Search Results (446)

Previous studies have indicated that polyethylene terephthalate (PET) micro/nanoplastics (MNPs) may coexist with antibiotics in environmental and biological matrices; however, their combined behavior in blood remains insufficiently characterized. This study examined PET MNPs prepared from water bottles at 0.5, 2.5, and 7.5 mg/mL together with tetracycline (2–50 µg/mL) in defibrinated horse blood. After 24 h exposure and particle removal, UV–visible absorbance, intrinsic fluorescence, redox indicators, and subsequent Escherichia coli responses were evaluated. The blood biochemical results showed condition-dependent changes in hemoglobin-associated absorbance, tryptophan-dominated fluorescence, reactive oxygen species, reduced glutathione, superoxide dismutase, and lipid peroxidation. When treated blood supernatants were applied to Escherichia coli, tetracycline alone reduced bacterial OD600, whereas selected PET MNP–tetracycline co-exposures partially restored bacterial proliferation and modified oxidative-stress responses. ATR–FTIR analysis of Escherichia coli pellets showed dose-dependent modulation of phosphate-, lipid-, and protein-associated bands, indicating changes in bacterial biochemical fingerprints under specific exposure combinations. Overall, the findings suggest that PET MNPs can modify tetracycline-associated spectral, redox, and bacterial response patterns in a blood matrix. Future studies incorporating adsorption assays, free tetracycline quantification, protein-corona profiling, time-course exposure designs, and antibiotic susceptibility testing would further clarify the mechanistic basis and biological relevance of these matrix-dependent interaction effects. Full article

Isotopic and nuclear techniques, including fallout radionuclides (FRNs), fingerprinting (FP), and isotope hydrology (IH), are essential tools for assessing soil and water degradation. However, as in state-of-the-art reviews, isolated application of FRNs, FP and IH limits their potential to address complex, interconnected environmental processes across landscapes. This study proposes a novel methodology based on the concept of Synergistic Convergence to integrate FRNs, FP, and IH into a unified framework for assessing soil and water degradation. Validated through a case study in Cuba’s Hanabanilla sub-basin, the five-stage methodology improves the identification of erosion hotspots, sediment sources, and water resource vulnerabilities. Results demonstrate that combining FRNs (quantifying soil redistribution), FP (tracing sediment sources), and IH (evaluating water dynamics) provides a robust approach for sustainable landscape management, according to the United Nations Sustainable Development Goals (SDGs) and the One Health framework. The integrated methodology proves superior to the non-integrated approach, connecting causes (erosion), drivers (water), and consequences (sedimentation, pollution). The Synergistic Convergence methodology effectively integrates isotopic and nuclear techniques (INTs) (FRNs, FP, and IH), enabling a holistic assessment of soil and water degradation and their interactions within terrestrial and aquatic ecosystems. Full article

Network pharmacology studies of medicine–food homology plants have identified broad injury response pathways and hubs that cannot support compound-level or Parkinson’s disease (PD)-specific claims. We developed a traceable, non-weighted framework that separates regulatory provenance, PD-context evidence, structural support, and safety/developability liabilities. A ten-plant feasibility panel was locked before overlap with a 1631-gene PD union, yielding 382 plant-associated targets and 190 strict intersections. Leave-one-plant-out analysis retained 173–190 targets, whereas disease source and threshold stress tests showed curation dependence. Whole-blood classifiers showed modest five-fold discrimination (area under the curve, 0.606–0.682) and were excluded from candidate decisions. Redocking-validated AutoDock Vina and protein–ligand interaction fingerprints retained baicalein–MMP9, baicalein–AKT1, and baicalein–BCL2 as caution-tagged follow-up pairs. Quercetin–MMP9 was retained as a liability-tagged comparator, while KCNH2 relations were safety-only. Because no biological validation is presented, these pairs remain hypotheses for prospective MPP⁺-treated SH-SY5Y testing with orthogonal injury, dopaminergic phenotypes, target dependency, material confirmation and safety controls. Baicalein remains source-pending for the material chain. Full article

This study investigated the interactions between phenolic compounds from Israeli and Chilean white wines and human serum carrier proteins, including human serum albumin (HALB), gamma-globulin (HGLO), and fibrinogen (HFB), to characterize their antioxidant capacity and serum protein-binding behavior under controlled experimental conditions. The analyzed wines included Israeli Chardonnay (ICR), Chilean Chardonnay (CCR), Israeli Sauvignon Blanc (ISB), and Chilean Sauvignon Blanc (CSB). HPLC and FTIR fingerprinting revealed cultivar- and region-dependent differences in phenolic composition, with Chardonnay wines showing stronger protein-binding behavior and Sauvignon Blanc samples displaying high antioxidant efficiency relative to their phenolic content. ICR exhibited the highest total binding capacity, 46.44%, and the strongest albumin interaction, with a binding constant (Kb) of 8.44 × 10⁴ M⁻¹ and a Gibbs free energy (ΔG) value of −35.03 kJ/mol. Empirical fluorescence quenching kinetics demonstrated that white wine phenolics establish stable physical complexes with human serum proteins, displaying a distinct preferential affinity for HALB as the protein showing the strongest apparent interaction among the proteins tested. Two- and three-dimensional fluorescence spectroscopy confirmed substantial quenching of the intrinsic tryptophan and tyrosine residues, indicating meaningful microenvironmental alterations within the protein’s active transport sites. These empirical interactions were closely mirrored by complementary molecular docking simulations, which provided a structural visualization of the physical binding interactions. ICR also showed the highest antioxidant capacity, with DPPH and CUPRAC values of 1.66 and 2.91 mmol TE/L, respectively. Ethanol control showed negligible effects, indicating that the observed bioactivity was mainly associated with the polyphenolic matrix. Among the investigated samples, Chardonnay showed higher apparent protein-binding capacity, whereas Sauvignon Blanc showed relatively high antioxidant efficiency in relation to its phenolic content. Full article

Triple-negative breast cancer (TNBC) is the most clinically aggressive subtype of breast cancer, characterized by the absence of targetable hormone receptors and HER2 amplification, significantly constraining treatment choices. Estrogen Receptor Beta (ERβ) has emerged as a biologically relevant yet underutilized target in TNBC, with its re-expression linked to tumor suppression and improved prognosis, prompting the development of selective ERβ modulators as a precision therapeutic approach. We introduce ERβ-Score, an interpretable machine learning scoring system developed using a curated dataset of 1699 ERβ bioactive chemicals obtained from ChEMBL, characterized by 39 physicochemical and three-dimensional molecular descriptors. After implementing scaffold-disjoint train/test partitioning to avert structural data leakage, a Gradient Boosting Classifier, fine-tuned through Bayesian hyperparameter optimization, attained in five-fold cross-validation a Precision–Recall AUC (Area Under the Curve) of 0.891, a ROC-AUC (Receiver Operating Characteristic) of 0.888, a Matthews Correlation Coefficient of 0.664, an F1-score of 0.838, and a balanced accuracy of 0.831; on the scaffold-disjoint hold-out test set it attained a Precision–Recall AUC of 0.905, a ROC-AUC of 0.864, and a Matthews Correlation Coefficient of 0.578, indicating strong and balanced discrimination between active and inactive ERβ modulators. We note explicitly that this scaffold-disjoint hold-out constitutes internal validation, since it derives from the same curated ChEMBL workflow used for model development, and it is therefore reported throughout as scaffold-disjoint internal validation rather than as independent external validation. The applicability domain boundaries were established using a k-nearest-neighbor Tanimoto-similarity method with ECFP4 (Extended-Connectivity Fingerprint with a Diameter of 4) fingerprints, offering a quantitative confidence metric that identifies structurally new molecules beyond the model’s reliable prediction range. External validation against independent Tox21 ERβ bioassay data confirmed genuine, statistically significant predictive signal (ROC-AUC = 0.71) while revealing reduced sensitivity for structurally novel active compounds. The model was subsequently used for extensive virtual screening of natural product and drug-like compound libraries, with prioritized candidates undergoing structure-based molecular docking against the ERβ co-crystal structure (PDB: 7XWQ) using Smina, facilitating a comprehensive evaluation of hits based on both ligand and structural properties. To enhance accessibility, the complete pipeline was implemented as an open-access interactive web application utilizing Streamlit, enabling researchers to input any SMILES string and obtain, in real time, an activity prediction with a probability score, applicability domain classification, Lipinski drug-likeness assessment, interactive three-dimensional visualization of protein–ligand interactions, and on-demand docking within the ERβ active site. Full article

Background: The Mediterranean Diet (MedDiet) is a global benchmark for the prevention of cardiometabolic diseases and healthy aging, with Olea europaea L. (O. europaea L.) and Citrus bergamia Risso et Poiteau (bergamot) serving as primary sources of bioactive molecules such as phenols, in particular flavonoids. Recent evidence highlights a paradigm shift from traditional agricultural yield toward a precision-nutrition model, in which the health-promoting potential of these species is increasingly recognized to arise from the interplay between genetic background and environmental factors. Objectives This review summarised current evidence on how genetic variability drives the metabolic fingerprints of olive and bergamot, exploring the integration of genomic, transcriptomic, and metabolomic data to identify cultivars with superior nutraceutical value. Results: Evidence suggests that phytochemical profiles of O. europaea L. and Citrus species, including bergamot, are shaped by the interaction between genetic background and environmental conditions, with genotype contributing significantly to metabolic variability. Specific O. europaea L. and Citrus genotypes display distinct metabolic fingerprints characterized by different bioactive compound profiles, potentially underlying variations in antioxidant, cardioprotective, lipid-lowering, and anti-inflammatory properties. Conclusions: The integration of high-resolution genotyping and metabolic profiling supports the selection of superior genotypes for standardized, evidence-based nutraceuticals. Future advances in precision breeding are expected to further enhance the health-promoting traits of these Mediterranean species. Full article

The condensation reaction of pyromellitic dianhydride and (aminomethyl)phosphonic acid in imidazole yielded N,N′-bis(phosphonomethyl)pyromellitimide ([(H₂O₃P)CH₂-(C₁₀H₂N₂O₄)-CH₂(PO₃H₂)]∙2H₂O). Recrystallization of this compound from deionized water, by placing the solution in a desiccator to allow slow diffusion of HCl, afforded suitable single crystals for X-ray crystallographic studies. The compound crystallizes in the monoclinic space group P2₁/n. The flexible methylene phosphonic acid groups appended to both nitrogen termini adopt a trans configuration. The phosphonate and carbonyl groups (acceptors: P=O and C=O), together with water molecules [donor: O(6)—H∙∙∙O], participate in an extensive network of hydrogen-bonding interactions. Two of the phosphonate groups are protonated as P—OH (donors) and interact with oxygen atoms of neighboring phosphonate groups and water molecules. Hirshfeld surface analysis and associated two-dimensional fingerprint plots indicate that O∙∙∙H/H∙∙∙O (56.1%) contacts are the primary contributors to the crystal packing, followed by H∙∙∙H (16.3%) and C∙∙∙O/O∙∙∙C (13.4%) interactions. No significant π–π interactions were observed. The direct optical bandgap value, estimated from the Tauc plot, is 3.24 eV, indicating semiconducting behavior. The compound also exhibits thermal stability up to ~270 °C. These properties suggest that this compound may be a promising candidate for future investigation in organic electronic and optoelectronic materials. Full article

Background: Anaplastic Lymphoma Kinase (ALK) is an oncogenic receptor tyrosine kinase implicated in several cancers. Despite the clinical success of ALK inhibitors, acquired resistance continues to drive the search for novel chemotypes. We developed a multiclass machine learning framework to classify ALK inhibitory activity using a curated ChEMBL dataset. Methods: Models were built using 2D molecular descriptors together with MACCS and ECFP4 fingerprints. Three widely used algorithms, Support Vector Machine (SVM), Random Forest (RF), and XGBoost, were applied for model development. Results: RF and XGBoost models demonstrated the best performance, achieving accuracies of ~0.75–0.79 with consistently high ROC–AUC values, particularly for fingerprint-based features. Bemis–Murcko scaffold analysis identified enriched chemotypes and underexplored scaffolds for further prioritization. The validated models were subsequently used to screen the Maybridge library, and compounds predicted to possess potential ALK inhibitory activity were prioritized for further computational evaluation. Applicability-domain filtering confirmed that the selected compounds occupied the predicted ALK inhibitor chemical space across multiple activity classes. The shortlisted compounds were subsequently evaluated by molecular docking to characterize their binding modes and interactions. Three candidate hits (SCR00078, SCR00073, and AW01085) were selected for further evaluation using 500 ns molecular dynamics simulations alongside the reference inhibitor Brigatinib. Simulation analyses revealed stable protein–ligand complexes and reduced conformational fluctuations relative to apo ALK, while MM/PBSA calculations identified SCR00078 and AW01085 as the most favorable binders. Conclusions: This integrated ML-to-simulation workflow prioritizes structurally novel candidate hits with predicted ALK inhibitory activity and provides an effective strategy for scaffold discovery and hit prioritization. Full article

Phenolic acids exhibit structure-dependent vibrational responses governed by aromatic substitution, π-conjugation, oxygenated functional groups, and molecular environment. In this work, p-coumaric, caffeic, trans-ferulic, and gallic acids were investigated through an integrated experimental Raman, density functional theory (DFT), and chemometric approach to establish molecular relationships between hydroxylation, methoxylation, conjugation, and Raman spectral behavior. Raman spectra were recorded in the solid state and in an ethanol/water (1:1, v/v) mixture. At the same time, DFT calculations were used to optimize the molecular structures, simulate Raman spectra, assign vibrational modes, and evaluate molecular electrostatic potential, frontier orbitals, electronic descriptors, and localized orbital locator maps. The solid-state Raman spectra provided the most resolved molecular fingerprints, with hydroxycinnamic acids exhibiting intense bands corresponding to aromatic and conjugated ν(C=C) modes. In contrast, gallic acid displayed a distinct hydroxybenzoic vibrational pattern dominated by phenolic C–O/O–H and carboxylic contributions. DFT-assisted assignments confirmed that the main spectral differences arise from coupled vibrations involving ν(C=C), ν(C=O), ν(C–O), δ(O–H), aromatic ring deformations, and methoxy-related modes. Molecular electrostatic potential (MEP) and localized orbital locator (LOL) analyses showed that oxygen-centered regions are the most electrostatically and electronically localized sites, thereby explaining the sensitivity of C–O, O–H, and C=O bands to solvent-mediated interactions. HOMO–LUMO analysis revealed extended frontier-orbital delocalization in hydroxycinnamic acids, in contrast to the more localized hydroxybenzoic electronic structure of gallic acid. Principal component analysis confirmed that solid-state Raman spectra provide stronger chemometric discrimination than solution spectra, with PC1 and PC2 explaining 79.0% of the total variance in the solid-state dataset. Overall, the results show that specific functional groups define the principal vibrational domains of the studied phenolic acids. In contrast, the exact band positions, relative intensities, and coupling patterns are additionally modulated by aromatic substitution, π-conjugation, electronic distribution, physical state, and molecular environment. Within the limitations of a single-conformer isolated-molecule model, the combined Raman–DFT–chemometric approach provides a comparative interpretation of the vibrational fingerprints of the four selected compounds. Full article

Background: HER2 is a key oncogenic gene in breast cancer, involved in tumor progression, metastasis, and therapeutic resistance. This study aimed to find new HER2 inhibitors using a hybrid of machine learning (ML) and structure-based virtual screening (VS), combined with molecular dynamics (MD) simulations on various scaffolds. Methods: Four supervised molecular fingerprint classification models were trained on a dataset of 10,000 validated compounds from ChEMBL. Random Forest was the top model for screening a large compound library. Selected compounds underwent molecular docking in the HER2 ATP binding site, ADMET, drug likeness, toxicity analysis, and 200 ns MD simulations. Methods like PCA, FEL, hydrogen-bond analysis, DCCM, RDF, salt-bridge analysis, and MM/PBSA were used to assess binding stability. Results: Virtual screening identified three compounds, CHMEBL193865 (Lead-1), CHMEBL46740 (Lead-2), and CHMEBL151318 (Lead-3)—with better binding affinity and interaction profiles than the reference inhibitor. MD simulations showed stable protein–ligand complexes with RMSD values of 2.32–2.76 Å. Among these, Lead-2 was the most structurally stable, and Lead-1 had the most favorable binding free energy. All three compounds showed good drug likeness, ADMET properties, and low predicted toxicity. Conclusions: These findings support further in vitro and in vivo testing for developing new therapeutics against HER2-overexpressing breast cancer, highlighting two scaffolds with promising lead optimization potential. Full article

This study explores the phytochemical composition and antimicrobial potential of Palustriella commutata, Philonotis calcarea, Cinclidotus riparius, Rhynchostegium riparioides, Plagiomnium ellipticum, and Porella platyphylla, with a focus on bryophyte species phenolic constituents and biological activities. HPLC analysis identified seven phenolic acids (4-hydroxybenzoic acid, 2,5-dihydroxybenzoic acid, 3,4-dihydroxybenzoic acid, caffeic acid, vanillic acid, syringic acid, and p-coumaric acid) across all species in varying concentrations. In in vitro antimicrobial tests, the bryophyte extracts used showed significant activity against Pseudomonas aeruginosa (ATCC 9027), while exhibiting relatively lower inhibition against Staphylococcus aureus (ATCC 25923). The study showed that all species extracts exhibited comparable activity to erythromycin against P. aeruginosa, while Cinclidotus riparius and Plagiomnium ellipticum extracts showed no activity against S. aureus, and the remaining species exhibited lower activity compared to erythromycin. Molecular docking studies demonstrated strong binding affinities of these phenolics to the active site of the target macromolecule (PDB ID: 3FRQ), suggesting meaningful interactions with residues such as ARG122 and ASN123, similar to erythromycin binding patterns. The findings indicate that bryophyte-derived phenolic compounds possess significant antimicrobial potential and may serve as promising candidates for the development of novel bioactive agents. This study provides a broad framework for the insufficiently researched antimicrobial potential of bryophytes by establishing direct links between species-specific phenolic fingerprints, experimentally observed antibacterial activities, and predicted receptor-level interactions. Further complementary biological studies are needed to confirm and expand upon these results. Full article

Internet-of-Things (IoT) devices in smart homes are vulnerable to passive traffic fingerprinting, where an adversary captures encrypted IEEE 802.11 frames and identifies devices using MAC-layer metadata such as packet sizes and inter-arrival times. Existing defenses based on padding, traffic shaping, or synthetic cover traffic can remain vulnerable because artificial timing signatures are detectable by machine learning classifiers. This paper proposes a game-theoretic framework for evaluating Wi-Fi MAC-layer cover-traffic injection defenses. We introduce donor-based mimicry injection, in which the access point injects a replica of a paired device’s authentic traffic into each device’s stream. We compare donor mimicry with fixed-rate, exponential, and uniform synthetic baselines across 198 scenario instances (156 unique defender configurations) and eight classifiers using 10-fold cross-validation. Donor mimicry at 100% bandwidth overhead reduces the best attacker’s balanced accuracy to 33.5%, whereas synthetic methods at equal overhead reach 93.9%, showing that behavioral realism, rather than injected volume alone, drives effectiveness. Modeling the interaction as a finite two-player zero-sum game yields a mixed-strategy Nash equilibrium with game value 0.247 within the evaluated strategy space; a deployable deterministic defense holds the best pairing-unaware attacker to 25.9% balanced accuracy, near the four-class random baseline of 25%. A pairing-aware robustness analysis shows that an attacker who can orient the donor-induced identity swap recovers near-baseline accuracy, so the four-class protection presumes pairing secrecy and the durable effect is pair-level anonymity. The defense operates at the access point and requires no IoT device modifications. Full article

Understanding olive tree metabolism and its interactions with biotic and abiotic factors is crucial for the sustainability and resilience of olive cultivation in a changing agroclimatic context. In response to biotic stress, plants activate complex signaling pathways that trigger the production of specialized metabolites, particularly volatile organic compounds (VOCs). This study investigates the volatolomic profile naturally emitted by whole olive fruits using an integrated metabolomic strategy that combines design of experiments (DoE), targeted and untargeted analyses, and multivariate statistics. Optimal headspace solid-phase microextraction (HS-SPME) conditions were established using 30 g of sample, a 50 °C extraction temperature, a 50 min extraction time, and a 3 min injection at 250 °C, identifying extraction time and temperature as the most critical factors influencing VOC recovery. The data demonstrated significant cultivar-dependent variation in the volatile emissions from healthy olive fruit among six representative varieties. Furthermore, robust partial least squares-discriminant analysis (PLS-DA) and random forest models provided a clear separation between healthy and damaged olive fruits, achieving high predictive accuracy (90%) and identifying key volatile biomarkers derived from the lipoxygenase (LOX) pathway. This novel multivariate optimization approach (SPME–GC/MS) represents a powerful tool for establishing a reliable chemical fingerprint of the olive fruit “volatilome” under evolving agroclimatic challenges. Full article

The voltage-gated sodium channel Nav1.7 is a strongly validated target for the development of novel, non-opioid analgesics due to its genetic link to pain signaling. To accelerate the discovery of safe Nav1.7 modulators, this study outlines an integrated computational pipeline to repurpose FDA-approved drugs. A structurally complete model of the Nav1.7 central pore was generated via homology modeling from a high-resolution cryo-EM structure (PDB: 7W9K) to ensure a physically consistent model suitable for dynamic simulations. We conducted a structure-based virtual screening of 2296 FDA-approved compounds, identifying four promising candidates (DB04868, DB00941, DB01419, and DB15982) with strong predicted affinities ranging from −11.38 to −12.57 kcal/mol. Interaction fingerprinting revealed that binding is predominantly driven by hydrophobic contacts with conserved pore-lining residues, including Phe1503, Leu1010, and Ile1500. To validate these static predictions, the top protein–ligand complexes were subjected to single-replica 250 ns molecular dynamics (MD) simulations. Comprehensive trajectory analyses, including RMSD, RMSF, and principal component analysis, revealed a notable discrepancy between static docking scores and dynamic stability. The highest-scoring docking candidate, DB04868, exhibited substantial conformational flexibility and reduced stabilization under simulated physiological conditions. Conversely, DB01419, despite a lower initial docking rank, demonstrated the highest structural stability across all metrics and uniquely formed intermittent stabilizing hydrogen bonds. These findings underscore the value of post-docking MD validation in computational drug discovery and nominate DB01419 and DB15982 as candidate scaffolds that warrant subsequent experimental validation, including electrophysiological characterization and Nav-isoform selectivity profiling. We emphasize that these are computational predictions: in silico binding stability is not equivalent to functional inhibition of Nav1.7 currents, and the lead designations reported here remain hypothesis-generating until confirmed by patch-clamp and biochemical assays. Full article

Background/Objectives: Obesity and related metabolic diseases arise from an interplay of lipid overload, insulin resistance and oxidative stress. Acetyl-CoA carboxylase 2 (ACC2) controls malonyl-CoA production and thereby gates mitochondrial fatty-acid oxidation, placing it at the intersection of lipid handling and redox-sensitive metabolic dysfunction. Dietary antioxidants such as polyphenols, flavonoids and terpenoids are increasingly studied as modulators of these pathways, yet systematic prioritization of food-derived antioxidant compounds against defined metabolic targets remains challenging. We developed an integrated deep learning and structure-based workflow to prioritize FooDB compounds with predicted ACC2-binding potential. Methods: A curated set of 3983 ACC2 bioactivity records from ChEMBL 36 was used to train scaffold-split models, including graph neural-network and graph–Morgan fingerprint-fusion architectures. The calibrated ensemble screened 139,988 FooDB compounds; 200 candidates with predicted activity probability above 0.70 were docked against the ACC2 carboxyltransferase domain (PDB ID: 3FF6), and six prioritized complexes underwent 500 ns molecular dynamics and MM/GBSA analysis. Results: Redocking of the co-crystallized ligand reproduced the experimental pose (RMSD 1.2 Å). Although the highest-ranked screening hits were antioxidant terpenoids and alkaloids, docking-based prioritization from the top candidates selected six larger, more polar food-derived compounds, including glycosides and two nucleotide/cofactor-like conjugates, which showed docking scores from −7.47 to −6.65 kcal/mol versus −6.21 kcal/mol for the reference ligand. Glu539 emerged as a recurrent interaction hotspot. All candidates gave more favourable MM/GBSA binding free energies than the reference (ΔG = −22.52 kcal/mol), led by FDB029596 (−35.65), FDB021568 (−34.14) and FDB017807 (−33.87 kcal/mol). Conclusions: This workflow provides a reproducible framework for prioritizing food-derived compounds as candidate ACC2 binders relevant to obesity and metabolic disease, generating structurally supported hypotheses for biochemical and nutritional validation. The prioritized compounds are computational candidates only and require biochemical and cellular (experimental) validation before any ACC2-related biological relevance can be established. Full article