According to the work goals and tasks determined by edition outline of the Chinese Pharmacopoeia 2025 Edition, the Chinese Pharmacopoeia 2025 has been completed. Among them, 52 new pharmaceutical excipients monographs have been added, and the total number has reached 387. 245 pharmaceutical excipients monographs have been revised, of which 109 monographs have only textual revisions and 136 monographs have substantive revisions. This article focuses on the general framework and the main characteristics of the standards of pharmaceutical excipients in the Chinese Pharmacopoeia 2025, which can contribute to accurately understand and utilize the standards in Chinese Pharmacopoeia.

Xanthine oxidase (XO) is an important therapeutic target for the treatment of hyperuricemia and gout. Based on the previously identified potent XO inhibitor 1, seventeen oxadiazoles and their ring-opening analogues were designed and synthesized via the bioisostere replacement strategy. Among them, compounds 2l, 2n, and 3b showed obvious XO inhibitory activity at the concentration of 10 μmol·L^-1, and compound 3b exhibited an IC₅₀ value of 1.45 μmol·L^-1.

Background: s/Aims: Non-invasive models stratifying clinically significant portal hypertension (CSPH) are limited. Herein, we developed a new non-invasive model for predicting CSPH in patients with compensated cirrhosis and investigated whether carvedilol can prevent hepatic decompensation in patients with high-risk CSPH stratified using the new model. Methods: Non-invasive risk factors of CSPH were identified via systematic review and meta-analysis of studies involving patients with hepatic venous pressure gradient (HVPG). A new non-invasive model was validated for various performance aspects in three cohorts, i.e., a multicenter HVPG cohort, a follow-up cohort, and a carvediloltreating cohort. Results: In the meta-analysis with six studies (n=819), liver stiffness measurement and platelet count were identified as independent risk factors for CSPH and were used to develop the new “CSPH risk” model. In the HVPG cohort (n=151), the new model accurately predicted CSPH with cutoff values of 0 and –0.68 for ruling in and out CSPH, respectively. In the follow-up cohort (n=1,102), the cumulative incidences of decompensation events significantly differed using the cutoff values of <–0.68 (low-risk), –0.68 to 0 (medium-risk), and >0 (high-risk). In the carvediloltreated cohort, patients with high-risk CSPH treated with carvedilol (n=81) had lower rates of decompensation events than non-selective beta-blockers untreated patients with high-risk CSPH (n=613 before propensity score matching [PSM], n=162 after PSM). Conclusions Treatment with carvedilol significantly reduces the risk of hepatic decompensation in patients with high-risk CSPH stratified by the new model.

Background: s/Aims: Non-invasive models stratifying clinically significant portal hypertension (CSPH) are limited. Herein, we developed a new non-invasive model for predicting CSPH in patients with compensated cirrhosis and investigated whether carvedilol can prevent hepatic decompensation in patients with high-risk CSPH stratified using the new model. Methods: Non-invasive risk factors of CSPH were identified via systematic review and meta-analysis of studies involving patients with hepatic venous pressure gradient (HVPG). A new non-invasive model was validated for various performance aspects in three cohorts, i.e., a multicenter HVPG cohort, a follow-up cohort, and a carvediloltreating cohort. Results: In the meta-analysis with six studies (n=819), liver stiffness measurement and platelet count were identified as independent risk factors for CSPH and were used to develop the new “CSPH risk” model. In the HVPG cohort (n=151), the new model accurately predicted CSPH with cutoff values of 0 and –0.68 for ruling in and out CSPH, respectively. In the follow-up cohort (n=1,102), the cumulative incidences of decompensation events significantly differed using the cutoff values of <–0.68 (low-risk), –0.68 to 0 (medium-risk), and >0 (high-risk). In the carvediloltreated cohort, patients with high-risk CSPH treated with carvedilol (n=81) had lower rates of decompensation events than non-selective beta-blockers untreated patients with high-risk CSPH (n=613 before propensity score matching [PSM], n=162 after PSM). Conclusions Treatment with carvedilol significantly reduces the risk of hepatic decompensation in patients with high-risk CSPH stratified by the new model.

Background: s/Aims: Non-invasive models stratifying clinically significant portal hypertension (CSPH) are limited. Herein, we developed a new non-invasive model for predicting CSPH in patients with compensated cirrhosis and investigated whether carvedilol can prevent hepatic decompensation in patients with high-risk CSPH stratified using the new model. Methods: Non-invasive risk factors of CSPH were identified via systematic review and meta-analysis of studies involving patients with hepatic venous pressure gradient (HVPG). A new non-invasive model was validated for various performance aspects in three cohorts, i.e., a multicenter HVPG cohort, a follow-up cohort, and a carvediloltreating cohort. Results: In the meta-analysis with six studies (n=819), liver stiffness measurement and platelet count were identified as independent risk factors for CSPH and were used to develop the new “CSPH risk” model. In the HVPG cohort (n=151), the new model accurately predicted CSPH with cutoff values of 0 and –0.68 for ruling in and out CSPH, respectively. In the follow-up cohort (n=1,102), the cumulative incidences of decompensation events significantly differed using the cutoff values of <–0.68 (low-risk), –0.68 to 0 (medium-risk), and >0 (high-risk). In the carvediloltreated cohort, patients with high-risk CSPH treated with carvedilol (n=81) had lower rates of decompensation events than non-selective beta-blockers untreated patients with high-risk CSPH (n=613 before propensity score matching [PSM], n=162 after PSM). Conclusions Treatment with carvedilol significantly reduces the risk of hepatic decompensation in patients with high-risk CSPH stratified by the new model.

OBJECTIVE To innovatively apply the pre-intelligent precision dosing and verification system （hereinafter referred to as “the system”）， and to provide a reference for the high-level “intelligent” transformation of inpatient pharmacy. METHODS The limitations of the triple-serial dispensing mode， which comprised the automatic medicine packaging machine （ATC）， intelligent tablet dispensing table （ITDT） and medication detection machine （MDM）， were analyzed. The application of the system and the adoption of the barcode scanning verification method optimized the pre-dosing management， whole-tablet drug dispensing process and ATC temporary dosing management. The comparative analysis was conducted to assess dosing time， labor cost and packaging error of the eight-month period， before and after the system application. RESULTS The triple-serial dispensing mode had a weak ability to avoid error risks in the manual dosing stage， and also had errors in the verification stage. Through the innovative application system， the pre-dosing management had been upgraded， the whole-tablet drug dispensing process had been optimized， and the ATC temporary dosing management had been improved. The average time required for each drug for pre-dosing， whole-tablet drug dispensing and ATC temporary dosing was significantly shortened after the application of the system， compared with before the application of the system （P＜0.001）. The number of pharmacists was reduced from two to one. The error rate of ATC decreased significantly from 0.220‰ to 0.029‰ （P＜0.001）. Specifically， the rate of pharmacist-related errors （pre-dosing error， ITDT dosing error， and ATC temporary dosing error） decreased from 0.116‰ to 0.001‰ （P＜0.001）， and machine-related errors decreased from 0.096‰ to 0.023‰ （P＜0.001）. CONCLUSIONS This innovative integration mode greatly improves the working efficiency and quality of inpatient pharmacy. It enhances refined management of drug expiration and inventory， saves time and labor costs， improves the accuracy of drug dispensing， and ensures patient medication safety.

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.

Humans ; Aged ; Lung Diseases ; Pneumonia/drug therapy* ; Adrenal Cortex Hormones/therapeutic use* ; Pulmonary Disease, Chronic Obstructive/drug therapy* ; Administration, Inhalation ; Community-Acquired Infections/drug therapy*

OBJECTIVE: To determine the role of Tripterygium wilfordii multiglycoside (TGW) in the treatment of psoriatic dermatitis from a cellular immunological perspective. METHODS: Mouse models of psoriatic dermatitis were established by imiquimod (IMQ). Twelve male BALB/c mice were assigned to IMQ or IMQ₊TGW groups according to a random number table. Histopathological changes in vivo were assessed by hematoxylin and eosin staining. Ratios of immune cells and cytokines in mice, as well as PAM212 cell proliferation in vitro were assessed by flow cytometry. Pro-inflammatory cytokine expression was determined using reverse transcription quantitative polymerase chain reaction. RESULTS: TGW significantly ameliorated the severity of IMQ-induced psoriasis-like mouse skin lesions and restrained the activation of CD45⁺ cells, neutrophils and T lymphocytes (all P<0.01). Moreover, TGW significantly attenuated keratinocytes (KCs) proliferation and downregulated the mRNA levels of inflammatory cytokines including interleukin (IL)-17A, IL-23, tumor necrosis factor α, and chemokine (C-X-C motif) ligand 1 (P<0.01 or P<0.05). Furthermore, it reduced the number of γ δ T17 cells in skin lesion of mice and draining lymph nodes (P<0.01). CONCLUSIONS TGW improved psoriasis-like inflammation by inhibiting KCs proliferation, as well as the associated immune cells and cytokine expression. It inhibited IL-17 secretion from γ δ T cells, which improved the immune-inflammatory microenvironment of psoriasis.
Male ; Animals ; Mice ; Tripterygium ; Psoriasis/drug therapy* ; Keratinocytes ; Skin Diseases/metabolism* ; Cytokines/metabolism* ; Imiquimod/metabolism* ; Dermatitis/pathology* ; Disease Models, Animal ; Mice, Inbred BALB C ; Skin/metabolism*

Background Anxiety and depression are common perinatal mental health issues that often occur together and can have serious negative effects on both maternal and infant health. Objective To examine the relationships between lifestyle factors and comorbid anxiety and depression (CAD) among pregnant women in Shanghai. Methods The study estimated the prevalence of CAD during the first, second, and third trimesters of pregnancy using the Self-rating Anxiety Scale (SAS) and Center for Epidemiological Studies-Depression (CES-D) based on data from the China National Birth Cohort (CNBC) embryonic-derived diseases with assisted reproductive technology (ART) sub-cohort. Information on demographics, sleep status, nutritional intake, and exercise during each trimester was collected through self-made questionnaires, the Pittsburgh Sleep Quality Index (PSQI), and the Food Frequency Questionnaire (FFQ). Lifestyle factors (such as sleep status, nutritional intake, and exercise during each trimester) were analyzed using logistic regression and generalized linear mixed models (GLMM) to determine their impacts on the prevalence of CAD (yes or no) among pregnant women. Results A total of 2876 pregnant women were included in this study. The prevalence of CAD was 10.6% (305), 3.6% (103), and 5.5% (159) in the first, second, and third trimesters of pregnancy, respectively. The logistic regression analysis revealed that poor sleep quality throughout the entire pregnancy were statistically associated with an increased prevalence of CAD, and the odds ratios (OR) with 95% confidence intervals (CI) were 2.817 (1.845, 4.301), 2.840 (1.855, 4.347), and 9.316 (5.835, 14.876) for the first, second, and third trimesters, respectively, when compared to good sleep quality. Additionally, compared to an intake frequency of 7 times per week, the frequency of egg intake ≤3 times per week in the first trimester (OR=2.025, 95%CI: 1.197, 3.425) and the frequency of egg intake of 4–6 times per week (OR=1.896, 95%CI: 1.117, 3.216) or ≤3 times per week (OR=1.906, 95%CI: 1.082, 3.357) in the third trimester were associated with an increased risk of CAD (P<0.05). Moreover, when compared to a frequency of exercise >3 times per week, never or almost never exercising in the second trimester (OR=2.218, 95%CI: 1.220, 4.035) was associated with an increased risk of CAD (P<0.05). The GLMM analysis also demonstrated a significant association between poor sleep quality, lower exercise frequency, or lower intake frequency of vegetables, eggs, or milk and an increased risk of CAD (P<0.05). Conclusion The prevalence of CAD among pregnant women in Shanghai follows a U-shaped distribution, with the highest rate occurring in early pregnancy and the lowest rate in mid-pregnancy. Factors such as poor sleep quality, inadequate intake of vegetables, eggs, or milk, and lack of exercise during pregnancy may increase the risk of CAD. Implementing lifestyle interventions during pregnancy could potentially reduce the risk of mental health problems and improve the overall health of both mothers and babies.