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.

Thrombospondin 4 (THBS4; TSP4), a crucial component of the extracellular matrix (ECM), serves as an important regulator of tissue homeostasis and various pathophysiological processes. As a member of the evolutionarily conserved thrombospondin family, THBS4 is a multidomain adhesive glycoprotein characterized by six distinct structural domains that mediate its diverse biological functions. Through dynamic interactions with various ECM components, THBS4 plays pivotal roles in cell adhesion, proliferation, inflammation regulation, and tissue remodeling, establishing it as a key modulator of microenvironmental organization. The transcription and translation of THBS4 gene, as well as the activity of the THBS4 protein, are tightly regulated by multiple signaling pathways and extracellular cues. Positive regulators of THBS4 include transforming growth factor-β (TGF-β), interferon-γ (IFNγ), granulocyte-macrophage colony-stimulating factor (GM-CSF), bone morphogenetic proteins (BMP12/13), and other regulatory factors (such as B4GALNT1, ITGA2/ITGB1, PDGFRβ, etc.), which upregulate THBS4 at the mRNA and/or protein level. Conversely, oxidized low-density lipoprotein (OXLDL) acts as a potent negative regulator of THBS4. This intricate regulatory network ensures precise spatial and temporal control of THBS4 expression in response to diverse physiological and pathological stimuli. Functionally, THBS4 acts as a critical signaling hub, influencing multiple downstream pathways essential for cellular behavior and tissue homeostasis. The best-characterized pathways include: (1) the PI3K/AKT/mTOR axis, which THBS4 modulates through both direct and indirect interactions with integrins and growth factor receptors; (2) Wnt/β-catenin signaling, where THBS4 functions as either an activator or inhibitor depending on the cellular context; (3) the suppression of DBET/TRIM69, contributing to its diverse regulatory roles. These signaling connections position THBS4 as a master regulator of cellular responses to microenvironmental changes. Substantial evidence links aberrant THBS4 expression to a range of pathological conditions, including neoplastic diseases, cardiovascular disorders, fibrotic conditions, neurodegenerative diseases, musculoskeletal disorders, and atopic dermatitis. In cancer biology, THBS4 exhibits context-dependent roles, functioning either as a tumor suppressor or promoter depending on the tumor type and microenvironment. In the cardiovascular system, THBS4 contributes to both adaptive remodeling and maladaptive fibrotic responses. Its involvement in fibrotic diseases arises from its ability to regulate ECM deposition and turnover. The diagnostic and therapeutic potential of THBS4 is particularly promising in oncology and cardiovascular medicine. As a biomarker, THBS4 expression patterns correlate significantly with disease progression and patient outcomes. Therapeutically, targeting THBS4-mediated pathways offers novel opportunities for precision medicine approaches, including anti-fibrotic therapies, modulation of the tumor microenvironment, and enhancement of tissue repair. This comprehensive review systematically explores three key aspects of THBS4 research(1) the fundamental biological functions of THBS4 in ECM organization; (2) its mechanistic involvement in various disease pathologies; (3) its emerging potential as both a diagnostic biomarker and therapeutic target. By integrating recent insights from molecular studies, animal models, and clinical investigations, this review provides a framework for understanding the multifaceted roles of THBS4 in health and disease. The synthesis of current knowledge highlights critical research gaps and future directions for exploring THBS4-targeted interventions across multiple disease contexts. Given its unique position at the intersection of ECM biology and cellular signaling, THBS4 represents a promising frontier for the development of novel diagnostic tools and therapeutic strategies in precision medicine.

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.

The discussion on the connotation of children’s subjectivity is not only a response to the lack of children’s subjectivity at the current stage of health management, but also a reference for children’s medical science popularization. Based on the perspective of social critical theory, this study used empirical research methods to review the "Dream Medical College" project of Children’s Hospital of Fudan University. The current situation and influencing factors of health management experience of 1 520 children participating in the "Dream Medical College" project were analyzed. The study showed that 96.35% of 1 316 subjects had diagnosis and treatment experience in specialized hospitals, and the overall negative emotional performance was at a low level (0~12 points). There was significant correlation between diagnosis and treatment, invasive experience and children’s emotional performance (P<0.05). The study revealed that the diagnosis and treatment field is the main practice place of children’s health management, while the subjective of children with different diagnosis and experience perform significantly different. Children over 4 years old have better language anxiety than physical anxiety when receiving diagnosis and treatment. Although medical science popularization is an important practical form of children’s health management, it lacks the science popularization content of invasive diagnosis and treatment and emotional management, and creative popular science form is more suitable for children with long-term and frequent diagnosis and treatment experience.

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.

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

Objective To investigate the characteristics and influencing factors of mental health status of permanent residents in Yichang, Hubei Province. Methods A total of 9 576 permanent residents aged 18 years and older from Yichang City were selected by a multistage random sampling method between June and October 2022. The PHQ-9 was used to assess the residents’ depressive symptoms, the GAD-7 was used to assess their anxiety symptoms, the ISI was used to assess their insomnia status, and the PCL-5 was used to assess their stress status. The influence factors of depression and anxiety were analyzed using χ2 test and logistic regression. Results A total of 9 122 valid questionnaires were completed. The detection rate of depression, anxiety, insomnia, and stress symptoms were 29.98%, 19.03%, 11.97% and 1.58%, respectively. Gender, education level, monthly family income, self-rated health status, mental health literacy level, total GAD-7 score, total ISI score, and total PCL-5 score were the main factors that caused residents' anxiety symptoms, while gender, education level, self-rated health status, total PHQ-9 score, total ISI score, and total PCL-5 score were the main factors that caused residents' anxiety symptoms. Conclusion The prevalence of depression and anxiety is high among the permanent residents in Yichang, while the situation of insomnia and stress is relatively good. Measures such as improving the level of mental health literacy can be taken to improve mental health level of residents in Yichang.

italic>Schisandra chinensis is a traditional Chinese medicine with the functions of reinforcing deficiency, strengthening, and inducing astringency, appliable to treat the chronic cough and deficiency in breath, palpitation, and insomnia, etc. A hybrid mass spectrometry scanning strategy (high-definition data-independent/data-dependent acquisition, HDDIDDA), enabling the ion mobility separation and alternating data-independent acquisition/data-dependent acquisition, was established, which, in combination with in-house library-driven automatic peak annotation workflows facilitated by the UNIFI software, was utilized to systematically characterize the multi-classes of chemical components from S. chinensis. The use of an HSS T3 column (100 mm × 2.1 mm, 1.8 μm), 0.1% formic acid in H₂O-acetonitrile as the mobile phase running at the flow rate of 0.3 mL·min^-1, and column temperature at 35 ℃, could enable good separation of the S. chinensis components within 42 min. HDDIDDA scan in both the positive and negative ion modes was employed for data acquisition. Based on the automatic peak annotation, reference standards comparison, MS² data interpretation, and literature analysis, we were able to identify or tentatively characterize 105 compounds in the S. chinensis decoction, involving 56 terpenoids, 42 lignans, five glycosides, one organic acid, and one flavonoid. HDDIDDA scanning can improve the coverage of data acquisition and improve the accuracy of identification, while CCS prediction analysis provides the possibility to distinguish isomers by the ion mobility technology. The results provide reference for the intelligent material basis research of TCM.