Search Results (796)

Systemic sclerosis (SSc) is an autoimmune connective tissue disorder characterized by vascular abnormalities, immune dysfunction, and progressive fibrosis. One of the most common manifestations of SSc is interstitial lung disease (ILD), known by a progressive course leading to significant morbidity and mortality. Aim: to investigate autoantibodies, cytokines, and genetic markers in SSc-ILD through a systematic review and analysis of a Kazakh cohort of SSc-ILD patients. Methods: A PubMed search over the past 10 years was performed with “SSc-ILD”, “autoantibodies”, “cytokines”, and “genes”. Thirty patients with SSc were assessed for lung involvement, EScSG score, and modified Rodnan skin score. IL-6 was measured by ELISA, antinuclear factor on HEp-2 cells by indirect immunofluorescence, and specific autoantibodies by immunoblotting. Genetic analysis was performed using a 120-gene AmpliSeq panel on the Ion Proton platform. Results: The literature review identified 361 articles, 26 addressed autoantibodies, 20 genetic variants, and 12 cytokine profiles. Elevated levels of IL-6, TGF-β, IL-33, and TNF-α were linked to SSc. Based on the results of the systemic review, we created a preliminary immunogenic panel for SSc-ILD with following analysis in Kazakh patients with SSc (n = 30). Fourteen of them (46.7%) demonstrated signs of ILD and/or lung hypertension, with frequent detection of antibodies such as Scl-70, U1-snRNP, SS-A, and genetic variants in SAMD9L, REL, IRAK1, LY96, IL6R, ITGA2B, AIRE, TREX1, and CD40 genes. Conclusions: Current research confirmed the presence of the broad range of autoantibodies and variations in IRAK1, TNFAIP3, SAMD9L, REL, IRAK1, LY96, IL6R, ITGA2B, AIRE, TREX1, CD40 genes in of Kazakhstani cohort of SSc-ILD patients. Full article

Fasciola hepatica remains a global health and economic concern, and treatment still relies heavily on triclabendazole. At the parasite–host interface, F. hepatica calcium-binding proteins (FhCaBPs) have a unique EF-hand/DLC-like domain fusion found only in trematodes. This makes it a parasite-specific target for small compounds and vaccinations. To enable novel therapeutic strategies, we report the first elevated-resolution structure of a full-length FhCaBP4. The apo structure was determined at 1.93 Å resolution, revealing a homodimer architecture that integrates an N-terminal, calmodulin-like, EF-hand pair with a C-terminal dynein light chain (DLC)-like domain. Structure-guided in silico mutagenesis identified a flexible, 16-residue β4–β5 loop (LTGSYWMKFSHEPFMS) with an FSHEPF core that demonstrates greater energetic variability than its FhCaBP2 counterpart, likely explaining the distinct ligand-binding profiles of these paralogs. Molecular dynamics simulations and AlphaFold3 modeling suggest that EF-hand 2 acts as the primary calcium-binding site, with calcium coordination inducing partial rigidification and modest expansion of the protein structure. Microscale thermophoresis confirmed calcium as the major ligand, while calmodulin antagonists bound with lower affinity and praziquantel demonstrated no interaction. Thermal shift assays revealed calcium-dependent stabilization and a merger of biphasic unfolding transitions. These results suggest that FhCaBP4 functions as a calcium-responsive signaling hub, with an allosterically coupled EF-hand–DLC interface that could serve as a structurally tractable platform for drug targeting in trematodes. Full article

Background: RiBi is integral to cell proliferation, and its dysregulation is increasingly recognized as a hallmark of aggressive cancers. We sought to develop and validate a composite “PanRibo-515 score” reflecting RiBi activity across multiple tumor types, assess its prognostic significance, and explore its relationship with immune checkpoint therapy outcomes. Methods: We curated 515 RiBi–associated genes (PanRibo-515) and used a LASSO regression-based strategy on a training dataset (GSE202203) to select the prognostically most relevant subset of 68 genes (OncoRibo-68). Directionality (positive or negative impact on survival) was assigned based on the sign of the LASSO coefficients. We integrated a forward selection approach to identify a refined subset of genes for computing the OncoRibo-68 score. For validation, patients in The Cancer Genome Atlas (TCGA) were stratified into high or low OncoRibo-68 score groups for survival analyses. Additional validation for immunotherapy response was conducted using bioinformatic platforms used for immunotherapy response analysis. Results: A higher OncoRibo-68 score consistently correlated with poorer overall and progression-free survival across multiple cancers. Elevated OncoRibo-68 score was linked to an immunosuppressive tumor microenvironment, but interestingly to increased response to checkpoint inhibitors. Conclusions: Our findings highlight RiBi as an important determinant of tumor aggressiveness and identify the OncoRibo-68 score as a promising biomarker for risk stratification and therapy selection. Future research may evaluate whether targeting RiBi pathways could enhance treatment efficacy, particularly in combination with immunotherapy. Full article

Background: Breast cancer remains a major public health challenge in Latin America, where access to personalized risk assessment tools is still limited. This study aimed to evaluate the implementation of a polygenic risk score (PRS)-based stratification model combined with remote genomic counseling in Colombian women with sporadic breast cancer and healthy women. Methods: In 2023, an embedded mixed-methods observational study was conducted in Medellín involving 1997 women aged 40–75 years who underwent clinical PRS testing. The intervention integrated PRS-based risk categorization with individualized risk factor assessment and lifestyle recommendations delivered through a remote counseling platform. Results: PRS analysis classified 9.7% of women as high risk and 46% as low risk. Healthier lifestyle patterns were significantly associated with lower PRS categories (p = 0.034). Physical activity showed a protective effect (OR = 0.60, 95% CI: 0.5–0.8), while prior smoking, elevated BMI, and sedentary behavior were associated with higher risk. The counseling model achieved high delivery (93%) and satisfaction (85%) rates. Qualitative insights revealed improved understanding of genomic risk and greater engagement in preventive behaviors. Only one new case of breast cancer was detected among intermediate-risk participants, with a diagnostic lead time of 12 months. Conclusions: These findings support the feasibility, acceptability, and potential impact of integrating PRS and genomic counseling in cancer prevention strategies in middle-income settings. Full article

UAV platforms equipped with RTK positioning and LiDAR sensors are increasingly used for landslide monitoring, offering frequent, high-resolution surveys with broad spatial coverage. In this study, we applied high-frequency UAV-based monitoring to the active Baldiola earthflow (Northern Apennines, Italy), integrating 10 UAV–LiDAR and photogrammetric surveys, acquired at average intervals of 14 days over a four-month period. UAV-derived orthophotos and DEMs supported displacement analysis through homologous point tracking (HPT), with robotic total station measurements serving as ground-truth data for validation. DEMs were also used for multi-temporal DEM of Difference (DoD) analysis to assess elevation changes and identify depletion and accumulation patterns. Displacement trends derived from HPT showed strong agreement with RTS data in both horizontal (R² = 0.98) and vertical (R² = 0.94) components, with cumulative displacements ranging from 2 m to over 40 m between April and August 2024. DoD analysis further supported the interpretation of slope processes, revealing sector-specific reactivations and material redistribution. UAV-based monitoring provided accurate displacement measurements, operational flexibility, and spatially complete datasets, supporting its use as a reliable and scalable tool for landslide analysis. The results support its potential as a stand-alone solution for both monitoring and emergency response applications. Full article

Sensor fusion in intelligent suspension systems constitutes a fundamental technology for optimizing vehicle dynamic stability, ride comfort, and occupant safety. By integrating data from multiple sensor modalities, this study proposes a hierarchical multi-sensor fusion framework for active suspension control, aiming to enhance control precision. Initially, a binocular vision system is employed for target detection, enabling the identification of lane curvature initiation points and speed bumps, with real-time distance measurements. Subsequently, the integration of Global Positioning System (GPS) and inertial measurement unit (IMU) data facilitates the extraction of road elevation profiles ahead of the vehicle. A BP-PID control strategy is implemented to formulate mode-switching rules for the active suspension under three distinct road conditions: flat road, curved road, and obstacle road. Additionally, an ant colony optimization algorithm is utilized to fine-tune four suspension parameters. Utilizing the hardware-in-the-loop (HIL) simulation platform, the observed reductions in vertical, pitch, and roll accelerations were 5.37%, 9.63%, and 11.58%, respectively, thereby substantiating the efficacy and robustness of this approach. Full article

Oral delivery of hydrophobic compounds remains challenging due to their poor aqueous solubility and the potential toxicity associated with conventional surfactant-based emulsions. To address these issues, we present a surfactant-free encapsulation strategy using electrosprayed alginate hydrogel beads for the stable and controlled delivery of hydrophobic oils. Hydrophobic compounds were dispersed in high-viscosity alginate solutions without surfactants via ultrasonication, forming kinetically stable oil-in-water dispersions. These mixtures were electrosprayed into calcium chloride baths, yielding monodisperse hydrogel beads. Higher alginate concentrations improved droplet sphericity and suppressed phase separation by enhancing matrix viscosity. The resulting beads exhibited stimuli-responsive degradation and controlled release behavior in response to physiological ionic strength. Dense alginate networks delayed ion exchange and prolonged structural integrity, while elevated external ionic conditions triggered rapid disintegration and immediate payload release. This simple and scalable system offers a biocompatible platform for the oral delivery of lipophilic active compounds without the need for surfactants or complex fabrication steps. Full article

The Mediterranean region, identified by scientists as a ‘climate hot spot’, is experiencing warmer and drier conditions, along with an increase in the intensity and frequency of extreme weather events. One such extreme phenomena is droughts. The recent wildfires in this region are a concerning consequence of this phenomenon, causing severe environmental damage and transforming natural landscapes. However, droughts involve a two-way interaction: On the one hand, climate change and various human activities, such as urbanization and deforestation, influence the development and severity of droughts. On the other hand, droughts have a significant impact on various sectors, including ecology, agriculture, and the local economy. This study investigates drought dynamics in four Mediterranean countries, Greece, France, Italy, and Spain, each of which has experienced severe wildfire events in recent years. Using satellite-based Earth observation data, we monitored drought conditions across these regions over a five-year period that includes the dates of major wildfires. To support this analysis, we derived and assessed key indices: the Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), and Normalized Difference Drought Index (NDDI). High-resolution satellite imagery processed within the Google Earth Engine (GEE) platform enabled the spatial and temporal analysis of these indicators. Our findings reveal that, in all four study areas, peak drought conditions, as reflected in elevated NDDI values, were observed in the months leading up to wildfire outbreaks. This pattern underscores the potential of satellite-derived indices for identifying regional drought patterns and providing early signals of heightened fire risk. The application of GEE offered significant advantages, as it allows efficient handling of long-term and large-scale datasets and facilitates comprehensive spatial analysis. Our methodological framework contributes to a deeper understanding of regional drought variability and its links to extreme events; thus, it could be a valuable tool for supporting the development of adaptive management strategies. Ultimately, such approaches are vital for enhancing resilience, guiding water resource planning, and implementing early warning systems in fire-prone Mediterranean landscapes. Full article

Hydrogen is a potential key component of a carbon-neutral energy carrier and an input to marine industrial processes. This study examines the consequences of coupled hydrogen release and marine environmental factors during floating photovoltaic hydrogen production (FPHP) system failures. A validated three-dimensional numerical model of FPHP comprehensively characterizes hydrogen leakage dynamics under varied rupture diameters (25, 50, 100 mm), transient release duration, dispersion patterns, and wind intensity effects (0–20 m/s sea-level velocities) on hydrogen–air vapor clouds. FLACS-generated data establish the concentration–dispersion distance relationship, with numerical validation confirming predictive accuracy for hydrogen storage tank failures. The results indicate that the wind velocity and rupture size significantly influence the explosion risk; 100 mm ruptures elevate the explosion risk, producing vapor clouds that are 40–65% larger than 25 mm and 50 mm cases. Meanwhile, increased wind velocities (>10 m/s) accelerate hydrogen dilution, reducing the high-concentration cloud volume by 70–84%. Hydrogen jet orientation governs the spatial overpressure distribution in unconfined spaces, leading to considerable shockwave consequence variability. Photovoltaic modules and inverters of FPHP demonstrate maximum vulnerability to overpressure effects; these key findings can be used in the design of offshore platform safety. This study reveals fundamental accident characteristics for FPHP reliability assessment and provides critical insights for safety reinforcement strategies in maritime hydrogen applications. Full article

Myxoma virus (MYXV), a rabbit-specific poxvirus and non-pathogenic in humans and mice, is an excellent candidate oncolytic virus for cancer therapy. MYXV also has immunotherapeutic benefits. In ovarian cancer (OC), immunosuppressive tumor-associated macrophages (TAMs) are key to inhibiting antitumor immunity while hindering therapeutic benefit by chemotherapy and dendritic cell (DC) vaccine. Because MYXV favors binding/entry of macrophages/monocytes, we examined the therapeutic potential of MYXV against TAMs. We found previously that a replication-defective MYXV with targeted deletion of an essential gene, M062R, designated ΔM062R MYXV, activated both the host DNA sensing pathway and the SAMD9 pathway. Treatment with ΔM062R confers therapeutic benefit comparable to that of wild-type replicating MYXV in preclinical models. Here we found that ΔM062R MYXV, when integrated with cisplatin and DC immunotherapy, further improved treatment benefit, likely through promoting tumor antigen-specific T cell function. Moreover, we also tested ΔM062R MYXV in targeting human immunosuppressive TAMs from OC patient ascites in a co-culture system. We found that ΔM062R treatment subverted the immunosuppressive properties of TAMs and elevated the avidity of cytokine production in tumor antigen-specific CD4⁺ T cells. Overall, ΔM062R presents a promising immunotherapeutic platform as a beneficial adjuvant to chemotherapy and DC vaccine. Full article

Obesity and metabolic dysfunction are established risk factors for luminal breast cancer, yet current preclinical models inadequately recapitulate the complex metabolic and immune interactions driving tumorigenesis. To develop and characterize an immunocompetent rat model of luminal breast cancer induced by chronic exposure to a cafeteria diet mimicking Western obesogenic nutrition, female rats were fed a cafeteria diet or standard chow from weaning. Metabolic parameters, plasma biomarkers (including leptin, insulin, IGF-1, adiponectin, and estrone), mammary gland histology, tumor incidence, and gene expression profiles were longitudinally evaluated. Gene expression was assessed by PCR arrays and qPCR. A subgroup underwent dietary reversal to assess the reversibility of molecular alterations. Cafeteria diet induced significant obesity (mean weight 426.76 g vs. 263.09 g controls, p < 0.001) and increased leptin levels without altering insulin, IGF-1, or inflammatory markers. Histological analysis showed increased ductal ectasia and benign lesions, with earlier fibroadenoma and luminal carcinoma development in diet-fed rats. Tumors exhibited luminal phenotype, low Ki67, and elevated PAI-1 expression. Gene expression alterations were time point specific and revealed early downregulation of ID1 and COX2, followed by upregulation of MMP2, THBS1, TWIST1, and PAI-1. Short-term dietary reversal normalized several gene expression changes. Overall tumor incidence was modest (~12%), reflecting early tumor-promoting microenvironmental changes rather than aggressive carcinogenesis. This immunocompetent cafeteria diet rat model recapitulates key metabolic, histological, and molecular features of obesity-associated luminal breast cancer and offers a valuable platform for studying early tumorigenic mechanisms and prevention strategies without carcinogen-induced confounders. Full article

This study examined the integraftion of airborne Light Detection and Ranging (LiDAR), Simultaneous Localization and Mapping (SLAM)-based handheld LiDAR, and iPhone LiDAR to inspect forest road networks following forest operations. The goal is to overcome the challenges posed by dense canopy cover and ensure accurate and efficient data collection and mapping. Airborne data were collected using the DJI Matrice 300 RTK UAV equipped with a Zenmuse L2 LiDAR sensor, which achieved a high point density of 285 points/m² at an altitude of 80 m. Ground-level data were collected using the BLK2GO handheld laser scanner (HPLS) with SLAM methods (LiDAR SLAM, Visual SLAM, Inertial Measurement Unit) and the iPhone 13 Pro Max LiDAR. Data processing included generating DEMs, DSMs, and True Digital Orthophotos (TDOMs) via DJI Terra, LiDAR360 V8, and Cyclone REGISTER 360 PLUS, with additional processing and merging using CloudCompare V2 and ArcGIS Pro 3.4.0. The pairwise comparison analysis between ALS data and each alternative method revealed notable differences in elevation, highlighting discrepancies between methods. ALS + iPhone demonstrated the smallest deviation from ALS (MAE = 0.011, RMSE = 0.011, RE = 0.003%) and HPLS the larger deviation from ALS (MAE = 0.507, RMSE = 0.542, RE = 0.123%). The findings highlight the potential of fusing point clouds from diverse platforms to enhance forest road mapping accuracy. However, the selection of technology should consider trade-offs among accuracy, cost, and operational constraints. Mobile LiDAR solutions, particularly the iPhone, offer promising low-cost alternatives for certain applications. Future research should explore real-time fusion workflows and strategies to improve the cost-effectiveness and scalability of multisensor approaches for forest road monitoring. Full article

Microfluidic methods are an important tool for studying the microrheology of blood and the mechanical properties of blood cells—erythrocytes, leukocytes, and platelets. In patients with diabetes, hypertension, obesity, sickle cell anemia, or cerebrovascular or peripheral vascular diseases, hemorheological alterations are commonly observed. These include increased blood viscosity and red blood cell (RBC) aggregation, along with reduced RBC deformability. Such disturbances significantly contribute to impaired microcirculation and microvascular perfusion. In blood vessels, abnormal hemorheological parameters can elevate resistance to blood flow, exert greater mechanical stress on the endothelial wall, and lead to microvascular complications. Among these parameters, erythrocyte deformability is a potential biomarker for diseases including diabetes, malaria, and cancer. This review highlights recent advances in microfluidic technologies for in vitro assays of RBC deformability and aggregation, as well as leukocyte aggregation and adhesion. It summarizes the core principles of microfluidic platforms and the experimental findings related to hemodynamic parameters. The advantages and limitations of each technique are discussed, and future directions for improving these devices are explored. Additionally, some aspects of the modeling of the microrheological properties of blood cells are considered. Overall, the described microfluidic systems represent promising tools for investigating erythrocyte mechanics and leukocyte behavior. Full article

Hydrogen is widely recognized as a key enabler of the clean energy transition, but the lack of safe, efficient, and scalable storage technologies continues to hinder its broad deployment. Conventional hydrogen storage approaches, such as compressed hydrogen storage, cryo-compressed hydrogen storage, and liquid hydrogen storage, face limitations, including high energy consumption, elevated cost, weight, and safety concerns. In contrast, solid-state hydrogen storage using carbon-based adsorbents has gained growing attention due to their chemical tunability, low cost, and potential for modular integration into energy systems. This review provides a comprehensive evaluation of hydrogen storage using carbon-based materials, covering fundamental adsorption mechanisms, classical materials, emerging architectures, and recent advances in computationally AI-guided material design. We first discuss the physicochemical principles driving hydrogen physisorption, chemisorption, Kubas interaction, and spillover effects on carbon surfaces. Classical adsorbents, such as activated carbon, carbon nanotubes, graphene, carbon dots, and biochar, are evaluated in terms of pore structure, dopant effects, and uptake capacity. The review then highlights recent progress in advanced carbon architectures, such as MXenes, three-dimensional architectures, and 3D-printed carbon platforms, with emphasis on their gravimetric and volumetric performance under practical conditions. Importantly, this review introduces a forward-looking perspective on the application of artificial intelligence and machine learning tools for data-driven sorbent design. These methods enable high-throughput screening of materials, prediction of performance metrics, and identification of structure–property relationships. By combining experimental insights with computational advances, carbon-based hydrogen storage platforms are expected to play a pivotal role in the next generation of energy storage systems. The paper concludes with a discussion on remaining challenges, utilization scenarios, and the need for interdisciplinary efforts to realize practical applications. Full article

In earthquake-prone areas such as Tokyo, accurate estimation of bearing stratum depth is crucial for foundation design, liquefaction assessment, and urban disaster mitigation. However, traditional methods such as the standard penetration test (SPT), while reliable, are labor-intensive and have limited spatial distribution. In this study, 942 geological survey records from the Tokyo metropolitan area were used to evaluate the performance of three machine learning algorithms, random forest (RF), artificial neural network (ANN), and support vector machine (SVM), in predicting bearing stratum depth. The main input variables included geographic coordinates, elevation, and stratigraphic category. The results showed that the RF model performed well in terms of multiple evaluation indicators and had significantly better prediction accuracy than ANN and SVM. In addition, data density analysis showed that the prediction error was significantly reduced in high-density areas. The results demonstrate the robustness and adaptability of the RF method in foundation soil layer identification, emphasizing the importance of comprehensive input variables and spatial coverage. The proposed method can be used for large-scale, data-driven bearing stratum prediction and has the potential to be integrated into geological risk management systems and smart city platforms. Full article