ObjectiveTo explore the potential mechanisms by which Myristica fragrans prevents and treats atherosclerosis （AS）. MethodsThe major active components of Myristica fragrans and their shared targets with AS were obtained from databases. The shared targets were subjected to pathway enrichment analysis and PPI network construction using the ClusterProfile package and the STRING database. Molecular docking between key targets and major active components was performed using AutoDock. Gene expression data from early and late， as well as stable and unstable AS plaques， were used to validate changes of key targets and major pathways during AS progression. Western blot， flow cytometry， YO-PRO-1/PI staining， and TUNEL staining were applied to verify the main mechanisms. ResultsNine active components of Myristica fragrans interacted with 293 AS-related targets， among which eight components acted on an average of 57.0% of the shared targets. Gene ontology （GO） and Kyoto encyclopedia of genes and genomes （KEGG） enrichment analyses indicated that the anti-AS effects mainly involved oxidative stress， inflammation， lipid metabolism， fluid shear stress， and apoptosis pathways. PPI network revealed JUN， CASP3， MAPK3， and AKT1 as key targets mainly involved in regulating apoptosis. Molecular docking showed stable binding conformations and high affinities between major components and these targets. Integrated analysis of gene expression in early and late， as well as stable and unstable AS plaques， showed significant enrichment of leukocyte apoptosis pathways in late and unstable plaques. Cell experiments further confirmed that Myristica fragrans significantly reduced Cleaved-CASP3（P=0.04）and p-MAPK3（P=0.000 3）levels， increased p-AKT1（P=0.004）levels， and inhibited macrophage apoptosis. ConclusionMyristica fragrans potentially interferes with AS development by modulating pathways related to oxidative stress， inflammation， lipid metabolism， fluid shear stress， and apoptosis， with CASP3， MAPK3， and AKT1 serving as key targets mediating its anti-apoptotic and anti-AS effects.

Objective Through integrative bioinformatics analysis of multi-source transcriptomic data, potential biomarkers to asthma epithelial cells were identified. The expression of these candidate target was subsequently validated in lung tissues and epithelial cells from asthma models. Methods The gene expression profile data of epithelial cells from three asthma patient cohorts and corresponding healthy controls were integrated from the Gene Expression Omnibus (GEO) database. Differential expression analysis and gene co-expression network analysis were performed to identify key genes and biological pathways associated with asthma. The key genes were validated in lung tissues and epithelial cells in asthma animal models. Results Differential gene expression analysis revealed 1121 upregulated and 1484 downregulated genes in epithelial cells from asthma patients compared with healthy controls. The biological pathway enrichment analysis revealed that the upregulated genes were mainly involved in glycosylation processes, whereas the downregulated genes were mainly associated with immune cell differentiation process. The gene co-expression network analysis revealed that module 9, enriched in glycosylation-related pathways, was significantly positively correlated with asthma, whereas module 17, associated with insulin and other signaling pathways, showed a significant negative correlation with asthma. We identified the genes of polypeptide N-acetylgalactosaminyltransferase 5 (GALNT5), pyrroline-5-carboxylate reductase 1 (PYCR1), and carcinoembryonic antigen-related cell adhesion molecule 5 (CEACAM5) as key genes within module 9, all of which were significantly upregulated in asthma. Finally, we validated that the expression levels of GALNT5, PYCR1, and CEACAM5 were significantly upregulated in epithelial cells from asthmatic lung tissue. Additionally, using a rat asthma model, we further confirmed that the protein levels of these three genes were significantly upregulated in lung tissues of the model group. Conclusion Through data integration and experimental validation, this study identified key genes and biological pathways closely associated with asthma pathogenesis. These findings provide a novel theoretical basis and potential targets for the diagnosis and treatment of asthma.
Asthma/metabolism* ; Humans ; Epithelial Cells/metabolism* ; Animals ; Biomarkers/metabolism* ; Gene Expression Profiling ; Transcriptome ; Gene Regulatory Networks ; Rats ; Computational Biology

Dry eye disease (DED) is a prevalent and intractable ocular disease induced by a variety of causes. Elevated sphingomyelin (SM) levels and pro-inflammatory cytokines were detected on the ocular surface of DED patients, particularly in the meibomian glands. Sphingomyelin synthase 2 (SMS2), one of the proteins involved in SM synthesis, would light a novel way of developing a DED therapy strategy. Herein, we report the design and optimization of a series of novel thiophene carboxamide derivatives to afford 14l with an improved highly potent inhibitory activity on SM synthesis (IC_{50, SMS2} = 28 nmol/L). Moreover, 14l exhibited a notable protective effect of anti-inflammation and anti-apoptosis on human corneal epithelial cells (HCEC) under TNF-α-hyperosmotic stress conditions in vitro, with an acceptable ocular specific distribution (corneas and meibomian glands) and pharmacokinetics (PK) profiles (t _{1/2, cornea} = 1.11 h; t _{1/2, meibomian glands} = 4.32 h) in rats. Furthermore, 14l alleviated the dry eye symptoms including corneal fluorescein staining scores and tear secretion in a dose-dependent manner in mice. Mechanically, 14l reduced the mRNA expression of Tnf-α, Il-1β and Mmp-9 in corneas, as well as the proportion of very long chain SM in meibomian glands. Our findings provide a new strategy for DED therapy based on selective SMS2 inhibitors.

Background As electronic cigarettes (e-cigarettes) are becoming increasingly prevalent, adolescents are experiencing growing levels of environmental exposure to them. Investigating the status and influencing factors of such exposure is essential to inform the development of targeted tobacco control strategies for youth. Objective To investigate the environmental exposure to electronic cigarettes among vocational school students in Shanghai, identify its influencing factors, and assess its impact on e-cigarette use behavior. Methods By cluster sampling, a total of 4890 students were recruited from 4 vocational schools in Shanghai. A questionnaire was designed to collect information on general characteristics, tobacco use (cigarettes and e-cigarettes), peer/parental e-cigarette use, and environmental e-cigarette exposure (environmental aerosol exposure, e-cigarette sales exposure and e-cigarette-related information exposure). Data were collected via Wenjuanxing (an online survey platform) anonymously and analyzed using SPSS 24.0. Chi-square test and logistic regressionwere used to analyze the data. Results The current vaping rate and vaping attempt rate were 2.29% and 8.94% among all participants. The proportions of students exposed to e-cigarette aerosol, e-cigarette sales, and e-cigarette-related information were 41.0%, 63.3%, and 77.3%, respectively. The exposure to environmental e-cigarette aerosol was higher among male students (45.3%) than among female students (34.5%), while the exposure to e-cigarette sales (65.4%) and e-cigarette-related information (78.8%) were higher among female students than among males (61.9%, 76.2%), respectively (P<0.05). Having friends using e-cigarettes increased the risk of exposure to e-cigarette aerosol (OR=7.32, 95%CI: 6.37, 8.41), exposure to e-cigarette sales (OR=3.10, 95%CI: 2.70, 3.56), and exposure to e-cigarette-related information (OR=1.45, 95%CI: 1.25, 1.69). Current cigarette smoking, parental e-cigarette use, and not living on campus also increased the risk of environmental e-cigarette aerosol exposure and sales exposure among the participants (P<0.01). Higher pocket money was significantly associated with increased risks of exposure to e-cigarette sales and e-cigarette information (P<0.001). Environmental e-cigarette aerosol exposure increased the risk of current (OR=6.21, 95%CI: 3.75, 10.29) and attempt (OR=2.25, 95%CI: 1.82, 2.77) e-cigarette use among the participants. Furthermore, reporting exposure to two or more categories of e-cigarette sales and information was associated with a higher risk of e-cigarette use than those reporting no exposure (P<0.05). Conclusion The adolescents are severely exposed to environmental e- cigarette, which increases their risk of e-cigarette use. We recommend implementing measures to mitigate environmental exposures, strengthen tobacco control education, and reduce peer influence related to e-cigarette use, with particular attention to female adolescents and those with higher pocket money.

OBJECTIVE: Paridis Rhizoma (Chonglou in Chinese), a traditional Chinese herbal medicine, has been shown have strong anti-tumor effects. Paris saponin VII (PSVII), an active constituent isolated from Paridis Rhizoma, was demonstrated to significantly suppress the proliferation of BxPC-3 cells in our previous study. Here, we aimed to elucidate the anti-pancreatic ductal adenocarcinoma (PDAC) effect of PSVII and the underlying mechanism. METHODS: Cell viability was determined by CCK-8, colony formation, and cell migration assays. Cell apoptosis and reactive oxygen species (ROS) production were measured by flow cytometry with annexin V/propidine iodide (Annexin V/PI) and 2',7'-dichlorodihydrofluorescein diacetate (DCFH-DA), respectively. Pyroptosis was evaluated by morphological features, Hoechst 33342/PI staining assay, and release of lactate dehydrogenase (LDH). JC-1 fluorescent dye was employed to measure mitochondrial membrane potential. Western blotting and reverse transcription-quantitative polymerase chain reaction (RT-qPCR) were used to determine the levels of proteins or mRNAs. The effect in vivo was assessed by a xenograft tumor model. RESULTS: PSVII inhibited the viability of PDAC cells (BxPC-3, PANC-1, and Capan-2 cells) and induced gasdermin E (GSDME) cleavage, as well as the simultaneous cleavage of Caspase-3 and poly (ADP-ribose) polymerase 1 (PARP). Knockdown of GSDME shifted PSVII-induced pyroptosis to apoptosis. Additionally, the effect of PSVII was significantly attenuated by Z-Asp(OMe)-Glu(OMe)-Val-Asp(OMe)-fluoromethylketone (Z-DEVD-FMK), on the induction of GSDME-dependent pyroptosis. PSVII also elevated intracellular ROS accumulation and stimulated Bax and Caspase-3/GSDME to conduct pyroptosis in PDAC cells. The ROS scavenger N-acetyl cysteine (NAC) suppressed the release of LDH and inhibited Caspase-9, Caspase-3, and GSDME cleavage in PDAC cells, ultimately reversing PSVII-induced pyroptosis. Furthermore, in a xenograft tumor model, PSVII markedly suppressed the growth of PDAC tumors and induced pyroptosis. CONCLUSION These results demonstrated that PSVII exerts therapeutic effects through Caspase-3/GSDME-dependent pyroptosis and may constitute a novel strategy for preventing chemotherapeutic resistance in patients with PDAC in the future.

OBJECTIVE To optimize the water extraction technology for Kaixin granules. METHODS UPLC-MS/MS method was established for the simultaneous determination of ginsenoside Rg1， ginsenoside Re， ginsenoside Rb1， tenuifolin， polygalaxanthone Ⅲ and 3， 6′-disinapoyl-sucrose. An orthogonal test was designed with extraction times， extraction duration， and the volume of added water as factors. Using the contents of the aforementioned six indicator components and the extract yield as evaluation indexes， analytic hierarchy process-entropy weight method was employed to determine the combined weights of each indicator. Subsequently， process optimization and validation were conducted by integrating grey relational analysis （GRA） and back propagation （BP） neural network. RESULTS The water extraction technology optimized by the orthogonal test and GRA was 10- fold water for the first decoction and 8-fold water for the subsequent two， extracting 3 times，extracting for 1 h each time； the average comprehensive score of the validation experiment was 91.10 （RSD＝0.31%， n＝3）. The water extraction technology optimized by BP neural network was extracting 3 times with 10-fold water added each time， extracting for 1.5 h each time； the average comprehensive score of the validation experiment was 95.89 （RSD＝0.73%， n＝3）. Considering practical production requirements， the optimal water extraction technology was extraction performed three times， with 10-fold water for the first decoction and 8-fold water for the subsequent two extractions， with an extraction time of 1 h each. CONCLUSIONS The optimized water extraction technology for Kaixin granules is stable and feasible.

This paper reported a type 1 diabetes patient who had severe diabetic nephropathy, retinopathy, hypertension, and hypothyroidism before pregnancy. The patient's blood glucose control was poor before pregnancy, and the complications were not properly treated. This was an unintended pregnancy, with a pre-pregnancy glycated hemoglobin A1c of 7.8% and early pregnancy urine protein of 3.81-4.53 g/24 h. Considering the patient's poor blood glucose control before pregnancy and the lack of proper treatment for multiple complications including nephropathy, a multidisciplinary consultation at an external hospital recommended termination of the pregnancy. However, the patient was determined to continue the pregnancy and was referred to Peking University First Hospital. Through strict blood glucose control, monitoring and evaluation of complications, and comprehensive management, the patient's blood glucose and blood pressure were well controlled during pregnancy. Regular monitoring of urine protein, renal function, and ocular fundus was conducted. At 31 weeks and 4 days of gestation, the patient's 24-hour urine protein significantly increased. After promoting fetal lung maturity, a cesarean section was performed at 34 weeks and 1 day of gestation, resulting in a successful delivery with good maternal and neonatal outcomes. At the 42-day postpartum follow-up, the patient's blood glucose and blood pressure were stable, urine protein returned to pre-pregnancy levels, and the infant was in good general condition.

Objective To investigate the effect of miR-206 on LPS-induced apoptosis and inflamma-tory response in H9c2 cardiomyocytes.Methods An inflammatory injury model was established in H9c2 cells with treatment of 10 μg/ml LPS.Then the cells were divided into control group,LPS group,LPS1 group(LPS+anti-negative control),LPS2 group(LPS+anti-miR-206),LPS3 group(LPS+miR-206 negative control),and LPS4 group(LPS+miR-206 mimic).The LPS1,LPS2,LPS3,and LPS4 groups(n=3)were transfected with anti-negative control,anti-miR-206 se-quence,miR-negative control,or miR-206 mimic sequence,using Lipofectamine 3000 reagent.CCK-8 and EdU assays were used to detect cell proliferation,Caspase-Glo 3/7 activity kit was em-ployed to measure Caspase-3/7 activity,and ELISA was utilized to test the secretion of inflamma-tory cytokines,TNF-α,IL-1β,and IL-6 in the cells.Results Compared with the control group,the LPS group had significantly reduced cell survival rate and EDU-positive cell rate,while elevated miR-206 expression,Bax/Bcl-2,activated Caspase-3,Caspase-3/7,TNF-α,IL-1β,IL-6,p-p38 and p-p65(P＜0.05,P＜0.01).The expression of miR-206,Bax/Bcl-2,activated Caspase-3,Caspase-3/7,TNF-α,IL-1β,IL-6,p-p38 and p-p65 were obviously reduced,while the cell survival rate and EDU-positive cell rate were notably increased in the LPS 2 group than the LPS group(P＜0.05).In the LPS 4 group,the activity of Caspase-3/7 was remarkably stronger than the LPS group(1586±58 vs 816±51,P＜0.05).Conclusion Suppression of miR-206 can effectively inhibit LPS-induced apoptosis and inflammatory response in H9c2 cardiomyocytes.

Objective:To establish tumor specific protein (SP70) targeted tumor cell enrichment technology and to assess applicational value of next-generation sequencing (NGS) analysis for enriched circulating tumor cell (CTC) in precision medicines of non-small cell lung cancer (NSCLC).Methods:The monoclonal antibody NJ001 was covalently coupled to the surface of magnetic beads to build targeted magnetic bead enrichment technology based on SP70. The limit of detection, coincidence rate, interference experiment, recovery test and clinical performance were evaluated. From March 2016 to August 2017, NGS analysis with or without pre-treatment of targeted enrichment for serous fluids of 43 NSCLC in the First Affiliated Hospital with Nanjing Medical University were compared (Kappa or Fisher exact test).Results:The CTC enrichment technology based on SP70 targeted immunomagnetic beads can specifically enrich tumor cells. The limit of detection was 10 4 SPC-A1 cells/L, and the coincidence rate, sensitivity and specificity were 100% (3/3). The endogenous interfering substances such as red blood cells, hemoglobin, white blood cells, epithelial cells and triglycerides had no interfering effects, as well as the exogenous interfering substances such as EDTA-K2, cefoxitin, carboplatin and paclitaxel. The recovery rate was 56.0% (56 000/100 000). A total of 30 gene mutations including 65 loci were found in 43 NSCLC under SP70 targeted enrichment, with a higher detection rate compared with unenrichment method [95.0% (19/20) vs 65.0% (13/20), χ 2=5.625, P=0.044]. Conclusion:In this study, SP70-targeted enriched CTC liquid biopsy method was established, with higher sensitivity and specificity of NGS detection than unenrichment method.

Objective:This study aims to develop a laboratory-based predictive model for early neurological deterioration (END) in patients with acute cerebral infarction (ACI) using baseline data collected at hospital admission.Methods:This study was a retrospective cohort study. Clinical and baseline laboratory test data from 502 patients with ACI admitted to the Department of Neurology at our hospital between January 1, 2022 and May 31, 2025. Of these patients, 313 were male and 189 were female, with a median age of 67 years (interquartile range: 58-73). Patients were classified into an END group and a non-END group according to the occurrence of END within 7 days of admission. Subsequently, using the caret package in R (version 4.4.2), the dataset was randomly divided into a training set ( n=351) and a validation set ( n=151) at a 7∶3 ratio, with END status as the stratification variable and a fixed random seed to ensure reproducibility. Following baseline characteristic comparisons between groups, these datasets were used for model development and validation, respectively. The differences in clinical indicators between the two patients groups were assessed using the chi-square test and the Wilcoxon rank sum test. In the training group, Lasso regression was utilized to identify variables significantly associated with END. Seven machine learning algorithms-decision tree (DT), random forest (RF), light gradient boosting machine (LGBM), extreme gradient boosting (XGB), K-nearest neighbors (KNN), support vector machine (SVM), and logistic regression (LR)-were employed to develop predictive models. The optimal hyperparameters were determined via grid search integrated with 5-fold cross-validation. The final algorithm was selected based on comprehensive model performance evaluation. Additionally, clinical data of 79 patients with ACI, collected between June 1 to August 31, 2025, were compiled as an independent test set for external validation. The cohort comprised 49 males and 30 females, with a median age of 68 years (interquartile range: 57-72). The SHapley Additive exPlanations (SHAP) method was employed to access feature importance and model interpretability. SHAP dependence plots and interaction plots were utilized to emplore the nonlinear relationships and interaction effects among the featurevariables. Results:Among the 502 patients, 166 experienced END during 7 days of hospitalization. Lasso regression identified nine significant predictors: history of hyperlipidemia, admission NIHSS score, lymphocyte-to-monocyte ratio (LMR), hemoglobin, D-dimer, albumin, neuron-specific enolase (NSE), homocysteine (HCY), and vitamin B12. The area under the receiver operating characteristic curve (AUC) for the seven machine learning models ranged from 0.709 to 0.946. The XGB model achieved the highest predictive performance, with an AUC of 0.946 (95% CI 0.924-0.960) in the training cohort and 0.867 (95% CI 0.902-0.933) in the validation cohort. SHAP analysis revealed that the top five variables contributing to END prediction were admission NIHSS score, HCY, D-dimer, history of hyperlipidemia, and vitamin B12. Conclusion:This study successfully developed a laboratory-based prediction model for END using the XGB machine learning algorithm, which demonstrated strong predictive performance.