cytokine antagonists and drugs is essential for effective medication management in inflammatory conditions. Recent regulatory authority guidelines emphasise a systematic, risk-based approach to evaluating these interactions, underscoring the need for mechanistic insight. Proinflammatory cytokines, such as interleukin-6 (IL-6), modulate cytochrome P450 (CYP) enzymes, reducing the metabolism of CYP substrates. Cytokine antagonists (such as IL-6 receptor antagonists) can counteract this effect, restoring CYP activity and increasing drug clearance. However, quantitative prediction of cytokine-mediated drug interactions remains challenging, as existing models often lack the mechanistic detail needed to capture the dynamic relationship between cytokine signalling, receptor engagement, and downstream modulation of drug metabolism. Methods: A physiologically based pharmacokinetic (PBPK) framework incorporating cytokine–receptor binding, subsequent downregulation of CYP expression, and blockade of the cytokine signalling by a therapeutic protein antagonist was developed to simulate and investigate cytokine antagonist-drug interactions. Tocilizumab, a humanised IL-6 receptor antagonist used to treat several inflammatory conditions associated with elevated IL-6 levels, was selected as a model drug to demonstrate the utility of the framework. Results: The developed PBPK model accurately predicted the pharmacokinetics profiles of tocilizumab and captured clinically observed dynamic changes in simvastatin exposure before and after tocilizumab treatment in rheumatoid arthritis (RA) patients. Simulated IL-6 dynamics aligned with observed clinical profiles, showing transient elevation following receptor blockade and associated restoration of CYP3A4 activity. Prospective simulations with commonly co-administered CYP substrates (celecoxib, chloroquine, cyclosporine, ibuprofen, prednisone, simvastatin, and theophylline) in RA patients revealed dose regimen- and drug-dependent differences in interaction magnitude. Conclusions: This study demonstrated the utility of PBPK models in providing a mechanistic understanding of cytokine antagonist-drug interactions, supporting enhanced therapeutic decision-making and optimising patient care in inflammatory conditions. Full article

Background: Rheumatoid arthritis (RA) is a chronic autoimmune disorder that predominantly affects synovial joints, leading to inflammation, pain, and progressive joint damage. Despite therapeutic advancements, the molecular basis of established RA remains poorly defined. Methods: In this study, we conducted an untargeted plasma proteomic analysis using two-dimensional differential gel electrophoresis (2D-DIGE) and matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS) in samples from RA patients and healthy controls in the discovery phase. Results: Significantly (ANOVA, p ≤ 0.05, fold change > 1.5) differentially abundant proteins (DAPs) were identified. Notably, upregulated proteins included mitochondrial dicarboxylate carrier, hemopexin, and 28S ribosomal protein S18c, while CCDC124, osteocalcin, apolipoproteins A-I and A-IV, and haptoglobin were downregulated. Receiver operating characteristic (ROC) analysis identified CCDC124, osteocalcin, and metallothionein-2 with high diagnostic potential (AUC = 0.98). Proteins with the highest selected frequency were quantitatively verified by multiple reaction monitoring (MRM) analysis in the validation cohort. Bioinformatic analysis using Ingenuity Pathway Analysis (IPA) revealed the underlying molecular pathways and key interaction networks involved STAT1, TNF, and CD40. These central nodes were associated with immune regulation, cell-to-cell signaling, and hematological system development. Conclusions: Our combined proteomic and bioinformatic approaches underscore the involvement of dysregulated immune pathways in RA pathogenesis and highlight potential diagnostic biomarkers. The utility of these markers needs to be evaluated in further studies and in a larger cohort of patients. Full article

The stylistic emulation of historical building facades presents significant challenges for artificial intelligence (AI), particularly for complex and data-scarce styles like Krakow’s Eclecticism. This study aims to develop a methodological framework for a “typological transcoding” of style that moves beyond mere visual mimicry, which is crucial for heritage preservation and urban renewal. The proposed methodology integrates architectural typology with Low-Rank Adaptation (LoRA) for fine-tuning a Stable Diffusion (SD) model. This process involves a typology-guided preparation of a curated dataset (150 images) and precise control of training parameters. The resulting typologically guided LoRA-tuned model demonstrates significant performance improvements over baseline models. Quantitative analysis shows a 24.6% improvement in Fréchet Inception Distance (FID) and a 7.0% improvement in Learned Perceptual Image Patch Similarity (LPIPS). Furthermore, qualitative evaluations by 68 experts confirm superior realism and stylistic accuracy. The findings indicate that this synergy enables data-efficient, typology-grounded stylistic emulation, highlighting AI’s potential as a creative partner for nuanced reinterpretation. However, achieving deeper semantic understanding and robust 3D inference remains an ongoing challenge. Full article

Acoustic emission (AE) signals exhibit a strong correlation with concrete damage. However, the relationship between column damage and AE signals under eccentric loading conditions, combined with the application of traditional RA-AF classification methods for crack characterization, demonstrates limitations. These approaches provide insufficient resolution to accurately identify damage types throughout the entire structural failure process. This study employed K-means clustering algorithm and Gaussian mixture models (GMMs) to analyze AE signal features from reinforced concrete (RC) columns undergoing failure under the eccentric compression loading of different eccentricity. Subsequently, a random forest model was used for automated damage stage classification. Experimental results demonstrate that the damage progression in eccentrically compressed columns comprises four distinct stages, each exhibiting unique AE signal characteristics. The integrated approach of clustering and random forest modeling demonstrates robust feasibility in identifying AE signal patterns associated with specific damage stages, achieving an 85% recognition rate for damage stage classification. These findings provide quantitatively validated evidence supporting the efficacy of machine learning-based methodologies for enabling stage-specific damage characterization in structural health monitoring applications. Full article

As a crucial energy source and chemical raw material, coal’s micro-pore structure holds a pivotal influence on the occurrence and development of coalbed methane (CBM). This study systematically analyzed the nano-pore structure, surface roughness, and fractal characteristics of six coal samples with varying volatile matter content (V_daf) using Atomic Force Microscopy (AFM) combined with Scanning Electron Microscopy (SEM), revealing the correlation between volatile matter and the micro-physical properties of coal. Through AFM three-dimensional topographical observations, it was found that coal samples with higher volatile matter exhibited significant gorge-like undulations on their surfaces, with pores predominantly being irregular macropores, whereas low volatile matter coal samples had smoother surfaces with dense and regular pores. Additionally, the surface roughness parameters (R_a, R_q) of coal positively correlated with volatile matter content. Meanwhile, quantitative analysis of nano-pore parameters using Gwyddion software showed that an increase in volatile matter led to a decline in pore count, shape factor, and area porosity, while the average pore diameter increased. The fractal dimension of samples with different volatile matter contents was calculated, revealing a decrease in fractal dimension with rising volatile matter. Nano-ring analysis indicated that the total number of nano-rings was significantly higher in low volatile matter coal samples compared to high volatile matter ones, but the nano-ring roughness (R_r) increased with volatile matter content. SEM images further validated the AFM results. Through multi-scale characterization and quantitative analysis, this study clarified the extent to which volatile matter affects the nano-pore structure and surface properties of coal, providing critical data support for efficient CBM development and reservoir evaluation. Full article

Inflammation is a complicated physiological process that contributes to a variety of disorders including osteoarthritis (OA) and rheumatoid arthritis (RA). Endocannabinoids and the endocannabinoid system (ECS) play a pivotal role in the physiological response to pain and inflammation. A clinical study to investigate the role of the endocannabinoid system and related lipids in pain and inflammation in OA and RA was performed. In total, 80 subjects, namely, 25 patients with RA, 18 with OA, and 37 healthy participants, were included. Sixteen endocannabinoids and congeners, as well as 129 oxylipins, were quantified in plasma using specific, quantitative LC-MS/MS assays. The endocannabinoid analysis revealed significantly lower levels of 2-arachidonoylglycerol (2-AG) in RA and OA patients compared to healthy participants. In contrast, the EC levels of the ethanolamide group (anandamide, docosahexaenoyl-EA, palmitoleoyl-EA, and other ethanolamides) were higher in the RA study cohort and to a lesser extent also in the OA cohort. This analysis of oxylipins revealed lower levels of the pro-resolving lipid 9-oxo-octadecadienoic acid (9-oxoODE) and the ω-3 fatty acids EPA (eicosapentaenoic acid) and DHA (docosahexaenoic acid) in RA compared to all other study cohorts. 2-AG is a key regulator of nociception and inflammation, and its relatively low levels might be a mechanistic contributor to residual pain and inflammation in RA and OA. Several changes in pro- and anti-inflammatory lipid mediators were detected, including lower levels of EPA and DHA in RA, which might reveal the potential for nutritional supplementation with these anti-inflammatory fatty acids. Full article

As a national intangible cultural heritage in China, traditional Miao batik has encountered obstacles in contemporary dissemination and design due to its reliance on manual craftsmanship and other reasons. Existing generative models are difficult to fully capture the complex semantic and stylistic attributes in Miao batik patterns, which limits their application in digital creativity. To address this issue, we construct the structured CMBP-9 dataset to facilitate semantic-aware image generation. Based on stable diffusion v1.5, Low-Rank Adaptation (LoRA) is used to effectively transfer the structure, sign, and texture features that are unique to the Miao people, and the Latent Consistency model (LCM) is integrated to improve the inference efficiency. In addition, a Style-Conditioned Linear Fusion (SCLF) strategy is proposed to dynamically adjust the fusion of LoRA and LCM outputs according to the semantic complexity of input prompts, thereby overcoming the limitation of static weighting in existing frameworks. Extensive quantitative evaluations using LPIPS, SSIM, PSNR, FID metrics, and human evaluations show that the proposed Batik-MPDM framework achieves superior performance in terms of style fidelity and generation efficiency compared to baseline methods. Full article

Enterococcus faecalis (E. faecalis) is one of the most detected bacteria in persistent apical periodontitis (PAP), with alkaline tolerance enabling post-treatment survival. In this study, we will investigate how alkaline conditions alter proteomic and metabolomic profiles of E. faecalis membrane vesicles (MVs) and preliminarily investigate the role of MVs of E. faecalis in the regulation of macrophage inflammatory response. E. faecalis MVs were characterized using transmission electron microscopy and nanoparticle tracking analysis under varying pH conditions. MVs’ proteomic and metabolomic profiling across pH levels was compared. The effects of E. faecalis MVs on human dTHP-1 macrophages were evaluated using CCK-8 metabolic activity assays and ELISA-based quantitative analysis of inflammatory cytokines. In this study, the presence of E. faecalis MVs was verified, and the alkaline environment of pH 9.0 did not alter their production. Through proteomic and metabolomic analysis, we observed that ATP synthase and stress proteins, as well as lysine degradation and tryptophan metabolism pathways, were significantly enriched in the MVs at pH 9.0. Finally, we observed that both E. faecalis MVs at pH 7.0 and pH 9.0 could dose-dependently inhibit the activity of dTHP-1 cells. E. faecalis MVs promote the secretion of IL-6, TNF-α, IL-1β, IL-1ra, and TGF-β by macrophages. Compared to pH 7.0, pH 9.0 E. faecalis MVs have a reduced effect on IL-1ra and TGF-β secretion. Additionally, we observed a significant increase in the IL-1β/IL-1ra ratio after treatment with E. faecalis MVs. Our study indicated that E. faecalis can produce MVs in pH 7.0 and pH 9.0 environments. ATP synthase, stress proteins, as well as lysine degradation and tryptophan metabolism pathways, were significantly enriched in pH 9.0 MVs. Furthermore, E. faecalis MVs could promote inflammatory responses in macrophages and dose-dependently inhibit the viability of dTHP-1 cells. Full article

Effective analysis of central bank communications is critical for anticipating monetary policy changes and guiding market expectations. However, traditional natural language processing models face significant challenges in processing lengthy and nuanced policy documents, which often exceed tens of thousands of tokens. This study addresses these challenges by proposing a novel integrated deep learning framework based on Hyena Hierarchy architectures, which utilize sub-quadratic convolution mechanisms to efficiently process ultra-long sequences. The framework employs Delta-LoRA (low-rank adaptation) for parameter-efficient fine-tuning, updating less than 1% of the total parameters without additional inference overhead. To ensure robust performance across institutions and policy cycles, domain-adversarial neural networks are incorporated to learn domain-invariant representations, and a multi-task learning approach integrates auxiliary hawkish/dovish sentiment signals. Evaluations conducted on a comprehensive dataset comprising Federal Open Market Committee statements and European Central Bank speeches from 1977 to 2024 demonstrate state-of-the-art performance, achieving over 6% improvement in macro-F1 score compared to baseline models while significantly reducing inference latency by 65%. This work offers a powerful and efficient new paradigm for handling ultra-long financial policy texts and demonstrates the effectiveness of integrating advanced sequence modeling, efficient fine-tuning, and domain adaptation techniques for extracting timely economic signals, with the aim to open new avenues for quantitative policy analysis and financial market forecasting. Full article

Carbon Capture and Storage (CCS) technology presents a practical solution for reducing industrial carbon dioxide (CO₂) emissions through underground anthropogenic CO₂ storage in depleted hydrocarbon reservoirs. The long-term storage efficiency faces several CO₂ leakage challenges that need to be addressed in the planning phase of the CCS project. Thus, effective risk assessment (RA) methodologies are crucial for ensuring safety, regulatory compliance, and public acceptance of CCS projects. This review examines RA parts and their corresponding technical and non-technical challenges. The analysis critically compares over 20 qualitative, semi-quantitative, quantitative, and hybrid RA techniques employed throughout GCS operations. Available quantitative RA tools do not deliver dependable results because they require technical data that become available late in the CCS project development process. Qualitative approaches work well for the initial screening of storage sites with limited data available, yet quantitative methods enable quantification of CO₂ leakage. For the first time, a comparative analysis of two integrated assessment tools is presented in this paper. The techniques achieve success based on high-quality data and analysis of existing technical and non-technical challenges which this paper examines. The comparative analysis outlines the limitations and advantages of every methodology studied and emphasizes the need for integrated hybrid frameworks to boost decision-making in the RA process. Future research should focus on creating or improving existing hybrid frameworks for late-stage RA while utilizing qualitative frameworks in the initial site screening stage to advance GSC’s safe and effective implementation. Full article

Childhood neuroblastoma (NB) is a malignant tumour that is a member of a class of embryonic tumours that have their origins in sympathoadrenal progenitor cells. There are five stages in the clinical NB staging system: 1, 2A, 2B, 3, 4S, and 4. For those diagnosed with stage 4 neuroblastoma (NBS4), the treatment options are limited with a survival rate of between 40 and 50%. Since 1975, more than 15 targets have been identified in the search for a treatment for high-risk NBS4. This article is concerned with the search for a multi-target drug treatment for high-risk NBS4 and focuses on four possible treatment targets that research has identified as having a role in the development of NBS4 and includes the inhibitors Histone Deacetylase (HDAC), Bromodomain (BRD), Hedgehog (HH), and Tropomyosin Kinase (TRK). Computer-aided drug design and molecular modelling have greatly assisted drug discovery in medicinal chemistry. Computational methods such as molecular docking, homology modelling, molecular dynamics, and quantitative structure–activity relationships (QSAR) are frequently used as part of the process for finding new therapeutic drug targets. Relying on these techniques, the authors describe a medicinal chemistry strategy that successfully identified eight compounds (inhibitors) that were thought to be potential inhibitors for each of the four targets listed above. Results revealed that all four targets BRD, HDAC, HH and TRK receptors binding sites share similar amino acid sequencing that ranges from 80 to 100%, offering the possibility of further testing for multi-target drug use. Two additional targets were also tested as part of this work, Retinoic Acid (RA) and c-Src (Csk), which showed similarity (of the binding pocket) across their receptors of 80–100% but lower than 80% for the other four targets. The work for these two targets is the subject of a paper currently in progress. Full article

Behavioral Momentum Theory (BMT) and Resurgence as Choice (RaC) are two theoretical and quantitative models of behavior that, when applied prospectively, might improve behavioral treatments and increase the likelihood of long-term success. Despite the plausible benefit of using BMT and RaC to guide clinical decision-making, it is unclear how frequently these models are prospectively used in practice. We briefly review contemporary research on BMT and RaC as related to the treatment of problem behavior. We discuss potential barriers and solutions to their prospective application, as well as areas for future research. Full article