Search Results (2,434)

Background/Objectives: Natural compounds occupy a pharmacologically rich chemical space, characterized by abundant scaffolds, extensive functional group elaboration, and defined stereochemistry. In this context, Helichrysum italicum, a Mediterranean medicinal plant, represents a valuable source of polyphenols with multiple biological and pharmacological activities. Methods: Here, we introduce an inverse molecular docking fingerprint approach to systematically investigate eight major Helichrysum italicum polyphenols, including $\alpha$-pyrones (arzanol, ethylpyrone), flavonols (gnaphaliin, kaempferol, quercetin), and flavanones (naringenin, pinocembrin, hesperetin). More than 40,000 human protein structures from the Protein Data Bank were screened to generate target-based inverse docking score fingerprints for each compound. Results: Hierarchical clustering of these fingerprints revealed shared binding patterns among structurally related polyphenols and enabled hypothesis generation regarding potential synergistic effects. Notably, favorable interactions were identified with PPARG and CARM1, supporting therapeutic relevance in inflammation and cancer, alongside additional targets associated with neurodegeneration and bone metabolism. Conclusions: This study establishes inverse docking fingerprints as a robust, mechanism-oriented method for natural product research and highlights Helichrysum italicum polyphenols as starting points for medicinal chemistry and drug discovery. Full article

Generally, forest trees with medicinal value present diverse chemotypes considered key determinants of efficacy, safety, and commercial valuation. Such heterogeneity varies among tissues, genotypes, and seasons, and stress exposure. This review summarizes how regulatory controls and genome architecture affect the stability and synthesis of secondary metabolites in woody medicinally important taxa. Detailed haplotypic and chromosomal analyses have recently identified diverse and repeatable architectural drivers. Among these, LTR/transposon-mediated revamping, neofunctionalization, biosynthetic gene clusters, and tandem duplication play a special role in reshaping pathway capacity. The enzymatic regulation of these drivers translates this “capacity” into harvest-pertinent chemistry by employing conserved TF modules, hormone crosstalk, and emergent chromatin/epigenetic layers. Nevertheless, major parameters pertaining to the tissue-specific storage, transport, and compartmentalization of these chemotypes are contextualized with certain limitations. In this review, the integration of GWAS/eQTL/TWAS with multi-tissue is explained in addition to the replacement of a single reference with pangenome/haplotype frameworks, and explicit modeling of G × E further strengthen genotype-to-chemotype mapping. Therefore, in this review we summarize practical workflows for chemotype discovery utilizing staged validation models of heterologous reconstitution, isotope/spatial evidence, and chemistry. These findings were supported by data on saponins, alkaloids, iridoids, and defense response. Such an integration links mechanistic understanding to authentication, standardization, and sustainable utilization strategies in woody medicinal trees. Full article

With the rapid advancement in Generative Artificial Intelligence (GenAI), AI-generated content (AIGC) lacking human cognitive oversight is increasingly permeating open web environments and academic communication systems. This study integrates longitudinal retraction data (Retraction Watch Database, 1990–2026), web-scale analyses of AI-content penetration (Common Crawl, 2013–2026), and bibliometric mapping of governance scholarship (Web of Science Core Collection, Scopus, Google Scholar, 2020–2026) to diagnose the cross-level misalignment between synthetic-content diffusion, AI-related misconduct pressure, and governance attention. On this basis, it proposes a Normalized Coverage Index (NCI) to measure the relative relationship between scholarly attention to AI-related academic misconduct governance and the level of misconduct pressure observed through retraction data across disciplines. The results reveal pronounced asymmetries at the disciplinary level. Fields such as chemistry (0.04), physics, mathematics & statistics (0.11), and life sciences & biology (0.34) exhibit clear governance gaps, whereas Education shows a comparatively excessive level of attention (NCI = 29.26). Since 2022, AIGC has expanded rapidly across open web corpora, accompanied by a sharp rise in AI-related retractions, which also exhibit a longer detection lag than traditional forms of misconduct (2.77 years vs. 1.91 years). Although the volume of academic governance-related research has grown rapidly, its proportion within the broader body of AI-related research has declined, suggesting that scholarly attention to governance has not kept pace with technological diffusion. Consequently, a structural misalignment in governance—closely tied to the allocation of attention—has emerged within the academic system in the era of GenAI. This misalignment may pose potential risks to the robustness of the knowledge production system. Addressing it requires rebuilding epistemic infrastructure through provenance transparency, auditable workflows, and governance-aware seed corpora aligned with empirically concentrated risks. Full article

Natural products (NPs) have historically provided the foundational scaffolds for drug development, yet traditional bioprospecting faces critical limitations: high rediscovery rates, laborious isolation workflows, and substantial attrition during clinical translation. The emergence of big data technologies is fundamentally transforming this landscape, enabling a shift from serendipity-based discovery toward systematic, data-driven approaches. This review examines how the integration of artificial intelligence (AI), machine learning (ML), and multi-omics datasets is accelerating natural product research across three key domains: (1) genome mining for biosynthetic gene cluster identification using platforms such as antiSMASH, (2) cheminformatics-driven prediction of structure–activity relationships and ADMET properties, and (3) metabolomics-guided dereplication to prioritize novel bioactive scaffolds. We evaluate the convergence of genomics, metabolomics, and computational chemistry in enabling in silico lead optimization and the discovery of cryptic metabolites from previously inaccessible microbial taxa. While challenges in data standardization and scalability persist, the synergy between big data and NP research is accelerating clinical translation. Despite persistent challenges in data standardization, scalability, and equitable benefit-sharing, the convergence of big data and NP research is poised to redefine drug development. These advances position computational NP research as a cornerstone of next-generation drug development. Full article

Environmental pollutants, lifestyle factors, and intrinsic metabolism can amplify reactive oxygen and nitrogen species (ROS/RNS) generation beyond antioxidant capacity. The resulting oxidative stress damages macromolecules, perturbs redox signaling, and may accelerate biological aging. This review synthesizes evidence published mainly in 2020–2025 on how major pollutant classes (air pollutants, metals, pesticides, nanoparticles, and micro-/nanoplastics) induce ROS through shared nodes mitochondrial electron transport disruption, NADPH oxidase activation, and redox cycling/Fenton chemistry and how these signals propagate to epigenetic remodeling (DNA methylation, histone modifications, and non-coding RNAs). To move beyond descriptive cataloging, we grade the strength of evidence by study context (cell culture, animal models, human observational studies, and clinically oriented biomarker research), highlight convergent findings and unresolved controversies, and specify key methodological limits. We then compare oxidative-stress biomarker platforms by analytical specificity, pre-analytical susceptibility, and translational readiness, distinguishing validated markers from exploratory redox-epigenetic and multi-omics signatures. Finally, we discuss how exposomics and AI-assisted multi-omics integration may support biomarker discovery while emphasizing current constraints (confounding, batch effects, and limited prospective validation) that must be addressed for clinical translation. Full article

Background/Objectives: Carbon dots (CDs) are promising antimicrobial nanomaterials owing to their biocompatibility, environmental friendliness, and tunable surface chemistry. This study aimed to synthesize nitrogen-doped CDs (AS-CDs) and develop a light-responsive antibacterial system through conjugation with chlorin e6 (Ce6). Methods: AS-CDs were synthesized by a microwave-assisted method using L-ascorbic acid and spermidine, followed by conjugation with Ce6. The materials were characterized by transmission electron microscopy, zeta potential analysis, and spectroscopic methods, and their antibacterial activity was evaluated against Escherichia coli, Staphylococcus aureus, and methicillin-resistant S. aureus (MRSA) under both dark and visible-light conditions. Cytotoxicity was assessed using HaCaT cells. Results: The AS-CDs exhibited a uniform nanoscale morphology with an average diameter of 6.3 nm and a positive surface charge of +15.6 mV, together with intrinsic broad-spectrum antibacterial activity. Ce6 conjugation further enhanced antibacterial efficacy under light irradiation, with the CDs-Ce6 conjugate achieving complete eradication of S. aureus and MRSA and marked inhibition of E. coli at 2.5 μg/mL. Cytotoxicity studies demonstrated low toxicity in HaCaT cells within the effective antibacterial concentration range. Conclusions: These findings highlight the potential of microwave-synthesized, photosensitizer-conjugated CDs as next-generation antimicrobial agents. This platform offers a cost-effective, sustainable, eco-friendly, and efficient platform for combating bacterial infections, with broader potential in pharmaceutical and biomedical applications. Full article

Textile wastewater remains one of the most challenging industrial effluents to remediate due to its intense and persistent coloration, high organic load, elevated salinity, and fluctuating pH and the presence of recalcitrant dye structures and auxiliary chemicals. Conventional physicochemical and biological treatments frequently achieve incomplete removal, generate secondary wastes, or fail under high-salt and toxic dye matrices. Advanced oxidation processes (AOPs) provide molecular-level degradation via reactive oxygen species (ROS), yet their deployment is often constrained by narrow operating windows, catalyst instability, chemical/energy demand, and scale-up limitations. In this context, metal–organic frameworks (MOFs) have emerged as tunable porous catalytic platforms that integrate adsorption and oxidation within a single architecture through controllable metal nodes, functional linkers, and engineered pore environments. This critical review reimagines textile effluent treatment through the lens of MOF-based hybrid catalysts, synthesizing progress across Fenton/photo-Fenton catalysis, photocatalytic MOFs, persulfate activation, and MOF-derived/composite systems. Mechanistic pathways are discussed by linking pollutant enrichment, cyclic redox reactions, charge-transfer processes, and ROS-driven degradation toward mineralization, with emphasis on the distinction between rapid decolorization and true organic removal. A critical comparison highlights how hybridization improves charge transport, stability, and catalyst recovery, while persistent gaps remain in hydrolytic robustness, metal leaching control, intermediate toxicity assessment, real-wastewater validation, continuous-flow reactor integration, and techno-economic feasibility. Finally, the review outlines actionable research directions, including water-stable and defect-engineered MOFs, immobilized and structured catalysts, solar-driven operation, standardized performance metrics, and life-cycle-informed design, to accelerate translation toward scalable and sustainable textile wastewater remediation. By bridging material chemistry with reactor-level feasibility and sustainability assessment, this review provides an implementation-oriented perspective for next-generation textile wastewater treatment. Full article

Sulfide-alkaline wastewater (SAW) from petrochemical plants, particularly from pyrolysis and hydrotreating units, presents a significant environmental challenge due to its high toxicity, extreme alkalinity (pH > 12), and high concentrations of sulfides and organic pollutants. Traditional treatment methods like acid neutralization or air oxidation are often inefficient, generate secondary waste, or fail to recover valuable components. This study investigates the effectiveness of a novel electrochemical system for the simultaneous treatment of SAW and recovery of valuable products. A lab-scale four-chamber electrodialyzer, equipped with cation-exchange membranes and nickel bipolar electrodes, was designed and tested using real industrial wastewater. The wastewater was characterized by a pH of 13.06, chemical oxygen demand of 12,600 mg/L, and a sulfide content of approximately 5000 mg/L. The process leverages anodic oxidation to convert sulfide ions into elemental sulfur, while sodium cations migrate through cation-exchange membranes to the cathodic compartments. There, water reduction generates high-purity hydrogen (≥99.9%) and a concentrated, purified sodium hydroxide solution. The results demonstrate the ineffectiveness of electrodialysis with anion-exchange membranes due to rapid membrane degradation. In contrast, the proposed electrodialyzer with bipolar electrodes achieved excellent performance: a caustic soda solution with a concentration of 2.3–2.5% was recovered with a current efficiency of 83–85%, containing only trace amounts of sulfides (0.0052%) and organic impurities (0.053%). The process completely removed the original sulfide alkalinity. The study confirms the chemical and mechanical stability of the cation-exchange membranes under harsh SAW conditions. The proposed technology offers a path towards a closed-loop system in refineries by enabling the reuse of recovered caustic, utilization of hydrogen, and potential recovery of sulfur, aligning with the principles of green chemistry and circular economy. Full article

The development of efficient, sustainable, and low-cost catalysts for wastewater treatment remains a major environmental challenge. In this work, Zn-doped CuO nanostructures were successfully synthesized via a green route using Aloysia citrodora leaf extract as a natural reducing and stabilizing agent. The structural and morphological properties of the prepared catalysts were systematically characterized by XRD, Raman spectroscopy, FTIR, SEM, and EDX analyses. The results revealed the formation of highly crystalline monoclinic CuO nanoparticles, whose defect density and surface properties were significantly modified by Zn incorporation. The catalytic performance of the synthesized materials was evaluated through the heterogeneous Fenton-like degradation of Rhodamine B in aqueous solution under dark conditions. The Zn-doped CuO catalyst exhibited outstanding degradation efficiency (~99.97%) within only 30 min, using a low catalyst dosage of 15 mg and a minimal H₂O₂ amount of 25 μL. The enhanced catalytic activity is attributed to the synergistic interaction between Zn-induced lattice defects and the Cu²⁺/Cu⁺ redox cycle, which promotes efficient H₂O₂ activation and •OH radical generation. Radical scavenging experiments confirmed the dominant role of hydroxyl radicals in the degradation process. Compared with previously reported CuO-based catalysts, the present system demonstrates superior performance in terms of reaction rate, oxidant consumption, and energy efficiency. These findings highlight the potential of Zn-doped CuO synthesized via green chemistry as a promising and sustainable catalyst for advanced wastewater treatment applications. Full article

Arsenic contamination, mainly in the arsenate (As(V)) form, continues to pose a serious threat to groundwater quality worldwide due to its long-term stability and toxicity at very low levels. Herein, we demonstrate, for the first time, a three-dimensional graphene oxide-based nanocomposite composed of Cu nanoparticle-doped, amino-functionalized UiO-66 (Cu/UiO-66-NH₂) anchored on a graphene oxide framework (Cu/UiO-66-NH₂@GO) as a novel and efficient nanosorbent for the rapid removal of As(V) in groundwater-like solutions. The nanocomposite was characterized by SEM and HRTEM to confirm the hybrid structure and by XRD, N₂ adsorption–desorption isotherms, and XPS to investigate crystallinity, porosity, and surface chemistry. The derived material exhibited a highly dispersed morphology and performed rapid arsenate solid-phase extraction to attain equilibration within 10 min and was effective for a wide pH range of 2–11. The best fit for the kinetic profiles was provided by the pseudo-second-order model. Interestingly, the maximum adsorption capacity of 747.9 mg g⁻¹ at pH 6.8 was achieved, demonstrating the benefits of the complementary pairing of dispersive GO sheets and Zr-MOF adsorption domains with Cu-derived active sites. Mechanistically, the enhanced uptake is ascribed to a combination of effects, including electrostatic pre-concentration, ligand exchange, and inner-sphere complexation at metal-oxo nodes; spectroscopic analysis (XPS and FTIR) suggests that the majority of arsenate is immobilized via a strong Zr-O-As bond at coordinatively unsaturated Zr centers, which is in line with t-ZrO₂-like surface domains formed within the nanocomposite. The embedded GO support inhibits further framework interpenetration and enhances active site availability and mass transport, leading to fast and high-capacity arsenate capture in groundwater samples with related conditions. Taken together, this work presents a powerful design concept that integrates unique GO-supported, Cu-modified UiO-66-NH₂ with Zr-O binding motifs to afford high-rate remediation nanocomposites, providing an excellent platform for next-generation arsenate remediation materials. Full article

Accurate State of Charge (SOC) estimation is critical for the safety and efficiency of Battery Management Systems (BMS). While data-driven methods have shown promise, they often exhibit limited generalization capability due to the lack of physical constraints. Incorporating symmetry in the battery, such as through the use of Physics-Informed Neural Networks (PINNs), can mitigate this issue. However, PINNs typically rely on integer-order equivalent circuit model differential equations, which fail to accurately describe the complex electrochemical relaxation processes. To bridge this gap, we propose a novel Fractional Differential Physics-Informed Neural Network (FDE-PINN) framework. Unlike traditional approaches, this method embeds a Fractional-Order Equivalent Circuit Model (FO-ECM) into the Gated Recurrent Unit (GRU) architecture to explicitly capture the anomalous diffusion and long-memory effects inherent in battery polarization. Specifically, the network is trained by minimizing a composite loss function that integrates the data fitting error with residuals from fractional-order governing equations, including Coulomb counting and fractional voltage dynamics. Extensive experiments on the Panasonic 18650PF dataset and CALCE A123 dataset verify the method’s superiority. Results demonstrate that the proposed FDE-GRU model achieves an average MSE of $14.29\times {10}^{-4}$ (with an MAE of 2.43% and RMSE of 3.23%) on the NCA chemistry and $26.24\times {10}^{-4}$ (with an MAE of 3.75% and RMSE of 5.09%) on the LiFePO₄ chemistry, significantly outperforming traditional methods by reducing the estimation error by 35.6% and 26.2% compared to the standard GRU, respectively. Full article

Well-defined, small-molecule, platinum-centered coordination compounds are of continued interest in both basic and applied research, particularly in medicinal chemistry and pharmaceuticals (i.e., cisplatin). Organoplatinum(IV) complexes have been reported to exhibit substantial in vitro cytotoxicity across a range of cancer cell lines. Compared with coordinatively unsaturated platinum(II) species, electronically and coordinatively saturated platinum(IV) complexes are generally more inert, reducing undesirable side reactions in plasma and cellular environments and potentially improving their safety profiles as chemotherapeutic agents. In addition, the presence of organic ligands can enhance lipophilicity, facilitating passive diffusion across cell membranes. Here, we report the synthesis, structural characterization, and in vitro anticancer activity of a series of organoplatinum(IV) complexes of the general formula Pt(CH₃)₂I₂{n,n′-dimethyl-2,2′-bipyridine} (n,n′ = 4,4′; 5,5′; 6,6′). The 5,5′- and 6,6′-dimethyl isomers were characterized by single-crystal X-ray diffraction. All three dimethyl-substituted complexes, along with the parent compound, Pt(CH₃)₂I₂{2,2′-bipyridine}, were evaluated for cytotoxic activity against a panel of 60 human cancer cell lines. Whereas Pt(CH₃)₂I₂{2,2′-bipyridine} and the 4,4′- and 5,5′-dimethyl derivatives displayed limited cytotoxicity, the 6,6′-dimethyl isomer exhibited notable activity, particularly against the colon cancer cell line HCT-116 (LC₅₀ = 8.17 μM) and the ovarian cancer cell line OVCAR-3 (LC₅₀ = 7.34 μM). The enhanced cytotoxicity of the 6,6′-dimethyl derivative is attributed, at least in part, to the relatively facile dissociation of the 6,6′-dimethyl-2,2′-bipyridine ligand from the platinum(IV) center, suggesting that sterically induced ligand lability plays an important role in modulating biological activity in this particular compound, giving new structural activity impetus for potential drug molecules. Full article

In this work, the debonding behavior under quasi-static Mode I fracture loading of adhesive joints made on two types of composite materials with the same epoxy matrix and unidirectional carbon or glass fiber reinforcement was analyzed. Standard DCB tests were used to quantify the influence of adhesive type and substrate surface preparation on interlaminar fracture toughness. For the fabrication of the joints under study, three commercial structural adhesives from different manufacturers were selected, two epoxy-based and one acrylic-based. Substrate surface preparation was carried out using three different procedures: manual abrasion, sanding with P220 Al₂O₃ sandpaper, grit blasting with Al₂O₃, and peel ply PA80 polyamide fabric. The experimental results revealed the same trend for both epoxy-based adhesives: sanding provided the best results, regardless of the substrate used. Surface preparation by grit blasting proved highly sensitive to the applied parameters, generally yielding poorer results than manual sanding. Surface preparation using PA80 peel ply fabric may be a viable option. However, its main drawback is that it must be incorporated during composite manufacturing. The results demonstrate that fracture performance is governed by the interaction between adhesive chemistry and surface morphology rather than by surface roughness alone. Full article

Platinum chemistry covers a wide range of applications, including homogeneous and heterogeneous catalysis as well as cancer therapy. Numerous Pt complexes have been synthesized and studied in recent years, with NMR spectroscopy serving as the primary technique for structural characterization. The ¹⁹⁵Pt nucleus has favorable features for NMR studies, being highly sensitive to ligand type and structural environment. From a computational perspective, factors such as solvent effects, relativistic corrections, and the electronic structure of the ligands strongly influence the calculated NMR parameters. Consequently, establishing a general computational protocol for ¹⁹⁵Pt NMR prediction remains a challenging task. In this work, we present a systematic validation and extension of our previously developed computational protocol, originally proposed for Pt(II) complexes, in studying ¹⁹⁵Pt NMR chemical shifts in Pt(II)-Sn(II) complexes. A benchmark set of 100 Pt(II)-Sn(II) complexes was analyzed, yielding good agreement with experimental data (R² = 0.86, MRD = 3.6%, MAD = 163 ppm), which is remarkable given the structural diversity and broad range of chemical shifts covered. Full article

This study analyses the behaviour of brass CB773S with extra-low-lead content in relation to corrosion and the corrosion–cavitation phenomenon. Electrochemical corrosion tests, both potentiodynamic and potentiostatic, as well as corrosion–cavitation tests, were conducted. Various potentials were applied to brass, alongside cavitation generated by an ultrasonic bath. Artificial seawater and artificial brackish water were used as electrolytes. Surface damage was evaluated using a stereo microscope and scanning electron microscopy. The results indicate that the interfaces between alpha and beta phases of brass serve as preferential sites for the nucleation and collapse of vapour bubbles under cavitation conditions, leading to a deep pitting, especially in artificial brackish water under this synergy. Susceptibility to a selective corrosion of the Zn-rich phase was observed, highly dependent on the test solution, as well as on the applied potential during the tests. The corrosion–cavitation synergistic damage was strongly dependent on the electrochemical parameters, particularly the applied potential, which plays a key role under cathodic protection conditions. In general, it can be concluded that low-lead brass behaviour is governed by a complex interaction between applied potential, electrolyte chemistry, microstructure, and mechanical effect. These findings provide valuable insights into brass’s performance under service conditions where corrosion and cavitation may appear simultaneously in marine environments. Full article