Search Results (348)

Lung cancer is one of the most prevalent and deadly forms of cancer, accounting for a significant portion of cancer-related deaths worldwide. It typically originates in the lung tissues, particularly in the cells lining the airways, and early detection is crucial for improving patient survival rates. Computed tomography (CT) imaging has become a standard tool for lung cancer screening, providing detailed insights into lung structures and facilitating the early identification of cancerous nodules. In this study, an improved Faster R-CNN model is employed to detect early-stage lung cancer. To enhance the performance of Faster R-CNN, a novel dual-attention optimized multi-scale CNN (DA OMS-CNN) architecture is used to extract representative features of nodules at different sizes. Additionally, dual-attention RoIPooling (DA-RoIpooling) is applied in the classification stage to increase the model’s sensitivity. In the false-positive reduction stage, a combination of multiple 3D shift window transformers (3D SwinT) is designed to reduce false-positive nodules. The proposed model was evaluated on the LUNA16 and PN9 datasets. The results demonstrate that integrating DA OMS-CNN, DA-RoIPooling, and 3D SwinT into the improved Faster R-CNN framework achieves a sensitivity of 96.93% and a CPM score of 0.911. Comprehensive experiments demonstrate that the proposed approach not only increases the sensitivity of lung cancer detection but also significantly reduces the number of false-positive nodules. Therefore, the proposed method can serve as a valuable reference for clinical applications. Full article

Alzheimer’s disease (AD) is characterized by β-amyloid plaques, neurofibrillary tangles, neuroinflammation, and oxidative stress—pathological features that pose significant challenges for the development of therapeutic interventions. Given these challenges, this review comprehensively evaluates the neuroprotective mechanisms of bioactive compounds derived from red algae, including polysaccharides and phycobiliproteins, which are considered a promising source of natural therapeutics for AD. Red algal constituents exhibit neuroprotective activities through multiple mechanisms. Sulfated polysaccharides (e.g., carrageenan, porphyran) suppress NF-κB-mediated neuroinflammation, modulate mitochondrial function, and enhance brain-derived neurotrophic factor (BDNF) expression. Phycobiliproteins (phycoerythrin, phycocyanin) and peptides derived from their degradation scavenge reactive oxygen species (ROS) and activate antioxidant pathways (e.g., Nrf2/HO-1), thus mitigating oxidative damage. Carotenoids (lutein, zeaxanthin) improve cognitive function through the inhibition of acetylcholinesterase and pro-inflammatory cytokines (TNF-α, IL-1β), while phenolic compounds (bromophenols, diphlorethol) provide protection by targeting multiple pathways involved in dopaminergic system modulation and Nrf2 pathway activation. Emerging extraction technologies—including microwave- and enzyme-assisted methods—have been shown to optimize the yield and maintain the bioactivity of these compounds. However, the precise identification of molecular targets and the standardization of extraction techniques remain critical research priorities. Overall, red algae-derived compounds hold significant potential for multi-mechanism AD interventions, providing novel insights for the development of therapeutic strategies with low toxicity. Full article

Science has made significant progress in detecting reactive oxygen species (ROS) in tobacco smoke, which is an important step for precision cancer therapy. An important advance is also the understanding that superoxide can be produced by electrophilic molecules. The dual action of hydrogen peroxide, directly or via electrophilic molecules, in the development of oxidative stress allows for the identification of target proteins that can potentially stop unwanted signals in cancer development. However, despite advances in proteomics, reliable inhibitors to stop ROS-associated cancer progression have not yet been proposed for the treatment of tobacco cigarette smokers. This is likely due to an imperfect understanding of the diversity of molecular mechanisms of anti-ROS action. Fluorescent protein detection in living cells, called in-gel, offers a direct route to a better understanding of the rapid interaction of ROS and electrophilic compounds with targeted proteins. It seemed that the traditional paradigm of pharmaceutical innovation “one drug, one disease” did not solve the problem of tobacco smoking causing cancer. However, among the various therapeutic treatments for tobacco smokers, the best way to combat cancer today is smoking cessation, which fits into the “one-cure” paradigm. Full article

The opioid drug overdose death rate remains a significant public health crisis in the U.S., where an opioid epidemic has led to a dramatic rise in overdose deaths over the past two decades. Since 1999, opioids have been implicated in approximately 75% of the nearly one million drug-related deaths. Research indicates that the epidemic is caused by both over-prescribing and social and psychological determinants such as economic stability, hopelessness, and social isolation. Impeding this research is the lack of measurements of these social and psychological constructs at fine-grained spatial and temporal resolution. To address this issue, we sourced data from Reddit, where people share self-reported experiences with opioid substances, specifically using opioid drugs through different routes of administration. To achieve this objective, an opioid overdose dataset is created and manually annotated in binary and multi-classification, along with detailed annotation guidelines. In traditional manual investigations, the route of administration is determined solely through biological laboratory testing. This study investigates the efficacy of an automated tool leveraging natural language processing and transformer model, such as RoBERTa, to analyze patterns of substance use. By systematically examining these patterns, the model contributes to public health surveillance efforts, facilitating the identification of at-risk populations and informing the development of targeted interventions. This approach ultimately aims to enhance prevention and treatment strategies for opioid misuse through data-driven insights. The findings show that our proposed methodology achieved the highest cross-validation score of 93% for binary classification and 91% for multi-class classification, demonstrating performance improvements of 9.41% and 10.98%, respectively, over the baseline model (XGB, 85% in binary class and 81% in multi-class). Full article

With the increasing popularity of low-cost, clean, and environmentally friendly new energy sources, the proportion of grid-connected new energy units has increased significantly. However, since these units are frequency decoupled from the grid through a power electronic interface, they are unable to provide inertia support during active power perturbations, which leads to a decrease in system inertia and reduced frequency stability. In this study, the urgent need to accurately assess inertia is addressed by developing an energy-function-based inertia identification technique that eliminates the effect of damping terms. By integrating vibration mechanics, the proposed method calculates the inertia value after a perturbation using port measurements (active power, voltage phase, and frequency). Simulation results of the Western System Coordinating Council (WSCC) 9-bus system show that the inertia estimation error of the method is less than 1%, which is superior to conventional methods such as rate-of-change-of-frequency (RoCoF) and least squares methods. Notably, the technique accurately evaluates the inertia of synchronous generators and doubly fed induction generators (DFIGs) under virtual inertia control, providing a robust inertia evaluation framework for low-inertia power systems with high renewable energy penetration. This research deepens the understanding of inertial dynamics and contributes to practical applications in grid stability analysis and control strategy optimalization. Full article

In this study, we introduce an Unmanned Aerial Vehicle (UAV) centric visual semantic simultaneous localization and mapping (SLAM) framework that integrates RGB–D cameras, inertial measurement units (IMUs), and a 5G–enabled remote interaction module. Our system addresses three critical limitations in existing approaches: (1) Distance constraints in remote operations; (2) Static map assumptions in dynamic environments; and (3) High–dimensional perception requirements for UAV–based applications. By combining YOLO–based object detection with epipolar–constraint-based dynamic feature removal, our method achieves real-time semantic mapping while rejecting motion artifacts. The framework further incorporates a dual–channel communication architecture to enable seamless human–in–the–loop control over UAV–Unmanned Ground Vehicle (UGV) teams in large–scale scenarios. Experimental validation across indoor and outdoor environments indicates that the system can achieve a detection rate of up to 75 frames per second (FPS) on an NVIDIA Jetson AGX Xavier using YOLO–FASTEST, ensuring the rapid identification of dynamic objects. In dynamic scenarios, the localization accuracy attains an average absolute pose error (APE) of 0.1275 m. This outperforms state–of–the–art methods like Dynamic–VINS (0.211 m) and ORB–SLAM3 (0.148 m) on the EuRoC MAV Dataset. The dual-channel communication architecture (Web Real–Time Communication (WebRTC) for video and Message Queuing Telemetry Transport (MQTT) for telemetry) reduces bandwidth consumption by 65% compared to traditional TCP–based protocols. Moreover, our hybrid dynamic feature filtering can reject 89% of dynamic features in occluded scenarios, guaranteeing accurate mapping in complex environments. Our framework represents a significant advancement in enabling intelligent UAVs/UGVs to navigate and interact in complex, dynamic environments, offering real-time semantic understanding and accurate localization. Full article

Short informal texts are increasingly prevalent in modern communication, often containing fragmented grammar, personal opinions, and limited context. Traditional NLP tasks for the texts ordinarily focus on the subjective aspect learning, such as sentiment analysis and polarity classification. The study of learning objectivity from the texts is similarly significant, which can benefit many real-world applications including information filtering, content verification, etc. Unfortunately, this study is not being explored. This paper proposes a novel task that aims at identifying objectivity in short informal texts. Inspired by the characteristics of objective statements that normally need complete syntax structures for knowledge expression and delivery, we try to leverage the viewpoint of subjects (U), the tense of predicates (V), and the viewpoint of objects (O) as critical factors for objectivity learning. Upon that, we further propose a two-stage objectivity identification approach: (1) a UVO quantification module is implemented via a proposed OpenIE and large language model (LLM)-based triple feature quantification procedure; (2) an objectivity identification module employs pre-trained base models like BERT or RoBERTa that are constrained with the quantified UVO. The experimental result demonstrates our approach can outperform the base models up to $5.91\%$ in objective-F1 and up to $6.97\%$ in accuracy. Full article

Microplastics (MPs), emerging contaminants of significant global concern, have a substantially increased environmental impact due to their biological persistence and accumulation in the body. Exposure to MPs has been associated with oxidative stress, systemic inflammation, and cellular dysfunction, notably affecting critical tissues such as the stomach, colon, and brain. This review explores the correlation between MPs and cancer risk along the gastric–colon–brain axis, identifying the signaling pathways altered by MP exposure. Furthermore, it highlights the role of functional nutrition and bioactive flavonoids—including chlorogenic acid, coumaric acid, and naringin—as well as the use of highly bioavailable combined polyphenol nanoparticles as potential detoxifying agents. Functional nutrients are effective in enhancing cellular resilience against reactive oxygen species (ROS) production and MP-induced toxicity, offering protective effects at the gastric, intestinal, and brain barriers. Activation of the Nrf2 pathway by bioactive compounds promotes the expression of detoxifying enzymes, suggesting a promising nutritional strategy to mitigate MP-related damage. This review underscores how functional nutrition may represent a viable therapeutic approach to reduce the harmful effects of MP exposure. The integration of advanced technologies—such as microfluidic systems, organ-on-a-chip platforms, and machine learning—and the identification of key molecular targets lay the foundation for developing preventive and personalized medicine strategies aimed at lowering the risk of environmentally induced carcinogenesis. Full article

In the context of China’s accelerating maritime judicial digitization, automatic summarization of lengthy and terminology-rich judgment documents has become a critical need for improving legal efficiency. Focusing on the task of automatic summarization for Chinese maritime judgment documents, we propose HybridSumm, an “extraction–abstraction” hybrid summarization framework that integrates a maritime judgment lexicon to address the unique characteristics of maritime legal texts, including their extended length and dense domain-specific terminology. First, we construct a specialized maritime judgment lexicon to enhance the accuracy of legal term identification, specifically targeting the complexity of maritime terminology. Second, for long-text processing, we design an extractive summarization model that integrates the RoBERTa-wwm-ext pre-trained model with dilated convolutional networks and residual mechanisms. It can efficiently identify key sentences by capturing both local semantic features and global contextual relationships in lengthy judgments. Finally, the abstraction stage employs a Nezha-UniLM encoder–decoder architecture, augmented with a pointer–generator network (for out-of-vocabulary term handling) and a coverage mechanism (to reduce redundancy), ensuring that summaries are logically coherent and legally standardized. Experimental results show that HybridSumm’s lexicon-guided two-stage framework significantly enhances the standardization of legal terminology and semantic coherence in long-text summaries, validating its practical value in advancing judicial intelligence development. Full article

Background: Although endothelial mesenchymal transition (EndMT) has been characterized as a basic process in embryogenesis, EndMT is the mechanism that accelerates the development of cardiovascular diseases, including heart failure, aging, and complications of diabetes or hypertension as well. Endothelial cells lose their distinct markers and take on a mesenchymal phenotype during EndMT, expressing distinct products. Methods: In this study, type 1 Diabetes mellitus (T1DM) was induced in rats with streptozotocin (STZ) by intraperitoneal injection at a 60 mg/kg dose. Diabetic rats were randomly divided into two groups, namely, control and diabetic rats, for 4 weeks. Heart, aorta, and plasma samples were collected at the end of 4 weeks. Sequentially, biochemical parameters, cytokines, reactive oxygen species (ROS), protein expression of EndMT markers (Chemokine C-X-C motif ligand-1 (CXCL-1), vimentin, citrullinated histone H3 (H3Cit), α-smooth muscle actin (α-SMA), and transforming growth factor beta (TGF-β) and versican), components of the extracellular matrix (matrix metalloproteinase 2 (MMP-2), tissue inhibitor of metalloproteinase-1(TIMP-1), and discoidin domain tyrosine kinase receptor 2 (DDR-2)) were detected by ELISA or Western blot, respectively. Results: Cytokines and ROS were increased in diabetic hearts, which induced partial EndMT. Among EndMT markers, histone citrullination, α-SMA, and CXCL-1 were increased; vimentin was decreased in DM. The endothelial marker endothelin-1 was significantly higher in the aortas of DM rats. Interestingly, TGF-β showed a significant decrease in the diabetic heart, plasma, and aorta. Additionally, MMP-2/TIMP-1 levels also decreased in DM. Conclusions: To sum up, the identification of molecules and regulatory pathways involved in EndMT provided novel therapeutic approaches for cardiac pathophysiological conditions. Full article

Freshwater ecosystems are increasingly threatened by climate change and anthropogenic activities, necessitating innovative and scalable monitoring solutions. Artificial intelligence (AI) has emerged as a transformative tool in aquatic biodiversity research, enabling automated species identification, predictive habitat modeling, and conservation planning. This systematic review follows the PRISMA framework to analyze AI applications in freshwater biodiversity studies. Using a structured literature search across Scopus, Web of Science, and Google Scholar, we identified 312 relevant studies published between 2010 and 2024. This review categorizes AI applications into species identification, habitat assessment, ecological risk evaluation, and conservation strategies. A risk of bias assessment was conducted using QUADAS-2 and RoB 2 frameworks, highlighting methodological challenges, such as measurement bias and inconsistencies in the model validation. The citation trends demonstrate exponential growth in AI-driven biodiversity research, with leading contributions from China, the United States, and India. Despite the growing use of AI in this field, this review also reveals several persistent challenges, including limited data availability, regional imbalances, and concerns related to model generalizability and transparency. Our findings underscore AI’s potential in revolutionizing biodiversity monitoring but also emphasize the need for standardized methodologies, improved data integration, and interdisciplinary collaboration to enhance ecological insights and conservation efforts. Full article

Accurate Total Organic Carbon (TOC) prediction in the deeply buried Lower Cambrian Qiongzhusi Formation shale is constrained by extreme heterogeneity (TOC variability: 0.5–12 wt.%, mineral composition Coefficient of Variation > 40%) and ambiguous geophysical responses. This study introduces three key innovations to address these challenges: (1) A Dynamic Weighting–Calibrated Random Forest Regression (DW-RFR) model integrating high-resolution Gamma-Ray-guided dynamic time warping (±0.06 m depth alignment precision derived from 237 core-log calibration points using cross-validation), Principal Component Analysis-Deyang–Anyue Rift Trough Shapley Additive Explanations (PCA-SHAP) hybrid feature engineering (89.3% cumulative variance, VIF < 4), and Bayesian-optimized ensemble learning; (2) systematic benchmarking against conventional ΔlogR (R² = 0.700, RMSE = 0.264) and multi-attribute joint inversion (R² = 0.734, RMSE = 0.213) methods, demonstrating superior accuracy (R² = 0.917, RMSE = 0.171); (3) identification of Gamma Ray (r = 0.82) and bulk density (r = −0.76) as principal TOC predictors, contrasted with resistivity’s thermal maturity-dependent signal attenuation (r = 0.32 at Ro > 3.0%). The methodology establishes a transferable framework for organic-rich shale evaluation, directly applicable to the Longmaxi Formation and global Precambrian–Cambrian transition sequences. Future directions emphasize real-time drilling data integration and quantum computing-enhanced modeling for ultra-deep shale systems, advancing predictive capabilities in tectonically complex basins. Full article

Expansins are cell wall-modifying proteins that play a pivotal role in plant growth, development, and stress adaptation to abiotic stress. This manuscript explores the functions of expansins in salt and drought stress responses across multiple plant species, highlighting their involvement in cell wall loosening, transcriptional regulation, ion and osmotic homeostasis, and phytohormone signalling. Genome-wide identification and expression analyses revealed differential regulation of expansin genes under abiotic stress conditions. In Nicotiana tabacum, overexpression of NtEXPA4 and NtEXPA11 promoted root elongation and ion homeostasis, improving salt and drought tolerance. Similarly, Brassica rapa BrEXLB1 was found to modulate root architecture and phytohormone-mediated stress responses. In Oryza sativa, OsEXPA7 was linked to cation exchange and auxin signalling under salt stress conditions. Conversely, in Populus trichocarpa, PtEXPA6 exhibited a negative regulatory role in salt stress tolerance, highlighting species-specific differences in expansin function. Expansins also contribute to reactive oxygen species (ROS) homeostasis, as observed in transgenic plants with increased activities of SOD, POD, APX, and CAT, which reduced oxidative damage under stress. Additionally, enhanced accumulation of soluble sugars and proline in expansin-overexpressing plants suggests their involvement in osmotic adjustment mechanisms. The interplay between expansins and ABA, auxins, and ethylene further underscores their role in integrating mechanical and hormonal stress responses. Despite substantial progress, limitations remain in understanding the broader regulatory networks influenced by expansins. Future research should focus on elucidating their downstream molecular targets, transcriptional interactions, and functional diversity across different plant species. Expansins represent promising candidates for improving crop resilience to environmental stress, making them valuable targets for future breeding and biotechnological approaches. Full article

This research proposes to systematically investigate the cardioprotective mechanisms of Pericarpium Trichosanthis injection (PTI) against acute myocardial ischemia through an integrated approach combining ultra-performance liquid chromatography quadrupole time-of-flight mass spectrometry (UPLC-Q-TOF/MS) constituent profiling, UNIFI database-assisted component identification, network pharmacology-guided target prediction, molecular docking verification, and in vivo experimental validation. The multimodal methodology is designed to comprehensively uncover the therapeutic benefits and molecular pathways underlying this traditional Chinese medicine formulation. Methods: UPLC-Q-TOF/MS and the UNIFI database were used in conjunction with a literature review to screen and validate the absorbed components of PTI. Using network pharmacology, we constructed protein-protein interaction (PPI) networks for pinpointing prospective therapeutic targets. In addition, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed to identify potential signaling pathways. In vivo experiments were conducted to investigate the mechanisms by which PTI ameliorated isoproterenol-induced myocardial injury in rats. All animal experiments have adhered to ARRIVE guidelines. Results: UPLC-Q-TOF/MS revealed 11 core active components in PTI. Network pharmacology prioritization identified pseudoaspidin, ciryneol C, cynanoside M, daurinol, and n-butyl-β-D-fructopyranoside as central bioactive constituents within the compound-target interaction network. Topological analysis of the protein interactome highlighted AKT1, EGFR, MMP9, SRC, PTGS2, STAT3, BCL2, CASP3, and MAPK3 as the most interconnected nodes with the highest betweenness centrality. Pathway enrichment analysis established the PI3K/Akt signaling cascade as the principal mechanistic route for PTI’s cardioprotective effects. Molecular docking simulations demonstrated high-affinity interactions between characteristic components (e.g., cynanoside M, darutigenol) and pivotal targets including PTGS2, MAPK3, CASP3, and BCL2. In vivo investigations showed PTI treatment markedly attenuated myocardial tissue degeneration and collagen deposition (p < 0.05), normalized electrocardiographic ST-segment deviations, and suppressed pro-inflammatory cytokine production (IL-6, TNF-α). The formulation concurrently reduced circulating levels of cardiac injury indicators (LDH, cTnI) and oxidative stress parameters (ROS, MDA), Regarding apoptosis regulation, PTI reduced Bax, caspase-3, and caspase-9, while elevating Bcl-2 (p < 0.05), effectively inhibiting myocardial cell apoptosis with all therapeutic outcomes reaching statistical significance. These findings highlight PTI’s protective effects against myocardial injury through multi-target modulation of inflammation, oxidation, and apoptosis. Conclusions: PTI exerts its therapeutic effects in treating acute myocardial ischemia by regulating and suppressing inflammatory responses, and inhibiting cardiomyocyte apoptosis. Full article

Research has delved into the main controlling factors for the evolution of the pore-fracture structure in deep coal samples. The gas content is influenced by multiple factors, among which the pore-fracture structure in deep coal samples stands as one of the key determinants. To ascertain the evolution of the pore-fracture structure and the main controlling factors of the gas content in deep coal samples of the Yan’an Gas Field, 16 coal samples were collected from the Yan’an Gas Field in the Ordos Basin in this study. A series of laboratory tests and analyses were then carried out. According to the test results, the major controlling factors for the evolution of the pore-fracture structure of the samples were analyzed in accordance with the proximate analysis components, maceral components, mineral composition of the coal samples, and R_o,max, in conjunction with the pore volume and specific surface area of nanopores. Meanwhile, based on the in situ desorption experiment, the major controlling factors of the gas content in coal were explored. First, based on the SEM and hand specimen identification, the pore-fracture structure of the samples is relatively well developed. Calcite filling the fractures of samples can be seen in the hand specimens of samples. This indicates that the mineral composition has a very important influence on the evolution of the pore-fracture structure of samples. Secondly, this study indicates that pore-fracture structure evolution is influenced by multiple factors, primarily ash content and fixed carbon. As ash content increased, the mesopore surface area and volume rose across all sample types, with Type C showing the highest increase (78.1% in surface area and 12.4% in volume compared to Type A). Conversely, micropore characteristics declined, with Type C exhibiting a 4.8% drop in surface area and a 4.7% reduction in volume. The R_o,max of the samples is generally higher than 2.8%, which has a multifaceted impact on pore-fracture structure evolution. Finally, the gas content is mainly controlled by pore volume and the specific surface area of nanopores, with industrial components and maceral compositions showing minimal direct influence. This suggests that gas content results from the combined effects of material composition and pore-fracture structure evolution. Inorganic minerals like quartz and calcite indirectly affect gas content by influencing pore structure development—occupying spaces while also creating new pores, especially through calcite dissolution. Conversely, clay minerals generally hinder pore development by filling spaces with limited fracture-forming capacity. The main purpose of this study is to evaluate the gas content of coal samples in Yan‘an Gas Field. There are few studies on this area by previous scholars. Full article