Plant-derived extracellular vesicles (PDEVs) are nanoscale extracellular vesicles secreted by plant cells, characterized by a lipid bilayer structure. These vesicles carry a variety of bioactive molecules, including proteins, nucleic acids, and lipids, and play essential roles in intercellular communication and physiological regulation in plants. Compared to animal-derived extracellular vesicles, PDEVs offer several advantages, such as a broad range of sources, high biocompatibility, low immunogenicity, and low production costs. Furthermore, PDEVs have demonstrated remarkable potential as natural nanocarriers for drug delivery, due to their ability to efficiently traverse biological barriers, such as the blood-brain barrier, making them promising candidates for drug delivery systems. This review systematically elaborates on the complex composition of PDEVs, which consists of lipids, proteins, and nucleic acids, the typical structural characteristics of their lipid bilayers ranging from 30 to 150 nm, and their versatile loading capabilities as drug carriers, efficiently encapsulating various types of therapeutic agents such as hydrophilic small molecules, hydrophobic drugs, nucleic acids, and proteins. We systematically summarize the recent advancements in strategies for enhancing the loading efficiency of PDEVs, which include methods such as co-incubation, ultrasound-assisted loading, electroporation, freeze-thaw cycles, and microfluidic technology. These techniques are evaluated based on their underlying principles, suitable drug types, and their respective advantages. In addition to loading strategies, we focus on the engineered approaches to achieve targeted delivery using PDEVs, such as genetic engineering modifications, chemical ligand conjugation, membrane fusion technology, and polyethylene glycol (PEG) modification. We discuss the mechanisms of these strategies in enhancing targeting efficiency, prolonging in vivo circulation time, and improving therapeutic efficacy. Further, this review highlights the application of PDEVs in various disease models, including tumor, skin inflammation, metabolic disorders, and neurodegenerative diseases, showcasing their therapeutic potential as multifunctional delivery platforms. The ability of PDEVs to encapsulate diverse therapeutic agents and target specific tissues or cells opens up new avenues for the treatment of complex diseases, offering advantages over conventional drug delivery systems. However, despite the promising applications of PDEVs, several challenges remain in their development and clinical translation. These challenges include variability in source materials, standardization of preparation processes, quality control, scalability of production, and the need for clinical validation. To overcome these obstacles, the integration of advanced technologies such as artificial intelligence-assisted design and multi-omics analysis is proposed as a way to facilitate the precise development of PDEVs. These emerging technologies hold the potential to further enhance the precision and effectiveness of plant-based drug delivery systems, ultimately advancing the field of precision medicine. In conclusion, the use of PDEVs as a platform for drug delivery represents a promising area of research with the potential to revolutionize therapeutic strategies. Their ability to encapsulate and deliver a wide variety of bioactive molecules, along with their inherent advantages in biocompatibility and versatility, makes them a valuable tool in the development of more efficient and targeted therapeutic interventions. Continued research and innovation in this field will pave the way for the clinical implementation of PDEVs in the treatment of various diseases, offering new hope for more effective and sustainable therapeutic options.

High-performance liquid chromatography(HPLC) was used to determine the content of three major components in Citri Reticulatae Semen(CRS), including limonin, nomilin, and obacunone. The chromaticity of the CRS sample during salt processing and stir-frying was measured using a color difference meter. Next, the relationship between the color and content of the salt-processed CRS sample was investigated through correlation analysis. By integrating the oil bath technique for processing simulation with HPLC, the changes in the relative content of nomilin and its transformation products were analyzed, with its structural transformation pattern during processing identified. Additionally, RAW264.7 cells were induced with lipopolysaccharides(LPSs) to establish an inflammatory model, and the anti-inflammatory activity of nomilin and its transformation product, namely obacunone was evaluated. The results indicated that as processing progressed, E~*ab and L~* values showed a downward trend; a~* values exhibited a slow increase over a certain period, followed by no significant changes, and b~* values remained stable with no significant changes over a certain period and then started to decrease. The limonin content remained barely unchanged; the nomilin content decreased, and the obacunone increased significantly. The changing trends in content and color parameters during salt-processing and stir-frying were basically consistent. The content of nomilin and obacunone was significantly correlated with the colorimetric values(L~*, a~*, b~*, and E~*ab), while limonin content showed no significant correlation with these values. By analyzing HPLC patterns of nomylin at different heating temperatures and time, it was found that under conditions of 200-250 ℃ for heating of 5-60 min, the content of nomilin significantly decreased, while the obacunone content increased pronouncedly. The in vitro anti-inflammatory activity results indicated that compared to the model group, the group with a high concentration of nomilin and the groups with varying concentrations of obacunone showed significantly reduced release of nitric oxide(NO)(P<0.01). When both were at the same concentration, obacunone showed better performance in inhibiting NO release. In this study, the obvious correlation between the color and content of major components during the processing of CRS samples was identified, and the dynamic patterns of quality change in CRS samples during processing were revealed. Additionally, the study revealed and confirmed the transformation of nomilin into obacunone during processing, with the in vitro anti-inflammatory activity of obacunone significantly greater than that of nomilin. These findings provided a scientific basis for CRS processing optimization, tablet quality control, and its clinical application.
Mice ; Animals ; Drugs, Chinese Herbal/pharmacology* ; RAW 264.7 Cells ; Limonins/chemistry* ; Chromatography, High Pressure Liquid ; Citrus/chemistry* ; Color ; Benzoxepins/chemistry* ; Anti-Inflammatory Agents/chemistry*

OBJECTIVE: To investigate the effects of thymoquinone on the proliferation of acute myeloid leukemia (AML) cells and its molecular mechanism, so as to provide theoretical basis for the basic research on the anti-leukemia of traditional Chinese medicine. METHODS: The HL-60 and THP-1 cells were treated with thymoquinone at different concentration gradients, cell proliferation was detected by CCK-8 method, morphological changes were detected by Wright-Giemsa method, apoptosis was detected by Annexin V/PI double staining flow cytometry, and apoptosis and signal pathway protein expression were detected by Western blot. Real-time quantitative fluorescence PCR and Western blot were used to detect the expression changes of high mobility family members of SRY-related proteins (SOX). RESULTS: Thymoquinone inhibited the malignant proliferation of HL-60 and THP-1 cells, up-regulated the expression of pro-apoptotic protein Bax, down-regulated the expression of anti-apoptotic protein Bcl-2 and Survivin, and hydrolyzed Caspase-3 to induce the apoptosis of HL-60 and THP-1 cells. Thymoquinone could also significantly down-regulate the phosphorylation of PI3K, Akt and mTOR, and inhibit the malignant biological characteristics of HL-60 and THP-1 cells by inhibiting the activation of PI3K/Akt/mTOR pathway. After thymoquinone intervention in HL-60 and THP-1 cells, the expression of SOX2 and SOX4 could be down-regulated significantly. At low concentration ( < 10 μmol/L), the expression of SOX12 was weakly affected by thymoquinone. With increasing concentration, the expression of SOX12 could be down-regulated, however, thymoquinone had no effect on SOX11 expression. CONCLUSION Thymoquinone can inhibit the proliferation of AML cells, and its mechanism may be related to inhibiting the activation of PI3K/Akt/mTOR signaling pathway, regulating the expression of apoptotic proteins and core members of SOX family.
Humans ; Benzoquinones/pharmacology* ; Cell Proliferation/drug effects* ; Leukemia, Myeloid, Acute/metabolism* ; Apoptosis/drug effects* ; HL-60 Cells ; Signal Transduction/drug effects* ; Proto-Oncogene Proteins c-akt/metabolism* ; TOR Serine-Threonine Kinases/metabolism* ; Proto-Oncogene Proteins c-bcl-2/metabolism* ; bcl-2-Associated X Protein/metabolism* ; Cell Line, Tumor ; Phosphatidylinositol 3-Kinases/metabolism* ; THP-1 Cells

Objective:To investigate the association of blood lipids and body mass index (BMI) with hyperuricemia (HUA) in the elderly.Methods:It was a cross-sectional study. A total of 114 391 elderly individuals received health examinations at primary healthcare institutions in Wujin District from January to December in 2022. The health examination included questionnaire survey, physical examination and laboratory examination. The multivariate logistic regression and restricted cubic spline (RCS) plots were used to analyze the association and dose-response relationship of blood lipid and BMI with HUA. The mediating effect model was used to explore the mediation effect of BMI on the association between blood lipid and HUA.Results:Among the 112 415 subjects, 18 506 (16.46%) were checked with HUA. After adjusting for relevant confounders, total cholesterol (TC) ( OR=1.20, 95% CI: 1.16-1.23), triglyceride (TG) ( OR=1.46, 95% CI: 1.44-1.49), high density lipoprotein cholesterol (HDL-C) ( OR=0.74, 95% CI: 0.73-0.76), low density lipoprotein cholesterol (LDL-C) ( OR=1.14, 95% CI: 1.12-1.15) and BMI ( OR=1.42, 95% CI: 1.39-1.44) were all associated with HUA (all P0.05). The RCS analysis revealed that TG, HDL-C, and LDL-C each exhibited a nonlinear dose-response relationship with HUA, the inflection points was 3.00 mmol/L, 1.57 mmol/L and 2.50 mmol/L, respectively (all P-nonlinear0.001). The results of interaction showed that there were additive interaction between high TC( S=1.27 , 95% CI: 1.17-1.37), high TG( S=1.32 , 95% CI: 1.25-1.40), high LDL-C( S=1.23 , 95% CI: 1.14-1.34) and overweight/obesity with HUA (all P0.05). The results of mediation effect analysis showed that the mediation effect of BMI on the association between blood lipids (HDL-C, LDL-C, TG and TC) and HUA, from high to low, were as follows: 22.5% (95% CI: 20.8%-24.2%), 13.9% (95% CI: 12.0%-16.2%), 13.5% (95% CI: 12.7%-14.4%) and-3.9% (95% CI:-6.6%--1.8%). Conclusion:The blood lipid levels and BMI are positively correlated with HUA in the elderly.

The pathogenesis of neurodegenerative diseases (NDDs) is fundamentally linked to complex and profound alterations in metabolic networks within the brain, which exhibit marked spatial heterogeneity. While conventional bulk metabolomics is powerful for detecting global metabolic shifts, it inherently lacks spatial resolution. This methodological limitation hampers the ability to interrogate critical metabolic dysregulation within discrete anatomical brain regions and specific cellular microenvironments, thereby constraining a deeper understanding of the core pathological mechanisms that initiate and drive NDDs. To address this critical gap, spatial metabolomics, with mass spectrometry imaging (MSI) at its core, has emerged as a transformative approach. It uniquely overcomes the limitations of bulk methods by enabling high-resolution, simultaneous detection and precise localization of hundreds to thousands of endogenous molecules—including primary metabolites, complex lipids, neurotransmitters, neuropeptides, and essential metal ions—directly in situ from tissue sections. This powerful capability offers an unprecedented spatial perspective for investigating the intricate and heterogeneous chemical landscape of NDD pathology, opening new avenues for discovery. Accordingly, this review provides a comprehensive overview of the field, beginning with a discussion of the technical features, optimal application scenarios, and current limitations of major MSI platforms. These include the widely adopted matrix-assisted laser desorption/ionization (MALDI)-MSI, the ultra-high-resolution technique of secondary ion mass spectrometry (SIMS)-MSI, and the ambient ionization method of desorption electrospray ionization (DESI)-MSI, along with other emerging technologies. We then highlight the pivotal applications of spatial metabolomics in NDD research, particularly its role in elucidating the profound chemical heterogeneity within distinct pathological microenvironments. These applications include mapping unique molecular signatures around amyloid β‑protein (Aβ) plaques, uncovering the metabolic consequences of neurofibrillary tangles composed of hyperphosphorylated tau protein, and characterizing the lipid and metabolite composition of Lewy bodies. Moreover, we examine how spatial metabolomics contributes to constructing detailed metabolic vulnerability maps across the brain, shedding light on the biochemical factors that render certain neuronal populations and anatomical regions selectively susceptible to degeneration while others remain resilient. Looking beyond current applications, we explore the immense potential of integrating spatial metabolomics with other advanced research methodologies. This includes its combination with three-dimensional brain organoid models to recapitulate disease-relevant metabolic processes, its linkage with multi-organ axis studies to investigate how systemic metabolic health influences neurodegeneration, and its convergence with single-cell and subcellular analyses to achieve unprecedented molecular resolution. In conclusion, this review not only summarizes the current state and critical role of spatial metabolomics in NDD research but also offers a forward-looking perspective on its transformative potential. We envision its continued impact in advancing our fundamental understanding of NDDs and accelerating translation into clinical practice—from the discovery of novel biomarkers for early diagnosis to the development of high-throughput drug screening platforms and the realization of precision medicine for individuals affected by these devastating disorders.

Objective: To construct an index system of public health risk assessment for air pollution emergency, so as to provide a tool of evaluating the public health risks of air pollution emergency. Methods: Index system of public health risk assessment for air pollution emergency was established through literature review and group discussions. The index system was determined through two rounds of Delphi expert consultations involving specialists in environmental health, toxicology, epidemiology, health emergency response, and atmospheric monitoring. The effectiveness of the consultation was evaluated by positive coefficient, authority coefficient and coordination coefficient. The weights of index were determined using a combination weighting method of the expert scoring method and the entropy weight method. Results: Fifteen experts participated in the consultation, including 11 males and 4 females. There were 8 experts with a doctor degree, 6 experts with a master degree, 1 experts with a bachelor degree. A total of 11 experts with senior professional titles, and 4 experts with associate senior professional titles. The average work experience was (23.73±10.48) years. The expert positive coefficients for the two rounds of consultations were 83.33% and 100%, respectively. The expert authority coefficients were 0.794 and 0.811, respectively. The coefficients of variation for the importance, feasibility, and sensitivity scores of each index in the two rounds of comsultations were 0.097 to 0.352, 0.078 to 0.478, 0.115 to 0.388, and 0.049 to 0.133, 0.052 to 0.153, 0.049 to 0.178, respectively. The Kendall's coefficients of concordance were 0.237 and 0.440 (both with P<0.05) for the two rounds of consultations. The constructed assessment index system included "likelihood" "hazard" "vulnerability" "controllability" with comprehensive weights of 0.206 7, 0.059 6, 0.378 1, and 0.355 5, respectively. Among the 13 second indicators, "monitoring capability" had the highest comprehensive weight of 0.192 6. Among the 40 tertiary indicators, "real-time monitoring of atmospheric pollutants" "retrospective evaluation of early forecasting results" "types, quantities, and combined effects of atmospheric pollutants" "exposure modes of the population to atmospheric pollutants" had relatively high comprehensive weights of 0.089 5, 0.043 1, 0.041 1 and 0.040 3, respectively. Conclusion The constructed index system of public health risk assessment for air pollution emergency can be applied to the public health risk assessment for air pollution emergencies.

Human naïve pluripotent stem cells (PSCs) hold great promise for embryonic development studies. Existing induction and culture strategies for these cells, heavily dependent on MEK inhibitors, lead to widespread DNA hypomethylation, aberrant imprinting loss, and genomic instability during extended culture. Here, employing high-content analysis alongside a bifluorescence reporter system indicative of human naïve pluripotency, we screened over 1,600 chemicals and identified seven promising candidates. From these, we developed four optimized media-LAY, LADY, LUDY, and LKPY-that effectively induce and sustain PSCs in the naïve state. Notably, cells reset or cultured in these media, especially in the LAY system, demonstrate improved genome-wide DNA methylation status closely resembling that of pre-implantation counterparts, with partially restored imprinting and significantly enhanced genomic stability. Overall, our study contributes advancements to naïve pluripotency induction and long-term maintenance, providing insights for further applications of naïve PSCs.
Humans ; DNA Methylation/drug effects* ; Genomic Instability ; Pluripotent Stem Cells/metabolism* ; Cell Culture Techniques/methods* ; Cells, Cultured

Traditional Chinese medicine (TCM) is an ancient medical system distinctive and effective in treating cancer, depression, coronavirus disease 2019 (COVID-19), and other diseases. However, the relatively abstract diagnostic methods of TCM lack objective measurement, and the complex mechanisms of action are difficult to comprehend, which hinders the application and internationalization of TCM. Recently, while breakthroughs have been made in utilizing methods such as network pharmacology and virtual screening for TCM research, the rise of machine learning (ML) has significantly enhanced their integration with TCM. This article introduces representative methodological cases in quality control, mechanism research, diagnosis, and treatment processes of TCM, revealing the potential applications of ML technology in TCM. Furthermore, the challenges faced by ML in TCM applications are summarized, and future directions are discussed.

OBJECTIVE: To investigate the associations between eight serum per- and polyfluoroalkyl substances (PFASs) and regional fat depots, we analyzed the data from the National Health and Nutrition Examination Survey (NHANES) 2011-2018 cycles. METHODS: Multiple linear regression models were developed to explore the associations between serum PFAS concentrations and six fat compositions along with a fat distribution score created by summing the concentrations of the six fat compositions. The associations between structurally grouped PFASs and fat distribution were assessed, and a prediction model was developed to estimate the ability of PFAS exposure to predict obesity risk. RESULTS: Among females aged 39-59 years, trunk fat mass was positively associated with perfluorooctane sulfonate (PFOS). Higher concentrations of PFOS, perfluorohexane sulfonate (PFHxS), perfluorodecanoate (PFDeA), perfluorononanoate (PFNA), and n-perfluorooctanoate (n-PFOA) were linked to greater visceral adipose tissue in this group. In men, exposure to total perfluoroalkane sulfonates (PFSAs) and long-chain PFSAs was associated with reductions in abdominal fat, while higher abdominal fat in women aged 39-59 years was associated with short-chain PFSAs. The prediction model demonstrated high accuracy, with an area under the curve (AUC) of 0.9925 for predicting obesity risk. CONCLUSION PFAS exposure is associated with regional fat distribution, with varying effects based on age, sex, and PFAS structure. The findings highlight the potential role of PFAS exposure in influencing fat depots and obesity risk, with significant implications for public health. The prediction model provides a highly accurate tool for assessing obesity risk related to PFAS exposure.
Humans ; Fluorocarbons/blood* ; Female ; Adult ; Middle Aged ; Male ; Environmental Pollutants/blood* ; Abdominal Fat ; Nutrition Surveys ; Alkanesulfonic Acids/blood* ; Obesity ; Environmental Exposure

OBJECTIVE: To identify the key features of facial and tongue images associated with anemia in female populations, establish anemia risk-screening models, and evaluate their performance. METHODS: A total of 533 female participants (anemic and healthy) were recruited from Shuguang Hospital. Facial and tongue images were collected using the TFDA-1 tongue and face diagnosis instrument. Color and texture features from various parts of facial and tongue images were extracted using Face Diagnosis Analysis System (FDAS) and Tongue Diagnosis Analysis System version 2.0 (TDAS v2.0). Least Absolute Shrinkage and Selection Operator (LASSO) regression was used for feature selection. Ten machine learning models and one deep learning model (ResNet50V2 + Conv1D) were developed and evaluated. RESULTS: Anemic women showed lower a-values, higher L- and b-values across all age groups. Texture features analysis showed that women aged 30-39 with anemia had higher angular second moment (ASM)and lower entropy (ENT) values in facial images, while those aged 40-49 had lower contrast (CON), ENT, and MEAN values in tongue images but higher ASM. Anemic women exhibited age-related trends similar to healthy women, with decreasing L-values and increasing a-, b-, and ASM-values. LASSO identified 19 key features from 62. Among classifiers, the Artificial Neural Network (ANN) model achieved the best performance [area under the curve (AUC): 0.849, accuracy: 0.781]. The ResNet50V2 model achieved comparable results [AUC: 0.846, accuracy: 0.818]. CONCLUSION Differences in facial and tongue images suggest that color and texture features can serve as potential TCM phenotype and auxiliary diagnostic indicators for female anemia.
Humans ; Female ; Tongue/diagnostic imaging* ; Adult ; Anemia/diagnosis* ; Middle Aged ; Face/diagnostic imaging* ; Young Adult ; Machine Learning