Search Results (664)

As typical environmental hormones, endocrine-disrupting chemicals (EDCs) have become a global environmental health issue of high concern due to their property of interfering with the endocrine systems of organisms. As a commonly used substitute for bisphenol A (BPA), bisphenol E (BPE) has been frequently detected in environmental matrices such as soil and water in recent years. Existing research has unveiled the developmental and reproductive toxicity of BPE; however, only one in vitro cellular experiment has preliminarily indicated potential neurotoxic risks, with its underlying mechanisms remaining largely unelucidated in the current literature. Potential toxic mechanisms and action targets of BPE were predicted using the zebrafish model via network toxicology and molecular docking, with RT-qPCRs being simultaneously applied to uncover neurotoxic effects and associated mechanisms of BPE. A significant decrease (p < 0.05) in the frequency of embryonic spontaneous movements was observed in zebrafish at exposure concentrations ≥ 0.01 mg/L. At 72 hpf and 144 hpf, the larval body length began to shorten significantly from 0.1 mg/L to 1 mg/L, respectively (p < 0.01), accompanied by a reduced neuronal fluorescence intensity and a shortened neural axon length (p < 0.01). By 144 hpf, the motor behavior in zebrafish larvae was inhibited. Through network toxicology and molecular docking, HSP90AB1 was identified as the core target, with the cGMP/PKG signaling pathway determined to be the primary route through which BPE induces neurotoxicity in zebrafish larvae. BPE induces neuronal apoptosis and disrupts neurodevelopment by inhibiting the cGMP/PKG signaling pathway, ultimately suppressing the larval motor behavior. To further validate the experimental outcomes, we measured the expression levels of genes associated with neurodevelopment (elavl3, mbp, gap43, syn2a), serotonergic synaptic signaling (5-ht1ar, 5-ht2ar), the cGMP/PKG pathway (nos3), and apoptosis (caspase-3, caspase-9). These results offer crucial theoretical underpinnings for evaluating the ecological risks of BPE and developing environmental management plans, as well as crucial evidence for a thorough comprehension of the toxic effects and mechanisms of BPE on neurodevelopment in zebrafish larvae. Full article

Background: Often, neoadjuvant therapy, which relies on the induction of double-strand breaks (DSBs), is used prior to surgery to shrink tumors by inducing cancer cell apoptosis. However, recent studies have suggested that this treatment may also induce a fluctuating state between senescence and stemness in PA-1 embryonal carcinoma cells, potentially affecting therapeutic outcomes. Thus, the respective epigenetic pathways are up or downregulated over a time period of days. These fluctuations go hand in hand with changes in spatial DNA organization. Methods: By means of Single-Molecule Localization Microscopy in combination with mathematical evaluation tools for pointillist data sets, we investigated the organization of euchromatin and heterochromatin at the nanoscale on the third and fifth day after etoposide treatment. Results: Using fluorescently labeled antibodies against H3K9me3 (heterochromatin tri-methylation sites) and H3K4me3 (euchromatin tri-methylation sites), we found that the induction of DSBs led to the de-condensation of heterochromatin and compaction of euchromatin, with a peak effect on day 3 after the treatment. On day 3, we also observed the co-localization of euchromatin and heterochromatin, which have marks that usually occur in exclusive low-overlapping network-like compartments. The evaluation of the SMLM data using topological tools (persistent homology and persistent imaging) and principal component analysis, as well as the confocal microscopy analysis of H3K9me3- and H3K4me3-stained PA-1 cells, supported the findings that distinct shifts in euchromatin and heterochromatin organization took place in a subpopulation of these cells during the days after the treatment. Furthermore, by means of flow cytometry, it was shown that the rearrangements in chromatin organization coincided with the simultaneous upregulation of the stemness promotors OCT4A and SOX2 and senescence promotors p21Cip1 and p27. Conclusions: Our findings suggest potential applications to improve cancer therapy by inhibiting chromatin remodeling and preventing therapy-induced senescence. Full article

Verticillium wilt is characterized by chlorosis in leaves and is a devastating disease in eggplant. Early diagnosis, prior to the manifestation of symptoms, enables targeted management of the disease. In this study, we aim to detect early leaf wilt in eggplant leaves caused by Verticillium dahliae by integrating multispectral imaging with machine learning and deep learning techniques. Multispectral and chlorophyll fluorescence images were collected from leaves of the inbred eggplant line 11-435, including data on image texture, spectral reflectance, and chlorophyll fluorescence. Subsequently, we established a multispectral data model, fusion information model, and multispectral image–information fusion model. The multispectral image–information fusion model, integrated with a two-dimensional convolutional neural network (2D-CNN), demonstrated optimal performance in classifying early-stage Verticillium wilt infection, achieving a test accuracy of 99.37%. Additionally, transfer learning enabled us to diagnose early leaf wilt in another eggplant variety, the inbred line 14-345, with an accuracy of 84.54 ± 1.82%. Compared to traditional methods that rely on visible symptom observation and typically require about 10 days to confirm infection, this study achieved early detection of Verticillium wilt as soon as the third day post-inoculation. These findings underscore the potential of the fusion model as a valuable tool for the early detection of pre-symptomatic states in infected plants, thereby offering theoretical support for in-field detection of eggplant health. Full article

Investigating brain area functions requires advanced technologies, but meaningful insights depend on correlating neural signals with behavior. Traditional mice behavior annotation methods, including manual and semi-automated approaches, are limited by subjectivity and time constraints. To overcome these limitations, our study employs the YOLO neural network for precise mice tracking and composite RGB frames for behavioral scoring. Our model, trained on over 10,000 frames, accurately classifies sitting, running, and grooming behaviors. Additionally, we provide statistical metrics and data visualization tools. We further combined AI-powered behavior labeling to examine hippocampal neuronal activity using fluorescence microscopy. To analyze neuronal circuit dynamics, we utilized a manifold analysis approach, revealing distinct functional patterns corresponding to transgenic 5xFAD Alzheimer’s model mice. This open-source software enhances the accuracy and efficiency of behavioral and neural data interpretation, advancing neuroscience research. Full article

The GABA_A receptors, through a short-term interaction with a mediator, induce hyperpolarization of the membrane potential (V_m) via the passive influx of chloride ions (Cl⁻) into neurons. The massive (or intense) activation of the GABA_ARs by the agonist could potentially lead to depolarization/excitation of the V_m. Although the ionic mechanisms of GABA_A-mediated depolarization remain incompletely understood, a combination of the outward chloride current and the inward bicarbonate current and the resulting pH shift are the main reasons for this event. The GABA_A responses are determined by the ionic gradients—neuronal pH/bicarbonate homeostasis is maintained by carbonic anhydrase and electroneutral/electrogenic bicarbonate transporters and the chloride level is maintained by secondary active cation–chloride cotransporters. Massive activation can also induce the rundown effect of the receptor function. This rundown effect partly involves phosphorylation, Ca²⁺ and the processes of receptor desensitization. In addition, by various methods (including fluorescence and optical genetic methods), it has been shown that massive activation of GABA_ARs during pathophysiological activity is also associated with an increase in [Cl⁻]_i and a decline in the pH and ATP levels in neurons. Although the relationship between the neuronal changes induced by massive activation of GABAergic signaling and the risk of developing neurodegenerative disease has been extensively studied, the molecular determinants of this process remain somewhat mysterious. The aim of this review is to summarize the data on the relationship between the massive activation of inhibitory signaling and the ionic changes in neurons. The potential role of receptor dysfunction during massive activation and the resulting ionic and metabolic disruption in neurons during the manifestation of network/seizure activity will be considered. Full article

Real-time, high-resolution monitoring of chemically diverse water pollutants remains a critical challenge for smart water management. Here, we report a fully integrated, multi-modal nano-sensor array, combining graphene field-effect transistors, Ag/Au-nanostar surface-enhanced Raman spectroscopy substrates, and CdSe/ZnS quantum dot fluorescence, coupled to an edge-deployable CNN-LSTM architecture that fuses raw electrochemical, vibrational, and photoluminescent signals without manual feature engineering. The 45 mm × 20 mm microfluidic manifold enables continuous flow-through sampling, while 8-bit-quantised inference executes in 31 ms at <12 W. Laboratory calibration over 28,000 samples achieved limits of detection of 12 ppt (Pb²⁺), 17 pM (atrazine) and 87 ng L⁻¹ (nanoplastics), with R² ≥ 0.93 and a mean absolute percentage error <6%. A 24 h deployment in the Cherwell River reproduced natural concentration fluctuations with field R² ≥ 0.92. SHAP and Grad-CAM analyses reveal that the network bases its predictions on Dirac-point shifts, characteristic Raman bands, and early-time fluorescence-quenching kinetics, providing mechanistic interpretability. The platform therefore offers a scalable route to smart water grids, point-of-use drinking water sentinels, and rapid environmental incident response. Future work will address sensor drift through antifouling coatings, enhance cross-site generalisation via federated learning, and create physics-informed digital twins for self-calibrating global monitoring networks. Full article

Anaplastic thyroid carcinoma (ATC) is a highly aggressive malignancy with poor prognosis and limited treatment options. Precision oncology seeks personalized therapies that selectively modulate tumor behavior, which is critical for improving patient outcomes. In this study, we evaluated the therapeutic potential of human dental pulp stem cell-derived extracellular vesicles (hDPSC-EVs) in three ATC cell lines (8505C, HTH83, KTC-2). Fluorescence and confocal microscopy confirmed the efficient, time-dependent internalization of hDPSC-EVs by ATC cells, with increased fluorescence intensity over 48 h. Functional assays revealed the selective inhibition of migration and invasion in a cell line-dependent manner, without affecting cell proliferation, viability, or tumorigenic traits, indicating a non-cytotoxic, context-specific modulation of tumor behavior. After 72 h of EV treatment, targeted qPCR of 92 cancer-related genes showed the strongest response in 8505C cells (24 genes; 16 up, 8 down), moderate changes in KTC-2 (16 genes; 14 up, 2 down), and few alterations in HTH83 (6 genes; 4 up, 2 down). Across all lines, FN1 emerged as a context-dependent target, downregulated in 8505C but upregulated in the other two. No broad pathway enrichment was observed, indicating the fine-tuning of key networks rather than wholesale reprogramming. Despite variations across cell lines, hDPSC-EVs consistently demonstrated no impact on cell proliferation and no evidence of cytotoxicity or tumorigenic behavior, with no adverse outcomes. These findings provide preclinical evidence for hDPSC-EVs as a promising, safe, and targeted therapeutic platform in precision oncology, particularly for aggressive cancers, like ATC, warranting further exploration in preclinical and clinical studies. Full article

To broaden clean asphalt modification methods, this study employs a composite polymer of maleic anhydride-grafted styrene-butadiene-styrene (MA-g-SBS) and styrene-butadiene-styrene (SBS) as a modifier. The composite is formulated into polymer latex and used to modify emulsified asphalt. Routine performance tests were conducted on MA-g-SBS/SBS composite latex-modified emulsified asphalt (MSMEA) with varying ratios to determine the optimal composition. The ideal ratio was found to be MA-g-SBS:SBS = 1:4. Subsequently, conventional property tests, rheological analyses, microphase structure observations, and bending beam creep tests were conducted on MSMEA with the optimal ratio to assess the impact of the composite latex on asphalt performance. Findings indicated that increasing the latex content significantly enhanced the softening point and ductility while reducing penetration. These macroscopic improvements were notably superior to those achieved with single SBS latex modification. Fluorescence microscopy revealed that at low dosages, the MA-g-SBS/SBS composite dispersed uniformly as point-like structures within the asphalt. At higher dosages (above 5%), a distinct network structure emerged. The addition of the composite latex raised the complex shear modulus and rutting factor while reducing the phase angle, with pronounced fluctuations observed between 4% and 5% dosages. This suggests a substantial enhancement in the high-temperature performance of the emulsified asphalt, attributed to the formation of the network structure. FT-IR results confirmed that a chemical reaction occurred during the modification process. Additionally, the bending beam creep test demonstrated that the composite latex reduced asphalt brittleness and improved its low-temperature performance. Full article

Background: Chitosan (CS) crosslinked with genipin (GNP) provides a mild, non-toxic route to generate nanogels (NGs) with enhanced integrity and colloidal stability. Objectives: To develop and characterise CS-GNP NG as a novel platform for targeted cellular delivery, optimising design through physicochemical characterisation and biocompatibility evaluation. Methods: NGs were synthesised under optimised conditions by adjusting the pH of the CS solution, followed by high-intensity ultrasound (HIUS) to achieve disaggregation. Physicochemical characterisation was carried out using UV-Vis spectroscopy, FTIR, dynamic light scattering (DLS), and scanning electron microscopy (SEM). Rheological studies and SAXS analysis assessed structural properties. Biocompatibility was evaluated via MTT assay, and internalisation was monitored by fluorescence microscopy on mammalian cell lines. Results: NG formation was highly pH-dependent, with optimal configuration at pH 4.5, yielding stable, uniformly sized particles (~200 nm, ζ-potential +29 mV). Kinetic modelling showed a sigmoidal formation pattern, suggesting nucleation, growth, and stabilisation. FTIR confirmed covalent bonding between CS and GNP via primary amide bonds and Schiff bases. Rheology indicated pseudoplastic behaviour, and SAXS revealed a compact network formation. Biocompatibility assays confirmed non-cytotoxicity below 100 µg/mL and efficient cellular uptake. Conclusions: This study presents a rapid, reproducible protocol for generating colloidally stable, biocompatible NGs suitable for drug delivery. Full article

USP (usual temperature pitch)-modified asphalt optimizes its rheological properties through reactions between the modifier and the asphalt. This significantly enhances the high- and low-temperature adaptability and environmental friendliness of asphalt. It has now become an important research direction in the field of highway engineering. This article systematically investigates the impact of different dosages of USP warm mix modifier on asphalt binders through rheological and microstructural analysis. Base asphalt and SBS-modified asphalt were blended with USP at varying ratios. Conventional tests (penetration, softening point, ductility) were combined with dynamic shear rheometry (DSR, AASHTO T315) and bending beam rheometry (BBR, AASHTO T313) to characterize temperature/frequency-dependent viscoelasticity. High-temperature performance was quantified via multiple stress creep recovery (MSCR, ASTM D7405), while fluorescence microscopy and FTIR spectroscopy elucidated modification mechanisms. Key findings reveal that (1) optimal USP thresholds exist at 4.0% for base asphalt and 4.5% for SBS modified asphalt, beyond which the rutting resistance factor (G*/sin δ) decreases by 20–31% due to plasticization effects; (2) USP significantly improves low-temperature flexibility, reducing creep stiffness at −12 °C by 38% (USP-modified) and 35% (USP/SBS composite) versus controls; (3) infrared spectroscopy displays that no new characteristic peaks appeared in the functional group region of 4000–1300 cm⁻¹ for the two types of modified asphalt after the incorporation of USP, indicating that no chemical changes occurred in the asphalt; and (4) fluorescence imaging confirmed that the incorporation of USP led to disintegration of the spatial network structure of the control asphalt, explaining the reason for the deterioration of high-temperature performance. Full article

The chlorophyll index (CHI) is a crucial indicator for assessing the photosynthetic capacity and nutritional status of crops. However, traditional methods for measuring CHI, such as chemical extraction and handheld instruments, fall short in meeting the requirements for efficient, non-destructive, and continuous monitoring at the canopy level. This study aimed to explore the feasibility of predicting rice canopy CHI using nighttime multi-source spectral data combined with machine learning models. In this study, ground truth CHI values were obtained using a SPAD-502 chlorophyll meter. Canopy spectral data were acquired under nighttime conditions using a high-throughput phenotyping platform (HTTP) equipped with active light sources in a greenhouse environment. Three types of sensors—multispectral (MS), visible light (RGB), and chlorophyll fluorescence (ChlF)—were employed to collect data across different growth stages of rice, ranging from tillering to maturity. PCA and LASSO regression were applied for dimensionality reduction and feature selection of multi-source spectral variables. Subsequently, CHI prediction models were developed using four machine learning algorithms: support vector regression (SVR), random forest (RF), back-propagation neural network (BPNN), and k-nearest neighbors (KNNs). The predictive performance of individual sensors (MS, RGB, and ChlF) and sensor fusion strategies was evaluated across multiple growth stages. The results demonstrated that sensor fusion models consistently outperformed single-sensor approaches. Notably, during tillering (TI), maturity (MT), and the full growth period (GP), fused models achieved high accuracy (R² > 0.90, RMSE < 2.0). The fusion strategy also showed substantial advantages over single-sensor models during the jointing–heading (JH) and grain-filling (GF) stages. Among the individual sensor types, MS data achieved relatively high accuracy at certain stages, while models based on RGB and ChlF features exhibited weaker performance and lower prediction stability. Overall, the highest prediction accuracy was achieved during the full growth period (GP) using fused spectral data, with an R² of 0.96 and an RMSE of 1.99. This study provides a valuable reference for developing CHI prediction models based on nighttime multi-source spectral data. Full article

Citral, an organic compound found in lemongrass (Cymbopogon citratus) oil and Litsea cubeba essential oil, has been reported to exhibit notable antifungal activity against Magnaporthe oryzae (M. oryzae), the pathogen of rice blast, which causes significant economic losses in rice production. However, the role of citral in inducing oxidative stress related to antifungal ability and its underlying regulatory networks in M. oryzae remain unclear. In this study, we investigated the oxidative effects of citral on M. oryzae and conducted transcriptomic and widely targeted metabolomic (WTM) analyses on the mycelia. The results showed that citral induced superoxide dismutase (SOD), catalase (CAT), ascorbate peroxidase (APX) activities but reduced glutathione S-transferase (GST) activity with 25% maximal effective concentration (EC₂₅) and 75% maximal effective concentration (EC₇₅). Importantly, citral at EC₇₅ reduced the activities of mitochondrial respiratory chain complex I, complex III and ATP content, while increasing the activity of mitochondrial respiratory chain complex II. In addition, citral triggered a burst of reactive oxygen species (ROS) and a loss of mitochondrial membrane potential (MMP) through the observation of fluorescence. Furthermore, RNA-seq analysis and metabolomics analysis identified a total of 466 differentially expression genes (DEGs) and 32 differential metabolites (DAMs) after the mycelia were treated with citral. The following multi-omics analysis revealed that the metabolic pathways centered on AsA, GSH and melatonin were obviously suppressed by citral, indicating a disrupted redox equilibrium in the cell. These findings provide further evidences supporting the antifungal activity of citral and offer new insights into the response of M. oryzae under oxidative stress induced by citral. Full article

To achieve an efficient, non-destructive, and intelligent identification of tea plant seedlings under high-temperature stress, this study proposes an improved YOLOv11 model based on chlorophyll fluorescence imaging technology for intelligent identification. Using tea plant seedlings under varying degrees of high temperature as the research objects, raw fluorescence images were acquired through a chlorophyll fluorescence image acquisition device. The fluorescence parameters obtained by Spearman correlation analysis were found to be the maximum photochemical efficiency (Fv/Fm), and the fluorescence image of this parameter is used to construct the dataset. The YOLOv11 model was improved in the following ways. First, to reduce the number of network parameters and maintain a low computational cost, the lightweight MobileNetV4 network was introduced into the YOLOv11 model as a new backbone network. Second, to achieve efficient feature upsampling, enhance the efficiency and accuracy of feature extraction, and reduce computational redundancy and memory access volume, the EUCB (Efficient Up Convolution Block), iRMB (Inverted Residual Mobile Block), and PConv (Partial Convolution) modules were introduced into the YOLOv11 model. The research results show that the improved YOLOv11-MEIP model has the best performance, with precision, recall, and mAP50 reaching 99.25%, 99.19%, and 99.46%, respectively. Compared with the YOLOv11 model, the improved YOLOv11-MEIP model achieved increases of 4.05%, 7.86%, and 3.42% in precision, recall, and mAP50, respectively. Additionally, the number of model parameters was reduced by 29.45%. This study provides a new intelligent method for the classification of high-temperature stress levels of tea seedlings, as well as state detection and identification, and provides new theoretical support and technical reference for the monitoring and prevention of tea plants and other crops in tea gardens under high temperatures. Full article

Foxtail millet (Setaria italica), a significant C4 model crop known for its exceptional photosynthetic efficiency and robust environmental adaptability, serves as an excellent model for investigating C4 photosynthesis and crop stress resilience. When subjected to abiotic stress, foxtail millet employs a sophisticated signal transduction network to regulate its physiological processes, ensuring sustained high photosynthetic efficiency and normal growth. The mitogen-activated protein kinase (MAPK) family plays a key role in plant growth, development, and stress response. Here, we identified and named a MAPK in S. italica as SiMPK6. Fluorescence quantitative PCR analysis revealed that SiMPK6 is mainly expressed in the leaves during the early shooting stage, with induction under various abiotic stresses such as low temperature, high osmotic pressure, high salt, high temperature, and high light. Overexpressing the SiMPK6 in Arabidopsis thaliana mitigated damage to photosystem II induced by stress, underscoring the gene’s crucial role in foxtail millet’s stress signal transduction and maintenance of high photosynthetic efficiency. Full article

Neurotransmitters such as serotonin regulate key physiological and cognitive functions, yet real-time detection remains challenging due to the limitations of conventional techniques like amperometry and microdialysis. Fluorescent molecularly imprinted polymer nanoparticles (fMIP-NPs) offer a promising alternative and are typically synthesized via solid-phase synthesis, in which template molecules are covalently immobilized on a solid support to enable site-specific imprinting. However, strong template–template interactions during this process can compromise selectivity. To overcome this, we incorporated a poly(methacrolein-co-methacrylamide)-based template anchoring strategy to minimize undesired template interactions and enhance imprinting efficiency. We optimized the synthesis of poly(methacrolein-co-methacrylamide) under three different conditions by varying the monomer compositions and reaction parameters. The poly(methacrolein-co-methacrylamide) synthesized under Condition 3 (5:1 methacrolein-to-methacrylamide molar ratio, 1:150 initiator-to-total monomer ratio, and 4.59 M total monomer concentration) yielded the most selective fMIP-NPs, whose fluorescence intensity increased with an increase in serotonin concentration, rising by up to 37% upon serotonin binding. This improvement is attributed to higher aldehyde functionality in the poly(methacrolein-co-methacrylamide) which enhances template immobilization and generates a rigid imprinted cavity to interact with serotonin. These findings suggest that the developed fMIP-NPs hold significant potential as imaging probes for neurotransmitter detection, contributing to advanced studies in neural network analysis. Full article