Background Gestational weight gain is closely related to maternal and infant health outcomes. Pregnant women are simultaneously exposed to four metals—lithium (Li), vanadium (V), uranium (U), and bismuth (Bi)—through inhalation of fine particulate matter and consumption of contaminated food and water. Existing studies suggest that exposure to these metals may be associated with gestational weight gain. However, no study has yet explored the complex relationships between exposure to mixtures of these four metals and weight gain at different stages of pregnancy. Objective To investigate the associations between mixed exposure to Li, V, U, and Bi in early pregnancy and the average weekly gestational weight gain during both early pregnancy and mid-to-late pregnancy. Methods This prospective study recruited eligible women in early pregnancy from an obstetrics clinic of a tertiary hospital in Jinan, China, between September 2021 and July 2023. Pre-pregnancy weight, current weight (at 11⁺⁰ to 13⁺⁶ weeks of gestation), and spot urine samples (≥5.0 mL) were collected at enrollment. Urinary concentrations of Li, V, Bi, and U were determined using inductively coupled plasma mass spectrometry. Participants were followed up in late pregnancy (≥28 weeks of gestation) to collect information on physical activity via questionnaire; weight measurements at the last antenatal visit (35⁺⁰ to 37⁺⁶ weeks of gestation) were obtained from the hospital information system. After adjusting for covariates, multiple linear regression and generalized additive models were used to assess the associations of individual metals with weekly weight gain in early pregnancy and in mid-to-late pregnancy. Bayesian kernel machine regression (BKMR) and quantile-based g-computation (Qgcomp) were applied to evaluate the joint effects of the metal mixture exposure on weekly weight gain at the two gestational stages. Results A total of 313 pregnant women were included. The geometric means of urinary Li, V, U, and Bi concentrations were 37.07, 0.20, 0.06, and 0.04 μg·L⁻¹, respectively; after creatinine adjustment, the corresponding values were 46.82, 0.25, 0.07, and 0.05 μg·g⁻¹ (Cr). The mean weekly gestational weight gain was (0.19±0.25) kg in early pregnancy and (0.53 ± 0.18) kg in mid-to-late pregnancy. Both multiple linear regression and generalized additive models showed that urinary V concentration was positively associated with average weekly gestational weight gain in early pregnancy, while no significant associations were found for other metals or for gestational weight gain in mid-to-late pregnancy. In the BKMR model with early-pregnancy weight gain as the outcome, V had the strongest association [posterior inclusion probability (PIP)=0.773]. When other metals were fixed at their medians, V showed a positive non-linear association with the outcome. A significant single-metal effect of V and its interaction with Li were observed. Compared with the 50th percentile of the metal mixture, the average weekly weight gain in early pregnancy increased by 0.016 (95%CI: 0.003, 0.029) and 0.018 (95%CI: 0.001, 0.036) at the 60th and 65th percentiles, respectively; conversely, at the 25th percentile, it decreased by 0.026 (95%CI: 0.002, 0.050). Overall, the joint effect of the metal mixture on early- pregnancy weight gain showed an upward trend. In the BKMR model for mid-to-late pregnancy gestational weight gain, all PIPs were<0.5, and no significant single-metal effects, interactions, or joint effects were identified. Qgcomp results confirmed a positive association between the metal mixture and early-pregnancy weight gain (b=0.031, 95%CI: 0.010, 0.051; P<0.01), with V contributing the highest positive weight (0.71). No significant association was found for weight gain in mid-to-late pregnancy (b=0.007, P=0.339). Conclusion Higher levels of co-exposure to the Li, V, Bi, and U metal mixture during early pregnancy may be associated with increased average weekly weight gain in early pregnancy. Among these metals, V exhibits a predominant role and appears to interact with Li. No association is observed between early-pregnancy metal mixture exposure and average weekly gestational weight gain in mid-to-late pregnancy. These findings suggest that monitoring and managing metal exposure during early pregnancy may be crucial for the rational regulation of gestational weight gain.

Intravitreal anti-vascular endothelial growth factor(anti-VEGF)therapy has been widely used, but the variability in its therapeutic efficacy limits individualized treatment. In recent years, the application of artificial intelligence(AI)has opened up new avenues for personalized treatment response prediction, and its core branches include machine learning(ML)and deep learning(DL). This review systematically retrieved and analyzed 41 relevant studies published up to April 2025. Comprehensive analysis reveals that AI predictive models are evolving from forecasting single endpoints(such as visual acuity or central retinal thickness)to integrating multi-dimensional endpoints(encompassing anatomical, functional, and treatment demand parameters)and generating predictive imaging outputs. In terms of technical approaches, DL models(28 studies, accounting for 68.3%)dominate this field due to their robust image interpretation capabilities, while ML models(10 studies, 24.4%)retain significant value in the analysis of structured clinical data. Cross-disease comparisons indicate that research efforts are most concentrated on age-related macular degeneration(ARMD)and diabetic macular edema(DME), with shared conceptual frameworks for model construction, yet distinct anatomical and functional indicators are prioritized for each disease. Currently, the field confronts several key challenges, including insufficient prospective clinical validation, limited model interpretability(the “black box problem”), and a scarcity of high-quality multi-center datasets. Moving forward, it is imperative to advance real-world validation and develop explainable AI techniques to expedite the clinical translation of these predictive models.

Objective: To explore the correlation between traditional Chinese medicine (TCM) constitution and depressive symptom among school aged children and adolescents, so as to provide evidences for informing constitution based regulation and prevention of depressive symptom. Methods: From June to December 2024, a total of 4 729 students aged 6-14 were recruited by cluster random sampling from 10 primary schools in Baoding (Hebei Province), Heze and Liaocheng (Shandong Province). General information, TCM constitution and depressive symptom were collected. Restricted cubic spline (RCS) models were used to analyze related factors and threshold effects of depressive symptom. Binary Logistic regression was applied to examine the association between depressive symptom and TCM constitution, with subgroup analyses conducted. Results: The detection rate of depressive symptom among the included children and adolescents was 25.82%. RCS analyses indicated non linear associations between depressive symptom and age (inflection point at 10 years old), bedtime (inflection point at 22:00), and wake up time (inflection point at 6:30 ) (all P non linearity <0.01). Linear associations were observed with body mass index (BMI) and sleep duration (all P non linearity > 0.05 ). After adjusting for covariates such as age, BMI and sleep status, binary Logistic regression analyses showed that Yin deficient constitution ( OR =1.26, 95% CI =1.09-1.45) and Phlegm-dampness constitution ( OR =1.42, 95% CI =1.11-1.82) were significantly associated with depressive symptom among children and adolescents (all P <0.05). Conclusions Depressive symptom among school aged children and adolescents is primarily associated with Yin deficiency and Phlegm dampness constitutions in TCM constitution. Active attention should be paid to susceptible TCM constitution among children and adolescents. Targeted health guidance and interventions should be implemented to improve TCM constitution health status for preventing the occurrence of depressive symptom.

Perinatal depression (PND) is a critical public health issue affecting maternal and offspring health. Digital health intervention platforms, leveraging advantages in accessibility, privacy, and cost-effectiveness, demonstrate good application in PND prevention and treatment. This review systematically searched literature and policy documents published between January 2018 and March 2025 in CNKI, PubMed, Web of Science and World Health Organization. It summarized the development trajectory of digital health intervention platforms and their current applications and effectiveness in PND prevention and treatment. Existing evidence was evaluated across dimensions of efficacy, systematicity, and practicality, identifying major challenges faced by these platforms. Studies indicate that while PND digital health intervention platforms have achieved preliminary success in alleviating PND symptoms, widespread issues persist, including incomplete service closed-loop systems, low user adherence, and insufficient sustainability. Future efforts should focus on optimizing intervention content and interactive design, advancing intelligent assessment and tiered intervention strategies, strengthening continuous monitoring and crisis response mechanisms, and constructing a multidisciplinary collaborative support system. These steps are essential for achieving efficient, intelligent, and sustainable development of digital health intervention platforms for PND.

Interferon regulatory factor 4 (IRF4) is a critical transcription factor that governs the differentiation of cluster of differentiation 4⁺ (CD4⁺) T cells. The pathogenesis and progression of psoriasis are primarily attributed to an immune imbalance stemming from the overproduction of interleukin-17A (IL-17A) by T lymphocytes. However, the role of IRF4 in psoriasis remains unexplored. In this study, we found that IRF4 activity is increased in the cutaneous lesions of patients with psoriasis in response to stimulation by IL-23A and IL-1β. This IRF4 elevation heightens its binding to the E1A binding protein p300 (EP300) promoter, triggering the transcription of downstream retinoic acid receptor-related orphan receptor-γt (RORγt) and increasing the secretion of IL-17A, thereby establishing the IL-1β/IL-23A-IRF4-EP300-RORC-IL-17A inflammatory cascade in psoriasis. The alleviation of imiquimod (IMQ)-induced psoriatic-like symptoms was achieved through the creation of a Irf4 ^-/- gene deletion mouse model and pharmacological inhibition using antisense oligonucleotides targeted for Irf4. This amelioration was accompanied by a decreased number of IL-17A-producing CD4⁺ T cells in the skin. The findings of this study suggest that IRF4 plays a crucial role in the promotion of inflammation and exacerbation of IMQ-induced psoriasiform dermatitis. Consequently, IRF4 targeting could be a promising therapeutic strategy.

Natural products (NPs) have long held a significant position in various fields such as medicine, food, agriculture, and materials. The chemical space covered by NPs is extensive but often underexplored. Therefore, high-throughput and efficient methodologies for the annotation and discovery of NPs are desired to address the complexity and diversity of NP-based systems. Mass spectrometry (MS) has emerged as a powerful platform for the annotation and discovery of NPs. MS databases provide vital support for the structural characterization of NPs by integrating extensive mass spectral data and sample information. Additionally, the released annotation methodologies, based on a variety of informatics tools, continuously improve the ability to annotate the structure and properties of compounds. This review examines the current mainstream databases and annotation methodologies, focusing on their advantages and limitations. Prospects for future technological advancements are then discussed in terms of novel applications and research objectives. Through a systematic overview, this review aims to provide valuable insights and a reference for MS-based NPs annotation, thereby promoting the discovery of novel natural entities.
Biological Products/chemistry* ; Mass Spectrometry/methods* ; Databases, Factual ; Drug Discovery/methods* ; Humans

[Objective]To investigate the role of endothelial microparticles(EMPs)in diabetic macrovascular complications(DMCs),summarize the research progress and analyze the research progress on traditional Chinese medicine(TCM)regulation of EMPs for the prevention and treatment of DMCs.[Methods]Base on the research on the pathological mechanism of DMCs,the biological characteristic of EMPs and their mechanism of action in DMCs in recent years,combined with the experimental and clinical studies on the regulation of EMPs by TCM for disease prevention and treatment,their paper summarizes the action targets of EMPs in regulating the pathological process of DMCs and the resarch progress of TCM intervention,on this basis,the limitations of current research and future prospects are summarized.[Results]EMPs are important biomarkers of endothelial injury,promoting mechanisms such as inflammation,oxidative stress,thrombosis and atherosclerosis,contributes to the pathological progression of DMCs.TCM intervenes in the generation and release of EMPs through mechanisms such as regulating inflammatory responses,alleviating oxidative stress,relieving endothelial dysfunction and promoting vascular repair.This approach helps maintain endothelial homeostasis and plays a crucial role in the prevention and treatment of DMCs.[Conclusion]EMPs play a significant role in the onset and progression of DMCs,serving as potential intervention targets.TCM offers novel therapeutic approaches for the prevention and treatment of DMCs by inhibiting inflammation and oxidative stress,promoting vascular remodeling and repair,and suppressing the formation of thrombosis and atherosclerosis.

Autophagy is the essential mechanism for cell adaptation and survival. Under normal physiological conditions, autophagy activates hair follicle stem cells, induces the transition of hair follicles from the telogen phase to the anagen phase, and maintains the hair growth cycle. Moreover, autophagy can protect dermal papilla cells from damage under pathological conditions. Intervention in autophagy can slow the progress of hair loss due to the existence of impaired autophagy in androgenetic alopecia and alopecia areata. The review summarizes the role of autophagy in hair cycle regulation, cell homeostasis maintenance, and hair regeneration, providing new insights into therapeutic options targeting follicular autophagy signaling pathways.

Objective:Investigating the association of the systemic immune-inflammation index and the pan-inflammation index with incident non-alcoholic fatty liver disease (NAFLD).Methods:This retrospective cohort study included 42 891 participants who underwent at least two health examinations at the Second Affiliated Hospital of Dalian Medical University between 2014 and 2023. Based on their levels of the systemic immune inflammation index (SII) and the pan immune inflammation value (PIV), participants were respectively divided into four quartile groups (Q1 to Q4). Cox proportional hazards models were employed to analyze the association of different SII and PIV levels, as well as their quartile groups, with new onset NAFLD in the total population and across various subgroups. Restricted cubic splines were used to examine the dose response relationship between these inflammatory indices and incident NAFLD. Additionally, sensitivity analyses were conducted to confirm the robustness of the findings.Results:After adjusting for confounding factors, the natural logarithm-transformed lnSII ( HR=1.247, 95% CI: 1.184-1.314, P<0.001) and lnPIV ( HR=1.192, 95% CI: 1.148-1.238, P<0.001) were significantly positively associated with the risk of NAFLD. When the subjects were grouped by SII quartiles (Q1-Q4), compared with those in Q1, participants in Q2, Q3, and Q4 exhibited progressively higher risks of incident NAFLD:11.9% ( HR=1.119, 95% CI: 1.051-1.192, P<0.001), 17.1% ( HR=1.171, 95% CI: 1.100-1.248, P<0.001), and 29.1% ( HR=1.291, 95% CI: 1.211-1.377, P<0.001), respectively. Quartile analysis of PIV yielded similar trends: the risk of incident NAFLD increased for 10.4% ( HR=1.104, 95% CI: 1.034-1.179, P=0.003), 18.7% ( HR=1.187, 95% CI: 1.112-1.266, P<0.001), and 30.5% ( HR=1.305, 95% CI: 1.223-1.393, P<0.001) in Q2, Q3, and Q4 group respectively when compared to that in Q1 group. Subgroup analysis confirmed consistent associations of SII and PIV with elevated NAFLD risk across all subgroups. Conclusion:Elevated levels of SII and PIV are significantly associated with increased risk of NAFLD.

Objective:To investigate transition pattern of health status among middle-aged and elderly population in China based on frailty index.Methods:In this retrospective cohort study, middle-aged and elderly people were selected from the China Health and Retirement Longitudinal Study (CHARLS) in 2011; and 1 434 subjects were followed up to 2015. The frailty index was calculated from the prevalence of chronic diseases, daily activity ability and blood biomarkers, and the frailty state was divided by quartiles of the frailty index. Markov models were constructed to determine the transition probabilities of different frailty states.Results:The mean age of the 1 434 subjects was (59.0±9.4) years and the mean frailty index was 0.11±0.05. In the healthy individuals, 63.0% remained healthy after a four-year follow-up; during the same follow-up period, 40.9% of the mildly frail individuals and 23.0% of the moderately frail individuals remained in their baseline frailty status. Increasing age leaded to a gradual increase in the probability of the population shifting to a severely frailty state. Women were more likely to shift to severe frailty status than men (0.029 vs 0.019, Z=3.03, P=0.002). Conclusion:Among middle-aged and elderly population in China, the transition of health states follows a pattern where higher frailty levels are associated with lower stability. Advanced age and female gender are identified as risk factors for progression to severe frailty.