ObjectiveTo establish a geographical origin identification model for Foeniculi Fructus from Haiyuan， providing a new technical reference for the protection of Haiyuan's geo-authentic medicinal materials and its designation as a national geographical indication agricultural product. MethodsSamples of Foeniculi Fructus were collected from eight producing areas， including Minqin （Gansu）， Bozhou （Anhui）， Qingdao （Shandong）， Dezhou （Shandong）， Urumqi （Xinjiang）， Nujiang （Yunnan）， Gutuo （Inner Mongolia）， and Haiyuan （Ningxia）. Gas chromatography-ion mobility spectrometry （GC-IMS） was used to detect the volatile organic compounds （VOCs） in samples from these geographic origins. VOCs were qualitatively analyzed through dual matching with the National Institute of Standards and Technology （NIST） mass spectral database and the IMS drift time database. Using the Reporter module and Gallery Plot visualization tools within the LAV analytical platform， VOC fingerprint profiles characterizing geographic origins were constructed. A non-targeted analytical strategy was adopted， and 97 VOCs detected via GC-IMS were subjected to principal component analysis （PCA） and partial least squares discriminant analysis （PLS-DA） based on their differential distribution patterns to construct an origin identification model for Foeniculi Fructus from Haiyuan region. Key discriminative markers were screened using variable importance in projection （VIP） values greater than 1. ResultsA total of 97 VOCs were identified， including alcohols， aldehydes， ketones， esters， organic acids， terpenoids， ethers， alkenes， and benzenes. The PLS-DA model， based on VOCs data obtained by GC-IMS， effectively distinguished Foeniculi Fructus in Haiyuan region from those of other origins. During cross-validation， the model achieved a prediction parameter （Q²） of 0.976 and a goodness-of-fit parameter （R²） of 0.936， with no overfitting observed in permutation testing. Twelve key flavor markers with VIP > 1 were identified as characteristic indicators of Haiyuan origin. ConclusionA stable and highly predictive origin identification model for Foeniculi Fructus from Haiyuan was successfully established using GC-IMS technology， PLS-DA， and VIP-based marker screening. This model provides a novel technical strategy for accurately distinguishing Foeniculi Fructus in Haiyuan region from other regional varieties and offers new technical support for its protection as a geo-authentic medicinal material and a nationally designated geographical indication agricultural product in China.

ObjectiveTo establish a geographical origin identification model for Foeniculi Fructus from Haiyuan， providing a new technical reference for the protection of Haiyuan's geo-authentic medicinal materials and its designation as a national geographical indication agricultural product. MethodsSamples of Foeniculi Fructus were collected from eight producing areas， including Minqin （Gansu）， Bozhou （Anhui）， Qingdao （Shandong）， Dezhou （Shandong）， Urumqi （Xinjiang）， Nujiang （Yunnan）， Gutuo （Inner Mongolia）， and Haiyuan （Ningxia）. Gas chromatography-ion mobility spectrometry （GC-IMS） was used to detect the volatile organic compounds （VOCs） in samples from these geographic origins. VOCs were qualitatively analyzed through dual matching with the National Institute of Standards and Technology （NIST） mass spectral database and the IMS drift time database. Using the Reporter module and Gallery Plot visualization tools within the LAV analytical platform， VOC fingerprint profiles characterizing geographic origins were constructed. A non-targeted analytical strategy was adopted， and 97 VOCs detected via GC-IMS were subjected to principal component analysis （PCA） and partial least squares discriminant analysis （PLS-DA） based on their differential distribution patterns to construct an origin identification model for Foeniculi Fructus from Haiyuan region. Key discriminative markers were screened using variable importance in projection （VIP） values greater than 1. ResultsA total of 97 VOCs were identified， including alcohols， aldehydes， ketones， esters， organic acids， terpenoids， ethers， alkenes， and benzenes. The PLS-DA model， based on VOCs data obtained by GC-IMS， effectively distinguished Foeniculi Fructus in Haiyuan region from those of other origins. During cross-validation， the model achieved a prediction parameter （Q²） of 0.976 and a goodness-of-fit parameter （R²） of 0.936， with no overfitting observed in permutation testing. Twelve key flavor markers with VIP > 1 were identified as characteristic indicators of Haiyuan origin. ConclusionA stable and highly predictive origin identification model for Foeniculi Fructus from Haiyuan was successfully established using GC-IMS technology， PLS-DA， and VIP-based marker screening. This model provides a novel technical strategy for accurately distinguishing Foeniculi Fructus in Haiyuan region from other regional varieties and offers new technical support for its protection as a geo-authentic medicinal material and a nationally designated geographical indication agricultural product in China.

AIM: To investigate the correlation of serum interleukin-35(IL-35), immunoglobulin 4/immunoglobulin(IgG4/IgG), thyroid stimulating immunoglobulin(TSI)levels with the activity and severity of thyroid associated ophthalmopathy(TAO).METHODS:Prospective study. A total of 148 TAO patients admitted to our hospital from January 2023 to July 2024 were selected as the observation group. They were assigned into an active group(75 cases)and an inactive group(73 cases)based on their activity level, and were assigned into a severe group(95 cases)and a mild group(53 cases)based on the severity of their condition; another 148 healthy patients who underwent physical examinations were regarded as the control group. The levels of IL-35, IgG4/IgG, and TSI in serum were compared between the two groups. The correlation between serum IL-35, IgG4/IgG, and TSI levels and TAO activity and severity of illness were analyzed. A multivariate Logistic regression was performed to analyze the influencing factors of TAO patients developing severe symptoms. ROC curve was applied to analyze the diagnostic value of serum IL-35, IgG4/IgG, and TSI levels for the development of severe TAO in patients.RESULTS: Compared with the control group, the serum IL-35 level in the observation group was significantly lower, while IgG4/IgG and TSI levels were significantly higher(all P<0.01). Compared with non-active TAO patients, active TAO patients had significantly lower serum IL-35 level and significantly higher IgG4/IgG and TSI levels(all P<0.01). Compared with the mild TAO patients, severe TAO patients had significantly lower serum IL-35 level and significantly higher disease duration, IgG4/IgG, and TSI levels(all P<0.01). The serum IL-35 level was negatively correlated with TAO activity and disease severity(r=-0.529, -0.554, both P<0.01), while serum IgG4/IgG level was positively correlated with TAO activity and disease severity(r=0.625, 0.663, both P<0.01). Serum TSI levels were positively correlated with TAO activity and disease severity(r=0.594, 0.607, both P<0.01). Multivariate Logistic regression analysis showed that serum IL-35, IgG4/IgG, and TSI levels were all factors influencing the progression of TAO patients to severe disease(all P<0.01). The areas under the curve(AUC)for diagnosing the progression of TAO patients to severe disease using serum IL-35, IgG4/IgG, and TSI levels were 0.868, 0.859, and 0.830, respectively. The combined AUC for the three markers was 0.955, significantly higher than that of each individual marker(Zthree factors combination-IL-35=2.893, Zthree factors combination-IL-35=3.510, Zthree factors combination-IL-35=4.157, P=0.004, <0.01, <0.01).CONCLUSION: Serum IL-35 level is significantly downregulated in TAO patients, while IgG4/IgG and TSI levels are significantly upregulated. The levels of IL-35, IgG4/IgG, and TSI are correlated with the activity and severity of TAO, and their combination has high diagnostic value for TAO developing into severe.

ObjectiveTo develop a nomogram-based risk prediction model for chronic obstructive pulmonary disease (COPD) incidence among the community-dwelling population aged 40 years old and above, so as to provide targeted references for the screening and prevention of COPD. MethodsBased on a natural population cohort in suburban Shanghai, a total of 3 381 randomly selected participants aged ≥40 years underwent pulmonary function tests between July and October 2021. Cox stepwise regression analysis was used to develop overall and gender-specific risk prediction models, along with the construction of corresponding risk nomograms. Model predictive performance was evaluated using the C-indice, area under the curve (AUC) values, and Brier score. Stability was assessed through 10-fold cross-validation and sensitivity analysis. ResultsA total of 3 019 participants were included, with a median follow-up duration of 4.6 years. The COPD incidence density was 17.22 per 1 000 person-years, significantly higher in males (32.04/1 000 person-years) than that in females (7.38/1 000 person-years) (P<0.001). The overall risk prediction model included the variables such as gender, age, education level, BMI, smoking, passive smoking, and respiratory comorbidities. The male-specific model incorporated the variables such as age, BMI, respiratory comorbidities, and smoking, while the female-specific model included age, marital status, respiratory comorbidities, and pulmonary tuberculosis history. The C-indices for the overall, male-specific, and female-specific models were 0.829, 0.749, and 0.807, respectively. The 5-year AUC values were 0.785, 0.658, and 0.811, with Brier scores of 0.103, 0.176, and 0.059, respectively. Both 10-fold cross-validated C-indices and sensitivity analysis (excluding participants with a follow-up duration of <6 months) yielded C-indices were above 0.740. ConclusionThis study developed concise and practical overall and gender-specific COPD risk prediction models and corresponding nomograms. The models demonstrated robust performance in predicting COPD incidence, providing a valuable reference for identifying high-risk populations and formulating targeted screening and personalized management strategies.

The accumulation of uremic toxins such as urea nitrogen, blood creatinine, and uric acid of patients with renal failure in vivo would lead to aggravated kidney damage. In this study, coated aldehyde oxy-starch (CAO) was used as an adsorbent to investigate its in vitro adsorption performance on renal failure indexes for urea, indoxyl sulfate (INS), monomethylamine (MMA), dimethylamine (DMA), uric acid (UA), and creatinine (Cr). The effects of variables such as pH, temperature, concentration, dosage, and time on the adsorption capacity of CAO were systematically investigated, employing analytical techniques of high-performance liquid chromatography (HPLC) and gas chromatography (GC). The results revealed that CAO exhibited a strong adsorption capacity for urea, INS, and MMA, alongside a moderate adsorption capacity for DMA, UA, and Cr. The adsorption kinetics and thermodynamic studies indicated that the adsorption of urea and UA by CAO were fitted in pseudo-first-order kinetics, and the adsorption isotherm aligned with the Freundlich adsorption model. The enthalpy change ΔH of urea in adsorption was in the range of 40 to 60 kJ·mol^-1, which demonstrated the presence of strong adsorption force due to the interactions of coordinating groups. The ΔH of UA was greater than 80 kJ·mol^-1, indicating the generation of chemical bonds during the adsorption. Both of them, Gibbs free energy ΔG was less than 0, within the range of -20 to 0 kJ·mol^-1, suggested that the adsorption of urea and UA by CAO occurred spontaneously as physical adsorption process. The adsorption entropy ΔS of urea and UA was > 0, which indicated an increase in entropy throughout the adsorption. The infrared spectroscopygram showed the formation of a chemical bond, specifically the imine bond, following the adsorption of urea by CAO, thereby indicating a chemical reaction during the adsorption. This study elucidates the adsorption mechanism of CAO on various indexes of renal failure, providing a scientific basis for its clinical usage.

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.

Background Salidroside (SAL) has a protective effect on multiple organ systems. Exposure to fine particulate matter (PM_2.5) in the atmosphere may lead to disruptions in gut microbiota and impact intestinal health. The regulatory effect of SAL on the gut microbiota of mice exposed to PM_2.5 requires further investigation. Objective To evaluate gut microbiota disruption in mice after being exposed to PM_2.5 and the potential effect of SAL. Methods Forty male C57BL/6 mice, aged 6 to 8 weeks, were randomly divided into four groups: a control group, an SAL group, a PM_2.5 group, and an SAL+PM_2.5 group, each containing 10 mice. In the SAL group and the SAL+PM_2.5 group, the mice were administered SAL (60 mg·kg⁻¹) by gavage, while in the control group and the PM_2.5 group, sterile saline (10 mL·kg⁻¹) was administered by gavage. In the PM_2.5 group and the SAL+PM_2.5 group, PM_2.5 suspension (8 mg·kg⁻¹) was intratracheally instilled, and in the control group and SAL group, sterile saline (1.5 mL·kg⁻¹) was intratracheally administered. Each experiment cycle spanned 2 d, with a total of 10 cycles conducted over 20 d. Histopathological changes in the ileum tissue of the mice were observed after HE staining. Colon contents were collected for gut microbiota sequencing and short-chain fatty acids (SCFAs) measurements. Results The PM_2.5 group showed infiltration of inflammatory cells in the ileum tissue, while the SAL+PM_2.5 group exhibited only a small amount of inflammatory cell infiltration. Compared to the control group, the PM_2.5 group showed decreased Shannon index (P<0.05) and increased Simpson index (P<0.05), indicating that the diversity of gut microbiota in this group was decreased; the SAL+PM_2.5 group showed increased Shannon index compared to the PM_2.5 group (P<0.05) and decreased Simpson index (P<0.05), indicating that the diversity of gut microbiota in mice intervened with SAL was increased. The principal coordinates analysis (PCoA) revealed a significant separation between the PM_2.5 group and the control group, while the separation trend was less evident among the control group, the SAL group, and the SAL+PM_2.5 group. The unweighted pair-group method with arithmetic means (UPGMA) clustering tree results showed that the control group and the SAL group clustered together first, followed by clustering with the SAL+PM_2.5 group, and finally, the three groups clustered with the PM_2.5 group. The PCoA and UPGMA clustering results indicated that the uniformity and similarity of the microbiota in the PM_2.5 group were significantly decreased. Compared to the control group, the PM_2.5 group showed decreased abundance of phylum Bacteroidetes and Candidatus_Saccharimonas (P<0.05) and increased abundance of phylum Proteobacteria, genus Escherichia, genus Bacteroides, genus Prevotella, genus Enterococcus, and genus Proteus (P<0.05). Compared to the PM_2.5 group, the SAL+PM_2.5 group showed decreased abundance of phylum Proteobacteria, phylum Actinobacteria, genus Prevotella, and genus Proteus (P<0.05), and increased abundance of Candidatus_Saccharimonas (P<0.05). The PM_2.5 group showed reduced levels of propionic acid, valeric acid, and hexanoic acid compared to the control group (P<0.05), while the SAL+PM_2.5 group showed increased levels of propionic acid, isobutyric acid, butyric acid, valeric acid, and hexanoic acid compared to the PM_2.5 group (P<0.05). Conclusion Exposure to PM_2.5 can cause pathological alterations, microbial dysbiosis, and disturbing production of SCFAs in intestinal tissue in mice. However, SAL can provide a certain degree of protective effect against these changes.

The World Health Organization (WHO) released the “Global report on hypertension” on September 19, 2023. This report systematically summarizes the prevalence, mortality, diagnosis and treatment of hypertension in various countries, and elucidates the current situation of hypertension management, and gives a series of suggestions on how to manage hypertension, providing new thinking and inspiration for countries to optimize hypertension management. Through the summary of relevant studies and reports, this paper further reviews the present situation, early identification and management of hypertension.

Aim To investigate the effect of ellagic acid (EA) on cognitive function in APP/PS 1 double- transgenic mice, and to explore the regulatory mechanism of ellagic acid on the level of oxidative stress in the hippocampus of double-transgenic mice based on the phosphatidylinositol 3-kinase/protein kinase B/glycogen synthase kinase-3 (PI3K/AKT/GSK-3 β) signaling pathway. Methods Thirty-two SPF-grade 6-month-old APP/PS 1 double transgenic mice were randomly divided into four groups, namely, APP/PS 1 group, APP/PS1 + EA group, APP/PS1 + LY294002 group, APP/PS 1 + EA + LY294002 group, with eight mice in each group, and eight SPF-grade C57BL/6J wild type mice ( Wild type) were selected as the blank control group. The APP/PS 1 + EA group was given 50 mg · kg

Aim To investigate the effect of quercetin on the aging model of bone marrow mesenchymal stem cells established under microgravity. Methods Using 3D gyroscope, a aging model of bone marrow mesenchymal stem cells was constructed, and after receiving quercetin and microgravity treatment, the anti-aging effect of the quercetin was evaluated by detecting related proteins and oxidation indexes. Results Compared to the control group, the expressions of age-related proteins p21, pi6, p53 and RB in the microgravity group significantly increased, while the expressions of cyclin D1 and lamin B1 significantly decreased, with statistical significance (P<0.05). In the microgravity group, mitochondrial membrane potential significantly decreased (P<0.05), ROS accumulation significantly increased (P <0.05), SOD content significantly decreased and MDA content significantly increased (P<0.05). Compared to the microgravity group, the expressions of age-related proteins p21, pi6, p53 and RB in the quercetin group significantly decreased, while the expressions of cyclin D1 and lamin B1 significantly increased, with statistical significance (P<0.05). In the quercetin group, mitochondrial membrane potential significantly increased (P<0.05), ROS accumulation significantly decreased (P<0.05), SOD content significantly increased and MDA content significantly decreased (P<0.05). Conclusions Quercetin can resist oxidation, protect mitochondrial function and normal cell cycle, thus delaying the aging of bone marrow mesenchymal stem cells induced by microgravity.