Alzheimer’s disease (AD) is a prevalent neurodegenerative condition characterized by progressive cognitive decline and memory loss. As the incidence of AD continues to rise annually, researchers have shown keen interest in the active components found in natural plants and their neuroprotective effects against AD. Quercetin, a flavonol widely present in fruits and vegetables, has multiple biological effects including anticancer, anti-inflammatory, and antioxidant. Oxidative stress plays a central role in the pathogenesis of AD, and the antioxidant properties of quercetin are essential for its neuroprotective function. Quercetin can modulate multiple signaling pathways related to AD, such as Nrf2-ARE, JNK, p38 MAPK, PON2, PI3K/Akt, and PKC, all of which are closely related to oxidative stress. Furthermore, quercetin is capable of inhibiting the aggregation of β‑amyloid protein (Aβ) and the phosphorylation of tau protein, as well as the activity of β‑secretase 1 and acetylcholinesterase, thus slowing down the progression of the disease.The review also provides insights into the pharmacokinetic properties of quercetin, including its absorption, metabolism, and excretion, as well as its bioavailability challenges and clinical applications. To improve the bioavailability and enhance the targeting of quercetin, the potential of quercetin nanomedicine delivery systems in the treatment of AD is also discussed. In summary, the multifaceted mechanisms of quercetin against AD provide a new perspective for drug development. However, translating these findings into clinical practice requires overcoming current limitations and ongoing research. In this way, its therapeutic potential in the treatment of AD can be fully utilized.

Cardiovascular disease (CVD), chronic kidney disease (CKD), and metabolic disorders are the 3 major chronic diseases threatening human health, which are closely related and often coexist, significantly increasing the difficulty of disease management. In response, the American Heart Association (AHA) proposed a novel disease concept of “cardiovascular-kidney-metabolic (CKM) syndrome” in October 2023, which has triggered widespread concern about the co-treatment of heart and kidney diseases and the prevention and treatment of metabolic disorders around the world. This review posits that effectively managing CKM syndrome requires a new and multidimensional paradigm for diagnosis and risk prediction that integrates biological insights, advanced technology and social determinants of health (SDoH). We argue that the core pathological driver is a “metabolic toxic environment”, fueled by adipose tissue dysfunction and characterized by a vicious cycle of systemic inflammation and oxidative stress, which forms a common pathway to multi-organ injury. The at-risk population is defined not only by biological characteristics but also significantly impacted by adverse SDoH, which can elevate the risk of advanced CKM by a factor of 1.18 to 3.50, underscoring the critical need for equity in screening and care strategies. This review systematically charts the progression of diagnostic technologies. In diagnostics, we highlight a crucial shift from single-marker assessments to comprehensive multi-marker panels. The synergistic application of traditional biomarkers like NT-proBNP (reflecting cardiac stress) and UACR (indicating kidney damage) with emerging indicators such as systemic immune-inflammation index (SII) and Klotho protein facilitates a holistic evaluation of multi-organ health. Furthermore, this paper explores the pivotal role of non-invasive monitoring technologies in detecting subclinical disease. Techniques like multi-wavelength photoplethysmography (PPG) and impedance cardiography (ICG) provide a real-time window into microcirculatory and hemodynamic status, enabling the identification of early, often asymptomatic, functional abnormalities that precede overt organ failure. In imaging, progress is marked by a move towards precise, quantitative evaluation, exemplified by artificial intelligence-powered quantitative computed tomography (AI-QCT). By integrating AI-QCT with clinical risk factors, the predictive accuracy for cardiovascular events within 6 months significantly improves, with the area under the curve (AUC) increasing from 0.637 to 0.688, demonstrating its potential for reclassifying risk in CKM stage 3. In the domain of risk prediction, we trace the evolution from traditional statistical tools to next-generation models. The new PREVENT equation represents a major advancement by incorporating key kidney function markers (eGFR, UACR), which can enhance the detection rate of CKD in primary care by 20%-30%. However, we contend that the future lies in dynamic, machine learning-based models. Algorithms such as XGBoost have achieved an AUC of 0.82 for predicting 365-day cardiovascular events, while deep learning models like KFDeep have demonstrated exceptional performance in predicting kidney failure risk with an AUC of 0.946. Unlike static calculators, these AI-driven tools can process complex, multimodal data and continuously update risk profiles, paving the way for truly personalized and proactive medicine. In conclusion, this review advocates for a paradigm shift toward a holistic and technologically advanced framework for CKM management. Future efforts must focus on the deep integration of multimodal data, the development of novel AI-driven biomarkers, the implementation of refined SDoH-informed interventions, and the promotion of interdisciplinary collaboration to construct an efficient, equitable, and effective system for CKM screening and intervention.

Based on the pharmacokinetics theory, this study investigated the pharmacokinetic characteristics of albiflorin, paeoniflorin, wogonoside, and wogonin in normal and febrile rats and summarized absorption and elimination rules of Dayuanyin in them to provide reference for further development and clinical application of Dayuanyin. Blood samples were taken from the fundus venous plexus of normal and model rats after intragastric administration of Dayuanyin at different time points. The concentration of each substance in blood was determined by ultra performance liquid chromatography-triple quadrupole mass spectrometry(UPLC-MS/MS) technique at different time points. DAS 2.0, a piece of pharmacokinetics software, was used to calculate the pharmacokinetic parameters of each component. The results show that the 4 components had good linear relationship in their respective ranges, and the results of methodological investigation met the requirements. The pharmacokinetic parameters of C_(max), T_(max), t_(1/2), AUC_(0-t), AUC_(0-∞), and MRT_(0-t) were calculated by the DAS 2.0 non-compartmental model. Compared with those in the normal group, C_(max) and AUC_(0-t) of the 4 components in the model group were significantly increased. There were significant differences in the pharmacokinetic characteristics between the normal and model groups, suggesting that the absorption and elimination of Dayuanyin may be affected by the changes of internal environment of the body in different physiological states.
Animals ; Rats ; Drugs, Chinese Herbal/administration & dosage* ; Male ; Rats, Sprague-Dawley ; Fever/metabolism* ; Tandem Mass Spectrometry ; Chromatography, High Pressure Liquid ; Glucosides/pharmacokinetics* ; Monoterpenes

Based on the ultra performance liquid chromatography-quadrupole Exactive Orbitrap mass spectrometry(UPLC-Q-Exactive Orbitrap-MS) technology, combined with related literature, databases, and reference material information, this study qualitatively analyzed the components of Dayuanyin in the tissue of rats after gavage and employed molecular docking technology to predict the rationality of the mechanism behind the antipyretic effect of the in vivo components in Dayuanyin. A total of 21, 26, 20, 21, 14, and 31 prototype components and 3, 16, 3, 7, 5, and 24 metabolites were identified from the heart, liver, spleen, lung, kidney, and hypothalamus of the rats, respectively, and the binding ability of key components and targets was further verified by molecular docking. The results showed that all components had good binding ability with targets. The established UPLC-Q-Exactive Orbitrap-MS could effectively and quickly identify the Dayuanyin components distributed in tissue and preliminarily identify their metabolites. Many components were identified in the hypothalamus, which suggested that the components delivered to the brain should be focused on in the study on Dayuanyin in the treatment of febrile diseases. The molecular docking technology was used to predict the rationality of the mechanism behind its antipyretic effect, which lays the foundation for the clarification of the material basis and action mechanism of Dayuanyin, the development of new preparations, and the prediction of quality markers.
Animals ; Drugs, Chinese Herbal/administration & dosage* ; Rats ; Molecular Docking Simulation ; Male ; Antipyretics/metabolism* ; Rats, Sprague-Dawley ; Tissue Distribution ; Mass Spectrometry ; Chromatography, High Pressure Liquid ; Hypothalamus/metabolism*

Gene regulation relies on the precise binding of transcription factors (TFs) at regulatory elements, but simultaneously detecting hundreds of TFs on chromatin is challenging. We developed cFOOT-seq, a cytosine deaminase-based TF footprinting assay, for high-resolution, quantitative genome-wide assessment of TF binding in both open and closed chromatin regions, even with small cell numbers. By utilizing the dsDNA deaminase SsdAtox, cFOOT-seq converts accessible cytosines to uracil while preserving genomic integrity, making it compatible with techniques like ATAC-seq for sensitive and cost-effective detection of TF occupancy at the single-molecule and single-cell level. Our approach enables the delineation of TF footprints, quantification of occupancy, and examination of chromatin influences on TF binding. Notably, cFOOT-seq, combined with FootTrack analysis, enables de novo prediction of TF binding sites and tracking of TF occupancy dynamics. We demonstrate its application in capturing cell type-specific TFs, analyzing TF dynamics during reprogramming, and revealing TF dependencies on chromatin remodelers. Overall, cFOOT-seq represents a robust approach for investigating the genome-wide dynamics of TF occupancy and elucidating the cis-regulatory architecture underlying gene regulation.
Transcription Factors/genetics* ; Humans ; Chromatin/genetics* ; Animals ; Binding Sites ; Mice ; DNA Footprinting/methods*

The compounds were isolated and purified by silica gel, MCI, Sephadex LH-20 and semi-preparative high performance liquid chromatography. The structures of the compounds were determined by NMR and MS spectroscopic data. Twenty monomer compounds were isolated from the ethyl acetate extract of Lindera reflexa root from Hunan province, and identified as: (1″S,3″S,6″R)-2′,4′,6′-trihydroxy-3′-[1″-(3″-hydroxy)-p-menthanyl]-chalcone (1), sumadain D (2), quercetin (3), catechin (4), epicatechin (5), N-cis-feruloyltyramine (6), N-trans-feruloyltyramine (7), N-trans-feruloyl-3-methoxytyramine (8), flavifloramides B (9), northalifoline (10), isolariciresinol (11), syringaresinol (12), pinoresinol (13), medioresinol (14), dehydroconiferyl alcohol (15), 4-methoxyl-denudaquinol (16), miliusanal (17), syringic acid (18), abscisic acid (19), (E)-cinnamyl-(E)-cinnamate (20). Compound 1 is a new compound, and compounds 2‒20 have been isolated from Lindera reflexa for the first time. The results showed that compounds 1, 2, 3 and 16 could significantly reduce the survival ability of MGC-803 cells with IC₅₀ values of 5.58, 23.41, 25.72 and 20.96 μmol·L^-1, respectively. Compounds 5 and 20 can reduce the survival ability of MGC-803 cells with IC₅₀ values of 98.83 and 89.26 μmol·L^-1, respectively.

Objective To explore the correlation between healthcare-associated infection(HAI)and partial inde-xes in the diagnosis-related groups(DRGs)of patients in thoracic surgery intensive care unit(ICU).Methods DRGs,case mix index(CMI),relative weight(RW),and HAI of patients in thoracic surgery ICU and four subspe-cialty departments(pulmonary surgery group,esophageal surgery group,mediastinum group[mainly thymic sur-gery],and trachea group)in a tertiary chest hospital in Shanghai from January to December 2022 were retrospec-tively analyzed and compared through DRGs index grouping.Results A total of 1 429 patients in the department of thoracic surgery ICU were analyzed,including 59 HAI cases,with a HAI rate of 4.13％.The incidences of HAI in pulmonary surgery group,esophageal surgery group,mediastinum group and trachea group were 3.74％(30/803),5.84％(25/428),1.27％(2/157)and 4.88％(2/41),respectively.There was no statistically significant differ-ence in the incidences of HAI among different subspecialty groups(P＞0.05).A total of 35 DRGs were involved,with CMI of 2.75,3.41,2.35 and 1.25 in pulmonary surgery group,esophageal surgery group,mediastinum group and trachea group,respectively,and RW ranged from 0.53 to 12.62.In the pulmonary surgery group,inci-dence of HAI in male patients was higher than that in female patients.Higher RW score level was associated with higher incidence of HAI.Differences were all statistically significant(all P 0.05).Among patients in the esophageal surgery group,the age of HAI group was higher than that of the non-HAI group(P＜0.05).Higher RW score level was associated with higher incidence of HAI(P＜0.05).Among patients in the mediastinum sur-gery group,the age of patients in the infected group was higher than that in the non-infected group(P＜0.05).Among the 59 HAI cases,31 were infected with MDROs.Conclusion Focusing on CMI and RW in the DRGs in-dex system,analyzing HAI from the perspectives of disease complexity and overall technical difficulties of medical services can provide reference for the precise management of HAI in the new era.

Objective To investigate the associations of reproductive health indicators with lung function and chronic obstructive pulmonary disease(COPD)among women aged 40 years and above.Methods From Jul to Sep,2021,female subjects aged 40 years and above were randomly selected from the Shanghai Suburban Adult Cohort and Biobank for COPD screening.A questionnaire was used to obtain information on demographic characteristics and reproductive health indicators.Linear regression was used to analyze the effects of reproductive health indicators on forced vital capacity(FVC)and forced expiratory volume in the first second(FEV1).Logistic regression was also used to analyze the effects of reproductive health factors on FVC as a percentage of the predicted value(FVC%Pred)and FEV1%Pred as well as on COPD.Results A total of 1876 women aged 40 years and above were enrolled with mean age of(62.1±8.2)years old,among them,78.1%were menopausal,and 40.9%had been pregnant≥3 times.Multivariate analysis showed that FVC and FEV1 decreased in postmenopausal women,but menopause was not associated with a decrease in their percentage of predicted values.Pregnancies≥3 times was a risk factor for COPD(for 3 times,OR=4.92,95%CI:1.48-19.95,P<0.05;for≥4 times,OR=9.06,95%CI:2.32-41.57,P<0.01),while pregnancies of 2 times did not increase the risk of COPD.Conclusion In women aged 40 years and above,menopause is associated with poorer FVC and FEV1,and excessive pregnancy(≥3 times)is a risk factor for COPD.

Objective To investigate the effect of microplastic(MPs)exposure on learning and memory in mice,and its mechanism by observing the protein expression of brain-derived neurotrophic factor(BDNF)/tyrosine receptor kinase B(TrkB)/N-methyl-D-aspartate receptor subtype 2B(NR2B)signaling pathway and neurogenesis.Methods Forty male C57BL/6 mice were randomly divided into control group(Ctrl)and microplastics exposure group(MPs).Mice in MPs group were treated with 0.3 mg/(kg·d)microplastics,administered by gavage at a volume of 200 μl for 30 consecutive days.Morris water maze test was used to evaluate the spatial learning and memory ability of mice.Western blotting was used to detect the protein levels of BDNF,TrkB and NR2B in hippocampus of mice.Immunofluorescent staining was used to observe the number of doublecortin(DCX)and neuronal nuclei antigen(NeuN)positive cells in the hippocampus of mice to evaluate hippocampal neurogenesis.Results Compared with the control group,the ability of learning and memory decreased significantly in MPs group mice(P＜0.01).The expression levels of BDNF,TrkB and NR2B in the hippocampus of MPs group mice were significantly lower than those of control group(P＜0.05).The number of DCX and NeuN positive cells in the hippocampus of MPs group was significantly lower than that of control group(P＜0.01).Conclusion MPs exposure induces learning and memory impairment which may be related to inhibiting BDNF/TrkB/NR2B signaling pathway and reducing hippocampal neurogenesis.

Objective To investigate the diagnostic value of immunophenotype in distinguishing acute promyelocytic leukemia(APL)from HLA-DR negative acute myeloid leukemia(AML)using flow cytometry.Methods A retrospective observational study was con-ducted including 42 APL patients and 28 newly diagnosed or relapsed HLA-DR negative AML patients admitted to our hospital from 2014 to 2024.Immunophenotype analysis was performed on bone marrow aspirate samples using flow cytometry.The positive expression rates of CD64,MPO,CD7,CD11c,CD9,CD123 and other antigens were compared between the two groups using the Chi-square test.The diagnostic efficiency of the CD9/123 and CD64+MPO+CD7 CD11c-models for APL was evaluated using receiver operating charac-teristic(ROC)curves.Results The HLA-DR negative AML group exhibited significantly lower positive rates of CD64,CD9 and MPO(P＜0.05),and higher positive rates of CD11c and CD7(P＜0.05)compared to APL group.The CD64+MPO+CD7-CD11c-model had an area under the curve(AUCROC)of 0.859,sensitivity of 93.8％and specificity of 75.0％for distinguishing APL.The CD9/CD123 expression pattern had AUCROC of 0.919,sensitivity of 83.3％and specificity of 84.0％for APL diagnosis.The combined CD9/123 and CD64+MPO+CD7-CD11c-model had AUCROC of 0.955,sensitivity of 83.3％and specificity of 100％.Conclusion The combined CD9/123 and CD64+MPO+CD7-CD11c-expression pattern may serve as a helpful tool for differentiating APL from HLA-DR negative AML.