Search Results (638)

Background: Epimedium is a natural herb with immunomodulatory potential, but its vaccine adjuvant properties remain poorly understood. Objective: The aim of this study was to elucidate the adjuvant effects of Epimedium and the underlying molecular mechanisms. Methods: Network pharmacology was used to identify bioactive compounds and targets of Epimedium from the TCMSP database, and immunomodulation-related targets from GeneCards and OMIM. PPI networks, KEGG/GO enrichment, molecular docking, and molecular dynamics (MD) simulations were performed. In vivo, female BALB/c mice were immunized with the Staphylococcus aureus (S. aureus) vaccine subunit HI antigen, either alone or with low- or high-dose icariin (ICA). Serum antibody responses (IgG, IgG1, IgG2a, IgG2b) were measured by ELISA. Survival against lethal S. aureus USA300 challenge was monitored. Results: Network pharmacology predicted 488 targets and 13 pathways. Core targets included IL6, TP53, EGFR, CTNNB1, HIF1A, HSP90AA1, JUN, MTOR, SRC, and AKT1. KEGG/GO analysis indicated involvement of T cell receptor and NOD-like receptor signaling pathways in inflammatory responses. Molecular docking and MD simulations confirmed stable ligand-target binding. Experimental validation showed that ICA significantly enhanced HI-specific antibody responses and induced a Th2-biased humoral immune response (IgG1/IgG2a ratio > 1), which is particularly relevant for vaccines targeting extracellular pathogens such as S. aureus. ICA also improved survival after lethal bacterial challenge. Conclusions: This study identifies potential bioactive compounds, core targets, and key pathways of Epimedium as a vaccine adjuvant. Experimentally, ICA, as a representative component, enhanced HI-specific antibody responses and conferred protection against lethal S. aureus challenge. Together, these findings offer a computational–experimental basis that may guide further mechanistic investigation. Full article

Background: Inflammatory bowel disease (IBD) is frequently complicated by secondary bone loss driven by chronic inflammation and gut–bone axis dysregulation. Although dried ginger has pharmacological activities relevant to intestinal inflammation, the effects of dried ginger milk extract (DGME), a lipophilic constituent-enriched preparation, on IBD-associated bone loss (IBD-BL) remain unknown. This study evaluated the preventive and therapeutic effects of DGME on IBD-BL and explored the underlying mechanisms. Methods: Mice with DSS-induced IBD-BL were treated with DGME (250, 125, or 62.5 mg/kg) or sulfasalazine. Colitis severity, bone microarchitecture, osteoclast activity and Th17 cells were assessed by histology, micro-computed tomography, histomorphometry and flow cytometric analysis. UHPLC-Q-TOF MS, network pharmacology, 16S rRNA sequencing, fecal metabolomics, and in vitro assays were used for mechanistic investigation. Results: DGME ameliorated colitis, improved trabecular bone microarchitecture, and reduced osteoclast-related bone destruction. These effects were associated with selective suppression of pathogenic bone marrow TNF-α⁺ Th17 cells and downregulation of Il17a, Rorc, Tnfα, Ccr2, Ccr6, Cxcr4, Csf1, and Tnfsf11. Compared with aqueous extract, DGME was enriched in 19 lipophilic constituents. Multi-omics analyses showed that DGME remodeled gut microbiota and metabolite profiles, characterized by enrichment of Lactobacillus, Anaerotruncus, vanillin, and spermidine. Both vanillin and spermidine suppressed Th17 effector genes and inhibited MEK/ERK signaling in vitro. Conclusions: DGME alleviated IBD-BL by suppressing pathogenic TNF-α⁺ Th17 responses and remodeling the gut microbiota–metabolite axis. This study not only extends the therapeutic application of dried ginger from intestinal inflammation to IBD-BL, but also identifies vanillin and spermidine as candidate functional mediators linked to MEK/ERK inhibition. Full article

Background: The combination of pharmacological therapy and exercise is frequently recommended for osteoporosis management; however, whether antiresorptive agents may interfere with exercise-induced bone adaptation remains unclear. This study aimed to investigate the independent and combined effects of zoledronate and treadmill exercise on bone microarchitecture and mechanical strength in an ovariectomized rat model. Methods: Twenty-four female Sprague Dawley rats underwent ovariectomy and were assigned to four groups: Control, zoledronate (ZA), treadmill exercise (T), and combined zoledronate and exercise (ZA + T). An additional sham-operated group was included. Zoledronate was administered as a single subcutaneous injection, and a 6-week treadmill exercise routine was implemented. Bone microarchitecture was assessed using micro-computed tomography, and a three-point bending test was employed for evaluation of mechanical properties. Results: The combined ZA + T group demonstrated significant improvements in trabecular bone parameters, including bone volume/tissue volume and trabecular number, compared with the Control group. Mechanical strength parameters, including maximum load and stiffness, were also significantly enhanced in the ZA + T group. Cortical bone parameters exhibited no significant changes. Conclusions: Treadmill exercise did not attenuate the effects of zoledronate, and may offer additive benefits in enhancing trabecular bone microarchitecture and mechanical strength. These findings suggest that exercise therapy can complement bisphosphonate treatment and contribute to optimizing therapeutic strategies for osteoporosis, supporting the potential utility of combined pharmacological and exercise-based interventions for improving bone health. Full article

The evaluation of the safety of chemical substances requires the identification of a safe dose, which has no adverse effects on humans. This is obtained through animal studies, with exposure prolonged for months. Repeated-dose toxicity is a term in toxicology and pharmacology referring to the highest tested dose of a substance, so-called No Observed Adverse Effect Level (NOAEL). Experimental data on NOAEL taken from the literature and the OpenFoodTox database (total n = 848). To speed up the processing of the enormous number of substances we are exposed to, in silico models are an attractive solution. Monte Carlo technique, incorporating the Las Vegas algorithm, was applied to develop models for repeated-dose toxicity in rats. Optimal descriptors were calculated using correlation weights for attributes of the Simplified Molecular Input Line Entry System (SMILES). Computational experiments were conducted 5 times, with splits obtained using the Las Vegas algorithm. Good predictive potential was observed for these models, with an average determination coefficient on the validation set of 0.77 ± 0.04. Full article

Background: In the last few decades, a growing body of evidence has confirmed that vestibular rehabilitation (VR) can improve the symptoms of many unilateral and bilateral vestibular disorders, by facilitating vestibular compensation mechanisms, such as adaptation, substitution, and habituation. However, the usefulness of the vestibular rehabilitation approach in Persistent Postural–Perceptual Dizziness (PPPD) is currently highly debated and unclear. The aim of the present study was to evaluate the efficacy of VR using computerized dynamic posturography in PPPD patients as a single treatment and without other associated psychological or pharmacological therapies. Results were compared with patients with unilateral and bilateral vestibular disfunction, in order to define the role of our rehabilitation model within a framework of personalized therapy for different disorders. Methods: We evaluated 44 patients (23 F, 21 M; ranged from 28 to 82 years; mean age 63.72) affected by unilateral vestibular vestibulopathy (UVP) (n = 19), bilateral vestibular vestibulopathy (BVP) (n = 10) and PPPD (n = 15). For each patient, a comprehensive clinical bedside vestibular assessment was carefully performed by expert clinicians, as well as Bithermal caloric tests with videonystagmography (VNG), Video Head Impulse Test (vHIT) and Computed Dynamic Posturography (CDP). The impact of dizziness on quality of life (QoL) was assessed by the Italian Dizziness Handicap Inventory (DHI). All subjects evaluated in this study underwent five rehabilitative therapy sessions in our centre, once a week for 45 min and exercised daily for 30 min at home. All the exercises progressively became more difficult each week. Results: Our study showed that vestibular rehabilitation improved quality of life and reduced the level of self-perceived handicap in patients affected by unilateral and bilateral vestibular dysfunction, with significant improvement in DHI total score and posturographic parameters. In PPPD patients, rehabilitation did not significantly modify posturographic performances and the improvement in total DHI score did not reach statistical significance, although a significant change was observed in the functional sub-score. Conclusions: Vestibular rehabilitation confirmed its effectiveness in unilateral and bilateral peripheral vestibulopathies. In patients with PPPD, rehabilitation performed with computerized dynamic posturography may reduce subjective handicap and improve some aspects of daily functioning, although the small sample size and the absence of a control group do not allow definitive conclusions about its efficacy. Full article

Respiratory syncytial virus (RSV) is a major pathogen responsible for severe lower respiratory tract infections in infants, the elderly, and immunocompromised individuals. Because the RSV F protein mediates viral entry and 15-lipoxygenase (15-LOX) amplifies virus-induced inflammatory responses, dual targeting of these proteins may provide both antiviral and anti-inflammatory benefits. In this study, we combined computational prediction with experimental validation to identify natural dual-target inhibitors from the Traditional Chinese Medicine Systems Pharmacology Database (TCMSP). A total of 13,131 natural compounds were screened by drug-likeness evaluation, molecular docking, ADME assessment, and molecular dynamics simulations, yielding 31 potential dual-target candidates with favorable drug-like properties. Among them, rhoeadine (MOL001473) maintained stable binding conformations with both targets throughout 100 ns simulations. In BEAS-2B cells, rhoeadine exhibited significant anti-RSV activity (EC50 = 1.82 µM), low cytotoxicity (IC50 = 34.50 µM), and a selectivity index (SI) of 18.97. Time-of-addition experiments suggested that rhoeadine primarily acts at the early stage of viral infection. Additionally, ELISA results indicated that rhoeadine significantly inhibited RSV-induced secretion of CCL5 and IL-6, highlighting its anti-inflammatory potential. In summary, this study identified rhoeadine as a promising natural compound with antiviral and anti-inflammatory activities against RSV. Computational analyses suggested its potential association with RSV F protein and 15-LOX, although direct target-level validation is still required. Full article

Introduction: Drug side effects are a relevant problem for patient safety and public health, and traditional methods have limitations in capturing complex patterns between clinical and pharmacological variables. Objective: To evaluate machine learning models to probabilistically predict multiple side effects associated with drug use. Materials and methods: A cross-sectional computational study was carried out with data from 1000 medications that included clinical condition, dosage and duration of treatment. Random Forest, Decision Tree, Support Vector Classifier and KNN were trained and optimized using Grid Search and an 80:20 split for training and testing. Chi-square tests and Principal Component Analysis were applied to explore associations and overlap between categories. Results: Significant associations were found between side effects and clinical condition (p < 0.05) and the drug administered (p < 0.05). The PCA showed a high overlap between categories, which justified a probabilistic approach. Tree-based models showed better performance (accuracy ≈ 0.35). Conclusions: Prediction of side effects is a multifactorial and non-deterministic problem; probabilistic machine learning models allow for estimating several plausible adverse events and can support clinical decision-making and pharmacovigilance. Full article

Background/Objectives: Neurodegenerative diseases like Alzheimer’s, Parkinson’s, and Amyotrophic lateral sclerosis (ALS) share common molecular pathways, including neuroinflammation and oxidative stress, which complicate the effectiveness of single-target treatments. Garcinia mangostana L. (mangosteen) has shown neuroprotective properties, but previous studies focused on lipophilic xanthones, which have poor bioavailability and uncertain blood–brain barrier permeability. Methods: In the current study, polar metabolites from G. mangostana peel aqueous extract (GMPE) were assessed for potential multi-target interactions via UHPLC-QTOF-MS-based metabolomics, systems pharmacology, and molecular docking analysis. Further, in silico ADMET screening and network-based analyses assessed for overlap between GMPE compounds and genes associated with neurodegeneration (AD, PD, ALS). Results: Analysis of genes linked to AD, PD, and ALS revealed 121 common molecular targets influenced by GMPE metabolites. Network and enrichment analyses indicated that the compounds derived from GMPE may be involved in common pathways related to oxidative stress, neuroinflammation, and neuronal survival. Molecular docking analyses suggest that selected metabolites are likely to exhibit moderate binding affinities to their respective protein targets. Conclusions: The results presented in this study provide evidence that GMPE may possess potential multi-target interactions within common neurodegenerative pathways. However, since the data are based on computational and predictive approaches, these results should be considered hypothesis-generating and warrant further experimental validation. Full article

Acute postoperative pain remains a major clinical challenge, affecting both recovery and resource utilisation. Beyond nociceptive input, pain is shaped by cognitive and emotional factors, including patient expectations. This narrative review examines the role of expectations in perioperative pain modulation, framed within predictive coding and Bayesian inference models. These models conceptualise pain as a probabilistic process that integrates sensory input with prior expectations, weighted by precision. In theory, positive expectations may enhance analgesic efficacy, whereas negative expectations may amplify pain via nocebo mechanisms. Control modifies expectations and may reduce perceived pain, while uncertainty diminishes these benefits. Evidence from observational studies links preoperative pain self-efficacy and anticipated pain scores to postoperative outcomes, yet interventional trials remain scarce. In this narrative review, we propose that expectation-sensitive strategies, including structured communication and computational modelling, may inform individualised anaesthesia and analgesia. Future research should validate these frameworks in clinical trials, optimise preoperative expectation management, and explore synergistic approaches that combine pharmacology with cognitive modulation. Understanding and leveraging expectations may offer a promising conceptual direction for more individualised perioperative care, although this approach remains hypothesis-generating at present. Full article

Familial thoracic aortic aneurysm and dissection (FTAAD), caused by the pathogenic Myh11 K1256del variant, is characterized by impaired aortic contractility; however, how reduced contractility predisposes the aorta to dissection remains incompletely understood. In this study, we performed a data-driven trans-omic upstream analysis using Genome Enhancer to identify key regulatory mechanisms in aortas from Myh11 K1256del mice under baseline conditions, without exposure to exogenous pathological stimuli. Transcriptome analysis revealed enrichment of genes related to smooth muscle contraction and regulation of myosin light chain phosphatase activity. Upstream computational analysis of regulatory regions identified nuclear factor of activated T cells 1 and lymphoid enhancer-binding factor 1 as major transcription factors, and further highlighted Rho-associated, coiled-coil-containing protein kinase 1 (ROCK1) as a predicted central regulator of the dysregulated transcriptional network. Druggability analysis suggested ROCK1 and the JunB proto-oncogene AP-1 transcription factor subunit as potential therapeutic targets. Furthermore, it predicted 51 candidate therapeutants, including atorvastatin, GSK-269962A, and atovaquone. These findings indicate that even in the absence of overt pathological stimulation, aortic tissue carrying the Myh11 K1256del variant exhibits a transcriptional program centered on ROCK signaling, which may prime the aorta for maladaptive responses to additional stress and may enhance susceptibility to dissection. This computational analysis requires experimental validation, but may provide a hypothesis-generating framework for development of preventive pharmacological interventions against FTAAD. Full article

Background: Genome-scale metabolic models (GEMs) are foundational tools for systems biology, enabling quantitative interrogation of human metabolism across physiological and pathological states. However, many legacy reconstructions exhibit heterogeneous identifier usage, incomplete pathway integration, and limited thermodynamic refinement, constraining reproducibility, interoperability, and translational applicability. Methods: We present EHMN 2026, an update of the Edinburgh Human Metabolic Network. The reconstruction was refined through systematic identifier reconciliation using MetaNetX and ChEBI mappings, duplicate reaction consolidation, thermodynamic directionality assessment, and structured pathway annotation via Reactome. The final model was encoded in Systems Biology Markup Language (SBML) Level 3 Version 2 with the Flux Balance Constraints (FBC2) package, ensuring explicit gene–protein–reaction (GPR) representation and compatibility with modern constraint-based modelling toolchains. Results: EHMN 2026 comprises 11 compartments, 14,321 metabolites (species), and 22,642 reactions, supported by 3996 gene products. Of all reactions, 9638 (42.6%) contain GPR associations, linking metabolic transformations to 2887 unique Ensembl gene identifiers (ENSG). Pathway integration yielded 2194 unique Reactome identifiers, providing structured pathway-level organisation of metabolic functions. Thermodynamic refinement reduced infeasible energy-generating cycles and improved reaction directionality coherence while preserving global network connectivity. The reconstruction is fully SBML-compliant and portable across major modelling platforms. Compared with Recon3D and Human1, EHMN 2026 uniquely combines native Reactome reaction-level annotation, systematic MetaNetX identifier harmonisation, documented thermodynamic cycle elimination (37 cycles, 0 remaining), and an 11-compartment architecture supporting organelle-specific modelling—features designed for QSP and multi-layer integration applications. Conclusions: EHMN 2026 delivers a rigorously harmonised, thermodynamically refined, and pathway-annotated human metabolic reconstruction with enhanced annotation depth and standards-based interoperability. By combining genome-scale coverage with structured gene and pathway integration, the model establishes a robust computational backbone for reproducible metabolic analysis and provides a scalable foundation for future multi-layer systems pharmacology and integrative modelling frameworks. Full article

Hydroxyurea (HU) is a ribonucleotide reductase inhibitor widely used for the treatment of sickle cell disease and myeloproliferative disorders, yet a precise nitric oxide (NO) synthase (NOS)-dependent mechanism remains incompletely defined. The role of NOS3 in HU-mediated proliferation, cell cycle, and apoptosis was analyzed in HEL92.1.7 erythroleukemic cells and primary mouse erythroid progenitors upon genetic knockdown/knockout and pharmacological NOS2/NOS3 inhibition. NOS3 expression, phosphorylation, NO and citrulline production, and protein nitrosylation were assessed via immunoblotting and biochemical assays. Computational docking and molecular dynamics simulations were performed to examine the interaction between HU and NOS3. HU enhanced NOS3 expression and phosphorylation, leading to increased NO and citrulline production. Computational analysis predicted HU binding within the NOS3 active site, whereas functional activation was AKT1-dependent. A biotin switch assay revealed cooperative NOS2-/NOS3-mediated protein nitrosylation under HU treatment. NOS3 depletion or inhibition abrogated HU-induced S-phase accumulation and restored cell proliferation. NOS3 protein depletion increased late apoptosis in erythroleukemic cells, while in murine erythroid cells, both Nos3 deficiency and inhibition decreased early and increased late apoptosis. NOS2 and NOS3 act as complementary mediators of proliferation and apoptosis, with NOS3 playing a distinct role in HU-induced proliferation arrest in erythroid cells. These findings highlight the therapeutic potential of NOS targeting to enhance the efficacy of HU and overcome resistance in hematologic malignancies. Full article

HIV latency, driven by a complex interplay of host factors, remains a key barrier to viral clearance. Current latency-reversing agents (LRAs) demonstrate limited efficacy and specificity, and none have been approved for clinical use. Although natural products have shown promise as LRAs, the therapeutic potential of fungal metabolites remains underexplored. Candida albicans, a prevalent human commensal and opportunistic pathogen, produces diverse secondary metabolites that can influence host pathways, affecting latency dynamics. This study aimed to investigate the latency-modulating potential of secondary metabolites of C. albicans using an integrative network pharmacology and computational pipeline. C. albicans secondary metabolites were retrieved from the literature, screened for drug-likeness, and mapped to human targets and biological pathways annotated in HIV latency. Key metabolites, hub genes, and pathways were systematically characterized through network and computational analyses. Six drug-like candidates, identified from 185 absorption, distribution, metabolism, excretion, and toxicity (ADMET)-screened metabolites, collectively mapped to 369 human genes with a 6.5% overlap in HIV latency (176 shared and 20 hub genes). These overlapping genes were significantly enriched for signal transduction, membrane localization, and adaptive responses to chemical stimuli. Kyoto encyclopedia of genes and genomes (KEGG) enrichment revealed oncogenic diseases (non-small cell lung, pancreatic, and prostate cancers) and latency-associated cascades, including PD-L1/PD-1, HIF-1, Ras, PI3K-Akt, calcium, and cAMP signaling. Six hub targets (MAPK1, PIK3CA, MAPK3, EGFR, MTOR, and AKT1) were consistently annotated within the top 30 KEGG pathways and displayed strong binding affinities for MET 15 and MET 119. Molecular dynamics (MD) simulations confirmed favorable binding free energies (BFEs) and stable conformational dynamics for the top-ranked metabolite MET 15. C. albicans secondary metabolites preferentially target oncogenic signaling networks central to HIV latency maintenance, notably PI3K/AKT/MTOR and MAPK/ERK, which regulate cell survival, metabolic homeostasis, and viral transcriptional repression. MET 15 is a top-ranked candidate metabolite for HIV latency-reversing therapeutics and warrants experimental validation in established latency models. Full article

Monkeypox virus (MPXV) is emerging as a global public health concern due to its nature of spread. There are limited treatment options, as the sole drug for treatment is lacking, highlighting the need for new therapeutic options. The use of computer-aided drugs discovery such as molecular docking, molecular dynamic (MD) simulations and post-simulation analysis are important tools in identifying potential compounds that can target specific proteins of the virus, such as DNA polymerase to stop virus replication. This study employed molecular docking and molecular simulation with the aim to identify potential inhibitors for MPXV treatment from the ZINC Database. Molecular docking was performed using PyRx 0.8 version after virtual screening of the ZINC database using the Tranches tool; then, toxicity prediction of the selected compounds was performed using the ProTox-3.0 web server. Molecular dynamics simulation was conducted using GROMACS version 4.5 to evaluate the structural stability and dynamic behavior of the protein–ligand complex for the best interacting compound. Furthermore, post-simulation analysis was conducted using standard GROMACS utilities for visualizing time-dependent properties from MD simulations. A total of 16 compounds were shortlisted based on their molecular docking scores and interaction profiles with the monkeypox virus DNA polymerase (PDB ID: 8HG1). The leading compound, ZINC000019418450, demonstrated strong binding affinity (−7.4 kcal/mol). According to post-simulation analysis, all top compounds formed between one and five hydrogen bonds and up to eleven hydrophobic contacts with residues within the active site, thus providing strong geometric and energetic evidence for binding stability. Notably, our identification of ZINC000104288636 as a Class 6 compound with an LD₅₀ of 23,000 mg/kg adds translational value by highlighting candidates with low predicted acute toxicity. Overall, this study lays a solid foundation for the rational design of next-generation monkeypox antiviral therapeutics. Future work is needed for experimental validation of prioritized compounds to assess their biochemical efficacy and pharmacological potential. Full article

The escalating antimicrobial resistance (AMR) crisis necessitates the development of innovative anti-infectives with novel mechanisms of action. Nevertheless, research on natural products remains constrained by low-throughput screening and limited mechanistic insights. Artificial intelligence (AI) is catalyzing a pivotal paradigm shift—from the mere isolation of active compounds to precisely deciphering their modes of action. This review highlights AI’s transformative role in bridging ethnopharmacological knowledge and modern pharmacology to decode the mechanisms of plant-derived anti-infectives. Case studies on berberine, baicalein, danshensu derivatives, and rosmarinic acid derivatives from Coleus amboinicus illustrate AI’s capacity to map traditional therapeutic concepts to specific pathways (e.g., biofilm inhibition, inflammasome modulation) and to predict precise binding interactions and pharmacophores with high precision. Leveraging statistical correlations between ethnobotanical usage patterns and chemical similarity, we propose a “Knowledge–Data–Mechanism” three-layer framework centered on deep mechanistic insight. Integrating Chinese initiatives, such as the CNDR (China’s National Drug Repository) database and the TCM-AI platform, with global traditional medicine wisdom, this strategy provides an actionable roadmap for modernizing anti-infective discovery. Validated applications of this paradigm have demonstrated order-of-magnitude acceleration in mechanistic characterization, rapidly yielding structurally novel agents with well-defined, target-specific actions—a critical advancement in addressing the urgent global threat of antimicrobial resistance. Full article