Search Results (21,187)

Raman spectroscopy is a rapid, label-free, and non-destructive technique for probing molecular structures, making it a powerful tool for clinical pathogen identification. However, interpreting its complex spectral data remains challenging. In this study, we evaluate and compare a suite of machine learning models—including Support Vector Machines (SVM), XGBoost, LightGBM, Random Forests, k-nearest Neighbors (k-NN), Convolutional Neural Networks (CNNs), and fully connected Neural Networks—with and without Principal Component Analysis (PCA) for dimensionality reduction. Using Raman spectral data from 30 clinically important bacterial and fungal species that collectively account for over 90% of human infections in hospital settings, we conducted rigorous hyperparameter tuning and assessed model performance based on accuracy, precision, recall, and F1-score. The SVM with an RBF kernel combined with PCA emerged as the top-performing model, achieving the highest accuracy (0.9454) and F1-score (0.9454). Ensemble methods such as LightGBM and XGBoost also demonstrated strong performance, while CNNs provided competitive results among deep learning approaches. Importantly, interpretability was achieved via SHAP (Shapley Additive exPlanations), which identified class-specific Raman wavenumber regions critical to prediction. These interpretable insights, combined with strong classification performance, underscore the potential of explainable AI-driven Raman analysis to accelerate clinical microbiology diagnostics, optimize antimicrobial therapy, and improve patient outcomes. Full article

The ultrasound-assisted extraction (UAE) method was optimized to extract bioactive compounds from Licaria armeniaca tissues. Extraction time, solid–liquid ratio (m/v), and ethanol percentage were investigated using a central composite rotational design and response surface methodology (RSM). Antioxidant activity (DPPH) and total phenolic content (TPC) served as the response variables. Most efficient extraction conditions were obtained for leaves (64.88% ethanol, 26.07 min, 6.23% m/v; R² = 0.93) and thin branches (73.81% ethanol, 31.34 min, 11% m/v; R² = 0.74). For thick branches, no significant predictive model was obtained, and optimal points were defined based on the best observed TPC and DPPH results (50% ethanol, 35 min, 11% m/v). The optimized extracts were analyzed by liquid chromatography–tandem mass spectrometry associated with molecular networking, GNPS (Global Natural Products Social Molecular Network) library searching, and machine learning tools. Metabolomic profiling indicated that leaves contained mainly alkaloids (46.34%), amino acids and peptides (19.51%), and shikimate derivatives and phenylpropanoids (12.20%). Thin branches showed predominance of alkaloids (35.97%), amino acids and peptides (20.86%), and carbohydrates (12.23%), while thick branches contained alkaloids (46.34%), amino acids and peptides (25.00%), and fatty acids (14.26%). Additionally, the extracts displayed significant cytotoxic activity against cancer cell lines of AGP-01 (malignant gastric ascites), AHOL (Human glioblastoma) and A549 (lung cancer) with IC50 values less than 50 μg/mL. Full article

The classification of mucopolysaccharidoses (MPSs) includes the classical types (I; II; III with subtypes A, B, C, and D; IV with subtypes A and B; VI; VII; IX; X), associated with impaired lysosomal degradation of mucopolysaccharides, also known as glycosaminoglycans (GAGs), as a result of deficiency in the specific enzymes responsible for GAG degradation (MPS IIIE has so far been identified only in animal models) and MPS-plus syndrome (MPSPS), which is characterized by an accumulation of undegraded GAGs, arising from impaired endosomal trafficking and inefficient delivery of these compounds to lysosomes (due to the VPS33A protein deficiency with normal GAG-degrading enzyme activities assessed in vitro). The aim of this comprehensive review is to provide physicians with a clinical, biochemical, and molecular overview of MPS manifestation. A brief summary of available and emerging therapies is also presented. Full article

Glioblastoma (GBM) is the most common and aggressive malignant brain tumor in adults. This review explains the connections between the genesis and progression of GBM and particular cellular tumorigenic mechanisms, such as angiogenesis, invasion, migration, growth factor overexpression, genetic instability, and apoptotic disorders, as well as possible therapeutic targets that help predict the course of the disease. Glioblastoma multiforme (GBM) diagnosis relies heavily on histopathological features, molecular markers, extracellular vesicles, neuroimaging, and biofluid-based glial tumor identification. In order to improve miRNA stability and stop the proliferation of cancer cells, nanoparticles, magnetic nanoparticles, contrast agents, gold nanoparticles, and nanoprobes are being created for use in cancer treatments, neuroimaging, and biopsy. Targeted nanoparticles can boost the strength of an MRI signal by about 28–50% when compared to healthy tissue or controls in a preclinical model like mouse lymph node metastasis. Combining the investigation of CNAs and noncoding RNAs with deep learning-driven global profiling of genes, proteins, RNAs, miRNAs, and metabolites presents exciting opportunities for creating new diagnostic markers for malignancies of the central nervous system. Artificial intelligence (AI) advances precision medicine and cancer treatment by enabling the real-time analysis of complex biological and clinical data through wearable sensors and nanosensors; optimizing drug dosages, nanomaterial design, and treatment plans; and accelerating the development of nanomedicine through high-throughput testing and predictive modeling. Full article

Background: Degenerative, metabolic and oncologic diseases are scarcely amenable to the complete reconstruction of tissue structure and functionalities using common therapeutic modalities. On the nanoscale, extracellular vesicles (EVs) and nanoparticles (NPs) have emerged as attractive candidates in regenerative and personalised medicine. However, EV transfection is hindered by its heterogeneity and low yield, while NPs suffer from cytotoxicity, immunogenicity, and long-term safety issues. Scope of Review: This review synthesises data from over 180 studies as part of a narrative synthesis, critically evaluating the disease-specific utility, mechanistic insights, and translational obstacles. The focus is laid on comparative cytotoxicity profiles, the capacities of hybrid EV–NP systems to circumvent mutual shortcomings, and the increasing impact of artificial intelligence (AI) on predictive modelling, as well as toxicity appraisal and manufacturing. Key Insights: EVs have inherent biocompatibility, immune evasive and organotropic signalling functions; NPs present structural flexibility, adjustable physicochemical properties, and industrial scalability. Common molecular pathways for NP toxicity, such as ROS production, MAPK and JAK/STAT activation, autophagy, and apoptosis, are significant biomarkers for regulatory platforms. Nanotechnological and biomimetic nanocarriers incorporate biological tropism with engineering control to enhance therapeutic efficacy, as well as their translational potential. AI approaches can support rational drug design, promote reproducibility across laboratories, and meet safe-by-design requirements. Conclusions: The intersection of EVs, NPs and AI signifies a turning point in regenerative nanomedicine. To advance this field, there is a need for convergence on experimental protocols, the adoption of mechanistic biomarkers, and regulatory alignment to ensure reproducibility and clinical competence. If realised, these endeavours will not only transition nanoscale medicament design from experimental constructs into reliable and patient-specific tools for clinical trials, but we also have the strong expectation that they could revolutionise future treatments of challenging human disorders. Full article

Bottle-brush polymers with aggrecan-like side chains represent a class of biomimetic macromolecules that replicate key structural and functional features of natural complexes of aggrecans with hyaluronic acid (HA) which are the major components of articular cartilage. In this study, we employ numerical self-consistent field (SCF) modeling combined with analytical theory to investigate the conformational properties of cylindrical molecular bottle-brushes composed of aggrecan-like double-comb side chains tethered to the main chain (the backbone of the bottle-brush). We demonstrate that the architecture of the brush-forming double-comb chains and, in particular, the distribution of polymer mass between the root and peripheral domains significantly influences the spatial distribution of primary side chain ends, leading to formation of a “dead” zone near the backbone of the bottle-brush and non-uniform density profiles. The axial stretching force imposed by grafted double-combs in the main chain, as well as normal force acting at the junction point between the bottle-brush backbone and the double-comb side chain are shown to depend strongly on the side-chain architecture. Furthermore, we analyze the induced bending rigidity and persistence length of the bottle-brush, revealing that while the overall scaling behavior follows established power laws, the internal structure can be finely tuned without altering the backbone stiffness. These theoretical findings provide valuable insights into relations between architecture and properties of bottle-brush-like supra-biomolecular structures, such as aggrecan-hyaluronan complexes. Full article

Brain tumors are unfortunately the most common types of solid tumors in the pediatric population, superseded only by leukemias, and largely bode a poor prognosis. Despite advances in our ability to diagnose and treat pediatric brain tumors, there remains a large unmet need to develop novel therapies to improve patient outcomes. The recent understanding of the molecular drivers of oncogenesis for many of these tumors has led to the engineering of preclinical small animal models which serve as valuable tools for scientists to study the mechanisms of tumor biology, to understand interactions with the tumor microenvironment, and allow for translatable novel therapeutic discovery. This review focuses on the state-of-the art development of preclinical models of two difficult-to-treat pediatric brain tumors: (1) diffuse midline gliomas, the most lethal form of pediatric brain cancer; (2) medulloblastoma, the most common embryonal tumor of the central nervous system. We will then round off this review with a discussion on the emerging use of multi-omics and AI approaches to complement the testing of novel therapies using these in vivo animal models. Full article

Extracellular vesicles (EVs) play a crucial role in cell-to-cell communication by transporting functionally active molecules, including proteins, lipids, and nucleic acids. While extensive research has focused on EVs generated from traditional two-dimensional (2D) monolayer cultures (2D-EVs), the emergence of three-dimensional (3D) organoid systems has led to the development of organoid-derived EVs (OEVs), which more closely mimic the physiological conditions of native tissues. In contrast to 2D cultures, 3D systems offer improved EV yield and cargo specificity, enhancing their translational potential. This review discusses the distinctive features of OEVs, including their enhanced tissue relevance, diverse molecular composition, and promising therapeutic applications in areas like disease modeling, regenerative therapies, and targeted drug delivery. We also present an overview of the current organoid-based platforms used to produce OEVs, recent innovations in EV modification and bioengineering, and the practical barriers to their clinical adoption. By comparing the strengths and limitations of OEVs with those of 2D-EVs, we provide a comprehensive perspective on their future role in precision healthcare, biomarker identification, and advanced therapeutic strategies. Full article

A three-dimensional quantitative structure-activity relationship (3D-QSAR) analysis incorporating ligand-receptor docking alignment and molecular dynamic (MD) simulations was conducted to elucidate the potent inhibitory effects of a series of benzamide derivatives on histone deacetylase 1 (HDAC1). A comparison between ligand-based (LB) and receptor-based (RB) 3D-QSAR models using molecular docking alignment produced statistically significant results. Steric and electrostatic contour maps provided insights into the interactions surrounding the benzamide ring, revealing that an increase in electron density enhances inhibitory activity. Furthermore, MD simulations were employed to investigate protein-ligand interactions in greater detail, yielding outcomes consistent with those from 3D-QSAR and molecular docking studies. This integrated approach of molecular docking, 3D-QSAR, and energy decomposition analysis derived from MD simulations, provides a valuable framework for the rational design of more potent HDAC1 inhibitors, facilitating the synthesis of highly effective anti-tumor compounds based on benzamide scaffolds. Full article

Peripheral edema is a pathological condition caused by abnormal fluid accumulation in the interstitial space due to elevated vascular permeability and inflammation. This study evaluated the therapeutic efficacy of Terminalia chebula fruit extract (TCE) in inflammation-induced peripheral edema and clarified its molecular mechanisms. Using hydrogen peroxide (H₂O₂)-stimulated human umbilical vein endothelial cells (HUVECs), TCE was tested for effects on cell viability, inflammatory gene expression, intracellular reactive oxygen species, endothelial barrier integrity, and vascular endothelial growth factor (VEGF)-induced migration. Its influence on nuclear factor kappa-light-chain-enhancer of activated B cells (NF-κB) and mitogen-activated protein kinase (MAPK) signaling was examined. In vivo, TCE was assessed in acetic acid-induced peritoneal vascular permeability and carrageenan-induced paw edema models, followed by histological analysis and serum tumor necrosis factor-α (TNF-α) measurement. TCE restored cell viability (76.2% to 94.8%), reduced TNF, IL6, and PTGS2 mRNA expression, and decreased reactive oxygen species by 27.2%. It enhanced barrier integrity, increased transendothelial electrical resistance, and inhibited VEGF-induced migration. TCE suppressed NF-κB and MAPK activation. In vivo, TCE reduced Evans blue extravasation by 41.6% and paw edema by 67.5%. Histology showed reduced dermal thickening and inflammatory infiltration, and serum TNF-α levels were lowered. TCE attenuates peripheral edema by preserving endothelial barrier function and suppressing inflammatory signaling, supporting its potential as a therapeutic agent for inflammation-associated vascular dysfunction and edema. Full article

Astragalus aitosensis, also known as Astracantha arnacantha (M. Bieb.) Podlech subsp. aitosensis (Ivanisch.) Réer & Podlech, a Bulgarian endemic species, was investigated for its phenolic profile and neuroprotective potential. A targeted extraction approach led to the isolation of 14 phytochemicals. According to our literature review, none of the isolated chemicals have been reported before for A. aitosensis. Two of them are previously undescribed molecules—an isomer of odoratin and 6-hydroxy-3-(2-hydroxy-4-methoxyphenyl)-7-methoxy-4H-1-benzopyran-4-one—and four of them had not been observed before our study in the genus Astragalus: 3′-methoxydaidzein, fujikinetin, sayanedine, and 6,4′-dimethoxy-7,2′-dihydroxyisoflavone. Five of the phytochemicals—maackiain, cajanin, onogenin, afrormosin, and sayanedine—exhibited selective inhibition of human monoamine oxidase-B (MAO-B), with maackiain reducing activity by 45%, nearing the effect of selegiline. The investigated phytochemicals also showed significant antioxidant and neuroprotective effects in ex vivo models using isolated rat brain synaptosomes, mitochondria, and microsomes, mitigating oxidative stress by preserving glutathione levels and reducing lipid peroxidation. Molecular docking confirmed favorable binding of active phytochemicals, particularly maackiain, within the MAO-B active site. Structure–activity relationship (SAR) analysis highlighted the role of specific substituents and fused-ring systems in MAO-B inhibition. This study expands our knowledge of the phytochemical diversity of A. aitosensis and supports the therapeutic relevance of its phenolic compounds in neurodegenerative disorders such as Parkinson’s disease. Full article

The aim of the study is to investigate the influence of the physicochemical characteristics of the molecular and supramolecular structure of polymers on electroadhesive interactions and their change under the action of a constant electric field. Currently, this effect is modeled in electroadhesion studies, but the range of variable parameters is limited and includes permittivity, moisture content, and surface roughness. It is important to consider other physicochemical parameters, such as material crystallinity and surface characteristics, changes in which can affect the magnitude of electroadhesive forces. In this study, the electric field strength was varied by altering the constant voltage in the range of 3–8 kV. Polyethylene, ethylene-vinyl acetate copolymers, and polyvinyl acetate were used as substrates for adhesive systems. The influence of the concentration of vinyl acetate groups, which determine the energy characteristics of the surface, and the degree of crystallinity on electroadhesive interactions under conditions of an external constant electric field and without it was traced. The degree of crystallinity was varied both by the cooling rate and the orientation during drawing. It was shown that by changing the polar component of the surface energy and the proportion of the crystalline phase in the substrate, electroadhesive interactions can be increased by 4 times to 120 Pa compared to polyethylene. The obtained laws are explained by the local dipoles induced by polar functional groups, which enhance the polymer’s surface interactions with other materials and external fields. At the same time, the fixation of macromolecules in crystalline regions complicates polarization under the influence of an electric field. Full article

The inherent complexity and heterogeneity of tumors pose substantial challenges for the development of effective oncology therapeutics. Organoids, three-dimensional (3D) in vitro models, have become essential tools for predicting therapeutic responses and advancing precision oncology, with established correlations to clinical outcomes in patient-derived models. These systems have transformed preclinical drug screening by bridging the gap between conventional two-dimensional (2D) cultures and in vivo models, preserving tumor histopathology, cellular heterogeneity, and patient-specific molecular profiles. Despite their potential, limitations in tumor organoid biology, including inter-batch variability and microenvironmental simplification, can undermine their reliability and scalability in large-scale drug screening. To overcome these challenges, the integration of advanced technologies such as artificial intelligence (AI), automated biomanufacturing, multi-omics analytics, and vascularization strategies has been explored. This review highlights the “Organoid plus and minus” framework, which combines technological augmentation with culture system refinement to improve screening accuracy, throughput, and physiological relevance. We are convinced that the future of drug development hinges on the convergence of these multidisciplinary technologies with standardized biobanking and co-clinical validation frameworks. This integration will position organoids as a cornerstone for personalized drug discovery and therapeutic optimization, ultimately advancing the development of efficacy in oncology. Full article

Background: Low temperature stress is a major environmental challenge affecting the growth, metabolism, and survival of many aquaculture species, including Nile tilapia (Oreochromis niloticus). Understanding the molecular mechanisms underlying cold tolerance is therefore essential for improving fish resilience and aquaculture sustainability. Methods: In the present study, an acute cold stress model of Nile tilapia (Oreochromis niloticus) was established and it was found that uncoupling protein 1 (UCP1) was involved in the acute cold stress process of tilapia. Results: The upregulation of UCP1 in the liver under cold stimulation was regulated by stress hormones such as cortisol and adrenaline. UCP1 has a short half-life and is degraded by proteasomes. In tilapia primary hepatocytes, the addition of adrenergic receptor agonists resulted in mitochondrial membrane potential decreasing, while UCP1 siRNA transfection inhibited mitochondrial membrane potential. Biochemical characteristics indicate that UCP1 is a channel protein that mediates proton leakage. In addition, feeding and intraperitoneal injection of mitochondrial uncoupling agent BAM15 can alleviate the low-temperature stress of tilapia. Conclusions: UCP1 helps maintain the metabolic homeostasis of tilapia under acute cold stimulation and provides new insights into the mechanisms of cold resistance as well as potential treatment strategies in fish. Full article

Background: Inflammatory Bowel Disease (IBD), including Ulcerative Colitis and Crohn’s Disease, is a multifactorial inflammatory condition of the intestinal tract driven by a complex interplay of genetic factors, immune system dysfunction, and gut microbiota alterations. This review aims to synthesize current advancements in modern drug development strategies for IBD. It emphasizes the integration of computational modelling, cell-based experiments, and animal model studies to enhance translational outcomes. Methods: To compile this review, an extensive literature search was performed utilizing PubMed, Scopus, and Google Scholar databases for English-language research and review articles published between 2000 and 2025 using keywords such as “IBD,” “molecular docking,” “bioinformatics,” “organoids,” “animal models,” and “network pharmacology,” among others. A total of 199 peer-reviewed studies were identified for inclusion based on relevance, transparency, and methodological robustness. Results: The review outlines a range of cutting-edge approaches to IBD drug discovery. These include computer modelling, molecular docking, and network analysis to accelerate early-stage target prediction and drug screening. The review further highlights the critical importance of utilizing 2D and 3D cell culture systems in parallel with advanced animal models. It emphasizes the critical integration of computational predictions with biologically relevant in vitro and in vivo validations to improve the reliability and efficiency of drug development. Conclusions: The integration of computer modelling, cell culture systems, and animal studies provides a revolutionary paradigm for accelerating drug discovery to IBD and other diseases enabling personalized and more effective treatment approaches. Full article