Search Results (3,276)

Schistosoma mansoni infection is associated with hepatic inflammation and fibrosis, but its systemic metabolic effects remain poorly understood. This study aimed to investigate changes in the serum lipidomic profile associated with S. mansoni infection and parasite load in individuals from an endemic area. This cross-sectional analysis was nested within a longitudinal cohort study conducted in northeastern Brazil. Parasitological diagnosis and quantification were performed using the Kato–Katz technique. A total of 45 individuals were selected and divided into three groups: high parasite load (HL), low parasite load (LL), and uninfected controls (NegE). Serum samples were analyzed using mass-spectrometry-based lipidomics. The most abundant lipid subclasses across all groups were phosphatidylcholines (PC), triacylglycerols (TAG), and phosphatidylethanolamines (PE). However, individuals in the HL group exhibited distinct lipidomic profiles, with increased levels of specific phosphatidylinositols (PI) and reduced levels of certain TAG species compared to the NegE group. These changes may reflect host–parasite interactions and immune–metabolic alterations driven by intense infection. Our findings suggest that S. mansoni infection, particularly at higher parasite burdens, can influence the host’s serum lipid profile and may contribute to metabolic disturbances in endemic populations. Full article

Minimizing carbon dioxide emissions is crucial due to the generation of energy from fossil fuels. The significance of carbon capture and storage (CCS) technology, which is highly successful in mitigating carbon emissions, has increased. On the other hand, hydrogen is an important energy carrier for storing and transporting energy, and technologies that rely on hydrogen have become increasingly promising as the world moves toward a more environmentally friendly approach. Nevertheless, the integration of CCS technologies into power production processes is a significant challenge, requiring the enhancement of the combined power generation–CCS process. In recent years, there has been a growing interest in process intensification (PI), which aims to create smaller, cleaner, and more energy efficient processes. The goal of this research is to demonstrate the process intensification potential and to model and simulate a hybrid integrated–intensified adsorptive-membrane reactor process for simultaneous carbon capture and hydrogen production. A comprehensive, multi-scale, multi-phase, dynamic, computational fluid dynamics (CFD)-based process model is constructed, which quantifies the various underlying complex physicochemical phenomena occurring at the pellet and reactor levels. Model simulations are then performed to investigate the impact of dimensionless variables on overall system performance and gain a better understanding of this cyclic reaction/separation process. The results indicate that the hybrid system shows a steady-state cyclic behavior to ensure flexible operating time. A sustainability evaluation was conducted to illustrate the sustainability improvement in the proposed process compared to the traditional design. The results indicate that the integrated–intensified adsorptive-membrane reactor technology enhances sustainability by 35% to 138% for the chosen 21 indicators. The average enhancement in sustainability is almost 57%, signifying that the sustainability evaluation reveals significant benefits of the integrated–intensified adsorptive-membrane reactor process compared to HTSR + LTSR. Full article

MicroRNA-211 (miR-211) is a versatile regulatory molecule that plays critical roles in cellular homeostasis and disease progression through the post-transcriptional regulation of gene expression. This review comprehensively examines miR-211’s multifaceted functions across various biological systems, highlighting its context-dependent activity as both a tumor suppressor and oncogene. In physiological contexts, miR-211 regulates cell cycle progression, metabolism, and differentiation through the modulation of key signaling pathways, including TGF-β/SMAD and PI3K/AKT. miR-211 participates in retinal development, bone physiology, and protection against renal ischemia–reperfusion injury. In pathological conditions, miR-211 expression is altered in various diseases, particularly cancer, where it may be a useful diagnostic and prognostic biomarker. Its stability in serum and differential expression in various cancer types make it a promising candidate for non-invasive diagnostics. The review also explores miR-211’s therapeutic potential, discussing both challenges and opportunities in developing miRNA-based treatments. Understanding miR-211’s complex regulatory interactions and context-dependent functions is crucial for advancing its clinical applications for diagnosis, prognosis, and targeted therapy in multiple diseases. Full article

Prostate cancer remains one of the most prevalent malignancies among men, and emerging evidence proposed a potential role for periprostatic adipose tissue (PPAT) in tumor progression. However, its relationship with imaging-based risk stratification systems such as PI-RADS remains uncertain. This retrospective observational study aimed to evaluate whether periprostatic and subcutaneous fat thickness are associated with PI-RADS scores or PSA levels in biopsy-naïve patients. We retrospectively reviewed 104 prostate MRI scans performed between January 2020 and January 2024. Fat thickness was measured on axial T2-weighted images, and statistical analyses were conducted using Spearman’s correlation and multiple linear regression. In addition to linear measurements, we also assessed periprostatic fat volume and posterior fat thickness derived from imaging data. No significant correlations were observed between fat thickness (either periprostatic or subcutaneous) and PI-RADS score or PSA values. Similarly, periprostatic fat volume showed only a weak, non-significant correlation with PI-RADS, while posterior fat thickness demonstrated a weak but statistically significant positive association. Additionally, subgroup comparisons between low-risk (PI-RADS < 4) and high-risk (PI-RADS ≥ 4) patients showed no meaningful differences in fat measurements. These findings suggest that simple linear fat thickness measurements may not enhance imaging-based risk assessment in prostate cancer, though regional and volumetric assessments could offer modest added value. Full article

To improve the accuracy of Japanese finger-spelled character recognition using an RGB camera, we focused on feature design and refinement of the recognition method. By leveraging angular features extracted via MediaPipe, we proposed a method that effectively captures subtle motion differences while minimizing the influence of background and surrounding individuals. We constructed a large-scale dataset that includes not only the basic 50 Japanese syllables but also those with diacritical marks, such as voiced sounds (e.g., “ga”, “za”, “da”) and semi-voiced sounds (e.g., “pa”, “pi”, “pu”), to enhance the model’s ability to recognize a wide variety of characters. In addition, the application of a change-point detection algorithm enabled accurate segmentation of sign language motion boundaries, improving word-level recognition performance. These efforts laid the foundation for a highly practical recognition system. However, several challenges remain, including the limited size and diversity of the dataset and the need for further improvements in segmentation accuracy. Future work will focus on enhancing the model’s generalizability by collecting more diverse data from a broader range of participants and incorporating segmentation methods that consider contextual information. Ultimately, the outcomes of this research should contribute to the development of educational support tools and sign language interpretation systems aimed at real-world applications. Full article

Real-time wildlife monitoring on edge devices poses significant challenges due to limited power, constrained bandwidth, and unreliable connectivity, especially in remote natural habitats. Conventional object detection systems often transmit redundant data of the same animals detected across multiple consecutive frames as a part of a single event, resulting in increased power consumption and inefficient bandwidth usage. Furthermore, maintaining consistent animal identities in the wild is difficult due to occlusions, variable lighting, and complex environments. In this study, we propose a lightweight hybrid tracking framework built on the YOLOv8m deep neural network, combining motion-based Kalman filtering with Local Binary Pattern (LBP) similarity for appearance-based re-identification using texture and color features. To handle ambiguous cases, we further incorporate Hue-Saturation-Value (HSV) color space similarity. This approach enhances identity consistency across frames while reducing redundant transmissions. The framework is optimized for real-time deployment on edge platforms such as NVIDIA Jetson Orin Nano and Raspberry Pi 5. We evaluate our method against state-of-the-art trackers using event-based metrics such as MOTA, HOTA, and IDF1, with a focus on detected animals occlusion handling, trajectory analysis, and counting during both day and night. Our approach significantly enhances tracking robustness, reduces ID switches, and provides more accurate detection and counting compared to existing methods. When transmitting time-series data and detected frames, it achieves up to 99.87% bandwidth savings and 99.67% power reduction, making it highly suitable for edge-based wildlife monitoring in resource-constrained environments. Full article

Swertiamarin (SW), a natural iridoid glycoside primarily isolated from the genus Swertia, Gentianaceae family, has been extensively utilized in traditional medicine systems, including Ayurveda, Traditional Chinese Medicine, and Tibetan medicine, for treating fever, diabetes, liver disorders, and inflammatory conditions. Pharmacokinetic studies reveal that SW exhibits rapid absorption but demonstrates low oral bioavailability due to the first-pass effect. Pharmacological studies have demonstrated that SW possesses a wide range of pharmacological activities, including antioxidant, anti-inflammatory, anti-tumor, anti-diabetic, and neuroprotective activities. Our analysis demonstrates that SW exerts remarkable therapeutic potential across multiple pathological conditions through coordinated modulation of key signaling cascades, including Nrf2/HO-1, NF-κB, MAPK, PI3K/Akt, and PPAR pathways. This comprehensive review systematically consolidates current knowledge on SW’s pharmacokinetic characteristics, toxicity, diverse biological activities, and underlying molecular mechanisms based on extensive preclinical evidence, establishing a scientific foundation for future drug development strategies and potential clinical applications of the potential natural lead compound. Full article

Prolactin (PRL) is a hormone primarily associated with lactation, but it plays various roles in both men and women. PRL belongs to the family of peptide hormones, including placental lactogen and growth hormone. Interestingly, PRL is a pleiotropic hormone affecting several physiological and pathological conditions, including fertility. Moreover, several pathophysiological roles have been associated with this hormone, including those of the immune system, autoimmune disorders, asthma, and ageing. Additionally, PRL receptors are ubiquitously expressed in tissues, including the mammary gland, gonads, liver, kidney, adrenal gland, brain, heart, lungs, pituitary gland, uterus, skeletal muscle, skin blood cells, and immune system. Therefore, in the present paper, we cover the potential role that PRL may play in asthma by promoting inflammation and modulating immune responses. The detection of its receptor in lung tissue suggests a direct role in airway smooth muscle contractility through activation of signaling pathways such as JAK2-STAT5, MAPK/ERK1/2, and PI3K/Akt, as well as influencing ionic currents that regulate cell contraction, proliferation, and survival. In this sense, this review aims to explore the potential involvement of PRL in asthma pathophysiology by examining its interactions with intracellular signaling pathways and its possible impact on airway smooth muscle contractility and immune modulation. Full article

Modern microgrids face critical challenges in maintaining stability and efficiency due to renewable energy intermittency and dynamic load demands. This paper proposes a novel real-time energy management framework that synergizes a bio-inspired T-Cell optimization algorithm with decentralized voltage-based droop control to address these challenges. A JADE-based multi-agent system (MAS) orchestrates coordination between the T-Cell optimizer and edge-level controllers, enabling scalable and fault-tolerant decision-making. The T-Cell algorithm, inspired by adaptive immune system dynamics, optimizes global power distribution through the MAS platform, while droop control ensures local voltage stability via autonomous adjustments by distributed energy resources (DERs). The framework is rigorously validated through Hardware-in-the-Loop (HIL) testing using OPAL-RT, which interfaces MATLAB/Simulink models with Raspberry Pi for real-time communication (MQTT/Modbus protocols). Experimental results demonstrate a 91% reduction in grid dependency, 70% mitigation of voltage fluctuations, and a 93% self-consumption rate, significantly enhancing power quality and resilience. By integrating centralized optimization with decentralized control through MAS coordination, the hybrid approach achieves scalable, self-organizing microgrid operation under variable generation and load conditions. This work advances the practical deployment of adaptive energy management systems, offering a robust solution for sustainable and resilient microgrids. Full article

Background/Objectives: Selective serotonin reuptake inhibitors (SSRIs), widely prescribed for anxiety disorders, may negatively impact the gut microbiota, contributing to dysbiosis. Considering the gut–brain axis’s importance in mental health, probiotics could represent an effective adjunctive strategy. This study evaluated the effects of Lactobacillus helveticus R0052 and Bifidobacterium longum R0175 on microbiota composition, metabolic activity, and immune markers in fecal samples from patients with anxiety on SSRIs, using the SHIME^® (Simulator of the Human Intestinal Microbial Ecosystem) model. Methods: The fecal microbiotas of four patients using sertraline or escitalopram were inoculated in SHIME^® reactors simulating the ascending colon. After stabilization, a 14-day probiotic intervention was performed. Microbial composition was assessed by 16S rRNA sequencing. Short-chain fatty acids (SCFAs), ammonia, and GABA were measured, along with the prebiotic index (PI). Intestinal barrier integrity was evaluated via transepithelial electrical resistance (TEER), and cytokine levels (IL-6, IL-8, IL-10, TNF-α) were analyzed using a Caco-2/THP-1 co-culture system. The statistical design employed in this study for the analysis of prebiotic index, metabolites, intestinal barrier integrity and cytokines levels was a repeated measures ANOVA, complemented by post hoc Tukey’s tests to assess differences across treatment groups. For the 16S rRNA sequencing data, alpha diversity was assessed using multiple metrics, including the Shannon, Simpson, and Fisher indices to evaluate species diversity, and the Chao1 and ACE indices to estimate species richness. Beta diversity, which measures microbiota similarity across groups, was analyzed using weighted and unweighted UniFrac distances. To assess significant differences in beta diversity between groups, a permutational multivariate analysis of variance (PERMANOVA) was performed using the Adonis test. Results: Probiotic supplementation increased Bifidobacterium and Lactobacillus, and decreased Klebsiella and Bacteroides. Beta diversity was significantly altered, while alpha diversity remained unchanged. SCFA levels increased after 7 days. Ammonia levels dropped, and PI values rose. TEER values indicated enhanced barrier integrity. IL-8 and TNF-α decreased, while IL-6 increased. GABA levels remained unchanged. Conclusions: The probiotic combination of Lactobacillus helveticus R0052 and Bifidobacterium longum R0175 modulated gut microbiota composition, metabolic activity, and inflammatory responses in samples from individuals with anxiety on SSRIs, supporting its potential as an adjunctive strategy to mitigate antidepressant-associated dysbiosis. However, limitations—including the small pooled-donor sample, the absence of a healthy control group, and a lack of significant GABA modulation—should be considered when interpreting the findings. Although the SHIME^® model is considered a gold standard for microbiota studies, further clinical trials are necessary to confirm these promising results. Full article

Accurate wellbore trajectory prediction is of great significance for enhancing the efficiency and safety of directional drilling in coal mines. However, traditional mechanical analysis methods have high computational complexity, and the existing data-driven models cannot fully integrate non-sequential features such as stratum lithology. To solve these problems, this study proposes a parallel input gated recurrent unit (Pi-GRU) model based on the TensorFlow framework. The GRU network captures the temporal dependencies of sequence data (such as dip angle and azimuth angle), while the BP neural network extracts deep correlations from non-sequence features (such as stratum lithology), thereby achieving multi-source data fusion modeling. Orthogonal experimental design was adopted to optimize the model hyperparameters, and the ablation experiment confirmed the necessity of the parallel architecture. The experimental results obtained based on the data of a certain coal mine in Shanxi Province show that the mean square errors (MSE) of the azimuth and dip angle angles of the Pi-GRU model are 0.06° and 0.01°, respectively. Compared with the emerging CNN-BiLSTM model, they are reduced by 66.67% and 76.92%, respectively. To evaluate the generalization performance of the model, we conducted cross-scenario validation on the dataset of the Dehong Coal Mine. The results showed that even under unknown geological conditions, the Pi-GRU model could still maintain high-precision predictions. The Pi-GRU model not only outperforms existing methods in terms of prediction accuracy, with an inference delay of only 0.21 milliseconds, but also requires much less computing power for training and inference than the maximum computing power of the Jetson TX2 hardware. This proves that the model has good practicability and deployability in the engineering field. It provides a new idea for real-time wellbore trajectory correction in intelligent drilling systems and shows strong application potential in engineering applications. Full article

Microglia, as the immune guardians of the central nervous system (CNS), have the ability to maintain neural homeostasis, respond to environmental changes, and remodel the synaptic landscape. However, persistent microglial activation can lead to chronic neuroinflammation, which can alter neuronal signaling pathways, resulting in accelerated cognitive decline. Phosphoinositol 3-kinase (PI3K) has emerged as a critical driver, connecting inflammation to neurodegeneration, serving as the nexus of numerous intracellular processes that govern microglial activation. This review focuses on the relationship between PI3K signaling and microglial activation, which might lead to cognitive impairment, inflammation, or even neurodegeneration. The review delves into the components of the PI3K signaling cascade, isoforms, and receptors of PI3K, as well as the downstream effects of PI3K signaling, including its effectors such as protein kinase B (Akt) and mammalian target of rapamycin (mTOR) and the negative regulator phosphatase and tensin homolog (PTEN). Experiments have shown that the overproduction of certain cytokines, coupled with abnormal oxidative stress, is a consequence of poor PI3K regulation, resulting in excessive synapse pruning and, consequently, impacting learning and memory functions. The review also highlights the implications of autonomously activated microglia exhibiting M1/M2 polarization driven by PI3K on hippocampal, cortical, and subcortical circuits. Conclusions from behavioral studies, electrophysiology, and neuroimaging linking cognitive performance and PI3K activity were evaluated, along with new approaches to therapy using selective inhibitors or gene editing. The review concludes by highlighting important knowledge gaps, including the specific effects of different isoforms, the risks associated with long-term pathway modulation, and the limitations of translational potential, underscoring the crucial role of PI3K in mitigating cognitive impairment driven by neuroinflammation. Full article

Accurate land cover classification is vital for informed water resource management, especially in irrigation-dependent regions facing increased climate variability. Using fused multi-sensor remote sensing imagery from Landsat 8 and Sentinel-2, this study assesses the effectiveness of three machine learning classifiers: Random Forest (RF), Gradient Tree Boosting (GTB), and Naive Bayes (NB) in creating land cover maps for the Tisza-Körös Valley Irrigation System (TIKEVIR) in Hungary. Water bodies, built-up areas, forests, grasslands, and major crops were among the important land cover categories that were classified for the two agricultural seasons (2018 and 2022). RF performed consistently in 2022 and reached its best accuracy in 2018 (OA = 0.87, KC = 0.83, PI = 0.94). While NB’s performance in 2022 remained less consistent, GTB’s performance increased. The findings show that RF works effectively for generating accurate land cover data, providing useful information for regional monitoring, and assisting in water and environmental management decision-making. Full article

The scientific investment decision of Park-level Integrated Energy System (PIES) projects is of great significance to energy enterprises for improving the efficient utilization of funds, promoting green and low-carbon transformation, and achieving the goal of carbon neutrality. This paper proposed a two-stage investment framework that integrates a multi-objective 0–1 programming model with a multi-criteria decision-making (MCDM) technique to determine the optimal PIES project investment portfolios under the constraint of quota investment. First, a multi-objective (MO) 0–1 programming model was constructed for typical PIES projects in Stage-I, which considers economic and environmental benefits to obtain Pareto frontier solutions, i.e., PIES project portfolios. Second, an evaluation index system from multiple dimensions was established, and a hybrid MCDM technique was adopted to comprehensively evaluate the Pareto frontier solutions in Stage-II. Finally, the proposed model was applied to an empirical case, and the simulation results show that the decision framework can achieve the best overall benefit of PIES project portfolios with maximal economic benefit and minimum carbon emissions. In addition, the robustness analysis was performed by changing the indicator weights to verify the stability of the proposed framework. This research work could provide a theoretical tool for investment decisions regarding PIES projects for energy enterprises. Full article

Coastal areas are prone to thunderstorms. Lightning strikes can damage power facilities and communication systems, thereby leading to serious consequences. The lightning location network achieves lightning location through data fusion from multiple lightning locator nodes and can detect the location and intensity of lightning in real time. It is an important facility for thunderstorm warning and protection in coastal areas. However, when a sensor node in a lightning location network experiences a soft fault, it causes distortion in the lightning location. To achieve fault diagnosis of lightning locator nodes in a multi-node data fusion mode, this study proposes a new lightning location mode: the observer pattern. This paper first analyzes the main factors contributing to the error of the lightning location algorithm under this mode, proposes an observer pattern estimation algorithm (OPE) for lightning location, and defines the proportion of improvement in lightning positioning accuracy (PI) caused by the OPE algorithm. By analyzing the changes in PI in the process of lightning location, this study further proposes a diagnostic algorithm (OPSFD) for soft-fault nodes in a lightning location network. The simulation experiments in the paper demonstrate that the OPE algorithm can effectively improve the positioning accuracy of existing lightning location networks. Therefore, the OPE algorithm is also a low-cost and efficient method for improving the accuracy of existing lightning location networks, and it is suitable for the actual deployment and upgrading of current lightning locators. Meanwhile, the experimental results show that when a soft fault causes the observation error of the node to exceed the normal range, the OPSFD algorithm proposed in this study can effectively diagnose the faulty node. Full article