Search Results (4,909)

Triazolopyrimidine sulfonamide herbicides, a prominent class of acetohydroxyacid synthase (AHAS) inhibitors, are exceptionally effective in controlling weeds in agricultural settings. To overcome metabolic resistance caused by the 5-demethylation of pyroxsulam, we sought to replace its 5-methoxy group on the triazolopyrimidine ring with alkyl substituents. This led to the synthesis of a series of N-(7-oxo-4,7-dihydro-[1,2,4]triazolo[1,5-a]pyrimidin-2-yl)arylsulfon-amides, which displayed significant structural diversification potential, culminating in the identification of the herbicidal hit compound I-20. However, the suboptimal lipophilicity compromised its herbicidal efficacy. To rectify this limitation, we modified the 7-carbonyl group to a tert-butoxy group, resulting in the highly active compound I-29. This compound demonstrated herbicidal activity comparable to or exceeding that of penoxsulam against various tested weeds, establishing it as a promising new lead compound and a candidate herbicide for further investigation. Subsequent studies revealed that I-29 exhibited a receptor binding mode and herbicidal activity profiles that closely aligned with those of penoxsulam. Moreover, its spatial structure was found to be even more conducive to inhibiting flavin adenine dinucleotide (FAD)-mediated AHAS activity. This research not only sheds light on addressing the challenge of 5-demethylation metabolic resistance in triazolopyrimidine sulfonamide herbicides but also offers new avenues for the development of AHAS-inhibiting triazolopyrimidine sulfonamide herbicides. Full article

Per- and polyfluoroalkyl substances (PFAS) continue to be one of the most persistent global contaminants and are increasingly recognized as leading metabolic- and hepatic-dysfunction mediators. Despite extensive investigation of PFAS toxicity, a critical gap in the identification and integration of toxicokinetic drivers of hepatic bioaccumulation with mechanistic pathways driving mitochondrial and nuclear receptor-related injury, more specifically, with respect to alternative PFAS strategies, still remains. Legacy PFAS, including PFOA and PFOS, accumulate in the liver and disturb mitochondrial homeostasis as they disrupt β-oxidation, induce oxidative stress, and alter lipid and bile acid metabolism. Meanwhile, the next-generation PFAS variants (including short-chain and polymeric substitutes) are rapidly increasing in environmental concentrations, but remain insufficiently characterized and poorly regulated, raising concerns that substitution-based strategies may maintain their toxicological risk. We summarize the evidence of the association between PFAS bioaccumulation and mitochondrial dysfunction, metabolic reprogramming, and inflammatory signaling, and illustrate mechanistic convergence across legacy and emerging PFAS. We also review insights from recent experimental models, such as 3D hepatocyte systems and human-relevant receptor platforms that more closely mimic chronic exposure states. This review emphasizes mechanistic convergence across legacy and emerging PFAS, highlighting shared pathways that may persist despite chemical substitution. Thus, we discuss key gaps in monitoring, toxicity assessment, and policy, including the requirement of regulatory paradigms that treat PFAS as a class rather than individual compounds. Full article

The monitoring of harmful algal blooms (HABs) constitutes a vital component of marine environmental protection and the sustainable development of the marine economy. However, the highly dynamic nature of these small targets, compounded by the complex water color interference prevalent in the coastal waters where HABs frequently occur, has resulted in traditional remote sensing monitoring methods, particularly those relying on fixed spectral index thresholds and pixel-wise binarization, suffering from imprecise identification in turbid coastal waters where suspended sediments, cloud cover, and sun glint create spectral confusion. These methods also exhibit low automation due to manual threshold adjustment requirements and poor transferability across different spatiotemporal conditions. Consequently, these methods struggle to meet practical application requirements. This study establishes a U-net model-based remote sensing identification framework for red tides using HY-1D CZI imagery (50 m resolution, 1–3 day revisit), targeted negative sample strategies, and event-level accuracy validation methods to achieve efficient marine red tide detection. Targeted negative sample selection involves purposefully selecting spectrally ambiguous regions as negative samples, aiming to enhance recognition accuracy and sample selection efficiency. The combination of targeted sampling with deep learning enables portability to new spatiotemporal contexts by learning invariant spectral–spatial features rather than relying on scene-specific thresholds. Experimental results demonstrate that the targeted negative sample strategy reduces event-level model false negatives by 27%, false positives by 36%, and increases the F1 score by 0.3217. Using an identical sample size, the targeted sample selection strategy yields an F1 score 0.0479 higher than random sampling. To achieve equivalent recognition accuracy, an increased number of random samples would be required. Comparative experiments reveal that the proposed method enhances sample selection efficiency by 87.5%. Transferability is demonstrated through successful identification of red tide patches in Wenzhou waters on 13 April 2022, without model retraining. This demonstrates that red tide remote sensing recognition based on targeted sample selection enables efficient, precise, and automated identification without human intervention, providing a reliable technical solution for operational marine red tide monitoring. Full article

Phenolic acids are the major bioactive compounds in Perilla frutescens (L.) Britt; however, the regulatory roles of NAC transcription factors (TFs) in their biosynthesis remain unclear. Here, we performed a genome-wide identification and characterization of the NAC family in P. frutescens and explored their involvement in phenolic acid production. A total of 108 PfNAC genes were identified and classified into 17 subfamilies. Expression and promoter analyses suggested potential roles in secondary metabolism. PfNAC29 is located in the plasma membrane and necleus, while PfNAC40 and PfNAC80 are located in the nucleus.Yeast one-hybrid and dual-luciferase assays demonstrated that these TFs bind to the CATGTG motif in the PfC4H promoter and activate its transcription. Overexpression in transgenic hairy roots significantly increased rosmarinic acid, caffeic acid, and ferulic acid accumulation, accompanied by upregulation of key biosynthetic genes. These results indicate that PfNAC29, PfNAC40, and PfNAC80 act as positive regulators of phenolic acid biosynthesis and provide promising targets for metabolic engineering in medicinal plants. Full article

Three-dimensional (3D) spheroid cancer models provide enhanced physiological relevance relative to traditional monolayer cultures but often demonstrate restricted drug responsiveness due to their dense architecture, hypoxic gradients, and diminished therapeutic penetrance. This study overcomes these limitations by establishing a sensitized 3D spheroid cancer cell model that employs the adenovirus-mediated gene expressions of tumor-suppressor and pro-apoptotic genes consisting of MOAP-1, BAX, and RASSF1A. The optimization of adenoviral infectivity led to the discovery of an intermediate multiplicity of infection (MOI) that facilitated effective and uniform transduction while reducing cytotoxicity. Adenovirus-infected 3D spheroid cells demonstrated enhanced apoptotic activities, evidenced by increased cell death relative to untreated spheroids. When exposed to the anti-cancer compound such as piperonal and pyrazole, the sensitized spheroids exhibited significantly enhanced drug responsiveness and synergistic effects over a five-day treatment period, surpassing the effects of adenovirus or anti-cancer drug treatment alone. Notably, similar responses were noted between low- and high drug doses, suggesting an enhancement of therapeutic efficacy at lower concentrations. This sensitized 3D spheroid model constitutes a more predictive in vitro system for anti-cancer drug discovery, facilitating enhanced mechanistic evaluation and the identification of potent drug candidates with greater translational significance. Full article

There has continued to be an increase in the production of gluten-free products, including beer. This interest is a combination of responses to both consumers addressing food sensitivities as well as personal preferences. Beer produced from gluten-free grains has a distinct flavor that differs greatly from traditional barley beer. Recently, the use of millet to produce gluten-free beer has increased with larger-scale malting of millet. It is the goal of this project to investigate the chemical composition of the millet beer aroma. The fermentation of millet-based beers was compared to sorghum and barley beers. Beyond this, the impact of common yeast strains on the fermentation of millet-based beers weas also investigated. All brews were regularly monitored for pH, gravity, total titratable acidity, total polyphenols, and free amino nitrogen. In addition, the aroma profile was sampled using Solid-Phase Microextraction (SPME) with chemical separation and identification and quantification using Gas Chromatography with Mass Spectroscopy (GC-MS). The analysis showed the production of acceptable beers; however, the fermentation there is obvious needed to optimize brewing conditions. In addition, the amount of total volatile compounds was found to be significantly different than beer produced using malted barley. Full article

Accurate and rapid identification of bacterial species is essential for public health, clinical diagnostics, and environmental monitoring. Although Raman spectroscopy offers a powerful, non-invasive alternative, reliance solely on spectral data often fails to distinguish species with highly similar signatures, particularly when the discriminating features are subtle. This difficulty is frequently compounded by a lack of integrated biological prior knowledge, which can hinder model performance. To address these challenges, we introduce BactoRamanBioNet, a novel multimodal neural network architecture. Our model employs a synergistic approach that utilizes a ResNet-Transformer architecture to capture complex spectral patterns and a CLIP text encoder to incorporate descriptive biological information, thereby enabling highly accurate multimodal classification of bacterial species. Empirical results demonstrate that BactoRamanBioNet achieves a classification accuracy of 98.2% and an F1-score of 98.0%. This performance surpasses the current state-of-the-art deep learning model, ResNet-1D, by 2.4% in accuracy and 2.0% in F1-score. Moreover, our model outperforms traditional classifiers, such as Support Vector Machine (SVM) and Random Forest (RF), by 9.8% and 7.9% in accuracy, respectively, while also exhibiting significant improvements in precision and recall. By establishing a new benchmark in performance and robustness, BactoRamanBioNet offers a powerful and reliable framework for automated microbiological analysis, paving the way for next-generation diagnostic systems. Full article

The genus Psiadia (Asteraceae), widely distributed in Madagascar and the Mascarene Islands (Mauritius, La Réunion, Rodrigues), is traditionally used to treat bronchitis, asthma, colds, abdominal pain, and other inflammatory disorders. However, few studies have scientifically validated these traditional medicinal uses. To assess P. dentata as a valuable source of bioactive natural products, a combined ¹H NMR-based metabolomic, molecular networking, and phytochemical study was conducted. Multivariate analysis (PLS-DA) of crude extracts from Psiadia species collected on Reunion Island enabled rapid discrimination of active extracts from P. dentata and revealed two methoxylated flavonoids and one coumarin as metabolites correlated with its antiplasmodial and anti-inflammatory activities. Additionally, UHPLC-DAD-ESI-QTOF-MS/MS molecular networking approach enabled detailed chemical profiling of this species, allowing the annotation of 25 compounds (1–25) in this species. Subsequent phytochemical investigation of P. dentata leaves led to the isolation and identification of 25 metabolites, including nine new diterpenes (26–34), one new coumarin (35), and 15 known compounds (1–8, 11, 18, 19 and 36–39) from the diterpenoid, flavonoid, and coumarin families. The structures of the new compounds were elucidated using spectroscopic methods, including extensive 1D and 2D NMR and HRESIMS analyses. Biological evaluation of the isolated compounds showed that compounds 1, 7, 26 and 27 showed antiplasmodial activity against Plasmodium falciparum (3D7 strain, IC₅₀ = 7.25–13.46 μM). Compounds 7, 26, 27, 31 and 32 inhibited nitric oxide production (IC₅₀ = 0.87–27.71 μM), indicating potential anti-inflammatory effects. Only compound 1 displayed moderate cytotoxicity against HepG2 and HT29 cancer cell lines (IC₅₀ = 25.67 and 18.35 μM, respectively). Full article

Saccharomyces cerevisiae is a useful model to understand the biochemistry and biology of aging. Yeast speeds up the aging study due to its short lifespan, well-established genetics, and simple measurement for lifespan. The chronological lifespan in yeast specifically emphasizes the survival rate of the population, providing data that offer more direct feedback on experimental treatments than replicative lifespan. The advancement of the yeast chronological lifespan assay has enabled researchers to efficiently screen numerous potential antiaging compounds and delve into aging theories. Through the integration of robust genetic screening and high-throughput technologies, the yeast model has facilitated the identification of various antiaging factors with potential applications in humans, shedding light on the genetic mechanisms of aging. Many natural products, similar to calorie restriction, have been shown to effectively extend the lifespan of yeast, a benefit that is also conserved in mammals. In this review, we highlight the nutrient factors, natural compounds, and genes that contribute to extending the yeast lifespan, as well as the genetic regulations underlying the aging process in yeast. Full article

Background: The visceral organs of sea cucumbers belonging to the family Stichopodidae, also known as Stichopodidae Viscus (SV), have been traditionally used for the management of gastrointestinal disorders. Experimental evidence has shown that the ethanol extract of SV (SVE) alleviates ulcerative colitis (UC) symptoms in a mouse model. However, the chemical constituents of SVE and the potential molecular targets mediating its effects in UC remain unclear. Methods: In this study, SVE was prepared from Apostichopus japonicus (Selenka). A reliable and sensitive strategy integrating advanced analytical and informatics tools was employed to profile the chemical components of SVE. Analyses were performed using ultra-performance liquid chromatography coupled with ion mobility spectrometry and quadrupole time-of-flight mass spectrometry operating in high-definition MS^E (UPLC-IMS-Q-TOF-HDMS^E), with data processed using the UNIFI scientific information system. Constituent identification relied on retention time (RT), accurate mass (MS1), experimentally acquired HDMS^E (MS2) spectra, and collision cross-section (CCS). Metabolomics-based approaches were further applied to characterize the in vivo exposure profile of SVE components in mouse serum and colon tissue after oral administration. Subsequently, the putative bioactive constituents and their underlying mechanisms of action were investigated using network pharmacology and molecular docking. Results: Based on the integrated identification strategy, a total of 78 compounds, including saponins, phenolic acids, fatty acids, and amino acids, were annotated in SVE, among which 6 compounds were verified using authentic reference standards to ensure unambiguous identification. Subsequently, 35 features in serum and 24 in the colon were found to be significantly altered following a single oral dose of SVE in mice, and were defined as SVE-related differential constituents. After network pharmacology analyses, 129 shared targets were identified between potential targets of SVE-related components in serum and UC-related targets, including PIK3CA, EGFR, and AKT1. Functional enrichment analysis suggested that SVE might exert its effects in UC through modulation of key nodes within the PI3K-Akt and EGFR signaling pathways, as well as lipid- and atherosclerosis-related pathways. Molecular docking results further indicated moderate binding affinities of representative SVE-related differential components toward PIK3CA, AKT1, and EGFR. Conclusions: This study clarifies the chemical basis and potential UC-related mechanisms of SVE, providing a scientific rationale for the development of SV-derived therapeutic candidates for UC. Full article

Rapid and actionable pathogen identification remains a major unmet need in the diagnosis of prosthetic joint infection (PJI). Current diagnostic approaches either provide rapid host response information without pathogen specificity or identify pathogens with delays of days to weeks. Here, we report a chemiresistive nanosensor array combined with machine learning analysis for same-day, pathogen-specific detection based on volatile organic compound (VOC) profiling. A 19-channel nanosensor array was first validated in vitro against a panel of ESKAPEE pathogens, achieving 96% mean classification accuracy using a radial-basis-function support vector machine (SVM) classifier. Data-driven optimization yielded a reduced six-sensor array with high signal-to-noise performance. The optimized platform was evaluated using pooled, uninfected human synovial fluid enriched 1:1 with nutrient media and spiked with Staphylococcus aureus, Staphylococcus epidermidis, or Pseudomonas aeruginosa across a range of 1–10⁶ CFU/mL. All infected samples were detected within 9 h, with distinct VOC signatures enabling accurate pathogen differentiation. Time-to-detection (TTD) demonstrated a strong inverse correlation with initial bacterial concentration, supporting semi-quantitative estimation of bacterial load. Negative controls remained at baseline throughout testing. This chemiresistive VOC-based biosensor platform demonstrates the potential to deliver rapid, integrated detection, identification, and burden estimation of metabolically active PJI pathogens, highlighting its promise for future point-of-care diagnostic applications. Full article

Indigofera stachyodes Lindl. (I. stachyodes), a fundamental herb in Miao ethnomedicine, possesses a broad pharmacological profile including antitumor potential. However, its antitumor bioactive compounds and their underlying mechanisms remain poorly characterized. Here, we developed a spectrum-effect relationship analysis integrated with UPLC-Q-TOF-MS/MS, which enabled the identification of 7 compounds with potential antitumor activity from I. stachyodes. A secondary screening of candidate compounds was performed using network pharmacology, which led to the identification of fisetin, luteolin, wogonin, and liquiritigenin as potential antitumor compounds. Enrichment analysis and molecular docking studies predicted the key involvement of the PI3K-AKT signaling pathway in mediating the antitumor activities of these compounds. Subsequently, in vitro cell experiments confirmed that the fisetin, wogonin, luteolin and liquiritigenin inhibited the proliferation of HepG2 cells, with IC₅₀ values of 82.13 ± 6.74, 123.38 ± 5.71, 141.76 ± 6.37, and 151.04 ± 3.08 µM, respectively, while exhibiting moderate antitumor activity compared to chemotherapeutic agents. This antiproliferative effect was further corroborated by confocal laser scanning microscopy (CLSM). These results not only validate the potential of I. stachyodes as a source for antitumor agents but also provide a foundation for its further development. Full article

Scaling up microalgae cultivation is key to commercial viability. Over the past two decades, the market value of microalgae has expanded exponentially, driven by their applications in the pharmaceutical, nutraceutical, cosmetic, and animal feed industries. High-value compounds such as omega-3 fatty acids, proteins, and pigments are in strong demand. However, supply remains constrained by suboptimal cultivation practices and high harvesting costs. Despite decades of progress in process modeling, control, and optimization, industrial adoption is still limited by dynamic cultivation conditions influenced by weather variability, biological adaptation, and integration challenges. Technical barriers, including limited data accuracy, modest control performance, and the fragility of low-cost devices, further restrict optimization efforts. In response, we examined recent advances in control, optimization, and automated machine learning applied to microalgae cultivation. We propose an automated architecture built on a closed-loop supervisory layer that embeds machine learning within the control loop, enabling real-time monitoring, prediction, and adaptive actuation. This approach aligns with real-time optimization and distributed control system practices, integrating system identification, controller optimization, fault diagnosis and tolerance, and perception to achieve autonomous, uncertainty-aware operation. Full article

Background/Objectives: Natural medicine analysis remains challenging due to chemical diversity. To the best of our knowledge, the comprehensive identification of multiple chemical constituents in Xiaoban Kangfu (XBKF) capsules has not been reported. Therefore, a combined approach utilizing ultra-high-performance liquid chromatography quadrupole-Exactive Orbitrap mass spectrometry (UHPLC-Q-Exactive Orbitrap MS) and molecular network analysis needs to be developed to comprehensively characterize the chemical constituents of XBK capsules in heat-clearing and toxin-eliminating granules, thereby enhancing annotation accuracy and enabling visualization. Methods: Firstly, chromatographic and mass spectrometry conditions were optimized to achieve good separation and a rich signal response. Subsequently, the literature searches, database consultations, and reference standards were employed to enhance annotation reliability. Finally, the raw data acquired under optimized conditions were uploaded to Global Natural Products Social (GNPSs), enabling component visualization by linking precursor ions of similar structural features with identical colors. Results: A total of 170 compounds were identified from this medicinal resource for the first time, including 50 flavonoids, 34 phenolic acids, 16 terpenoids, 14 quinones, 14 organic acids, eight coumarins, ive carbohydrates, and 29 other compounds. Conclusions: This study establishes a robust UHPLC-Q-Exactive Orbitrap MS/MS strategy for the comprehensive chemical profiling of XBKF capsules. The use of the presented validated analytical method for the comprehensive quality control of XBKF capsules is highly promising, offering fast, highly sensitive, and reliable analysis. Full article

Natural products such as polyketides are a fertile target for drug discovery. Methodologies relating to discovery, metabolism, synthesis and biosynthesis of polyketides have evolved considerably since they were first studied in the early 20th century. The antibiotic erythromycin, produced by the Streptomyces erythreus bacteria, was the first of the macrolide natural products to be discovered in 1952. The biosynthesis of erythromycin is catalysed by a large multifunctional enzyme, which constructs the polyketide intermediate that is acted upon by tailoring enzymes to produce the final construct. It is during this process that molecular diversity is generated, and commercial samples of erythromycin tend to be mixtures of co-metabolites. To fully identify these compounds, a full fragmentation scheme of the main component (erythromycin A) is required, which is absent from the literature. In this study, accurate-mass sequential mass spectrometry is used to propose a fragmentation scheme which is then used to assign structures to eight co-metabolites including the identification of a previously unpublished form of erythromycin. This clearly demonstrates the successful application of the methodology. Full article