Search Results (22,259)

Lung cancer remains a major public health challenge due to high incidence and mortality. The chemokine receptor CXCR4 and its ligand CXCL12 (SDF-1) constitute a critical axis in tumor biology, influencing tumor cell proliferation, invasion, angiogenesis, and immune evasion. Aberrant CXCR4 expression is frequently observed in lung cancer and is closely associated with adverse prognosis, enhanced metastatic potential, and therapeutic resistance. Mechanistically, CXCR4 activates signaling pathways including PI3K/AKT, MAPK/ERK, JAK/STAT, and FAK/Src, promoting epithelial–mesenchymal transition, stemness, and survival. The CXCL12/CXCR4 axis also orchestrates interactions with the tumor microenvironment, facilitating chemotaxis toward CXCL12-rich niches (e.g., bone marrow and brain) and modulating anti-tumor immunity via regulatory cells. Regulation of CXCR4 occurs at transcriptional, epigenetic, and post-transcriptional levels, with modulation by hypoxia, inflammatory signals, microRNAs, and post-translational modifications. Clinically, high CXCR4 expression correlates with metastasis, poor prognosis, and reduced response to certain therapies, underscoring its potential as a prognostic biomarker and therapeutic target. Therapeutic strategies targeting CXCR4 include small-molecule antagonists (e.g., AMD3100/plerixafor; balixafortide), anti-CXCR4 antibodies, and CXCL12 decoys, as well as imaging probes for patient selection and response monitoring (e.g., 68Ga-pentixafor PET). Preclinical and early clinical studies suggest that CXCR4 blockade can impair tumor growth, limit metastatic spread, and enhance chemotherapy and immunotherapy efficacy, although hematopoietic side effects and infection risk necessitate careful therapeutic design. This review synthesizes the molecular features, regulatory networks, and translational potential of CXCR4 in lung cancer and discusses future directions for precision therapy and biomarker-guided intervention. Full article

Background/Objectives: Trigeminal Neuralgia (TN) is a chronic neuropathic condition characterized by sudden, severe facial pain. Anticonvulsants are the cornerstone of pharmacological management, yet comparative evidence based on pharmacological class remains scarce. This systematic review aimed to evaluate the efficacy and safety of anticonvulsants in TN, stratified by their mechanism of action. Methods: A systematic search in PubMed, Scopus and Web of Science was conducted following PRISMA 2020 guidelines. Studies employing a pharmacological approach including human patients with TN, published in English since 2000, were included. Risk of bias was assessed using the Cochrane RoB 2, the ROBINS-I and the ROBINS-E tools, according to the study design. Results: Out of 922 initial records, 12 studies met the eligibility criteria. Sodium channel inhibitors showed high efficacy but frequent adverse effects, particularly hyponatremia and central nervous system symptoms. Calcium channel modulators offered a more favorable safety profile. Combination therapies showed benefits, levetiracetam and topiramate were moderately effective and well tolerated. Although the evidence has limitations, anticonvulsants continue to be the primary treatment for TN. Sodium-channel blockers demonstrate strong efficacy, whereas alternative agents generally provide superior tolerability. Conclusions: These findings support selecting drugs according to their underlying mechanisms of action. Equally important is tailoring therapy to pain phenotype and patient characteristics, balancing mechanism with tolerability and efficacy. Full article

Knowledge of weed community structure in agricultural systems is important for sustainable management, especially for evaluating the effects of different herbicides on soybean–corn succession crops. This study evaluated, over two crop seasons, weed community structure in response to chemical weed management strategies for soybean–corn succession in Brazil’s Triângulo Mineiro region. Phytosociological surveys of the weed community were conducted during harvest periods throughout the experimental phase, with referenced data for generating spatial distribution maps of biomass and density of the main present species. The survey identified 33 weed species, predominantly from the Poaceae and Asteraceae families. Regardless of the management system, the total weed biomass was lower in corn crops compared to soybean crops. In management systems using six different herbicides, the IVI of Commelina benghalensis was the lowest due to greater diversification of herbicide mechanisms of action. The results demonstrate that chemical weed management strategies strongly influence weed community structure, with significant effects on weed community structure and evenness in intensive agricultural regions. These changes also have implications for resistance management. Full article

Cost code verification in state-funded construction projects remains a labor-intensive and error-prone task, particularly given the structural heterogeneity of project estimates and the prevalence of malformed codes, inconsistent units of measurement (UoMs), and locally modified price components. This study evaluates a deterministic GPT-based assistant designed to automate Vietnam’s regulatory verification. The assistant was developed and iteratively refined across four Action Research cycles. Also, the system enforces strict rule sequencing and dataset grounding via Python-governed computations. Rather than relying on probabilistic or semantic reasoning, the system performs strictly deterministic checks on code validity, UoM alignment, and unit price conformity in material (MTR), labor (LBR), and machinery (MCR), given the provincial unit price books (UPBs). Deterministic equality is evaluated either on raw numerical values or on values transformed through explicitly declared, rule-governed operations, preserving auditability without introducing tolerance-based or inferential reasoning. A dedicated exact-match mechanism, which is activated only when a code is invalid, enables the recovery of typographical errors only when a project item’s full price vector well matches a normative entry. Using twenty real construction estimates (16,100 rows) and twelve controlled error-injection cases, the study demonstrates that the assistant executes verification steps with high reliability across diverse spreadsheet structures, avoiding ambiguity and maintaining full auditability. Deterministic extraction and normalization routines facilitate robust handling of displaced headers, merged cells, and non-standard labeling, while structured reporting provides line-by-line traceability aligned with professional verification workflows. Practitioner feedback confirms that the system reduces manual tracing effort, improves evaluation consistency, and supports documentation compliance with human judgment. This research contributes a framework for large language model (LLM)-orchestrated verification, demonstrating how Action Research can align AI tools with domain expectations. Furthermore, it establishes a methodology for deploying LLMs in safety-critical and regulation-driven environments. Limitations—including narrow diagnostic scope, unlisted quotation exclusion, single-province UPB compliance, and sensitivity to extreme spreadsheet irregularities—define directions for future deterministic extensions. Overall, the findings illustrate how tightly constrained LLM configurations can augment, rather than replace, professional cost verification practices in public-sector construction. Full article

Background: Mycoplasma pneumoniae pneumonia (MPP) is a common community-acquired pneumonia in children. Increasing drug resistance highlights the need for more effective treatments with fewer side effects. The Qingfei Tongluo Jiedu formula (QTJD) has demonstrated clinical efficacy against MPP; however, its underlying mechanisms remain unclear. This study aimed to explore the mechanism of QTJD on MPP using network pharmacology and in vitro experiments. Methods: Network pharmacology was used to identify the active compounds and signaling pathways of QTJD in MPP. QTJD-containing serum was prepared, and primary mouse lung and bone marrow cells were isolated to examine the effects of QTJD on macrophage polarization through butyric acid. Cell viability assays, flow cytometry, and quantitative reverse transcription-polymerase chain reaction were performed. GPR109^−/− cells were used to confirm the receptor mediating butyric acid’s action, and Western blotting was employed to assess the MAPK signaling pathway. Results: QTJD promoted macrophage polarization and alleviated the inflammatory response caused by Mycoplasma pneumoniae. High-performance liquid chromatography-electrospray ionization mass spectrometry combined with network pharmacology identified 20 active compounds. Protein-protein interaction analysis revealed 10 core target, including JUN and Tumor Necrosis Factor (TNF), while enrichment analysis highlighted pathways such as Mitogen-Activated Protein Kinase (MAPK) and Phosphoinositide 3-Kinase-Protein Kinase B. Experimental validation demonstrated that QTJD reduced M1 markers (CD86, CXCL10) by increasing butyrate levels (p < 0.01) and enhanced M2 markers (CD206, Arg-1, MRC-1), promoting M2 polarization. QTJD inhibited ERK1/2, p38, and JNK1/2 (p < 0.01). In GPR109A^−/− mice macrophages, QTJD suppressed p38 and JNK1/2 (p < 0.01) but showed no effect on ERK1/2 (p > 0.05), confirming involvement of the butyrate-GPR109A-MAPK pathway. Conclusions: QTJD effectively alleviates MPP by regulating macrophage polarization through the butyrate-GPR109A-MAPK pathway. Future studies should explore how QTJD modulates pulmonary immunity through gut microbiota and butyrate production and elucidate its immunoregulatory mechanisms along the gut-lung axis using multi-omics approaches. Full article

The article presents an extensive review of modern technological solutions for pulverized coal combustion, with emphasis on combustion modifiers and biomass co-firing. It highlights the role of coal in the national energy system and the need for its sustainable use in the context of energy transition. The pulverized coal combustion process is described, along with factors influencing its efficiency, and a classification of modifiers that improve combustion parameters. Both natural and synthetic modifiers are analyzed, including their mechanisms of action, application examples, and catalytic effects. Special attention is given to the synergy between transition metal compounds (Fe, Cu, Mn, Ce) and alkaline earth oxides (Ca, Mg), which enhances energy efficiency, flame stability, and reduces emissions of CO, SO₂, and NO_x. The article also examines biomass-coal co-firing as a technology supporting energy sector decarbonization. Co-firing reduces greenhouse gas emissions and increases the reactivity of fuel blends. The influence of biomass type, its share in the mixture, and processing methods on combustion parameters is discussed. Finally, the paper identifies directions for further technological development, including nanocomposite combustion modifiers and intelligent catalysts integrating sorption and redox functions. These innovations offer promising potential for improving energy efficiency and reducing the environmental impact of coal-fired power generation. Full article

Background: Achieving deep decarbonization in global supply chains is essential for advancing net-zero objectives; however, the integrative role of artificial intelligence (AI) and robotics in this transition remains insufficiently explored. This study examines how these technologies support carbon-emission reduction across supply chain operations. Methods: A curated corpus of 83 Scopus-indexed peer-reviewed articles published between 2013 and 2025 is analyzed and organized into six domains covering supply chain and logistics, warehousing operations, AI methodologies, robotic systems, emission-mitigation strategies, and implementation barriers. Results: AI-driven optimization consistently reduces transport emissions by enhancing routing efficiency, load consolidation, and multimodal coordination. Robotic systems simultaneously improve energy efficiency and precision in warehousing, yielding substantial indirect emission reductions. Major barriers include the high energy consumption of certain AI models, limited data interoperability, and poor scalability of current applications. Conclusions: AI and robotics hold substantial transformative potential for advancing supply chain decarbonization; nevertheless, their net environmental impact depends on improving the energy efficiency of digital infrastructures and strengthening cross-organizational data governance mechanisms. The proposed framework delineates technological and organizational pathways that can guide future research and industrial implementation, providing novel insights and actionable guidance for researchers and practitioners aiming to accelerate the low-carbon transition. Full article

The global consensus on sustainable development hinges on the coordinated advancement of economic, social, and environmental dimensions, with high-quality development serving as China’s pivotal pathway for practical implementation. As the primary implementers, cities are confronted with the dual challenge of defining the level of high-quality development and mapping out clear actionable pathways. Therefore, unraveling the driving mechanisms of high-quality urban development is significant. This study constructed a high-quality development evaluation index system, employing a sustainable development index to measure Lianyungang City’s development level from 2008 to 2023. The interrelationships among driving factors were revealed through the coupling coordination degree model, entropy weight method, and Pearson correlation coefficient. The study indicated that innovation stood out as the primary contributor, with contribution rising from 0.09 (2008–2017) to 0.10 (2017–2023). High-tech enterprises and valid invention patents were core drivers of the innovation index’s rise, with weights of 30.35% and 28.92%. Innovation investment promoted the transformation of cities toward technology-intensive development models while effectively supporting Sustainable Development Goals such as industrial upgrading, environmental improvement, and livelihood enhancement. Overall, advancing high-quality urban development required focusing on innovation-driven strategies while catalyzing other areas of development to achieve Sustainable Development Goals. Full article

Aging and neurodegenerative diseases are characterized by common features involving bioenergetics deficiencies, oxidative stress and alterations of calcium buffering. Mechanisms of mitochondrial-targeted drugs include the modulation of electron transport chain and oxidative phosphorylation, the binding to mitochondrial lipids, free-radical scavenging, calcium signaling, and possible effects on mitochondrial biogenesis and dynamics and on the regulation of mitophagic pathways. One of the main sites of action of mitochondria-targeted drugs is the interaction with respiratory chain components. Mitochondrial-targeted compounds such as Mito-Q, and Mito-apocynin have been developed by conjugating triphenylphosphonium (TPP⁺) lipophilic cation group with natural molecules, therefore obtaining promising drugs for reestablishing the correct functioning of the mitochondrial respiratory chain. Stabilization of cardiolipin at the inner mitochondrial membrane by elamipretide or SkQ1 and mitochondria-targeted ROS scavengers can also offer a therapeutic approach to prevent bioenergetic impairment associated with several diseases. In addition, the modulation of calcium signaling can be achieved using both MCU agonists and antagonists representing another mitochondrial target for drug therapies development. Finally, potential strategies for treating neurodegenerative diseases based on the modulation of mitochondrial biogenesis, dynamics and/or mitophagic pathways are discussed. Full article

Cardiovascular disease (CVD) continues to be the primary cause of morbidity and mortality worldwide, underscoring the urgent need for novel therapeutic alternatives. In recent years, marine ecosystems have garnered increasing attention as a promising source of bioactive compounds with unique structural and pharmacological properties. Marine-derived toxins and venoms, including tetrodotoxin, ω-conotoxins, anthopleurins, palytoxin, brevetoxin, aplysiatoxin, and asterosaponins, exert cardioprotective effects through diverse mechanisms such as modulation of ion channels, inhibition of sympathetic overactivity, antioxidative actions, and enhancement of myocardial contractility. These properties make them potential candidates for addressing various CVD manifestations, including arrhythmia, hypertension, ischemia–reperfusion injury, and heart failure. However, despite their therapeutic promise, the clinical application of these marine compounds remains limited due to poor tissue selectivity, narrow therapeutic indices, proinflammatory activity, and limited metabolic stability. Structural modifications, advanced drug delivery platforms, and in vivo validation studies are crucial for overcoming these challenges. This review highlights the pharmacological actions, molecular targets, and cardiovascular relevance of selected marine toxins and venoms while also addressing key translational barriers. Advances in biotechnology and peptide engineering are enabling the safer and more targeted use of these compounds. Collectively, marine-derived toxins and venoms represent a largely untapped but highly promising frontier in cardiovascular drug discovery. Strategic research focused on elucidating mechanisms, optimizing delivery, and translating clinical applications will be critical to unlocking their full therapeutic potential. Full article

Voluntary movement arises from a sequence of neural processes that involve planning, preparation, and execution within distributed cortical networks. The readiness potential, a slow negative brain signal preceding self-initiated actions, represents a sensitive indicator of motor preparation. However, it remains unclear how this signal reflects concurrent variations in mechanical and temporal demands. In this study, twenty-eight healthy participants performed self-paced elbow flexions under nine combinations of mechanical load and movement duration while brain electrical activity, muscle activity, and movement kinematics were simultaneously recorded. Linear mixed-effects analyses revealed that the amplitude of the readiness potential increased progressively with greater mechanical load, indicating that cortical readiness scales with the intensity of preparatory effort. In contrast, longer movement durations produced smaller amplitudes, suggesting that extended temporal windows reduce the efficiency of preparatory synchronization. No significant interaction between load and duration was observed, supporting the idea of partially independent neural mechanisms for effort and timing. These findings identify the readiness potential as a neural marker integrating the energetic and temporal dimensions of voluntary movement and provide a basis for understanding how cortical readiness dynamically optimizes human motor performance. Full article

Non-Intrusive Load Monitoring is an effective method for disaggregating the power consumption of individual appliances from the aggregate load data of a building. The advent of smart meters, Internet of Things devices, and artificial intelligence technologies has significantly advanced the capabilities of non-intrusive load monitoring. However, challenges such as varying sampling frequencies and measurement sensitivities remain. This paper introduces an innovative model incorporating an Adaptive Multi-Scale Attention Integration Module (AMSAIM) to address these issues. The model leverages deep learning and attention mechanisms to improve the accuracy and real-time performance of non-intrusive load monitoring. Validated on the standard UK-DALE dataset, the model consistently demonstrated superior performance. In seen scenarios, our model achieved average F1-scores approximating 0.94 and notably reduced Mean Absolute Error (MAE) values. For washing machines, it achieved an F1-score of 0.99 and MAE of 41.64, outperforming the next best method’s F1-score by 1 percentage point. In challenging unseen scenarios, the model showcased strong generalization, achieving an F1-score of 0.91 for washing machines and reducing MAE to 7.66. Furthermore, an ablation study rigorously confirmed the necessity of the AMSAIM module, showing that the synergistic integration of the efficient multi-scale attention (EMA) and the selective kernel (SK) adaptive receptive field unit is crucial for enhancing model robustness and generalization. Our results highlight the model’s potential for enhancing energy efficiency and providing actionable insights for energy management across various conditions. Full article

The natural polypeptide drug hirudin, a direct thrombin inhibitor, exhibits potent anticoagulant, anti-myocardial fibrotic, and anti-inflammatory effects in the treatment of cardiovascular diseases (CVD), but its clinical application remains limited by its low bioavailability, insufficient targeting capability, and bleeding risk. In recent years, the development of nanotechnology has enabled peptide drug delivery systems to demonstrate substantial promise in medical practice. Significant progress has been made in overcoming limitations and enhancing therapeutic efficacy against CVD through the use of Hirudin-based drug delivery systems by addressing drug stability in vivo, improving targeting ability, and ultimately achieving responsive release. This paper systematically reviews the mechanisms of action, clinical applications, and novel delivery strategies of the peptide drug hirudin in the treatment of CVD, with a particular focus on recent advances in hirudin-based drug delivery systems, and it also looks forward to future research directions for hirudin delivery systems, including the development of scalable intelligent carriers, the construction of real-time feedback systems, and the establishment of standardized in vitro and in vivo evaluation systems, aiming to present novel strategies for safe and efficient treatment of CVD. Full article

Accurate upper-limb motor assessment is critical for post-stroke rehabilitation but relies on subjective clinical scales. This study proposes the Action-Aware Multimodal Wavelet Fusion Network (AMWFNet), integrating surface electromyography (sEMG) and robotic kinematics for automated Fugl-Meyer Assessment (FMA-UE)-aligned quantification. Continuous Wavelet Transform (CWT) converts heterogeneous signals into unified time-frequency scalograms. A learnable modality gating mechanism dynamically weights physiological and kinematic features, while action embeddings encode task contexts across 18 standardized reaching tasks. Validated on 40 participants (20 post-stroke, 20 healthy), AMWFNet achieved 94.68% accuracy in six-class classification, outperforming baselines by 9.17% (Random Forest: 85.51%, SVM: 85.30%, 1D-CNN: 91.21%). The lightweight architecture (1.27M parameters, 922ms inference) enables real-time assessment-training integration in rehabilitation robots, providing an objective, efficient solution. Full article

In constrained waters, multi-USV cooperative encirclement of highly maneuverable targets is strongly affected by partial observability as well as obstacle and boundary constraints, posing substantial challenges to stable cooperative control. Existing deep reinforcement learning methods often suffer from low exploration efficiency, pronounced policy oscillations, and difficulties in maintaining the desired encirclement geometry in complex environments. To address these challenges, this paper proposes an adaptive difference-based multi-agent policy gradient method (MAADPG) under the centralized training and decentralized execution (CTDE) paradigm. MAADPG deeply integrates potential-field-inspired geometric guidance with a multi-agent deterministic policy gradient framework. Specifically, a guidance module generates geometrically interpretable candidate actions for each pursuer. Moreover, a difference-driven adaptive action adoption mechanism is introduced at the behavior policy execution level, where guided actions and policy actions are locally compared and the guided action is adopted only when it yields a significantly positive return difference. This design enables MAADPG to select higher-quality interaction actions, improve exploration efficiency, and enhance policy stability. Experimental results demonstrate that MAADPG consistently achieves fast convergence, stable coordination, and reliable encirclement formation across representative pursuit–encirclement scenarios, including obstacle-free, sparsely obstructed, and densely obstructed environments, thereby validating its applicability and stability for multi-USV encirclement tasks in constrained waters. Full article