Indobufen is a new generation of antiplatelet agents and has been shown to have antithrombotic effects in animal models. However, its therapeutic potential and mechanisms against cerebral ischemia/reperfusion (I/R) injury remain unclear. In this study, we evaluated the in vivo neuroprotective effects of indobufen through both pretreatment and posttreatment regimens in a rat model of middle cerebral artery occlusion/reperfusion (MCAO/R). In vitro, human umbilical vein endothelial cells (HUVECs) subjected to oxygen-glucose deprivation/reoxygenation (OGD/R) were employed to investigate the relationship between indobufen and the pyroptosis-associated NF-κB/Caspase-1/GSDMD pathway. The pharmacodynamic tests revealed that indobufen ameliorated I/R injury by decreasing the level of thromboxane B2 (TXB2), infarct size, brain edema and neurological impairment in rats and rescuing cell pyroptosis in HUVECs. The underlying mechanisms were probably related to pyroptosis suppression by regulating the NF-κB/Caspase-1/GSDMD pathway. Overall, these studies indicate that indobufen exerts protective and therapeutic effects against I/R injury by pyroptosis suppression via downregulating NF-κB/Caspase-1/GSDMD pathway.

Optical microscopy is essential for exploring biological and material structures, with resolution determining the level of observable detail. The advent of super-resolution fluorescence microscopy has broken the diffraction limit, achieving nanoscale resolution. However, traditional assessment methods, such as the Rayleigh criterion and point spread function (PSF) width measurement, rely on empirical judgments and diffraction-limited models, rendering them inadequate for modern super-resolution imaging. This review systematically traces the evolution of resolution assessment methodologies, from classical criteria to advanced strategies tailored for various super-resolution modalities. We first discuss Fourier-based quantitative methods. Fourier ring correlation (FRC) and its 3D counterpart, Fourier shell correlation (FSC), objectively determine resolution by evaluating the statistical correlation of two independent image reconstructions in frequency space. These methods offer robustness against noise and provide a global resolution metric, but they require data independence and are computationally intensive. They have become the prevailing standards in electron and super-resolution microscopy. Subsequently, we examine adaptations for specific super-resolution techniques. For single-molecule localization microscopy (SMLM) techniques such as PALM and STORM, the Fourier image resolution (FIRE) method extends FRC by incorporating a physical model that accounts for localization precision and labeling density. For stimulated emission depletion (STED) microscopy and other nonlinear techniques, assessment strategies differ. While PSF shrinkage measurements using fluorescent beads are useful for system calibration, evaluating the effective resolution directly on biological samples is more practical. This is typically performed via linewidth analysis of known structures (e.g., microtubules) or edge-spread function measurements, capturing the effects of photobleaching and sample-induced aberrations. A major paradigm shift is parameter-free resolution estimation based on decorrelation analysis. This method analyzes the autocorrelation decay of a single image’s Fourier spectrum to identify the cutoff spatial frequency without requiring dual datasets or user-defined thresholds. Its high efficiency and broad applicability have been validated across widefield, confocal, STED, SIM, and SMLM modalities. Optimized rendering strategies for SMLM data further enhance its accuracy, and it is emerging as a tool for real-time optimization of experimental parameters. The review also addresses the “gold standard” of resolution validation using well-defined nanostructures, such as DNA origami and nuclear pore complexes, which provide ground truth for verifying resolution claims and detecting artifacts. In the era of artificial intelligence, deep learning plays a dual role: it powerfully enhances image resolution but also introduces challenges, as models may generate “hallucinations” or false details. This underscores the need for new validation metrics to verify the physical fidelity of AI-generated content. Finally, we outline future directions: developing unified cross-modality standards, enabling real-time dynamic resolution monitoring for live-cell imaging, creating techniques for generating local resolution maps to capture sample heterogeneity, and integrating intelligent error correction to ensure data veracity. By providing a comprehensive overview of resolution assessment progress and challenges, this review aims to equip researchers with the knowledge to select appropriate tools, thereby fostering rigorous quantitative imaging in the life and material sciences.

A novel analytical method was developed in this study by combining online solid phase extraction with ultra performance liquid chromatography-tandem mass spectrometry(Online SPE-UPLC-MS/MS)for simultaneous determination of 50 kinds of steroid hormones in surface water.Specifically,after high-speed centrifugation of 4 mL water samples,the supernatant was directly injected into an Oasis HLB online SPE column for enrichment and purification.Subsequently,the target compounds were transferred to the analytical column via valve switching for separation and analysis.The chromatographic separation was performed on a Thermo Acclaim RSLC C18 column(100 mm×2.1 mm,2.2 μm),using a mobile phase composed of 5 mmol/L ammonium fluoride aqueous solution and acetonitrile.Mass spectrometric detection was conducted in positive ion mode,utilizing multiple reaction monitoring(MRM)with quantification achieved by the internal standard method.The method validation demonstrated that the limits of detection(LOD)for the 50 kinds of steroid hormones ranged from 0.02 to 0.50 ng/L,while the limits of quantification(LOQ)were between 0.08 and 1.67 ng/L.The average recoveries in surface water samples at spiked concentrations of 5,20 and 200 ng/L were between 74.1％and 119％,with relative standard deviations(RSDs)of 0.2％to 9.9％.This method was applied to analyze 11 surface water samples collected from sites surrounding a pharmaceutical and chemical industrial park.A total of 44 kinds of steroid hormones were detected,with concentrations ranging from 0.11 to 88.6 ng/L,revealing the presence of hormone contamination in the environmental waters surrounding industrial areas.Compared with the traditional offline SPE methods,the proposed online SPE technique significantly reduced sample volume requirements and pretreatment time,while minimizing the loss of target compounds during the pretreatment process.Moreover,compared to reported online SPE techniques,this method achieved high-throughput analysis of multiple classes of steroid hormones,with lower detection limits and higher recoveries.Overall,this method provided rapid sample preparation,high sensitivity,and excellent stability,making it suitable for the direct analysis of trace steroid hormones in surface water.

The major components of Olaflur raw material were characterized using ultra performance liquid chromatography-quadrupole time-of-flight-mass spectrometry(UPLC-Q-TOF/MS).The results revealed that cetyl amine fluoride(C16-AmF),octadecene amine fluoride(C18:1-AmF),and octadecyl amine fluoride(Olaflur)were the main components.The contents of C16-AmF,C18:1-AmF,and Olaflur in oral care products were determined via ultra performance liquid chromatography-charged aerosol detector coupled with solid-phase extraction(SPE-UPLC-CAD).The oral care sample was dispersed evenly with a 50%ethanol aqueous solution,and then vortexed with ethanol.The supernatant was collected by centrifugation,concentrated to near dryness,and redissolved with ultrapure water.The re-dissolved sample was loaded onto a Poly-Sery HLB Pro SPE column for purification and elution.The acetonitrile eluate was collected and concentrated to 1.0 mL.Finally,a prepared test solution was separated on a Thermo Acclaim Surfactant Plus chromatographic column(2.1 mm×150 mm,3 μm).Acetonitrile and 100 mmol/L acetic acid-ammonium acetate aqueous solution(pH=4.8)were used as the mobile phases for gradient elution.The flow rate was 0.3 mL/min and cloumn temperature was maintained at 40℃.The sample was detected using a charged aerosol detector,and quantified using an external standard method.The experimental results indicated that the three organic fluorinated amines showed good linear relationship in their respective concentration ranges.The correlation coefficients(r)were greater than 0.99.The limit of detection(LOD)and the limit of quantification(LOQ)of C16-AmF were 2.0 and 8.0 μg/mL,respectively.The LOD and LOQ of C18:1-AmF were 2.0 and 8.0 μg/mL,respectively.The LOD and LOQ of Olaflur were 3.0 μg/mL and 10.0 μg/mL,respectively.The spiked recoveries of the three organic fluorinated amines were 84.3%-104.2%,with relative standard deviations(RSDs)of 4.93%-5.82%.The 28 batches of commercial oral care samples were detected by this method and the results indicated that three organic fluorinated amines were detected in 18 samples and the total content were 22.2-11477.8 μg/g.This method had high sensitivity and good reproducibility.It was suitable for verifying the authenticity of the claims of oral care products promoted with Olaflur as the main efficacy ingredient and selling point,and provided a valuable reference for establishing and improving the standard analytical method for Olaflur.

Objective To investigate the relationship between brain white matter lesions (WML) and triglyceride glucose (TyG) index in non-diabetic elderly individuals based on magnetic resonance imaging. Methods A total of 523 non-diabetic elderly individuals aged ≥ 60 years were selected from Jinan, Shandong Province, China from June 2018 to December 2019. According to the quartiles of TyG index, there were 133 participants in the first quartile (Q1) group, 127 in the second quartile (Q2) group, 132 in the third quartile (Q3) group, and 131 in the fourth quartile (Q4) group. All participants underwent brain magnetic resonance imaging to evaluate paraventricular, deep, and total WML volumes, as well as Fazekas scores. Results Compared with Q1, Q2, and Q3 groups, Q4 group showed significant increase in periventricular, deep, and total WML volumes (P < 0.05). The proportion of participants with a Fazekas score ≥ 2 in the periventricular, deep, and total WML was higher in the Q4 group compared with the Q1 and Q2 groups (P < 0.05). The proportion of participants with a Fazekas score ≥ 2 in deep WML was higher in Q4 group than in Q3 group (P < 0.05). TyG index was significantly positively correlated with periventricular, deep, and total WML volumes (r = 0.401, 0.405, and 0.445, P < 0.001). After adjusting for confounding factors, TyG index was still significantly positively correlated with periventricular, deep, and total WML volumes (P < 0.001). Logistic regression analysis showed that compared with Q1 group, the risk of Fazekas score ≥ 2 in periventricular WML was 1.950-fold (95% confidence interval [CI]: 1.154-3.294, P = 0.013) in Q3 group and 3.411-fold (95% CI: 1.984-5.863, P < 0.001) in Q4 group, the risk of Fazekas score ≥ 2 in total WML was 2.529-fold (95%CI: 1.444-4.430, P = 0.001) in Q3 group and 4.486-fold (95%CI: 2.314-8.696, P < 0.001) in Q4 group. The risk of Fazekas score ≥ 2 in deep WML was 2.953-fold (95%CI: 1.708-5.106, P < 0.001) in Q4 group compared with Q1 group. Conclusion Increased TyG index is an independent risk factor for WML in non-diabetic elderly individuals.

This study investigated the mechanism of Croci Stigma in treating immune checkpoint inhibitor(ICI)-associated myocarditis based on network pharmacology and molecular docking. Network pharmacology was employed to screen the active ingredients and molecular targets of Croci Stigma in treating ICI-associated myocarditis. The "drug-ingredient-target-disease" network and protein-protein interaction network were constructed to screen the key ingredients and core targets. Gene Ontology functional enrichment analysis showed that the mechanism was related to the regulation of inflammation and apoptosis. The Kyoto Encyclopedia of Genes and Genomes pathway analysis revealed that the treatment was related to the advanced glycation end product-receptor for advanced glycation end products(AGE-RAGE) signaling pathway. Molecular docking result showed that crocins had close associations with RAC-alpha serine/threonine-protein kinase 1(AKT1), signal transducer and activator of transcription 3, and matrix metalloproteinase 9. Crocins were then selected as the therapeutic drug. The mouse model of ICI-associated myocarditis was established by subcutaneous injection of porcine cardiac myosin combined with intraperitoneal injection of pembrolizumab. The results suggested that Croci Stigma reduced the spleen index but had no effect on the heart index. The electrocardiogram showed that Croci Stigma increased the heart rate and shortened PR and QRS intervals. Echocardiographic data indicated that Croci Stigma increased the left ventricular stroke volume, cardiac output, ejection fraction, and fractional shortening. Hematoxylin-eosin and Masson staining results showed that Croci Stigma decreased the number of inflammatory cells infiltrating in the myocardium and alleviated myocardial fibrosis. Enzyme-linked immunosorbent assay results showed that Croci Stigma decreased the serum levels of inflammatory cytokines including tumor necrosis factor-alpha, interleukin-6, interleukin-12, and regulated on activation, normal T-cell expressed and secreted and lowered the levels of creatine kinase and creatine kinase isoenzyme MB. Biochemical data suggested that Croci Stigma inhibited the activities of superoxide dismutase and lactate dehydrogenase. Western blot result showed that Croci Stigma regulated the expression of myocardial AKT. The findings demonstrate that Croci Stigma may regulate AKT expression to effectively protect the cardiac tissue from ICI-associated myocarditis through antagonizing immune responses and inflammation, inhibiting oxidative stress, alleviating cardiac fibrosis, relieving cardiac block, and improving the cardiac function.
Animals ; Molecular Docking Simulation ; Myocarditis/metabolism* ; Immune Checkpoint Inhibitors/adverse effects* ; Mice ; Network Pharmacology ; Drugs, Chinese Herbal/administration & dosage* ; Male ; Humans ; Protein Interaction Maps/drug effects*

This study investigated the effects and underlying mechanisms of vanillic acid(VA) against cardiac fibrosis(CF) induced by isoproterenol(ISO) in mice. Male C57BL/6J mice were randomly divided into control group, VA group(100 mg·kg~(-1), ig), ISO group(10 mg·kg~(-1), sc), ISO + VA group(10 mg·kg~(-1), sc + 100 mg·kg~(-1), ig), ISO + dynamin-related protein 1(Drp1) inhibitor(Mdivi-1) group(10 mg·kg~(-1), sc + 50 mg·kg~(-1), ip), and ISO + VA + Mdivi-1 group(10 mg·kg~(-1), sc + 100 mg·kg~(-1), ig + 50 mg·kg~(-1), ip). The treatment groups received the corresponding medications once daily for 14 consecutive days. On the day after the last administration, cardiac functions were evaluated, and serum and cardiac tissue samples were collected. These samples were analyzed for serum aspartate aminotransferase(AST), lactate dehydrogenase(LDH), creatine kinase-MB(CK-MB), cardiac troponin I(cTnI), reactive oxygen species(ROS), interleukin(IL)-1β, IL-4, IL-6, IL-10, IL-18, and tumor necrosis factor-α(TNF-α) levels, as well as cardiac tissue catalase(CAT), glutathione(GSH), malondialdehyde(MDA), myeloperoxidase(MPO), superoxide dismutase(SOD), total antioxidant capacity(T-AOC) activities, and cytochrome C levels in mitochondria and cytoplasm. Hematoxylin-eosin, Masson, uranium acetate and lead citrate staining were used to observe morphological and mitochondrial ultrastructural changes in the cardiac tissues, and myocardial injury area and collagen volume fraction were calculated. Flow cytometry was applied to detect the relative content and M1/M2 polarization of cardiac macrophages. The mRNA expression levels of macrophage polarization markers [CD86, CD206, arginase 1(Arg-1), inducible nitric oxide synthase(iNOS)], CF markers [type Ⅰ collagen(Coll Ⅰ), Coll Ⅲ, α-smooth muscle actin(α-SMA)], and cytokines(IL-1β, IL-4, IL-6, IL-10, IL-18, TNF-α) in cardiac tissues were determined by quantitative real-time PCR. Western blot was used to detect the protein expression levels of Coll Ⅰ, Coll Ⅲ, α-SMA, Drp1, p-Drp1, voltage-dependent anion channel(VDAC), hexokinase 1(HK1), NOD-like receptor protein 3(NLRP3), apoptosis-associated speck-like protein(ASC), caspase-1, cleaved-caspase-1, gasdermin D(GSDMD), cleaved N-terminal gasdermin D(GSDMD-N), IL-1β, IL-18, B-cell lymphoma-2(Bcl-2), B-cell lymphoma-xl(Bcl-xl), Bcl-2-associated death promoter(Bad), Bcl-2-associated X protein(Bax), apoptotic protease activating factor-1(Apaf-1), pro-caspase-3, cleaved-caspase-3, pro-caspase-9, cleaved-caspase-9, poly(ADP-ribose) polymerase-1(PARP-1), and cleaved-PARP-1 in cardiac tissues. The results showed that VA significantly improved cardiac function in mice with CF, reduced myocardial injury area and cardiac index, and decreased serum levels of AST, CK-MB, cTnI, LDH, ROS, IL-1β, IL-6, IL-18, and TNF-α. VA also lowered MDA and MPO levels, mRNA expressions of IL-1β, IL-6, IL-18, and TNF-α, and mRNA and protein expressions of Coll Ⅰ, Coll Ⅲ, and α-SMA in cardiac tissues, and increased serum levels of IL-4 and IL-10, cardiac tissue levels of CAT, GSH, SOD, and T-AOC, and mRNA expressions of IL-4 and IL-10. Additionally, VA ameliorated cardiac pathological damage, inhibited myocardial cell apoptosis, inflammatory infiltration, and collagen fiber deposition, reduced collagen volume fraction, and alleviated mitochondrial damage. VA decreased the ratio of F4/80~+CD86~+ M1 cells and the mRNA expressions of CD86 and iNOS in cardiac tissue, and increased the ratio of F4/80~+CD206~+ M2 cells and the mRNA expressions of CD206 and Arg-1. VA also reduced protein expressions of p-Drp1, VDAC, NLRP3, ASC, caspase-1, cleaved-caspase-1, GSDMD, GSDMD-N, IL-1β, IL-18, Bad, Bax, Apaf-1, cleaved-caspase-3, cleaved-caspase-9, cleaved-PARP-1, and cytoplasmic cytochrome C, and increased the expressions of HK1, Bcl-2, Bcl-xl, pro-caspase-3, pro-caspase-9 proteins, as well as the Bcl-2/Bax and Bcl-xl/Bad ratios and mitochondrial cytochrome C content. These results indicate that VA has a significant ameliorative effect on ISO-induced CF in mice, alleviates ISO-induced oxidative damage and inflammatory response, and its mechanism may be closely related to the inhibition of Drp1/HK1/NLRP3 and mitochondrial apoptosis signaling pathways, suppression of myocardial cell inflammatory infiltration and collagen fiber deposition, reduction of collagen volume fraction and CollⅠ, Coll Ⅲ, and α-SMA expressions, thus mitigating CF.
Animals ; Isoproterenol/adverse effects* ; Male ; Mice ; Signal Transduction/drug effects* ; Vanillic Acid/administration & dosage* ; Dynamins/genetics* ; Mice, Inbred C57BL ; Fibrosis/genetics* ; Apoptosis/drug effects* ; Mitochondria/metabolism* ; NLR Family, Pyrin Domain-Containing 3 Protein/genetics* ; Myocardium/metabolism* ; Humans

Based on the interleukin-17(IL-17) signaling pathway, this study explores the effect and mechanism of Bufei Decoction on Klebsiella pneumoniae pneumonia in rats. SD rats were randomly divided into the control group, model group, Bufei Decoction low-dose group(6.68 g·kg~(-1)·d~(-1)), Bufei Decoction high-dose group(13.36 g·kg~(-1)·d~(-1)), and dexamethasone group(1.04 mg·kg~(-1)·d~(-1)), with 10 rats in each group. A pneumonia model was established by tracheal drip injection of K. pneumoniae. After successful model establishment, the improvement in lung tissue damage was observed following drug administration. Core targets and signaling pathways were screened using transcriptomics techniques. Real-time fluorescence quantitative polymerase chain reaction was used to detect the mRNA expression of core targets interleukin-6(IL-6), interleukin-1β(IL-1β), tumor necrosis factor-α(TNF-α), and chemokine CXC ligand 6(CXCL6). Western blot was used to assess key proteins in the IL-17 signaling pathway, including interleukin-17A(IL-17A), nuclear transcription factor-κB activator 1(Act1), tumor necrosis factor receptor-associated factor 6(TRAF6), and downstream phosphorylated p38 mitogen-activated protein kinase(p-p38 MAPK), and phosphorylated nuclear factor-κB p65(p-NF-κB p65). Apoptosis of lung tissue cells was detected by terminal deoxynucleotidyl transferase-mediated dUTP-biotin nick end labeling(TUNEL). The results showed that, compared with the control group, the model group exhibited significant pathological damage in lung tissue. The mRNA expression of IL-6, IL-1β, TNF-α, and CXCL6, as well as the protein levels of IL-17A, Act1, TRAF6, p-p38 MAPK/p38 MAPK, and p-NF-κB p65/NF-κB p65, were significantly increased, and the number of apoptotic cells was notably higher, indicating successful model establishment. Compared with the model group, both low-and high-dose groups of Bufei Decoction showed reduced pathological damage in lung tissue. The mRNA expression levels of IL-6, IL-1β, TNF-α, and CXCL6, and the protein levels of IL-17A, Act1, TRAF6, p-p38 MAPK/p38 MAPK, and p-NF-κB p65/NF-κB p65, were significantly decreased, with a significant reduction in apoptotic cells in the high-dose group. In conclusion, Bufei Decoction can effectively improve lung tissue damage and reduce inflammation in rats with K. pneumoniae. The mechanism may involve the regulation of the IL-17 signaling pathway and the reduction of apoptosis.
Animals ; Interleukin-17/metabolism* ; Drugs, Chinese Herbal/administration & dosage* ; Rats, Sprague-Dawley ; Signal Transduction/drug effects* ; Rats ; Male ; Klebsiella pneumoniae/physiology* ; Klebsiella Infections/immunology* ; Humans ; Lung/drug effects*

As a new type of production factor that can empower the development of new quality productivity, the data element is an important engine to promote the high quality development of the industry. Traditional Chinese medicine(TCM) dispensing is the most basic work of TCM clinical pharmacy, and its quality directly affects the clinical efficacy of TCM. The integration of data elements and TCM dispensing can stimulate the innovation and vitality of the TCM dispensing industry and promote the high-quality and sustainable development of the industry. A large-scale, detailed, and systematic study on TCM dispensing was conducted. The innovative practice path of data fusion construction in the whole process of TCM dispensing was investigated by integrating the digital resources "nine full activities" of TCM dispensing, creating the digital dictionary of "TCM clinical information data elements", and exploring innovative applications of TCM dispensing driven by data and technology, so as to promote the standardized, digital, and intelligent development of TCM dispensing in medical health services. The research content of this project was successfully selected as the second batch of "Data element×" typical cases of National Data Administration in 2024, which is the only selected case in the field of TCM.
Medicine, Chinese Traditional/methods* ; Drugs, Chinese Herbal ; Humans

ObjectiveTobacco-related diseases remain one of the leading preventable public health challenges worldwide and are among the primary causes of premature death. In recent years, accumulating evidence has supported the classification of nicotine addiction as a chronic brain disease, profoundly affecting both brain structure and function. Despite the urgency, effective diagnostic methods for smoking addiction remain lacking, posing significant challenges for early intervention and treatment. To address this issue and gain deeper insights into the neural mechanisms underlying nicotine dependence, this study proposes a novel graph neural network framework, termed TI-GNN. This model leverages functional magnetic resonance imaging (fMRI) data to identify complex and subtle abnormalities in brain connectivity patterns associated with smoking addiction. MethodsThe study utilizes fMRI data to construct functional connectivity matrices that represent interaction patterns among brain regions. These matrices are interpreted as graphs, where brain regions are nodes and the strength of functional connectivity between them serves as edges. The proposed TI-GNN model integrates a Transformer module to effectively capture global interactions across the entire brain network, enabling a comprehensive understanding of high-level connectivity patterns. Additionally, a spatial attention mechanism is employed to selectively focus on informative inter-regional connections while filtering out irrelevant or noisy features. This design enhances the model’s ability to learn meaningful neural representations crucial for classification tasks. A key innovation of TI-GNN lies in its built-in causal interpretation module, which aims to infer directional and potentially causal relationships among brain regions. This not only improves predictive performance but also enhances model interpretability—an essential attribute for clinical applications. The identification of causal links provides valuable insights into the neuropathological basis of addiction and contributes to the development of biologically plausible and trustworthy diagnostic tools. ResultsExperimental results demonstrate that the TI-GNN model achieves superior classification performance on the smoking addiction dataset, outperforming several state-of-the-art baseline models. Specifically, TI-GNN attains an accuracy of 0.91, an F1-score of 0.91, and a Matthews correlation coefficient (MCC) of 0.83, indicating strong robustness and reliability. Beyond performance metrics, TI-GNN identifies critical abnormal connectivity patterns in several brain regions implicated in addiction. Notably, it highlights dysregulations in the amygdala and the anterior cingulate cortex, consistent with prior clinical and neuroimaging findings. These regions are well known for their roles in emotional regulation, reward processing, and impulse control—functions that are frequently disrupted in nicotine dependence. ConclusionThe TI-GNN framework offers a powerful and interpretable tool for the objective diagnosis of smoking addiction. By integrating advanced graph learning techniques with causal inference capabilities, the model not only achieves high diagnostic accuracy but also elucidates the neurobiological underpinnings of addiction. The identification of specific abnormal brain networks and their causal interactions deepens our understanding of addiction pathophysiology and lays the groundwork for developing targeted intervention strategies and personalized treatment approaches in the future.