This study used a coupled computational fluid dynamics-discrete element method (CFD-DEM) simulation model to investigate the variation in flow velocity, the effective number of collisions, and interaction forces, and heat transfer between particles of green tea powder (GTP) during ball milling under different grinding times, rotational speeds, and ambient temperatures. The results indicate that with increasing grinding time, the maximum velocity and average velocity of particles gradually rise and stabilize, while the forces acting on particles initially increase, then slowly decrease and stabilize. However, the proportion of different collision types to total collision counts shows a relatively insignificant variation. The particle breakage effect improves with the increase of rotational speed. In addition, an increase in temperature significantly enhances the flow velocity of particles, and the force acting on the particles shows a significant increase and then a slow decrease. However, an increase in temperature leads to a reduction in the proportion of particle-ball collision types. For the heat transfer phenomenon of particles, higher rotational speeds lead to a faster increase in particle temperature, with a higher achievable maximum temperature. The particle temperature also increases with the extension of grinding time, but it is difficult to bring all particles to the maximum temperature. The research results provide guidance for the optimization of the production process of GTP. (c) 2026 Published by Elsevier B.V. on behalf of The Society of Powder Technology Japan. All rights are reserved, including those for text and data mining, AI training, and similar technologies.

The rapid and accurate detection of cadmium (Cd) in rice leaves is crucial for the prevention and control of heavy metal pollution. Compared to traditional chemical methods, laser-induced breakdown spectroscopy (LIBS) serves as a fast, chemical-free method for the analysis of hazardous metals. However, the detection capabilities of LIBS are influenced by matrix effects, which have long been a core bottleneck limiting the quantitative accuracy of LIBS. In this study, a data rolling method was introduced, which enhanced the local receptive field of the convolutional kernel and provides global interaction of spectral information through data rolling changes. By combining a ResNet18-based bidirectional convolutional network, this approach significantly improved the detection accuracy of Cd in rice leaves and effectively mitigates matrix effects in spectral analysis. The results indicate that data rolling bidirectional residual network (DR-BiResNet) outperforms traditional machine learning models, achieving correlation coefficient (R) of 0.9486, root mean square error (RMSE) of 6.4771 mg/kg and relative prediction deviation (RPD) of 3.16 on the prediction set. DR-BiResNet successfully captured the Cd I 228.80 nm and Cd II 214.44 nm signals and effectively learned the global interactions as observed by GradCAM++. Additionally, visual diagnostics were conducted on the external samples, allowing for a direct observation of the distribution of Cd in rice leaves, with R of 0.9618. The combination of DR-BiResNet and LIBS provides an accurate and efficient analytical method for the detection of Cd in rice leaves, which has great potential for environmental protection.

Grinding is an essential process in green tea powder production, which greatly affects the final quality. However, in-depth studies on process parameter-quality correlations are lacking, especially regarding the multiple process parameters induced variability. In this work, the influence of process parameters (ambient temperature, rotational speed, and grinding time) on physicochemical and sensory metrics of green tea powder was investigated with univariate and multivariate analysis. Both rotational speed and grinding time contribute to reducing the particle size and water holding capacity (WHC), and increasing lightness, greenness, and yellowness of green tea powder, while low ambient temperature can help to improve the WHC and flowability. Rotational speed is significantly related to the concentrations of gallic acid, catechin, epigallocatechin gallate, and epigallocatechin gallate (p < 0.01). Rotational speed and grinding time are closely related to the final appearance of green tea powder, while temperature is the dominant factor influencing its aroma.

Purpose Osteosarcoma (OS) is a prevalent primary malignant bone tumor that predominantly affects children, adolescents, and young adults. Proline-rich protein 11 (PRR11) is wellknown for its role in regulating cell cycle progression and promoting tumorigenesis. Nevertheless, the precise molecular mechanisms underlying PRR11-driven tumorigenesis in OS have yet to be elucidated. In the present study, we aimed to elucidate the role of PRR11 in OS and its underlying molecular mechanisms. Methods Genotype‒tissueexpression(GTEx) and The Cancer Genome Atlas (TCGA) data were analyzed for PRR11 expression (normal vs OS) and survival differences (low vs high expression). Immunohistochemistry(IHC) and western blotting(WB) were performed to examine the expression distribution of the PRR11 protein in OS tissues and cell lines. Three types of lentiviral vectors were used to establish stable 143B cell lines: (1) miRNA-based shRNA vectors, (2) Lenti-CRISPR-Cas9 vectors, and (3) overexpression vectors. RNA-seq analysis of the miRNA-based shRNAs. WB was used to elucidate the mechanisms by whichPRR11 affects DNA damage, DNA repair, the cell cycle, and the Hippo signaling pathway. Moreover, functional assays included colony formation, wound healing, and transwell assays in vitro and subcutaneous inoculation in vivo . Results This studyidentified PRR11 as a pivotal regulator that promotes OS cell migration, invasion, and proliferation in vitro and promotes OS subcutaneous inoculation. RNA-seq analysis revealed that PRR11 silencing regulates several signaling pathways, including the cell cycle, the DNA damage response, and DNA repair;subsequently, the detection of DNA damage/repair markers and cell cycle-related proteins further confirmed alterations in these signaling pathways. Subsequent flow cytometry experiments revealed that PRR11 could prolong the G0/G1 phase and shorten the G2/M phase. Conclusions PRR11 functions as an oncogene in OS, where the PRR11-Hippo axis drivestumor progression through a DNA damage-cell cycle coupling mechanism.

Rapid and accurate quantification of mineral elements in plants facilitates the optimization of cultivation strategies and provides theoretical support for heavy metal pollution control. Compared to traditional chemical detection methods, laser-induced breakdown spectroscopy (LIBS) offers rapid, simultaneous multi-element analysis. However, the quantitative accuracy of LIBS is often hindered by challenges such as sample heterogeneity and the inherent matrix effects arising from the physical and chemical properties of samples. These limitations highlight the need for innovative approaches to improve the reliability and precision of LIBS-based elemental quantification. In this study, we proposed a low-cost image-spectroscopy dual-modal rapid detection system combined with a dual-modal hierarchical fusion network (DMH-FNet). Compared with a standalone LIBS system, the quantification performance improved for the seven elements, namely P, Ca, Mg, Zn, Mn, K, and Si. During the validation phase, feature map visualization was used to interpret the feature extraction process of DMH-FNet. The results indicate that the model shifted its focus from low-level features of ablation crater details to high-level global features of the sample. Subsequently, SHapley additive exPlanations (SHAP) was used to explain the decision-making process of the optimal quantitative model and visualize key image features. The results demonstrate that DMH-FNet efficiently extracts features highly correlated with ablation crater information using its neural network capabilities and enhances the quantification of mineral elements through complementary fusion with LIBS spectral features. This study is the first to leverage the superior feature extraction capability of neural networks to capture valuable information from ablation images, thereby improving the quantification performance for multiple mineral elements. In conclusion, the proposed detection system, with its low-cost equipment, real-time data acquisition, and DMH-FNet, enables simultaneous, rapid, and accurate prediction of multiple mineral element contents.

Adding bovine milk into tea and tea-containing beverages has been popular nowadays, while this dietary system may affect the digestion and absorption of tea polyphenols. In this work, the interactions of tea polyphenols with bovine milk proteins and their impacts on the bioaccessibility of tea polyphenols were investigated. The results indicate that tea polyphenols interact with amino acid residues of bovine milk proteins through hydrogen bonds and van der Waals forces spontaneously. Tea polyphenols cause static fluorescence quenching of bovine milk proteins, with different interaction types through one binding site. The interaction between tea polyphenols and bovine milk proteins forms a complex, which reduces the contents of α-helix, β-turn, and random coil in the secondary structure of bovine milk proteins while increasing the β-sheet content. Tea polyphenol-bovine milk protein interaction can enhance the bioaccessibility of tea polyphenols, with esterified tea polyphenols epicatechin gallate and epigallocatechin gallate showing better improvement effects.

The geographical origin of Baishao (Radix Paeoniae Alba) affects the components and content, which in turn affects its pharmacological action. Laser-induced breakdown spectroscopy (LIBS) was combined with conventional machine learning and deep learning methods to rapidly discriminate the geographical origins of Baishao slices without sample preparation. The influence of spatial variation of Baishao slices on the LIBS signal was investigated. The spectra that were averaged using 16-point spectra showed the best origin identification performance, with an accuracy of 96.7% as determined by partial least squares-discriminant analysis (PLS-DA). Meanwhile, the spectra obtained from a single point after voting showed the best origin identification performance using ResNet, with an accuracy of 95.0%. The preliminary results indicate the feasibility of using LIBS and machine learning for rapid, accurate, in situ origin identification of Baishao slices, which provides an approach for quality and adulteration supervision.

The fast and precise visualization of mineral elements in contaminated plants is crucial for understanding nutrient dynamics, plant health, and environmental monitoring. In this study, a Wasserstein generative adversarial network (WGAN) is proposed to expand the data scale and combines laser-induced breakdown spectroscopy (LIBS) with three machine learning methods for mineral elements analysis in Cr-stressed rice (Oryza sativa L.) leaves. In the quantitative analysis of mineral elements, the proposed method improved the prediction performance for Cu, K, Mg, Mn, and Na, except for Ca and Fe, demonstrating the effectiveness of data augmentation in enhancing the quantitative models. Mapping the distribution of Ca, Fe, Mg, K, Mn, and Na in rice leaves shows higher concentrations towards the apical regions and approximately symmetrical distribution along the leaf vein. Additionally, Fe, K, and Mn concentrations are significantly lower in Cr-polluted leaves compared to uncontaminated leaves. These preliminary findings offer insights into the macro distribution of mineral elements in plants.

Ball milling is a crucial step in matcha powder processing. In this study, experimental and numerical simulation investigations on the mechanical and physical properties of matcha powder during processing were conducted. Based on the discrete element method (DEM), a scaling model was established to improve simulation efficiency. Furthermore, three typical models of matcha particles were established and the coefficients of rolling friction and static friction were calibrated which were 0.064 and 0.50 respectively. Above this, the correlation between the mechanical and physical properties of matcha powder was also investigated. The results suggest that the morphological characteristics of matcha powders showed a highly significant correlation with the collision velocity; the flowability of matcha particles exhibited a significant correlation with both the collision velocity and the number of collisions. Predictably, these findings may provide a reference for the selection of processing parameters during matcha processing and the optimal design of production equipment.

Minerals in rice leaves is a crucial indicator of plant health, and their concentrations can be used to guide plant management. It is important to predict mineral content in contaminated rice rapidly. In this study, laser-induced breakdown spectroscopy (LIBS) was applied to quantify minerals (Ca, Cu, Fe, K, Mg, Mn, and Na) in rice leaves under chromium (Cr) stress. Two feature extraction methods, including principal component analysis (PCA) and extreme gradient boosting (XGBoost), were compared to identify important variables that related to mineral concentrations. Results showed that partial least square regression (PLSR) achieved good performance in Ca, Fe Mg, K, Mn, and Na, with correlation coefficient of 0.9782, 0.8712, 0.8933, 0.9206, 0.9856, and 0.9865, root mean square error of 219.25, 14.78, 1192.47, 385.12, 9.56, and 124.32 mg/kg, respectively. In addition, the correlation between different spectral lines were further analyzed. Cr exhibited a positive correlation with Ca, Mg, and Na, and a negative correlation with Mn, Cu, and K. The proposed method provides a high-accuracy and fast approach for minerals prediction in rice leaves under Cr stress, which is important for environmental protection and food safety.

In this Letter, a rapid origin classification device and method for Baishao (Radix Paeoniae Alba) slices based on auto-focus laser-induced breakdown spectroscopy (LIBS) is proposed. The enhancement of spectral signal intensity and stability through auto-focus was investigated, as were different preprocessing methods, with area normalization (AN) achieving the best results—increasing by 7.74%—but unable to replace the improved spectral signal quality provided by auto-focus. A residual neural network (ResNet) was used as both a classifier and feature extractor, achieving higher classification accuracy than traditional machine learning methods. The effectiveness of auto-focus was elucidated by extracting LIBS features from the last pooling layer output using uniform manifold approximation and projection (UMAP). Our approach demonstrated that auto-focus could efficiently optimize the LIBS signal, providing broad prospects for rapid origin classification of traditional Chinese medicines.

Superfine grinding is a crucial step in green tea powder (GTP) processing. In this work, the time-resolved changes of physicochemical properties (physical properties, main active components, etc.) of GTPs were investigated, and ball milling time (BMT) for tea grinding was optimized. Variable importance in projection was used to evaluate the importance of modification of each physicochemical parameter on BMT, and a multi-objective fuzzy opti-mization model was used to obtain the optimal BMT. The BMT range was 2-30 h, and the results showed that BMT had a significant influence on the physicochemical properties of GTP. The mean particle size decreased from 257 to 10.6 im and the color became lighter as the BMT increased. The main active components decreased markedly when BMT increased form 18-24 h. Mean particle size and color difference dramatically correlated with BMT and could be described by a one-phase exponential decay function. The optimal BMT was 18 h and high quality GTP could be obtained under this condition with small particle size, bright green color and rich in theanine and total tea polyphenols (TTPs).

To meet the growing demand for food quality and safety, there is a pressing need for fast and visible techniques to monitor the food crop and product production processing, and to understand the chemical changes that occur during these processes. Herein, the fundamental principles, instruments, and characteristics of three major laser-based imaging techniques (LBITs), namely, laser-induced breakdown spectroscopy, Raman spectroscopy, and laser ablation-inductively coupled plasma-mass spectrometry, are introduced. Additionally, the advances, challenges, and prospects for the application of LBITs in food crops and products are discussed. In recent years, LBITs have played a crucial role in mapping primary metabolites, secondary metabolites, nanoparticles, toxic metals, and mineral elements in food crops, as well as visualizing food adulteration, composition changes, pesticide residue, microbial contamination, and elements in food products. However, LBITs are still facing challenges in achieving accurate and sensitive quantification of compositions due to the complex sample matrix and minimal laser sampling quantity. Thus, further research is required to develop comprehensive data processing strategies and signal enhancement methods. With the continued development of imaging methods and equipment, LBITs have the potential to further explore chemical distribution mechanisms and ensure the safety and quality of food crops and products.

Geographical origin is an important factor affecting the quality of Baishao. To precisely determine the geographical origin of Baishao, the feasibility of recognition technology of spectrum-image fusion was explored. Different convolutional neural networks (CNN), including InceptionNet, ResNet, and MobileNet, were studied for their performance on laser-induced breakdown spectroscopy (LIBS) spectra and laser ablation images, and gradient-weighted class activation mapping (Grad-CAM) was used to visualize the important spectrum lines and image regions. The feature-level fusion exhibited superior performance in identifying the geographical origin, boasting an impressive accuracy of 97.78%, and the decision-level fusion exhibited the highest accuracy for geographical origin recognition, achieving a remarkable 98.89%. This work demonstrated that the LIBS ablation images contained useful information for origin identification, and data fusion strategies, as well as deep learning, could be powerful tools for establishing discriminant models. It provides a theoretical basis and technical support for the origin recognition of Baishao based on the information fusion of the LIBS spectrum and ablation image.

The Radix Paeoniae Alba (Baishao) is a traditional Chinese medicine (TCM) with numerous clinical and nutritional benefits. Rapid and accurate identification of the geographical origins of Baishao is crucial for planters, traders and consumers. Hyperspectral imaging (HSI) was used in this study to acquire spectral images of Baishao samples from its two sides. Convolutional neural network (CNN) and attention mechanism was used to distinguish the origins of Baishao using spectra extracted from one side. The data-level and feature-level deep fusion models were proposed using information from both sides of the samples. CNN models outperformed the conventional machine learning methods in classifying Baishao origins. The generalized Gradient-weighted Class Activation Mapping (Grad-CAM++) was utilized to visualize and identify important wavelengths that significantly contribute to model performance. The overall results illustrated that HSI combined with deep learning strategies was effective in identifying the geographical origins of Baishao, having good prospects of real-world applications.

The geographical origin of Baishao (Radix Paeoniae Alba) affects the components and content, which in turn affects its pharmacological action. Laser-induced breakdown spectroscopy (LIBS) combined with conventional machine learning and deep learning methods was used to fast discriminate the origins of Baishao slices (without sample preparation). The influence of depth and spatial variation of Baishao slices on the LIBS signal were investigated. Spectra averaged using sixteen-point spectra had the best origin identification performance on partial least squares-discriminant analysis (PLS-DA) with an accuracy of 96.67%. Spectra from single point after voting had the best origin identification performance on ResNet with an accuracy of 95%. The preliminary results indicate the feasibility of using LIBS and machine learning for rapid, accurate, in situ origin identification of Baishao slices, which provides an approach for functional food quality and adulteration supervision.

Despite the great promise initially demonstrated by photothermal ablation (PTA) therapy, its inability to completely ablate large tumors is problematic, because this has been found to result in residual tumors at ablation margins and bring a relative high rate of subsequent recurrences and metastases. To address this issue, we herein report a smart photothermal nanosystem (PBM) based on FDA-approved Prussian blue (PB) nanoparticles, doped with Mn (III) to suppress the tumor debris left by incomplete ablation. Notably, our study demonstrated that PTA-induced hyperthermia plays a crucial role in initiating the cGAS-STING pathway by generating damaged cytosolic DNA. This PBM nanosystem, which consumes glutathione and continuously releases Mn(II), further amplifies the PTA-induced cGAS-STING pathway in CT26 colon and 4T1 breast tumor models. Moreover, treatment with PBM following PTA boosted the robust immune response in situ and extended to the whole body with a remarkable suppression effect on both local residual and distant tumors. This work, which improves the antitumor efficacy of nonablated areas utilizing hyperthermia-enhanced immune therapy, may therefore provide a promising adjuvant antitumor strategy for the issue of incomplete ablation.Statement of significanceThis work discovered, for the first time, that photothermal ablation-induced hyperthermia plays a crucial role in initiating the cGAS-STING pathway. Taking advantage of this finding, we developed a smart photothermal material (PBM) tailored for incomplete tumor ablation. This integrated Mn(III)-doped nanosystem (PBM) demonstrated superior therapeutic benefits due to the thermal ablation process and immune enhancement. As the photothermal ablation-induced cGAS-STING pathway was triggered, the released Mn(III) consumes GSH while continuously transferred to Mn(II), which further amplified STING activation and facilitated a more robust antitumor immunity, thereby remarkably inhibiting both local residual and distant tumors in virtue of the biological changes under thermal ablation.

Purpose: Perilla frutescens (L.) Britt., a traditional edible-medicinal herb in China, has been used to treat cardiovascular and cerebrovascular (cardio-cerebrovascular) diseases for thousands of years. However, knowledge of the mechanisms underlying the effects of essential oil from P. frutescens (EOPF) in the treatment of cardio-cerebrovascular diseases is lacking. The promotion of angiogenesis is beneficial in the treatment of ischemic cardiocerebrovascular diseases. The current study investigated the pro-angiogenic role of EOPF and its main component perillaldehyde in sunitinib-injured transgenic Tg (flk1:EGFP) zebra fish embryos and human umbilical vein endothelial cells (HUVECs) for the first time. Materials and Methods: The pro-angiogenic effects of EOPF and perillaldehyde were observed in vivo using transgenic Tg (flk1:EGFP) zebrafish embryos and in vitro using HUVECs. Cell viability, proliferation, migration, tube formation, and protein levels were detected by MTT, EdU staining, wound healing, transwell chamber, and Western blot assays, respectively. Results: EOPF and perillaldehyde exerted a significant stimulatory effect on the formation of zebrafish intersegmental vessels (ISVs). Moreover, EOPF and perillaldehyde promoted proliferation, migration, and tube formation in sunitinib-treated HUVECs. Additionally, our findings uncovered that the pro-angiogenic effects of EOPF and perillaldehyde were mediated by increases in the expression ratios of p-ERK1/2 to ERK1/2 and Bcl-2 to Bax. Conclusion: The present study is the first report to provide clear evidence that EOPF and perillaldehyde promote angiogenesis by stimulating repair of sunitinib-injured ISVs in zebrafish embryos and promoting proliferation, migration, and tube formation in sunitinib-injured HUVECs. The underlying mechanisms are related to increased p-ERK1/2 to ERK1/2 and Bcl2 to Bax expression ratios. EOPF and perillaldehyde may be used in the treatment of cardiocerebrovascular diseases, which is consistent with the traditional application of P. frutescens.

Traceability of honey is highly required by consumers and food administration with the consideration of food safety and quality. In this study, a technique named laser-induced breakdown spectroscopy (LIBS) was used to fast trace geographical origins of acacia honey and multi-floral honey. LIBS emissions from elements of Mg, Ca, Na, and K had significant differences among different geographical origins. The clusters of honey from different geographical origins were visualized with principal component analysis. In addition, support vector machine (SVM) and linear discrimination analysis (LDA) were used to quantitively classify the origins. The results indicated that SVM performed better than LDA, and the discriminant results of multi-floral honey were better than acacia honey. The accuracy and mean average precision for multi-floral honey were 99.7% and 99.7%, respectively. This study provided a fast approach for geographical origin classification, and might be helpful for food traceability.

PURPOSE:We aimed to compare chest HRCT lung signs identified in scans of differently aged patients with COVID-19 infections. METHODS:Case data of patients diagnosed with COVID-19 infection in Hangzhou City, Zhejiang Province in China were collected, and chest HRCT signs of infected patients in four age groups (<18 years, 18-44 years, 45-59 years, ≥60 years) were compared. RESULTS:Small patchy, ground-glass opacity (GGO), and consolidations were the main HRCT signs in 98 patients with confirmed COVID-19 infections. Patients aged 45-59 years and aged ≥60 years had more bilateral lung, lung lobe, and lung field involvement, and greater lesion numbers than patients <18 years. GGO accompanied with the interlobular septa thickening or a crazy-paving pattern, consolidation, and air bronchogram sign were more common in patients aged 45-59 years, and ≥60 years, than in those aged <18 years, and aged 18-44 years. CONCLUSIONS:Chest HRCT manifestations in patients with COVID-19 are related to patient's age, and HRCT signs may be milder in younger patients.