Search Results (80,981)

Ocular fungal diseases are associated with severe infection and pain and, in advanced stages, can lead to vision loss. Current treatment options are limited to the topical application of conventional drugs, and the bioavailability of these drugs is quite limited due to ocular barriers. In this study, a dual-drug nanodelivery system was developed to improve intraocular drug delivery by combining antifungal and anti-inflammatory therapies. Posaconazole (PSC), a broad-spectrum triazole antifungal agent, and dexketoprofen trometamol (DKP), a rapidly acting nonsteroidal anti-inflammatory drug, were co-loaded onto polymeric micelles and then incorporated into electrospun poly(vinyl alcohol)/poly(vinylpyrrolidone) (PVA/PVP) nanofiber intraocular implants. DSC, XRD, FTIR, and FESEM analyses showed that both APIs were successfully converted into nanofiber form without disrupting the micelle structure. Comparative studies with DKP solution and PSC commercial oral suspension (Noxafil^® 40 mg/mL) showed that the produced micelle-loaded nanofibers provided sustained release and significantly increased ex vivo ocular permeation and penetration. In vitro antifungal activity tests demonstrated efficacy against Candida albicans, and HET-CAM toxicity tests showed that the micelle-loaded nanofibers were non-irritating and suitable for ocular application. Overall, the micelle-loaded electrospun nanofiber ocular inserts developed in this study represent a promising platform for combined antifungal and anti-inflammatory ocular therapy. Full article

Due to the high water absorption of coal gangue aggregate, concrete prepared with a high content of this material exhibits a significantly reduced service life under freeze–thaw conditions. This study evaluates the frost resistance of gel-enhanced coal gangue aggregate concrete modified by incorporating nano-SiO₂ and polypropylene fibre (PPF) to generate more C-S-H gel and form a dense structure with different dosages of air-entraining agent (0, 0.004%, 0.008%, 0.012%, and 0.016%). The research results show that when the admixture content is 0.012%, the concrete still exhibits excellent frost resistance after 100 freeze–thaw cycles. The mass loss is only 4.7%, compressive strength loss is 37%, and dynamic elastic modulus loss is 39%, while the specimen maintains the best apparent integrity. In addition, the capillary water absorption rate, initial capillary water absorption rate, and cumulative water absorption all reach their lowest values under this condition, indicating optimal frost resistance performance. Finally, through regression analysis, a highly accurate predictive model for capillary water absorption was established, providing a theoretical basis for further research on the durability and frost resistance of coal gangue aggregate concrete. Full article

Although previous studies have considered sensing constraints and UAV dynamics, most of them have used unrealistic sensing limitations and simplified dynamic models. Thus, these approaches can suffer from a significant discrepancy between simulation results and real-world deployment. To address this issue, this study incorporates high-fidelity sensing constraints and UAV dynamics into a multi-agent reinforcement learning approach, focusing on the practical interplay between FOV limitations and pursuit strategies. First, the proposed reward considers the sensing constraints via a gaze-alignment reward, which varies with the field-of-view condition, and a capturability reward that encourages transitions toward a capturable region. Second, realistic UAV dynamics, including lateral motion, forward motion, and yawing, are modeled in a simulation environment to reduce the sim-to-real gap. Quantitative evaluations demonstrated that the proposed formulation significantly improved the capture performance under diverse sensing conditions. The capturability reward increases the capture success rate by 11.4%. When the maximum speed of the evading UAV was 2 m/s faster than that of the pursuing UAVs, all capture trials failed when lateral motion was not used. However, when lateral motion was enabled, the success rate increased to 99.2%, highlighting the need for lateral motion. Full article

Evaluating street interface morphology is essential for urban design, yet existing approaches often struggle to combine large-scale applicability with higher-level morphological interpretation. This study proposes a scalable framework for assessing street interface morphology using an automated multimodal large language model (MLLM) agent. Using street view imagery (SVI), the framework evaluates four core morphological dimensions—enclosure, continuity, transparency, and roughness–through two complementary analytical streams: objective geometric measurement and subjective morphological assessment. To support reliable evaluation, the framework incorporates a dual-benchmark strategy consisting of manually derived geometric measurements and expert-consensus ratings for calibration and validation. Applied in Shanghai, the framework demonstrated reliable performance across the evaluated dimensions. The optimized agent was further extended to continuous street-segment analysis, demonstrating its applicability to large-scale urban assessment. By integrating objective and subjective evaluation within a scalable and interpretable workflow, the proposed methodology provides a practical tool for street interface morphology analysis and urban design assessment. Full article

Background: Diabetic foot ulcers recur frequently after healing. The first three months carry the highest risk. Remission is a vulnerable phase that demands precise self-care and timely feedback. Evidence supports thermometry and protective footwear with gradual return to activity, yet adherence at home is inconsistent. Objective: To describe the design and planned evaluation of a conversational agent (chatbot) that guides patients through the remission phase following diabetic limb reconstruction. Methods: This protocol describes a conversational agent (chatbot) that turns remission guidance into daily actions, grounded in clinical expertise and established care guidelines. Walking is dosed like a drug, with careful titration based on tissue response. The agent integrates automatic data capture (smartphone step counts, skin temperature, shoe step streams, smartwatch step streams, Bluetooth thermometry when available, and app session timestamps) with manual patient entries (shoe wear time, skin redness persistence, and symptom checks). It doses walking activity, guides footwear break-in, prompts photo-confirmed concerns, following clinician-informed rules and escalation pathways. We define data quality checks for missingness and physiologic plausibility, and the agent reinforces reducing weight-bearing activity when risk signals appear. We outline device drift. The study is designed as a single-arm feasibility pilot (n = 30) to assess engagement, safety, and implementation fidelity. Results: No clinical outcome results are reported because this is a protocol study and enrollment has not yet begun. This study presents the prespecified sensing-to-decision workflow, escalation logic, and pilot endpoints, along with internal technical verification procedures (e.g., message delivery reliability, data completeness checks, and rule-engine consistency testing). Conclusions: A remission chatbot is a plausible method to extend specialist support into the home, reflecting integration of clinical expertise with digital health tools. This protocol defines how feasibility, safety, and usability will be evaluated. Clinical efficacy should be confirmed in future studies. Full article

Taxanes, such as paclitaxel and docetaxel, are microtubule-stabilizing agents widely used in oncology, either as monotherapy or in combination regimens. While highly effective, these first-generation taxanes face important limitations, including significant toxicity, reduced water solubility, and the emergence of multidrug resistance. To address these challenges, semi-synthetic taxoids have been developed, aiming to improve pharmacological profiles and overcome therapeutic barriers. Central to these efforts is the understanding of structure-activity relationships, which guides the rational design of taxane analogues with enhanced efficacy and safety. This review explores recent advances in taxoid development, highlights findings from clinical trials, and evaluates how these new agents compare with traditional taxanes in terms of therapeutic potential and tolerability. While novel delivery systems offer improved outcomes with existing drugs, the development of new taxane analogues remains a promising approach to address drug resistance, albeit with challenges related to toxicity, high costs, and historically low success rates in drug development. Furthermore, taxanes are already used in certain cardiovascular conditions and show emerging potential in neurodegenerative diseases, although current evidence remains largely limited to preclinical or early-phase clinical studies. These developments mark an important evolution in the field and offer new opportunities for future therapeutic strategies. Full article

With the rapid development of e-commerce and smart manufacturing, automated warehouse systems have become critical infrastructure for modern logistics. In China’s vast market, the dynamic scheduling of Rail-Guided Vehicles (RGVs) faces significant challenges due to complex task uncertainties, hierarchical supply chain structures, and real-time collision avoidance requirements. Traditional rule-based methods and static optimization models often fail to adapt to such dynamic environments. To address these issues, this paper proposes a novel hybrid deep reinforcement learning framework integrating a Dynamic Graph Neural Network (DGNN) and a Transformer model. The DGNN captures the spatiotemporal dependencies of the warehouse network topology, while the Transformer mechanism enhances long-range feature extraction for task prioritization. Furthermore, we design a centralized Deep Q-network (DQN) framework with parameterized action spaces to coordinate multiple RGVs collaboratively. While the system manages multiple physical vehicles, the learning architecture employs a single-agent global scheduler to avoid the non-stationarity issues inherent in multi-agent reinforcement learning. Experimental results based on real-world data from a large-scale electronics manufacturing warehouse demonstrate that our method reduces average task completion time by 18.5% and improves system throughput by 22.3% compared to state-of-the-art baselines. The proposed approach demonstrates potential for intelligent warehouse management in dynamic industrial scenarios. Full article

Thirty 2-methyl-quinazolinone fussed hydroxamic acids (3-OH) and their 3-OEt and 3-OBn derivatives were evaluated for their affinity towards calf-thymus (CT) DNA using UV-vis absorption, viscosity and fluorescence spectroscopy. DNA photocleavage activity was assessed by incubating the compounds with plasmid DNA followed by UV-A and visible light irradiation, which enabled identification of the most potent derivatives active at concentrations of 100 nΜ and 10 μΜ, respectively. Mechanistic studies on the most active compounds indicated the formation of oxygen radical species and a decrease in efficiency under argon. Measurements of singlet oxygen release verified these findings. Molecular docking studies provided further insight into the interactions between the compounds and DNA. UV-A irradiation of the most potent DNA photocleavers in three cell lines, two malignant melanoma lines (A375 and COLO-800) and the immortalized keratinocyte line HaCaT, identified three derivatives that, at a concentration up to 10 μΜ, reduced cell viability by approximately 50%. Taken together, these results indicate that these 2-methylquinazolinone-based hydroxamic acid derivatives are promising candidates for the development of photodynamic therapy agents. Full article

While l-tryptophan is a precursor of plant growth regulators, its effects on secondary metabolism, amino acid profile and cell wall organization in flax callus remain underexplored. This study aimed to optimize flax callus shaken cultures and evaluate the impact of l-tryptophan (0.1 mM and 1 mM) on structural properties of plant cell walls in tested callus using Fourier transform infrared spectroscopy. The impact of l-tryptophan on callus proliferation and metabolism was also determined, because amino acids (among them l-tryptophan) can promote the growth of callus. The results showed that 1 mM l-tryptophan is an effective elicitor, which stimulates flax callus to accumulate larger amounts of bioactive compounds, especially carotenoids and polyphenols, than control callus cultured without l-tryptophan. A lower concentration of l-tryptophan (0.1 mM) slightly improved the level of determined secondary metabolites (except flavonoids). The effect of l-tryptophan on polymers in plant cell walls was investigated. The data confirm that the plant cell wall is a dynamic structure, capable of remodelling in response to growth conditions and external agents. l-tryptophan (0.1 and 1 mM) reduced cellulose levels and induced structural changes in cellulose compared to the untreated control. The structural analyses also suggested a decrease in lignin level and increase in pectin amounts in flax callus after tryptophan addition in comparison to control callus. The results may reflect the relationship between tryptophan and auxins (which are derived from tryptophan) and confirm the role of these metabolites in shaping the structure of the plant cell wall. In fact, an increase in tryptophan level was confirmed in flax callus in tested experimental conditions (supplementation of cultures with both doses of l-tryptophan). These findings have practical significance, because l-tryptophan is also used as a fertilizer or component of fertilizers in plant cultivation. Full article

Fungal extracts have garnered considerable attention in recent years due to their diverse pharmaceutical potential. The present study investigates the secondary metabolite profile and biological activities of Alternaria citri, a fungal strain associated with citrus fruits. Metabolites were extracted from A. citri grown in Potato Dextrose Broth (PDB) using ethyl acetate and subsequently evaluated for antimicrobial, antioxidant, and cytotoxic activities, alongside gas chromatography–mass spectrometry (GC–MS) profiling. GC–MS analysis identified 14 bioactive compounds in the fungal extract. The extract exhibited antimicrobial activity against Aspergillus flavus, Trichoderma hamatum, Staphylococcus aureus, and Escherichia coli. Moderate total phenolic and flavonoid contents were observed, which correlated with concentration-dependent antioxidant activity as determined by the DPPH assay. Cytotoxic evaluation using NIH/3T3 cells demonstrated potential anticancer activity, with an IC₅₀ value of 126.63 µg/mL. A. citri is an interesting source of bioactive metabolites with potential therapeutic applications. These findings further strengthen the evidence that Alternaria species can serve as promising sources of natural antioxidants and antimicrobials, thereby supporting their potential applications in pharmaceutical and biomedical formulations. This study expands current knowledge of fungal metabolite diversity and establishes A. citri as a potential source of novel therapeutic agents. Full article

Background: Neuromyelitis optica spectrum disease (NMOSD) is a severe and highly disabling autoimmune astrocytopathy in which humoral immunity, mediated by the presence of autoantibodies, and cellular immunity, through Th17 cells and related cytokines, are key contributors to the pathogenesis. This neuroglial disease affects the central nervous system and is predominantly described in the young productive population. For many years, NMOSD treatment lacked disease-specific therapies and relied on conventional immunosuppressive agents. Progress in elucidating underlying mechanisms of the disease has led to the development and approval of highly specific and effective pathology-modifying drugs. Objective: The objective of this paper is to analyze current and emerging monoclonal antibody-based therapies for NMOSD. Methods: A systematic review of the literature was conducted focusing on approved and investigational monoclonal antibodies targeting major immunopathogenic pathways in NMOSD. Both long-term maintenance therapies and treatments for acute relapses were considered. Results: Targeted monoclonal antibody therapies have significantly transformed the therapeutic management of NMOSD. Drugs directed at B-cell depletion, IL-6 receptor inhibition, and complement blockade have demonstrated substantial efficacy in reducing relapse rates and improving clinical outcomes. Emerging therapies and biomolecular engineering represent promising strategies aimed at further modulating disease activity. These treatments offer improved specificity compared with traditional immunosuppressive regimens and contribute to better long-term disease control. Conclusions: The growing understanding of NMOSD immunopathogenesis has led to the development of highly specific monoclonal antibody-based therapies that have substantially redefined long-term maintenance strategies. Emerging biological targets may expand future therapeutic options. Continued research is essential to optimize individualized treatment approaches and improve outcomes for patients with NMOSD. Full article

While artemisinin and its derivatives demonstrate broad antitumor potential, the stereochemical influence on the bioactivity of higher-order artemisinin assemblies remains inadequately characterized. Herein, we report the synthesis, chromatographic separation, and structural elucidation of four stereoisomeric artemisinin trimers, followed by systematic evaluation of their antitumor efficacy against MCF-7 and MDA-MB-231 breast cancer cell lines. All trimers exhibited potent cytotoxicity against MCF-7 cells (IC₅₀ < 0.09 μM), with trimer 6a (β, β, β) demonstrating robust antitumor activity in both in vitro and in vivo xenograft models. Remarkably, pronounced stereochemistry-dependent activity emerged against MDA-MB-231 cells: 6a displayed approximately 100-fold greater potency than 6b (β, β, α) and 6.6-fold superiority over gemcitabine. Mechanistic investigations revealed that 6a downregulates Cyclin D1, CDK4, and CDK6 expression, thereby inducing G0/G1 phase cell cycle arrest. These findings underscore the pivotal role of stereochemical configuration in modulating artemisinin trimer bioactivity and provide rational guidance for structure-based design of artemisinin-derived anticancer therapeutics. Full article

Background/Objectives: Epirubicin, Olaparib, and Ribociclib are widely used anticancer agents whose serum concentrations exhibit significant inter-individual variability, supporting the need for reliable and robust analytical methods suitable for pharmacokinetic evaluation and therapeutic exposure assessment. Variations in metabolism, drug–drug interactions, organ function, and treatment regimens may substantially influence systemic exposure, highlighting the importance of accurate quantification in clinical practice. This study describes the development and validation of a solid-phase extraction–liquid chromatography–mass spectrometry (SPE–LC–MS) method for the simultaneous quantification of these drugs in human serum. Methods: Sample preparation was performed using Oasis PRiME HLB^® cartridges to ensure efficient clean-up, optimal recovery, and reduced matrix effects. Chromatographic separation was achieved using gradient elution with 0.1% formic acid and acetonitrile on a reversed-phase column, followed by single-quadrupole mass spectrometric (QDa) detection in the selected ion recording mode. The total run time was 13 min, enabling high-throughput analysis. Results: The method demonstrated good linearity (r > 0.997) over the tested concentration ranges, along with adequate selectivity, precision, accuracy, recovery, and stability, fulfilling the ICH M10 guideline validation criteria. No significant carry-over or interference from endogenous compounds was observed. Conclusions: Application to patient samples confirmed reliable performance in real clinical matrices and consistent quantification across different concentration levels. The proposed approach provides a potentially more accessible alternative in laboratories already equipped with LC-MS systems compared to LC-MS/MS platforms and can be applied in pharmacokinetic studies, representing a proof-of-concept for exposure assessment in oncology. Full article

High-availability (HA) systems—essential in many contemporary contexts—are designed to guarantee the availability of processes and data for more than 99% of their operational time. These systems are typically implemented as Cloud/Edge infrastructures that are properly maintained by human operators and intelligent agents in order to guarantee the required level of availability. Moreover, we are witnessing the widespread adoption of AI-based automation across many industries. AI-based software agents are increasingly being adopted to introduce more automation in highly available systems, particularly for monitoring and fault detection, fault prediction, recovery, and optimization processes. In this review paper, we discuss the state of the art of AI-based solutions for HA systems. In particular, we focus on the use of AI for the core operational mechanisms of monitoring, failure detection, and recovery. Our discussion begins by reviewing a few key background concepts of HA architectures, then we review recent work on AI-based solutions for monitoring, fault detection and recovery in HA systems. Full article

High-quality training data are essential for the performance and generalization of artificial intelligence systems, particularly in dynamic environments such as adaptive stream processing for disaster response. However, constructing large and representative datasets remains costly and time-consuming, especially in domains where real data are scarce or difficult to obtain. Large Language Models (LLMs) provide powerful capabilities for synthetic text generation, yet the quality of generated data strongly depends on the design of input prompts. Prompt engineering is therefore critical, but it remains largely manual and difficult to scale, particularly in black-box settings where model internals are inaccessible. This work introduces EVOLMD-MO, a multi-objective evolutionary framework for automated prompt learning aimed at generating high-quality synthetic text datasets using black-box LLMs. The proposed approach formulates prompt optimization as a multi-objective search problem in which candidate prompts evolve through genetic operators guided by two complementary objectives: semantic fidelity to reference data and generative diversity of the produced samples. To support scalable optimization, the framework integrates a modular multi-agent architecture that decouples prompt evolution, LLM interaction, and evaluation mechanisms. The evolutionary process is implemented using the NSGA-II algorithm, enabling the discovery of diverse Pareto-optimal prompts that balance semantic preservation and diversity. Experimental evaluation using large-scale disaster-related social media data demonstrates that the proposed approach consistently improves prompt quality across generations while maintaining a stable trade-off between fidelity and diversity. Compared with a single-objective baseline, EVOLMD-MO explores a significantly broader semantic search space and produces more diverse yet semantically coherent synthetic datasets. These results indicate that multi-objective evolutionary prompt learning constitutes a promising strategy for black-box LLM-driven data generation, with potential applicability to adaptive data analytics and real-time decision-support systems in highly dynamic environments, pending broader validation across domains and models. Full article