ObjectiveTo explore the changes in color， odor and chemical components during wine-processing of Chuanxiong Rhizoma（CR）， identify differential markers， and provide a basis for standardizing the process and establishing quality standards. MethodsFifteen batches of CR samples from 4 producing areas were collected. Colorimeter and electronic nose were used to detect the color changes and odor components of CR before and after wine-processing. Multivariate statistical methods including partial least squares-discriminant analysis（PLS-DA）， principal component analysis（PCA）， discriminant factor analysis（DFA） and Fisher discriminant analysis were applied to identify wine-processed CR at different processing stages and establish discriminant models， and differential components were screened out based on variable importance in the projection（VIP） value1. Then， high performance liquid chromatography（HPLC） was employed to detect the content changes of four components（ferulic acid， senkyunolide I， senkyunolide A and ligustilide） during the processing stages. ResultsThe differences of wine-processed CR at various stages were primarily reflected in color parameters L^*（brightness value）， a^*（red-green value） and b^*（yellow-blue value）. Based on chromaticity differences， the color reference ranges were established for moderately processed CR， including L^* of 46.75-48.24， a^* of 5.37-6.07 and b^* of 20.32-21.70. In odor analysis， DFA revealed significant differences among processing stages， and 11 odor markers were identified， with four differential markers（4-hydroxy-3-butylphthalide， isopropyl butyrate， L-limonene and 1-methoxyhexane） based on VIP values. HPLC results showed that there was no significant difference of the four components except for ligustilide in wine-processed CR at different stages. ConclusionThis study achieved rapid identification of wine-processed CR with different processing degrees by electronic sensory technology and differential component content detection， with discrimination accuracy rates of 92.4% and 93.272% for color and odor， respectively. This paper also established the reference ranges of main colorimetric parameters for wine-processed CR at different stages， and four differential components were screened out， providing a basis for standardizing the processing of wine-processed CR and establishing quality standards for this decoction pieces.

ObjectiveTo explore the changes in color， odor and chemical components during wine-processing of Chuanxiong Rhizoma（CR）， identify differential markers， and provide a basis for standardizing the process and establishing quality standards. MethodsFifteen batches of CR samples from 4 producing areas were collected. Colorimeter and electronic nose were used to detect the color changes and odor components of CR before and after wine-processing. Multivariate statistical methods including partial least squares-discriminant analysis（PLS-DA）， principal component analysis（PCA）， discriminant factor analysis（DFA） and Fisher discriminant analysis were applied to identify wine-processed CR at different processing stages and establish discriminant models， and differential components were screened out based on variable importance in the projection（VIP） value1. Then， high performance liquid chromatography（HPLC） was employed to detect the content changes of four components（ferulic acid， senkyunolide I， senkyunolide A and ligustilide） during the processing stages. ResultsThe differences of wine-processed CR at various stages were primarily reflected in color parameters L^*（brightness value）， a^*（red-green value） and b^*（yellow-blue value）. Based on chromaticity differences， the color reference ranges were established for moderately processed CR， including L^* of 46.75-48.24， a^* of 5.37-6.07 and b^* of 20.32-21.70. In odor analysis， DFA revealed significant differences among processing stages， and 11 odor markers were identified， with four differential markers（4-hydroxy-3-butylphthalide， isopropyl butyrate， L-limonene and 1-methoxyhexane） based on VIP values. HPLC results showed that there was no significant difference of the four components except for ligustilide in wine-processed CR at different stages. ConclusionThis study achieved rapid identification of wine-processed CR with different processing degrees by electronic sensory technology and differential component content detection， with discrimination accuracy rates of 92.4% and 93.272% for color and odor， respectively. This paper also established the reference ranges of main colorimetric parameters for wine-processed CR at different stages， and four differential components were screened out， providing a basis for standardizing the processing of wine-processed CR and establishing quality standards for this decoction pieces.

Extracellular vesicles(EVs)are nanoscale membrane vesicles released by cells that carry bioactive molecules such as proteins and nucleic acids. They play a key role in intercellular communication. In recent years, they have shown significant potential in the diagnosis and treatment of corneal and ocular surface diseases. As diagnostic biomarkers, EVs derived from tears or other sources can reflect disease-specific characteristics, providing a basis for the early disease detection. In terms of therapy, mesenchymal stem cells(MSCs)-derived EVs deliver miRNAs to modulate inflammatory pathways, promote corneal epithelial repair, and inhibit fibrosis. Furthermore, as natural drug vehicles, EVs enable precise delivery of therapeutic agents and promote corneal wound healing. This review systematically summarizes the recent research advances in the application of EVs in the field.

Acute-on-chronic liver failure (ACLF) is an acute deterioration of liver function occurring on the basis of chronic liver disease, accompanied by failure of the liver and extrahepatic organs, and is associated with a high short-term mortality rate. Liver transplantation is the only curative treatment for patients with ACLF. However, the shortage of donor livers and limitations of the organ allocation system mean that only a minority of patients can receive transplants. The current organ allocation system based on the model for end-stage liver disease (MELD) score may underestimate the urgency of liver transplantation for ACLF patients. Therefore, it is urgent to develop better assessment tools to determine which ACLF patients are most likely to benefit from liver transplantation. This article reviews the current mainstream definitions of ACLF, the selection of candidates for liver transplantation in ACLF, and the prognostic scoring systems for liver transplantation in ACLF, both domestically and internationally, in order to provide a reference for the prognostic assessment of liver transplantation in ACLF patients.

Azvudine and nirmatrelvir/ritonavir (Paxlovid) are recommended for COVID-19 treatment in China, but their safety and efficacy in the elderly population are not fully known. In this multicenter, retrospective, cohort study, we identified 5131 elderly hospitalized COVID-19 patients from 32,864 COVID-19 patients admitted to nine hospitals in Henan Province, China, from December 5, 2022, to January 31, 2023. The primary outcome was all-cause death, and the secondary outcome was composite disease progression. Propensity score matching (PSM) was performed to control for confounding factors, including demographics, vaccination status, comorbidities, and laboratory tests. After 2:1 PSM, 1786 elderly patients receiving azvudine and 893 elderly patients receiving Paxlovid were included. Kaplan-Meier and Cox regression analyses revealed that compared with Paxlovid group, azvudine could significantly reduce the risk of all-cause death (log-rank P = 0.002; HR: 0.71, 95% CI: 0.573-0.883, P = 0.002), but there was no difference in composite disease progression (log-rank P = 0.52; HR: 1.05, 95% CI: 0.877-1.260, P = 0.588). Four sensitivity analyses verified the robustness of above results. Subgroup analysis suggested that a greater benefit of azvudine over Paxlovid was observed in elderly patients with primary malignant tumors (P for interaction = 0.005, HR: 0.32, 95% CI: 0.18-0.57) compared to patients without primary malignant tumors. Safety analysis revealed that azvudine treatment had a lower incidence of adverse events and higher lymphocyte levels than Paxlovid treatment. In conclusion, azvudine treatment is not inferior to Paxlovid treatment in terms of all-cause death, composite disease progression and adverse events in elderly hospitalized COVID-19 patients.

Objective:To analyze the factors influencing recurrence after liver transplantation in patients with primary biliary cholangitis (PBC).Methods:Clinical data of 53 patients with PBC undergoing liver transplantation at the Department of General Surgery, Beijing Youan Hospital, Capital Medical University from August 2006 to January 2024 were retrospectively anaylyzed, including 11 males and 42 females, aged (55.1±7.9) years. Eight (15.1%) of the patients experienced recurrence within five years after liver transplantation. Univariate and multivariate logistic regression analyses were used to identify the factors influencing recurrence after transplantation. The receiver operating characteristic (ROC) curve was used to evaluate the predictive efficacy of each variable for post-transplant recurrence.Results:Univariate logistic regression analysis showed that severe postoperative complications, positive anti-centromere antibodies, and postoperative CD4/CD45<0.4 were associated with recurrence after liver transplantation (all P<0.05). Further multivariate logistic regression analysis revealed that severe postoperative complications ( OR=23.183, 95% CI: 1.667-322.447, P=0.019), postoperative CD4/CD45<0.4 ( OR=9.272, 95% CI: 1.244-69.099, P=0.030), and positive anti-centromere antibodies ( OR=17.106, 95% CI: 1.381-211.878, P=0.027) were associated with a higher risk of recurrence after liver transplantation in patients with PBC. ROC curve analysis showed that the area under the curve for predicting recurrence after liver transplantation based on severe postoperative complications was 0.731 (95% CI: 0.539-0.922, P=0.039), with a sensitivity of 75.0% and a specificity of 71.1%. Conclusion:Severe postoperative complications, CD4/CD45 <0.4, and positive anti-centromere antibodies are risk factors for recurrence after liver transplantation in patients with PBC. Severe postoperative complications showed a good predictive efficacy for recurrence.

Ischemic stroke is more common in the elderly, characterized by rapid onset, high incidence, high disability rate, and high mortality rate.It often causes severe cellular dysfunction and brain damage, imposing a significant burden on families and society.Death-associated protein kinase 1(DAPK1)is a calcium/calmodulin(Ca 2+ /CaM)-dependent serine/threonine(Ser/Thr)protein kinase that plays a crucial role in regulating brain damage caused by ischemic stroke.Studies have shown that DAPK1 activity is regulated by phosphorylation and mediates the pathophysiological processes of ischemic stroke through multiple signaling pathways, including DAPK1-N-methyl-D-aspartate(DAPK1-NMDA), DAPK1-NMDA), DAPK1-tumor suppressor protein p53(DAPK1-p53), DAPK1-microtubule-associated protein Tau(DAPK1-Tau), and DAPK1-dangerous signaling(DAPK1-DANGER), among others.Blocking these signaling pathways can significantly improve neuronal damage following ischemic stroke.This article reviews the related signaling pathways, mechanisms of action, and progress in targeted drug development of DAPK1 in ischemic stroke, aiming to provide references for future research and treatment of ischemic stroke.

Objective: To investigate the changing trends for associations of anxiety and depressive symptoms with smartphone addiction among middle school students, so as to provide a scientific basis for preventing smartphone addiction in middle school students. Methods: From 2022 to 2023, a method of combining convenient sampling with cluster sampling was used to select 8 923 middle school students from 27 junior high schools and 3 senior high schools in a district of Shenzhen City between September 2022 (baseline, T1) and September 2023 (follow up, T2). The Smartphone Addiction Scale-Short Version (SAS-SV), Patients Health Questionnaire-9 Item (PHQ-9), and Generalized Anxiety Disorder 7-item Scale (GAD-7) were administered to assess smartphone addiction, anxiety and depressive symptoms. Mixed effects models were used to analyze the association of anxiety and depressive symptoms with smartphone addiction among middle school students. Results: From September 2022 to September 2023, the reported prevalence of smartphone addiction increased from 24.22% to 25.25% ( χ 2=45.71); and smartphone addiction scores [ 24.00 (16.00, 32.00),25.00(16.00, 33.00)], anxiety symptom scores [2.00(0.00, 7.00),3.00(0.00, 7.00)] and depressive symptom scores[3.00(0.00, 8.00),5.00(0.00, 9.00)] all significantly increased ( Z =-17.43, -42.38, -41.57) (all P <0.05). There were statistically significant difference in the distribution of anxiety and depression symptom levels among middle school students in 2022 and 2023 ( χ 2=85.15, 106.85, both P <0.05). After adjusting for covariates such as age, gender and family background, mixed effects models revealed dose response associations of anxiety and depressive symptoms with smartphone addiction among middle school students:mild anxiety symptom( OR =3.22), moderate to severe anxiety symptom ( OR =5.36), mild depressive symptom ( OR =3.32) and moderate to severe depressive symptom ( OR =6.13) were significantly associated with higher risks of smartphone addiction (all P <0.05). Interaction effect analysis found that co existing anxiety and depressive symptoms synergistically increased addiction risk by 5.60 times ( OR =5.60) compared to the asymptomatic group, with 32% of the combined risk attributable to their interaction ( S=1.64, AP =0.32)(both P < 0.05 ). Conclusions Anxiety and depressive symptoms are significantly associated with smartphone addiction, exhibiting a synergistic effect. Attention should be paid to emotional issues and smartphone addiction among middle school students.

Objective: To investigate the value of the Chinese visceral adiposity index (CVAI) in predicting hypertension risk, so as to provide a tool for the early assessment of hypertension risk. Methods: Health examination individuals aged ≥18 years were selected from four medical institutes in Jinhua City, Zhejiang Province in 2022. Data on basic information, lifestyle, disease history, body mass index, waist circumference, blood pressure, and blood biochemical indicators were collected through questionnaire surveys and physical examinations. CVAI was calculated to assess levels of visceral fat accumulation, divided by quartiles into Q1, Q2, Q3, and Q4 groups. The relationship between CVAI and hypertension was analyzed using a multivariable logistic regression model, and their dose-response relationship was examined using a restricted cubic spline model. The value of CVAI in predicting hypertension risk was evaluated using receiver operating characteristic (ROC) curve. Results: A total of 23 791 individuals were enrolled, with a median age of 68.00 (interquartile range, 14.00) years. Among them, 10 178 (42.78%) were males and 13 613 (57.22%) were females. The median CVAI was 111.40 (interquartile range, 48.23). Hypertension was identified in 15 563 cases, with a prevalence of 65.42%. After adjusting for lifestyle, disease history, and blood biochemical indicators, the multivariable logistic regression analysis revealed that hypertension risk in the CVAI Q2, Q3, and Q4 groups were 2.012 (95%CI: 1.865-2.170), 3.059 (95%CI: 2.826-3.311), and 5.099 (95%CI: 4.672-5.565) times that of the Q1 group, respectively. The restricted cubic spline model revealed a non-linear relationship between CVAI and hypertension risk (Pnon linearity<0.05). Hypertension risk increased more rapidly when the CVAI was ≥81.03. The area under the ROC curve for CVAI in predicting hypertension risk was 0.691, with an optimal cutoff value of 106.01, which falls within the Q2 group. Conclusions There was a nonlinear dose-response relationship between CVAI and hypertension. CVAI can predict the risk of hypertension, and 106.01 can serve as an early warning threshold for risk screening.