Objective Orosomucoid1 （ORM1） is a novel target in the quest for anti-fatigue pharmacotherapy. Preliminary investigations have illuminated oleuropein （OLE） as a promising candidate molecule, poised to enhance ORM1 expression. To elucidate the influence of OLE on ORM1 protein expression and assess its ramifications on muscle endurance. Methods The impact of OLE on ORM1 protein expressions within hepatocytes and liver tissue was meticulously quantified through Western blotting; the effects of OLE on muscle endurance were evaluated via the rotarod and forced swimming tests; glycogen content within liver and muscle tissues was determined utilizing a specialized kit; and PAS staining was employed to visualize glycogen deposition in the gastrocnemius muscle. Results OLE demonstrated a capacity to elevate ORM1 protein expression in hepatocytes in a time- and dose-dependent manner, concurrently prolonging the duration of swimming and rotarod performance in mice, also in a time- and dose-dependent manner. Furthermore, OLE augmented ORM1 expression in liver tissue, elevated serum ORM1 levels, and enhanced glycogen reserves within the liver and muscle. Conclusion OLE may serve to amplify muscle endurance by elevating ORM1 levels in vivo and augmenting glycogen stores within skeletal muscle.

The inflammatory microenvironment is closely associated with the initiation and progression of osteoarthritis （OA）， specifically manifesting as macrophage activation， dysregulation of inflammatory cytokines， and redox imbalance. Following an overview of the pathological characteristics of the OA inflammatory microenvironment， this paper reviews the research progress on the mechanism of traditional Chinese medicine （TCM） intervening in OA by modulating the inflammatory microenvironment. It has been found that TCM monomers/active ingredients （such as total alkaloids from Strychnos nux-vomica ， quercetin， triptolide， etc.）， herb pairs （e.g. Angelica pubescens - Gentiana macrophylla ， Carthami Flos-Lycopodii Herba）， and TCM formulas （such as Zhuanggu jianxi formula， Duhuo jisheng decoction and Rongjin niantong formula， etc.） can inhibit macrophage activation， reduce the release of proinflammatory cytokines and the generation of reactive oxygen species by inhibiting multiple signaling pathways， including nuclear factor-κB， Wnt/ β -catenin， and mitogen-activated protein kinase， thereby alleviating the articular inflammatory microenvironment， restoring local joint homeostasis， and slowing the progression of OA.

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.

Objective: This study proposes a clustering framework for Chinese materia medica (CMM) based on a large language model (LLM), aiming to explore potential compatibility patterns among CMMs from the semantic perspective of CMM property theory. Methods: First, a CMM property knowledge base was constructed based on Chinese Materia Medica, including 567 commonly used CMMs characterized by four properties, five flavors, and meridian tropism. Then, 49 CMMs derived from 10 prescriptions for Zangdu (脏毒, pathogenic toxins) recorded in Waike Zhengzong (《外科正宗》, Orthodox Manual of External Medicine) and Yangke Xinde Ji (《疡科心得集》, Collected Insights on Ulcer Medicine) were selected as the experimental dataset. Five semantic representation methods—One-Hot, Word2Vec, Bidirectional Encoder Representations from Transformers (BERT), Beijing Academy of Artificial Intelligence General Embedding (BGE), and Qwen—were applied to encode CMM property information into vector representations. Subsequently, t-distributed Stochastic Neighbor Embedding (t-SNE) was used for nonlinear dimensionality reduction on high-dimensional semantic vectors, followed by k-means clustering (k = 7). Clustering performance was evaluated using the Silhouette Score (SS), Davies-Bouldin Index (DBI), and Calinski-Harabasz Index (CHI). Results: The Qwen-based clustering method, CMM-EmbedCluster, achieved the highest SS (0.607 4) and CHI (158.057 2), as well as the lowest DBI (0.499 5), indicating improved cluster separation and compactness compared with other methods. Visualization of CMM clustering results showed that the clusters were well separated in the low-dimensional space, with strong inter-cluster discrimination and high intra-cluster functional consistency. Further interpretability analysis of CMM clustering results revealed stable structural differences among clusters in terms of four properties, five flavors, and meridian tropism, forming functional partitions consistent with CMM property theory. Conclusion CMM-EmbedCluster utilizes an LLM to achieve semantic-level representation and clustering of CMMs within the framework of CMM property theory, providing support for exploring potential compatibility patterns among CMMs from the perspective of CMM property semantics.

Objective: To investigate the association between uric acid level and elevated blood pressure among children and adolescents in Guangzhou, so as to provide a scientific basis for prevention and control of hypertension in the pediatric population. Methods: From March to December 2019, using stratified cluster random sampling, 3 626 children and adolescents aged 6-17 years without elevated blood pressure at baseline were recruited from 6 primary and secondary schools in Guangzhou. Serum uric acid levels were measured at baseline, and divided into four groups ( Q1-Q 4) based on age and sex specific quartiles for analyses. Two waves of follow up were carried out from October 2020 to May 2021 and from October 2021 to May 2022, respectively. Multilevel robust Poisson regression was used to evaluate the association between uric acid level and elevated blood pressure among children and adolescents. The additive interaction between high uric acid level and sugar sweetened beverage (SSB) intake on elevated blood pressure was assessed by stratification analysis and calculation of the relative excess risk due to interaction (RERI). Results: A total of 1 131 new cases of elevated blood pressure were identified during the two follow ups, with an incidence density of 130.5 cases 1 000 person years. Multilevel robust Poisson regression analysis showed that after adjusting for confounders such as gender, age and annual family income, compared with those in the uric acid Q 1 level, the risk of elevated blood pressure in individuals in the Q 4 level increased by 25% ( RR=1.25, 95%CI =1.08-1.44), and the risk of elevated systolic blood pressure increased by 20% ( RR=1.20, 95%CI =1.05-1.36) (both P <0.05); no statistical association was observed between uric acid and elevated diastolic blood pressure (all P >0.05). In the stratified analysis, the statistical associations of uric acid Q 4 level with elevated blood pressure ( RR=1.34, 95%CI =1.13-1.58) and elevated systolic blood pressure ( RR=1.33, 95%CI =1.14-1.55) were only observed in the group with SSB intake ≥ 250 mL/week (both P <0.05). The RERI values for the effect of uric acid Q 4 and Q 3 levels with SSB intake ≥ 250 mL/week on elevated systolic and diastolic blood pressure were 0.29 and 0.44 (both P <0.05). Conclusions High uric acid level is a risk factor for elevated blood pressure among children and adolescents, with a predominant effect on systolic blood pressure. High uric acid level and SSB intake synergistically promote the increase of blood pressure of children and adolescents.

Objective To investigate the correlation of dietary lipid intake and apolipoprotein with Helicobacter pylori (Hp) infection combined with gastric mucosal lesions. Methods A retrospective analysis was conducted on 320 patients diagnosed with Hp infection during physical examinations from March 2024 to March 2025. The daily dietary lipid intake of all patients was statistically analyzed. The levels of apolipoprotein A1‌ (Apo-A1) and apolipoprotein B‌ (Apo B) were detected. Logistic regression was used to analyze the risk factors influencing Hp infection combined with gastric mucosal lesions. Results Compared with the non-gastric mucosal lesion group, patients in the gastric mucosal lesion group had higher daily dietary lipid intake, lower Apo-A1, higher Apo B, and higher proportion of family history of gastric cancer (P<0.05). Logistic regression analysis revealed that daily dietary lipid intake, low level of Apo-A1, and high level of Apo B were independent risk factors of Hp infection with gastric mucosal lesion (P<0.05). Conclusion Daily dietary lipid intake, Apo-A1, and Apo B are closely related to Hp infection with gastric mucosal lesion. For patients with high daily dietary lipid intake, decreased Apo-A1 level, increased Apo B level, and complicated Hp infection, it is necessary to further screen gastroscopy to determine whether gastric mucosal lesion occurs.

ObjectiveTo explore the function and related molecular mechanisms of magnoflorine against colon cancer via network pharmacology， molecular docking， and in vitro cell experiments. MethodsIn this study， the canonical SMILES of magnoflorine was obtained from the PubChem database， and the potential targets of magnoflorine were predicted by the Swiss Target Prediction database， while the disease targets of colon cancer were obtained from the DisGeNET， GeneCards and OMIM databases. The intersecting targets between magnoflorine's predicted targets and colon cancer disease targets were taken， and a protein-protein interaction （PPI） network was constructed and analyzed. The DAVID online database was employed to conduct Gene Ontology （GO） and KEGG pathway enrichment analyses on core targets. The top five key targets screened were docked with magnoflorine using AutoDock software. Finally， cellular experiments including CCK-8 assays， EdU experiments， cell scratch assays， and Transwell assays were conducted to validate the results from network pharmacology and molecular docking. Results44 key targets of magnoflorine in resisting colon cancer were acquired. The molecular docking results showed that magnoflorine had a strong binding activity with the core target signal transducer and activator of transcription 3 （STAT3） in the top five of the PPI network. Cellular experiments confirmed that magnoflorine could inhibit the proliferation and migration of colon cancer cells by suppressing the JAK/STAT3 signaling pathway. ConclusionMagnoflorine may inhibit the proliferation and migration of colon cancer cells by regulating the JAK/STAT3 signaling pathway.

Objective To investigated the effects of occupational exposure to N,N-dimethylformamide (DMF) on sterol regulatory element-binding protein (SREBP) and iron metabolism among workers. Methods A total of 20 workers occupationally exposed to DMF were selected as the exposure group, and 15 healthy workers without DMF exposure from the same factory were selected as the control group using the purposive sampling method. DMF exposure levels were assessed in individual samples of the workers. The urinary N-methylformamide (NMF) levels were measured by gas chromatography. The mRNA expression of SREBP-1a, SREBP-1c, and SREBP-2 in peripheral blood was measured by real-time quantitative polymerase chain reaction. Enzyme-linked immunosorbent assay was used to detect serum transferrin, soluble transferrin receptor (sTfR), and ferritin levels. Results The DMF exposure levels among the workers in the control group were below the detection limit, and urinary NMF was undetectable. The median DMF exposure level was 13.30 mg/m³, and the mean urinary NMF level was (9.72±1.78) mg/g Cr among the workers in the exposure group. Compared with the control group, the relative mRNA expression of SREBP-1a, SREBP-1c, and SREBP-2 in peripheral blood of workers in the exposure group increased (all P<0.05), while the levels of transferrin and sTfR decreased (both P<0.01). However, there was no statistically significant difference in serum ferritin levels between the two groups (P>0.05). In the exposure group, urinary NMF level of workers was negatively correlated with the relative mRNA expression level of SREBP-1c in blood [correlation coefficient (r)=-0.50, P=0.03], and positively correlated with the levels of serum transferrin, sTfR, and ferritin (r = 0.63, 0.58, and 0.52, respectively; all P<0.05). Conclusion Occupational exposure to DMF can upregulate the expression of the SREBP family and disrupt iron metabolism, with a potential association between these effects.

Objective: To analyze the factors associated with high level fear of negative evaluation (FNE) among junior high school students and to construct a nomogram risk prediction model, so as to provide scientific tools for psychological health intervention for junior high school students. Methods: A convenience sampling combined with cluster random sampling method was used to select 5 485 junior high school students from 4 cities (Wuhan, Huanggang, Xianning and Xiaogan) for an online questionnaire survey in March 2025. The total sample was randomly split into a training set ( n =3 839) and a validation set ( n =1 646). Univariate analysis was performed in the training set using Chi-square test and t-test. Variables with statistical significance were subsequently included in multivariate Logistic regression to identify independent predictors and to construct a nomogram based risk prediction model. The discriminative ability and clinical utility of the model were evaluated in the validation set using the area under the receiver operating characteristic curve (AUC), calibration curve, and decision curve analysis (DCA). Results: There were 1 649 junior high school students with low level FNE and 2 190 with high level FNE in the training set. The self control ability of junior high school students with lowlevel and high level FNE showed a statistically significant difference (23.96±3.96, 21.48±3.37, t=25.15, P < 0.01 ). Statistically significant differences in the detection rate of high level FNE were observed among junior high school students with different genders, family types, parenting styles, academic rankings, psychological flexibility, mobile phone addiction tendencies, emotional management training, exercise frequency, left behind experiences, and places of origin ( χ 2=82.01- 1 126.68 , all P <0.01). The results of Logistic regression analysis revealed that, the following factors were identified as significant factors influencing high level FNE among junior high school students:exercise frequency ( OR=0.21, 95%CI =0.17-0.26); parenting style ( OR=0.48, 95%CI =0.40-0.58); left behind experience ( OR=3.88, 95%CI =3.27-4.61); smartphone addiction proneness ( OR=2.19, 95%CI =0.89-0.93); self-control ability ( OR=0.91, 95%CI =0.89-0.93); and psychological flexibility ( OR=0.16, 95%CI =0.10-0.28) (all P <0.05). The AUC for the training and validation set were 0.88 (95% CI =0.87-0.89) and 0.87 (95% CI =0.85-0.89), respectively. The Hosmer-Lemeshow goodness of fit test yielded χ 2=8.57, 15.20 (both P >0.05). Conclusion The risk prediction model with high level FNE demonstrates good accuracy and can assist educators and parents in timely screening of junior high school students with high level FNE, thereby providing a basis for implementing targeted interventions.

Lung cancer is a leading cause of cancer-related deaths worldwide, with its high mortality rate primarily attributed to delayed diagnosis. Radiomics, by extracting abundant quantitative features from medical images, offers novel possibilities for early diagnosis and precise treatment of lung cancer. This article reviewed the latest advancements in radiomics for lung cancer management, particularly its integration with artificial intelligence (AI) to optimize diagnostic processes and personalize treatment strategies. Despite existing challenges, such as non-standardized image acquisition parameters and limitations in model reproducibility, the incorporation of AI significantly enhanced the precision and efficiency of image analysis, thereby improving the prediction of disease progression and the formulation of treatment plans. We emphasized the critical importance of standardizing image acquisition parameters and discussed the role of AI in advancing the clinical application of radiomics, alongside future research directions.
Humans ; Lung Neoplasms/diagnosis* ; Artificial Intelligence ; Early Detection of Cancer/methods* ; Precision Medicine ; Image Processing, Computer-Assisted/methods* ; Tomography, X-Ray Computed ; Radiomics