Molecules最新文献

Sclerotinia sclerotiorum is a soilborne fungal pathogen, and it is a major threat to lettuce production, causing lettuce drop. This study evaluated the antifungal effectiveness of five essential oils (EOs) (Rosmarinus officinalis, R. officinalis var. verbenone, Lavandula hybrida, Origanum majorana, and Thymus vulgaris) at 0.1%, 1%, and 10%, along with their phytotoxic effect in the field on three different crops (lettuce, tomato, and chard) following foliar application. T. vulgaris EO completely inhibited S. sclerotiorum mycelial growth at all tested concentrations. R. officinalis, L. hybrida, and O. majorana also showed full inhibition at 1% and 10%, while R. officinalis var. verbenone achieved 80-100% inhibition. R. officinalis had the least phytotoxic effects, with only a minimal effect on chard at 1%. R. officinalis var. verbenone caused low/moderate phytotoxicity in lettuce and chard but had no toxic effect on tomato. L. hybrida and O. majorana had moderate to low effects, while T. vulgaris was the most phytotoxic, significantly affecting lettuce and tomato at 1%. Further field trials are needed to define EO application protocols toward sustainable lettuce drop management without risks of phytotoxicity.

Glioblastoma (GBM) is the most common malignant primary brain tumor among adults and one of the deadliest human cancers. Its infiltrative growth pattern, high intratumor heterogeneity, and the existence of the blood-brain barrier severely limits current treatment approaches. Precision medicine-guided treatment decision-making based on unique molecular characteristics of patients' tumors and tumor microenvironments is highly desired. Gold nanoparticles (AuNPs) are promising nanoplatforms that enable precision medicine and personalized treatments for GBM. Their size- and shape-dependent tunable physiochemical properties, ease of surface functionalization, unique optical/electronic properties, and biocompatibility have facilitated the development of AuNP-based multimodal agents with the capability of delivering therapies, molecular imaging, and diagnosis in one platform. Recent research has shown that AuNPs can deliver chemotherapeutics, genes, and immunotherapeutics and aid in imaging, radiosensitization, and photothermal therapy for GBM therapy. Ligand-targeted and stimuli-responsive AuNPs enable site-selective targeting of GBM cells and the tumor microenvironment, allowing for personalized medicine approaches. Here, we review the progress made in biomedical applications of AuNPs for GBM treatment with a focus on precision-based drug/gene delivery, diagnosis/imaging, and therapy enhancement. We also discuss safety, biodistribution, scalability for translation, and regulatory challenges that need to be addressed for AuNP development. Future opportunities for AuNPs in personalizing GBM treatment are also highlighted.

Self-assembling antimicrobial complexes are a promising new technology for the development of antimicrobial, antifungal, and other bioactive agents with targeted delivery, adaptability, and the regulation of processes over time. Ribosomally synthesized antimicrobial peptides (AMPs) are most frequently considered as the basis for such complexes; however, we suggest that non-ribosomally synthesized peptides (NRPs) should be considered as molecules that also hold potential for engineering and already possess a set of qualities that AMPs are still to be engineered to have. This review examines the key features of NRP structure and self-assembly that determine their potential as antimicrobial agents, as well as NRP engineering methods through which new, more advanced agents for combating antibiotic-resistant microorganisms can be created.

Fermentation is a complex bioprocess that drives the transformation of raw materials into products with altered chemical makeup, improved nutritional value, and enhanced sensory and functional qualities [...].

Luteolin (Ltn), a natural flavonoid, effectively inhibits microglial activation in Alzheimer's disease (AD) with promising therapeutic potential, but its efficacy is severely limited by the blood-brain barrier (BBB). To overcome this obstacle, this study prepared poly (lactic-co-glycolic acid) (PLGA) nanoparticles (NPs)-designated as TGN/RAP12-RBC-NPs@Ltn-which were coated with red blood cell membranes (RBCm) functionalized with two peptides, TGN (TGNYKALHPHN) and RAP12 (EAKIEKHNHYQK). The results demonstrated that TGN significantly enhanced BBB permeability, while RAP12 enabled effective targeting and delivery of TGN/RAP12-RBC-NPs@Ltn to microglial mitochondria in the brain. In addition, the presence of RBCm significantly inhibited the phagocytosis of NPs by macrophages, exerting a notable role in immune evasion. Meanwhile, the study confirmed that encapsulating Ltn within NPs significantly enhanced cognitive function in APP/PS1 mice, modulated the expression of key mitochondrial metabolic enzymes-pyruvate dehydrogenase (PDH) and its phosphorylated forms (pS232PDH, pS293PDH, pS300PDH)-in microglia, thereby ameliorating mitochondrial dysfunction and effectively regulating the neuroinflammatory environment in the mouse brain, and ultimately contributed to therapeutic efficacy. From this, it could be seen that TGN/RAP12-RBC-NPs@Ltn could significantly enhance the therapeutic effect of Ltn on AD, providing an effective treatment strategy for delaying the progression of AD.

Human breast milk harbors commensal lactic acid bacteria with probiotic potential, and microbial fermentation may enhance the bioactivity of plant-derived exosome-like nanovesicles (EVs); this study evaluated whether L. plantarum BMSE-HMP251 isolated from breast milk could safely ferment Hordeum vulgare L. and improve the anti-inflammatory activity of derived EVs. BMSE-HMP251 was identified by 16S rRNA sequencing and characterized by biochemical, safety, and genomic analyses. EVs derived from Hordeum vulgare L. extract and BMSE-HMP251-fermented broth were evaluated for physicochemical properties, antioxidant activity, cytotoxicity, and anti-inflammatory activity in LPS-stimulated HT-29 and RAW 264.7 cells. EVs derived from Hordeum vulgare L. fermentation exhibited a distinct size distribution and significantly enhanced bioactivity, including higher DPPH radical scavenging activity and greater suppression of nitric oxide production and proinflammatory cytokine (TNF-α and IL-1β) mRNA expression, compared with EVs from unfermented extracts. These effects were observed following fermentation with the human breast milk-derived strain L. plantarum BMSE-HMP251, which showed species-consistent phenotypic and genomic characteristics and no safety concerns. Overall, fermentation markedly enhances the anti-inflammatory potential of plant-derived EVs, supporting fermentation as a safe and effective strategy to improve their functional value.

The c-Jun N-terminal kinase (JNK) pathway is a central driver of fibrosis, inflammation, and neurodegeneration. While direct JNK inhibitors have shown therapeutic promise, achieving high isoform selectivity remains a significant medicinal chemistry challenge. Furthermore, targeting the upstream 'gatekeepers' MKK4 and MKK7 offers a distinct mechanism to modulate pathway output with greater precision. Consequently, medicinal chemistry efforts have shifted upstream to the dual-specificity kinases MKK4 and MKK7. This review critically evaluates the structural biology and pharmacological evolution of small-molecule inhibitors targeting these nodes. We contrast the distinct therapeutic landscapes of the two kinases: while MKK4 inhibition has emerged as a breakthrough strategy for unlocking liver regeneration (exemplified by the first-in-class clinical candidate HRX215), MKK7 inhibition is primarily pursued for its anti-fibrotic and anti-inflammatory potential. Special attention is given to structure-based design strategies, including the exploitation of the unique hinge-region cysteine (Cys218) for MKK7-specific covalent targeting and the optimization of scaffold selectivity against off-targets like BRAF. Finally, we discuss emerging modalities, such as PROTACs and dual inhibitors, outlining a roadmap for the next generation of precision therapeutics targeting the MKK-JNK axis.

Poly(ethylene glycol) (PEG) has long stood as the prevailing standard in drug delivery, celebrated for its capacity to enhance solubility, extend circulation, and improve pharmacological performance. Nevertheless, the emergence of anti-PEG antibodies, accelerated clearance, and limited biodegradability increasingly undermine its role as a universal solution. In response, a new generation of polymers has been developed to address these shortcomings, offering the potential to sustain or surpass PEG's benefits while mitigating immunogenicity, improving biocompatibility, and enabling finer control over therapeutic fate. This review examines current research to articulate a coherent perspective on the replacement of PEG, tracing how advances in polymer design are reshaping the foundations of targeted drug delivery. Taken together, these developments signal not only a corrective to the limitations of PEG but also a broader paradigm shift toward safer, more versatile, and clinically translatable systems that define the next frontier in precision therapeutics.

Acmella oleracea (L.) R. K. Jansen (Asteraceae), commonly known as the "toothache plant" or "jambu," is a significant medicinal plant that has been traditionally used in Brazil and other tropical and subtropical regions for relieving dental pain, as an anti-inflammatory agent, and as a culinary spice. Due to its versatile utility, this plant has been extensively studied in modern medicine and pharmacy for its diverse pharmacological properties, including anesthetic, analgesic, anti-inflammatory, antioxidant, and antimicrobial activities. Analytical research on the chemical compositions responsible for these activities has led to the identification of approximately 120 secondary metabolites. These findings provide scientific validation for its traditional uses and have spurred research into the development of ingredients for functional foods and cosmetics. This review incorporates the latest research findings, focusing on biological activities and compounds that have been practically isolated or can be isolated based on quantitative experimental data, to serve as a practical reference for industrial development. Furthermore, factors influencing the content of alkylamides and phenolic compounds, two major bioactive groups, are summarized to support material development. Ultimately, this review aims to provide a clearer understanding of the plant's utility and contribute to the development of products that enhance human health.

This study aims to establish an origin identification method for Scutellariae radix that integrates multidimensional quality indicators and machine learning algorithms, enabling accurate and rapid traceability of Scutellariae radix medicinal materials from four production areas: Hebei (HB), Shanxi (SX), Shaanxi (SAX), and Chengde (CD). The study collected a total of 43 batches of Scutellariae radix samples from the aforementioned origins. It systematically measured 12 key quality indicators covering flavonoids, physicochemical parameters, chromaticity values, and biological activity. These specifically include four flavonoid components: baicalin, wogonoside, baicalein, and wogonin; three physicochemical parameters: moisture content, ash content, and alcohol-soluble extract; four chromaticity values: L*, a*, b*, and ΔE; and in vitro anti-inflammatory activity (IC₅₀ value for NO clearance). On the basis of these parameters, in this study there were five machine learning models constructed based on the following algorithms and methods: Random Forest (RF), Extreme Learning Machine (ELM), Backpropagation Neural Network (BP), and Radial Basis Function Neural Network (RBF). A comparative analysis was conducted to evaluate the origin identification performance of each model. The results indicate significant differences (p < 0.05) in the contents of baicalin, wogonoside, L*, a*, b*, ΔE, and alcohol-soluble extract among Scutellariae radix from different origins. The comparative analysis of four machine learning models reveals that RF outperforms ELM, BP, and RBF in multiclass classification, achieving a test accuracy of 75% and consistent precision, recall, and F1-score of 79.17%. In contrast, the three neural networks attain only 66.67% test accuracy, with RBF showing high precision but low recall, ELM delivering moderate performance, and BP performing poorly. These results underscore the strength of ensemble methods like RF in small-sample settings, where they mitigate overfitting and enhance generalization, whereas neural networks struggle with limited data. We therefore recommend RF for deployment under current data constraints and suggest future work should focus on data expansion, especially for under-performing classes, along with hyperparameter tuning to further improve classification.