Search Results (13,648)

Background: The increasing prevalence of antifungal resistance among Candida albicans isolates necessitates the exploration of alternative antifungal strategies and novel molecular targets. Plant-derived bioactive compounds represent a promising source of antifungal agents; however, their chemical composition and mechanisms of action remain insufficiently characterized. Methods: Ethanolic extracts of Azadirachta indica leaves and Ficus benghalensis aerial roots were subjected to qualitative phytochemical screening, gas chromatography–mass spectrometry (GC–MS), and Fourier transform infrared (FTIR) analysis to characterize their bioactive constituents. Molecular docking studies were performed to evaluate the interaction of selected phytochemicals with C. albicans N-myristoyltransferase (PDB ID: 1IYK), a validated antifungal drug target. Results: Phytochemical screening revealed the presence of alkaloids, carbohydrates, and phenolic compounds in both extracts, while saponins were detected only in A. indica. GC–MS analysis demonstrated greater chemical diversity in A. indica, including fatty acids and phenolic derivatives, compared with F. benghalensis, which was dominated by terpenoids and long-chain hydrocarbons. FTIR analysis confirmed functional groups relevant to protein–ligand interactions. Molecular docking showed favorable binding interactions between several A. indica–derived compounds and N-myristoyltransferase, whereas compounds from F. benghalensis did not exhibit detectable binding under the conditions tested. Conclusions: The integrated phytochemical and in silico analyses provide insight into the antifungal potential of A. indica, suggesting possible interactions between identified phytochemicals and the selected drug target protein. This study highlights the value of combining chemical characterization with target-based computational screening in the rational exploration of plant-derived antifungal agents. Full article

Warfarin is a coumarin-derived oral anticoagulant widely used for the prevention and treatment of thromboembolic disorders, particularly in patients with mechanical heart valves. The drug exerts its anticoagulant effect by inhibiting vitamin K epoxide reductase, thereby impairing γ-carboxylation of vitamin K-dependent coagulation factors. Despite its clinical efficacy, warfarin therapy is associated with a narrow therapeutic index, substantial interindividual variability in dose response, numerous drug interactions, and significant hemorrhagic risk. Maternal warfarin therapy during pregnancy is strongly associated with fetal warfarin syndrome (FWS), a characteristic pattern of embryopathy resulting from in utero exposure to the drug. This review summarizes current knowledge regarding the physicochemical properties, pharmacological mechanisms, dose variability, toxicity, and developmental effects associated with warfarin exposure. Evidence from human clinical studies and vertebrate animal models is discussed to elucidate conserved developmental and molecular mechanisms underlying warfarin teratogenicity. The review also examines the signaling pathways disrupted by warfarin exposure, highlighting that its teratogenic effects extend beyond anticoagulation to the disruption of vitamin K-dependent developmental signaling. Inhibition of γ-glutamyl carboxylation, together with alterations in Gas6/TAM, PXR, Ras, and Wnt/β-catenin signaling pathways, impairs skeletal, vascular, and neural development, contributing to the characteristic abnormalities of fetal warfarin syndrome. Collectively, this review integrates clinical, molecular, and experimental findings to provide a comprehensive understanding of warfarin-induced developmental toxicity. Current knowledge is insufficient to fully elucidate the complex mechanisms underlying warfarin-induced embryopathy and fetal toxicity. Further investigations are warranted to identify safer anticoagulant regimens during pregnancy and to inform the development of novel therapeutic strategies that minimize fetal risk while maintaining maternal anticoagulation. Full article

Autonomous microrobot teams have significant potential for distributed drug delivery, cooperative vascular intervention, and parallelized biomedical diagnostics. However, coordinated control in cardiovascular environments remains challenging due to partial observability, limited communication bandwidth, and hydrodynamically coupled pulsatile blood flow. This paper introduces Decentralized Hemodynamic-Aware Multi-Agent Reinforcement Learning (DH-MARL), a distributed learning and control framework in which individual microrobots learn decentralized policies from local observations while graph-based attention mechanisms model inter-agent interactions during centralized training. The proposed framework integrates turbulence-aware adaptive exploration, reduced-order hydrodynamic interaction modeling, diffusion-based local communication, and hemodynamic-aware counterfactual credit assignment to improve cooperative learning and role specialization in dynamic vascular environments. A scalable Unity-based simulator supporting coupled pulsatile flow for up to 32 agents was developed for training and evaluation. Our experimentalresults cover four therapeutic scenarios: distributed drug delivery, cooperative clot dispersion, stenosis mapping, and vessel bottleneck traversal. For 16-agent teams, DH-MARL reaches an 88.7% team success rate. This performance exceeds independent single-agent controllers and centralized MAPPO baselines, and inter-robot collision rates remain below 4%. The learned policies generalize to unseen team sizes with minimal performance degradation, highlighting the scalability and robustness of the proposed decentralized control strategy. These simulation-level results demonstrate the feasibility of distributed reinforcement learning and graph-based coordination as a control paradigm for future multi-agent microrobot systems in biomedical environments. The results also provide a foundation for the calibration of subsequent microfluidic, ex vivo, and preclinical experiments. Full article

Background: Pharmacokinetic/pharmacodynamic (PK/PD)-guided intravenous anesthesia and target-controlled infusion (TCI) have progressively transformed modern anesthetic practice from empirical drug administration toward individualized, model-informed precision anesthesia. Recent advances in neuromonitoring, closed-loop systems, and artificial intelligence (AI) are further expanding this paradigm. Methods: This Perspective article provides a conceptual and educational synthesis of the historical foundations, mathematical principles, clinical applications, and AI-based perspectives of PK/PD-guided intravenous anesthesia and TCI. Results: The concentration–effect relationship derived from Hill’s equation represents the conceptual basis of modern anesthetic pharmacology. Contemporary TCI systems integrate PK/PD models, effect-site targeting, synergistic drug interactions, and multimodal monitoring to improve anesthetic precision and safety. Recent evidence supports the advantages of total intravenous anesthesia (TIVA) in postoperative recovery outcomes, including reduced postoperative nausea and vomiting, emergence delirium, and improved quality of recovery. Emerging developments include increasingly generalizable PK “supermodels,” adaptive closed-loop control systems, multimodal AI integration, and patient-specific digital twins that may eventually support simulation of physiological responses and optimization of drug administration. However, biological variability, monitoring limitations, signal artifacts, model uncertainty, and the need for prospective validation, regulatory oversight, and continuous clinician supervision remain major challenges to the routine implementation of AI-assisted individualized anesthesia. Conclusions: PK/PD-guided anesthesia and TCI increasingly represent a clinically relevant framework for precision anesthesia, integrating pharmacology, monitoring, adaptive control, and AI-assisted systems. Future developments may progressively reduce the discrepancy between predicted and observed clinical effects, moving anesthetic practice toward continuously adaptive, feedback-driven, and individualized anesthesia care. Full article

Isoflurane remains a cornerstone of clinical anaesthesia, valued for its robust pharmacological profile. However, its long-term neurological impact, specifically its potential to trigger cognitive impairment, remains a subject of intense debate. As a highly lipophilic agent that readily penetrates the blood–brain barrier, isoflurane exerts complex modulatory effects on various neurotransmitter systems and signaling cascades. While traditional paradigms have predominantly focused on isoflurane-induced neurotoxicity, characterised by neuronal apoptosis, oxidative stress and neuroinflammation, emerging evidence reveals a more nuanced, “Janus-faced” reality. Intriguingly, exposure to low doses during specific developmental stages has been shown to enhance cognitive function and consolidate memory. This suggests that a unidimensional toxicity model fails to capture the drug’s full biological spectrum. In this review, we propose a “dose–age-effect” model, a novel two-dimensional framework designed to reconcile these divergent findings. By categorising isoflurane’s impact across distinct developmental and mature neurobiological stages, we systematically synthesise high-quality evidence ranging from molecular signaling to behavioural phenotypes. We also examine the underlying mechanistic landscape and evaluate potential intervention strategies. This integrated perspective advances our theoretical understanding of anaesthetic–brain interactions and provides a roadmap for optimising clinical protocols and mitigating the risk of adverse postoperative cognitive outcomes. Full article

Levetiracetam (LEV) is an antiepileptic drug and is used in the management of various types of seizures and other clinical conditions, including anxiety and neuropathic pain. Its unique mechanism primarily involves selective interaction with synaptic vesicle protein 2A (SV2A), which stabilizes synaptic function and the release of neurotransmitters. The objective of the present study was to evaluate LEV activity in morphine (MPH) dependence. In mice, LEV was used at doses ranging from 31.25 to 125 mg/kg, depending on the experimental paradigm. It was studied in three schedules: (1) MPH tolerance to antinociceptive effects (the hot plate test—HPT); (2) MPH withdrawal signs, manifested as jumps, induced by naloxone (NAL)—2 mg/kg, i.p.; (3) MPH sensitization to locomotor activity. In this study, LEV reduced MPH tolerance to analgesic effects. Moreover, LEV diminished the intensity of NAL-precipitated MPH withdrawal signs. This drug was also effective in attenuating MPH-induced sensitization. These findings suggest that LEV may modulate behavioral adaptations associated with repeated MPH exposure. However, further mechanistic studies are required to identify the molecular and neurochemical pathways involved in these effects. Full article

The Short Tandem Repeat 3D Structure Database (STR3SD) is a web-based database that provides a comprehensive resource for structural biology research of short tandem repeats (STRs) in cancers and neurodegenerative diseases. STR3SD contains three-dimensional (3D) structures of STRs surveyed from literature. The data are organized into three main categories, including 3D structures of nucleic acids only, nucleic acids–protein complexes, and nucleic acids–ligand complexes. Under these categories, each entry is annotated with repeat type, molecular type (DNA or RNA), sequence, PDB ID, structural component, ligand name, structural determination method, experimental conditions (temperature, pH, and ion), PubMed ID, and interactive 3D structure view. The database is built on direct literature investigation by human experts and serves as a crucial tool for studying structures and functions of STRs in cancers and neurodegenerative diseases, supporting research on structural polymorphisms and pathogenic mechanisms of STRs, and facilitating drug design targeting STRs for disease therapy. Full article

Breast cancer is a highly heterogeneous disease characterized by distinct molecular subtypes, dynamic tumor–microenvironment interactions, and variable therapeutic responses. Despite the availability of multiple preclinical platforms, a major challenge remains the lack of a coherent multiscale framework capable of integrating biological complexity across experimental systems. This limitation reduces the predictive power of individual models and highlights the need for complementary strategies that reproduce disease progression across multiple biological scales. In this context, advanced imaging technologies have emerged as essential tools for linking preclinical platforms and enhancing their translational relevance. This review examines how multimodal imaging supports the integration of in vitro, ex vivo, and in vivo breast cancer models. We discuss how optical imaging, high-frequency ultrasound, magnetic resonance imaging, positron emission tomography/computed tomography, and intravital microscopy provide complementary molecular, functional, anatomical, and cellular information for the longitudinal assessment of tumor growth, metastatic dissemination, microenvironment remodeling, and therapeutic response. Particular attention is given to emerging translational workflows that combine patient-derived models with advanced imaging to investigate drug sensitivity, treatment resistance, and tumor progression within a precision oncology perspective. We also highlight the role of multimodal imaging in biomarker validation across platforms and in the development of clinically relevant preclinical pipelines. Overall, advanced imaging represents a critical translational bridge across breast cancer model systems, improving the predictive value of preclinical studies and supporting imaging-guided precision oncology from bench to bedside. Full article

Silybum marianum (SM) is a rich source of flavonolignans with promising antioxidant and antidiabetic properties; however, its therapeutic application is limited by poor stability and bioavailability. This study combined experimental and computational approaches to develop and evaluate SM-loaded chitosan nanoparticles (CS–SM nanoparticles). Microwave-assisted extraction followed by LC-MS/MS profiling identified eleven metabolites, including major flavonolignans characteristic of SM. Nanoparticles prepared by ionic gelation exhibited favorable physicochemical properties, including a particle size of 173–189 nm, a polydispersity index of 0.23, a zeta potential of +41.5 mV, an encapsulation efficiency of 98%, and a drug loading capacity of 50%, indicating the formation of a stable colloidal delivery system. CS–SM nanoparticles showed enhanced antioxidant, anti-inflammatory, and α-amylase inhibitory activities compared with crude extracts. The formulation exhibited an α-amylase IC₅₀ value of approximately 0.40 mg/mL and maintained low hemolytic activity, suggesting favorable preliminary biocompatibility. Molecular docking demonstrated favorable interactions of neosilyhermin A, silibinin, and silyhermin with α-amylase and α-glucosidase active sites. Short-timescale molecular dynamics simulations revealed ligand-dependent behavior within the chitosan–TPP matrix, indicating different release tendencies among the investigated flavonolignans. Overall, the results support CS–SM nanoparticles as a promising platform for the delivery of bioactive phytochemicals with antioxidant and antidiabetic potential. Full article

Background/Objectives: Digoxin, a positive inotropic agent with a narrow therapeutic window, is used in heart failure but carries toxicity risks. This study aims to characterize the profile of digoxin-associated adverse drug reactions reported in the EudraVigilance database, focusing on drug–drug interactions, dosing errors, and off-label use. Methods: Descriptive and disproportionality analyses were performed on Individual Case Safety Reports (ICSRs) until 25 January 2026. Digoxin was compared with other inotropic agents by calculating the Reporting Odds Ratio and 95% confidence intervals. Results: Digoxin recorded 11,426 ICSRs, with a predominance in patients aged 65–85 years (46.4%) and over 85 years (25.1%). Higher reporting probabilities were identified for the terms “Drug interaction” and “Overdose” compared to other inotropes. Fatal outcomes were reported in 19 cases related to drug interactions and 41 cases related to overdose. Consistent disproportionality signals were found for gastrointestinal, renal, and nervous system disorders. Off-label use was reported in 0.8% of cases and was associated with unfavorable clinical outcomes. Risks are exacerbated by polypharmacy and renal decline in elderly patients. Conclusions: Digoxin toxicity and cardiac complications are the primary drivers of reported morbidity. This risk profile highlights the need for the rigorous clinical monitoring, precise dose adjustments and heightened vigilance regarding drug–drug interactions. Full article

Hepatocellular carcinoma (HCC) is an aggressive liver cancer requiring reliable biomarkers, while current approaches are limited by high-dimensional data and complex nonlinear gene interactions. In this study, differentially expressed genes were identified and fed to a deep autoencoder to reduce dimensionality, capture nonlinear interactions, and extract informative latent features. Predictive gene features were selected through mutual information (MI) ranking and LASSO regression and subsequently evaluated using logistic regression (LR), random forest (RF), and support vector machine (SVM) classifiers with 5-fold cross-validation (CV). The top 50 genes underwent enrichment analyses. Protein–protein interaction (PPI) networks were constructed to identify hub genes, followed by gene–drug interaction, transcription factor analysis, and survival validation. Enrichment analysis highlighted critical pathways involved in metabolic, signaling, and cell-cycle, and viral carcinogenesis. CV showed stable and high performance across classifiers (accuracy = 0.969, F1 ≈ 0.97), with RF and SVM achieving the highest AUC values (~0.983 and ~0.982). Independent tests showed excellent performance, with RF achieving perfect performance (1.0) across all evaluation metrics, confirming high feature discriminative power. Survival analysis showed that hub genes, including HSP90AB1, TUBA1B, PKM, H2AZ1, YWHAZ, ACLY, RAN, ILF2, KPNA2, and TXNRD1, were significantly associated with poor prognosis (HR > 1.5, p < 0.05), correlating with reduced overall, relapse-free, and disease-specific survival in HCC. The integrative novel framework effectively identifies biologically relevant biomarkers, providing insights into HCC mechanisms and potential targets for precision therapy. Full article

Oral delivery of bioactive compounds in veterinary medicine offers important opportunities to improve animal health, productivity, and welfare, but its effectiveness is often limited by species-specific gastrointestinal physiology. This review aims to provide a comprehensive overview of microencapsulation strategies for oral veterinary delivery, focusing on species-specific gastrointestinal barriers and the formulation approaches developed to overcome them. Monogastric and ruminant animals present distinct gastrointestinal environments that compromise the stability, bioavailability, and therapeutic performance of orally administered compounds. In monogastrics, gastric acidity, digestive enzymes, gastrointestinal transit, and microbiota-mediated interactions represent major barriers, whereas in ruminants, ruminal fermentation, prolonged retention, and physicochemical conditions may cause premature degradation of bioactive compounds. These barriers significantly hinder the successful use of probiotics, enzymes, essential oils, nutrients, vaccines, and veterinary drugs. However, microencapsulation has emerged as a promising solution, providing a protective barrier that improves compound stability, enhances gastrointestinal survival, and enables controlled or site-specific release. By preserving bioactivity and modulating release kinetics, microencapsulation contributes to improved delivery efficiency and functional performance. A distinctive feature of this review is the integration of species-specific gastrointestinal physiology with microencapsulation technologies to provide a rational framework for designing oral delivery systems in veterinary medicine, rather than focusing solely on individual encapsulation technologies. Current challenges related to material selection, formulation optimisation, and industrial scalability are discussed. Overall, this review highlights that integrating gastrointestinal physiology with advanced microencapsulation technologies is essential for developing effective, targeted, and sustainable oral delivery systems, ultimately supporting improved animal health, productivity, and welfare. Full article

Background/Objectives: The pharmacological management of inflammation remains a clinical challenge, driving the demand for advanced topical formulations. This study utilizes a “smart-NADES” (natural deep eutectic solvent) paradigm to develop an innovative anti-inflammatory topical system. The aim was to evaluate the integrated antioxidant potential, alongside the in vitro and in vivo anti-inflammatory activities, of a novel smart-NADES licorice root extract and its hydrogel formulation. Methods: A NADES system composed of D-sorbitol and L-lactic acid (3:1) was employed for licorice root extraction. The antioxidant capacity was assessed using three independent assays and integrated via the Relative Antioxidant Capacity Index (RACI), while phytochemical interactions were quantified using the Chou–Talalai Combination Index (CI). In vitro anti-inflammatory activity was evaluated via protein stabilization capacity, and in vivo efficacy was validated using a formalin-induced paw edema model in mice. Results: The NADES extract contained glycyrrhizic acid levels of 6.3 ± 0.3 mg/g and showed strong antioxidant synergism in the DPPH assay (CI = 0.49 ± 0.07). The extract exhibited potent total antioxidant capacity (IC₅₀ = 11.9 ± 0.6 μg/mL) with a superior RACI score (1.23). In vitro protein stabilization (IC₅₀ = 63 ± 5 μg/mL) was comparable to diclofenac sodium. The 5% hydrogel numerically surpassed the commercial 2% diclofenac Emulgel (67.5% vs. 32.9% inhibition, respectively) at 24 h, although the direct pairwise comparison did not reach statistical significance (p = 0.186). Conclusions: The developed smart-NADES licorice hydrogel represents an effective, green formulation with pronounced topical anti-inflammatory properties, establishing a robust pharmacological rationale for advanced topical drug delivery. Full article

Background: Adsorption-based solidification is a solvent-free route to prepare a solid form of self-nanoemulsifying drug delivery systems (SNEDDS). However, the limited drug loading and the low bulk density of the porous carrier restrict its pharmaceutical applicability. This study developed a pH-modulated SNEDDS in which sodium carbonate modulates the pH of the formulation microenvironment. Atorvastatin calcium and glibenclamide were used as high-dose and low-dose weakly acidic model drugs. Methods: The SNEDDS components were selected by solubility and emulsification screening. Sodium carbonate was incorporated as the pH-modulating agent, and liquid formulations were solidified by adsorption onto Syloid. The formulations were characterized by FTIR, PXRD, and SEM, and evaluated by an in vitro dissolution study. Results: The selected liquid SNEDDS (L-SNEDDS) consisted of polysorbate 80, polyethylene glycol 400, and glyceryl monocaprylate. Sodium carbonate increased the microenvironmental pH from 5.31 to 6.83. This increased drug loading by approximately 2.0-fold for atorvastatin calcium and 3.0-fold for glibenclamide. FTIR showed no chemical interaction between the components. SEM confirmed adsorption within the porous carrier, whereas PXRD showed no detectable drug crystallinity. The increased loading reduced the number of capsules required per dose from two to one for atorvastatin calcium and from four to one for glibenclamide. In vitro dissolution confirmed that pH modulation did not compromise drug dissolution despite the reduced SNEDDS content per dose. Conclusions: pH modulation with sodium carbonate enabled single-capsule dosing and provided a solvent-free route to boost drug loading for the two investigated model drugs. Full article

Background/Objective: Polypharmacy, conventionally defined as the concurrent use of five or more medications, has emerged as a critical public health challenge in aging populations worldwide. Polypharmacy in older adults, driven by multimorbidity and age-related physiological changes, increases the risk of adverse drug reactions, drug interactions, falls, cognitive impairment, non-adherence, and preventable hospitalization. This study aims to provide a structured narrative synthesis of the current evidence on the incidence, risk factors, and clinical management of polypharmacy in elderly patients, with particular emphasis on deprescribing strategies, interdisciplinary care models, patient education, and digital clinical decision support technologies. Methods: A structured narrative literature review was conducted across PubMed, Scopus, and Web of Science using Boolean combinations of MeSH and free text terms including ‘polypharmacy,’ ‘elderly,’ ‘deprescribing,’ and ‘medication review.’ Eligible sources were peer-reviewed primary studies published between January 2016 and June 2026 that enrolled adults aged ≥65 years and reported validated prescribing review approaches, such as the STOPP/START criteria. The review drew on 40 primary studies spanning randomized controlled trials (RCTs) and observational, qualitative, and mixed-methods designs, which were interpreted and synthesized narratively across the principal thematic domains of polypharmacy management. Results: Polypharmacy was independently and consistently associated with DDIs, preventable hospitalizations, and functional decline across diverse clinical settings. Pharmacist-led medication reviews and interdisciplinary, team-based interventions produced the most robust improvements in prescribing appropriateness and meaningful reductions in potentially inappropriate medications (PIMs). Electronic clinical decision support systems (CDSSs) have demonstrated measurable benefits for safe deprescribing at scale, although usability limitations, incomplete workflow integration, and clinician resistance are significant implementation obstacles. Conclusions: Effective management of polypharmacy in older adults requires a multicomponent, patient-centered strategy that integrates evidence-based deprescribing algorithms, principally the STOPP/START criteria, sustained interdisciplinary collaboration, structured patient education, and technology-assisted decision support. Full article